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Journal articles on the topic 'Image scene'
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1
Liu, Yongmei, Tanakrit Wongwitit, and Linsen Yu. "Automatic Image Annotation Based on Scene Analysis." International Journal of Image and Graphics 14, no. 03 (2014): 1450012. http://dx.doi.org/10.1142/s0219467814500120.
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Automatic image annotation is an important and challenging job for image analysis and understanding such as content-based image retrieval (CBIR). The relationship between the keywords and visual features is too complicated due to the semantic gap. We present an approach of automatic image annotation based on scene analysis. With the constrain of scene semantics, the correlation between keywords and visual features becomes simpler and clearer. Our model has two stages of process. The first stage is training process which groups training image data set into semantic scenes using the extracted semantic feature and visual scenes constructed from the calculation distances of visual features for every pairs of training images by using Earth mover's distance (EMD). Then, combine a pair of semantic and visual scene together and apply Gaussian mixture model (GMM) for all scenes. The second stage is to test and annotate keywords for test image data set. Using the visual features provided by Duygulu, experimental results show that our model outperforms probabilistic latent semantic analysis (PLSA) & GMM (PLSA&GMM) model on Corel5K database.
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Stevenson, Natasha, and Kun Guo. "Image Valence Modulates the Processing of Low-Resolution Affective Natural Scenes." Perception 49, no. 10 (2020): 1057–68. http://dx.doi.org/10.1177/0301006620957213.
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In natural vision, noisy and distorted visual inputs often change our perceptual strategy in scene perception. However, it is unclear the extent to which the affective meaning embedded in the degraded natural scenes modulates our scene understanding and associated eye movements. In this eye-tracking experiment by presenting natural scene images with different categories and levels of emotional valence (high-positive, medium-positive, neutral/low-positive, medium-negative, and high-negative), we systematically investigated human participants’ perceptual sensitivity (image valence categorization and arousal rating) and image-viewing gaze behaviour to the changes of image resolution. Our analysis revealed that reducing image resolution led to decreased valence recognition and arousal rating, decreased number of fixations in image-viewing but increased individual fixation duration, and stronger central fixation bias. Furthermore, these distortion effects were modulated by the scene valence with less deterioration impact on the valence categorization of negatively valenced scenes and on the gaze behaviour in viewing of high emotionally charged (high-positive and high-negative) scenes. It seems that our visual system shows a valence-modulated susceptibility to the image distortions in scene perception.
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Deng, Li Qiong, Dan Wen Chen, Zhi Min Yuan, and Ling Da Wu. "Attribute-Based Cartoon Scene Image Search System." Advanced Materials Research 268-270 (July 2011): 1030–35. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1030.
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In this paper, we present an interactive search system of cartoon scène images. Using a set of automatically extracted, semantic cartoon scene images’ attributes (such as category, time and pureness), the user can find a desired cartoon scene image, such as “a pure sky at sunset”. The system is fully automatic and scalable. It computes all cartoon scene images’ attributes offline, and then provides an interactive online search engine. Furthermore, the system contains different kinds of retrieval interface designs which aimed at users. The results show that our system can improve the facility and efficiency greatly.
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Wu, Xue Feng, and Yu Fan. "A Research for Fuzzy Image Restoration." Advanced Materials Research 955-959 (June 2014): 1085–88. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.1085.
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Computational photography and image processing technology are used to restore the clearness of images taken in fog scenes autmatically.The technology is used to restore the clearness of the fog scene,which includes digital image processing and the physical model of atmospheric scattering.An algorithm is designed to restore the clearness of the fog scene under the assumption of the albedo images and then the resolution algorithm is analysised.The algorithm is implemented by the software of image process ,which can improve the efficiency of the algorithm and interface.The fog image and defogging image are compared, and the results show that the visibility of the image is improved, and the image restoration is more clearly.
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Fan, Yu, and Xue Feng Wu. "A Research for Image Defogging Algorithm." Applied Mechanics and Materials 409-410 (September 2013): 1653–56. http://dx.doi.org/10.4028/www.scientific.net/amm.409-410.1653.
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Computational photography and image processing technology are used to restore the clearness of images taken in fog scenes autmatically.The technology is used to restore the clearness of the fog scene,which includes digital image processing and the physical model of atmospheric scattering.An algorithm is designed to restore the clearness of the fog scene under the assumption of the albedo images and then the resolution algorithm is analysised.The algorithm is implemented by the software of image process ,which can improve the efficiency of the algorithm and interface.The fog image and defogging image are compared, and the results show that the visibility of the image is improved, and the image restoration is more clearly .
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Xu, Yuanjin. "Application of Remote Sensing Image Data Scene Generation Method in Smart City." Complexity 2021 (January 28, 2021): 1–13. http://dx.doi.org/10.1155/2021/6653841.
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Remote sensing image simulation is a very effective method to verify the feasibility of sensor devices for ground observation. The key to remote sensing image application is that simultaneous interpreting of remote sensing images can make use of the different characteristics of different data, eliminate the redundancy and contradiction between different sensors, and improve the timeliness and reliability of remote sensing information extraction. The hotspots and difficulties in this direction are based on remote sensing image simulation of 3D scenes on the ground. Therefore, constructing the 3D scene model on the ground rapidly and accurately is the focus of current research. Because different scenes have different radiation characteristics, therefore, when using MATLAB to write a program generated by 3D scenes, 3D scenes must be saved as different text files according to different scene types, and then extension program of the scene is written to solve the defect that the calculation efficiency is not ideal due to the huge amount of data. This paper uses POV ray photon reverse tracking software to simulate the imaging process of remote sensing sensors, coordinate transformation is used to convert a triangle text file to POV ray readable information and input the RGB value of the base color based on the colorimetry principle, and the final 3D scene is visualized. This paper analyzes the thermal radiation characteristics of the scene and proves the rationality of the scene simulation. The experimental results show that introducing the chroma in the visualization of the scene model makes the whole scene have not only fidelity, but also radiation characteristics in shape and color. This is indispensable in existing 3D modeling and visualization studies. Compared with the complex radiation transmission method, using the multiple angle two-dimensional image generated by POV rays to analyze the radiation characteristics of the scene, the result is intuitive and easy to understand.
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Goel, Gaurav, and Dr Renu Dhir. "Characters Strings are Extracted Exhibit Morphology Method of an Image." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 6, no. 1 (2013): 272–78. http://dx.doi.org/10.24297/ijct.v6i1.4454.
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In understanding an image, extraction of characters existing in the image is considered to be important. Scene images are different from document images, which are composed of characters and complicated background i.e. photo, picture, or painting etc. instead of white one that makes it difficult to be dealt with. Extraction and localization of scene text are used in many applications. In this paper, we have proposed a connected component based method to extract text from natural images. The proposed method uses colour space processing. Character recognition is done through OCR that accepts the input in form of text boxes, which are generated through text detection and localization stages. The Proposed method is robust with respect to font size, colour, orientation, and style. Results of the proposed algorithm, by taking the real scenes, including indoor and outdoor images, shows that proposed method efficiently extracts and localizes the scene text. In this paper, we have introduced a new method to extract characters from scene images using mathematical morphology.Â
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Zhu, Zhiqin, Yaqin Luo, Hongyan Wei, et al. "Atmospheric Light Estimation Based Remote Sensing Image Dehazing." Remote Sensing 13, no. 13 (2021): 2432. http://dx.doi.org/10.3390/rs13132432.
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Remote sensing images are widely used in object detection and tracking, military security, and other computer vision tasks. However, remote sensing images are often degraded by suspended aerosol in the air, especially under poor weather conditions, such as fog, haze, and mist. The quality of remote sensing images directly affect the normal operations of computer vision systems. As such, haze removal is a crucial and indispensable pre-processing step in remote sensing image processing. Additionally, most of the existing image dehazing methods are not applicable to all scenes, so the corresponding dehazed images may have varying degrees of color distortion. This paper proposes a novel atmospheric light estimation based dehazing algorithm to obtain high visual-quality remote sensing images. First, a differentiable function is used to train the parameters of a linear scene depth model for the scene depth map generation of remote sensing images. Second, the atmospheric light of each hazy remote sensing image is estimated by the corresponding scene depth map. Then, the corresponding transmission map is estimated on the basis of the estimated atmospheric light by a haze-lines model. Finally, according to the estimated atmospheric light and transmission map, an atmospheric scattering model is applied to remove haze from remote sensing images. The colors of the images dehazed by the proposed method are in line with the perception of human eyes in different scenes. A dataset with 100 remote sensing images from hazy scenes was built for testing. The performance of the proposed image dehazing method is confirmed by theoretical analysis and comparative experiments.
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Zhou, Wen, Dongping Ming, Lu Xu, Hanqing Bao, and Min Wang. "Stratified Object-Oriented Image Classification Based on Remote Sensing Image Scene Division." Journal of Spectroscopy 2018 (June 3, 2018): 1–11. http://dx.doi.org/10.1155/2018/3918954.
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The traditional remote sensing image segmentation method uses the same set of parameters for the entire image. However, due to objects’ scale-dependent nature, the optimal segmentation parameters for an overall image may not be suitable for all objects. According to the idea of spatial dependence, the same kind of objects, which have the similar spatial scale, often gather in the same scene and form a scene. Based on this scenario, this paper proposes a stratified object-oriented image analysis method based on remote sensing image scene division. This method firstly uses middle semantic which can reflect an image’s visual complexity to classify the remote sensing image into different scenes, and then within each scene, an improved grid search algorithm is employed to optimize the segmentation result of each scene, so that the optimal scale can be utmostly adopted for each scene. Because the complexity of data is effectively reduced by stratified processing, local scale optimization ensures the overall classification accuracy of the whole image, which is practically meaningful for remote sensing geo-application.
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Qi, Guanqiu, Liang Chang, Yaqin Luo, Yinong Chen, Zhiqin Zhu, and Shujuan Wang. "A Precise Multi-Exposure Image Fusion Method Based on Low-level Features." Sensors 20, no. 6 (2020): 1597. http://dx.doi.org/10.3390/s20061597.
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Multi exposure image fusion (MEF) provides a concise way to generate high-dynamic-range (HDR) images. Although the precise fusion can be achieved by existing MEF methods in different static scenes, the corresponding performance of ghost removal varies in different dynamic scenes. This paper proposes a precise MEF method based on feature patches (FPM) to improve the robustness of ghost removal in a dynamic scene. A reference image is selected by a priori exposure quality first and then used in the structure consistency test to solve the image ghosting issues existing in the dynamic scene MEF. Source images are decomposed into spatial-domain structures by a guided filter. Both the base and detail layer of the decomposed images are fused to achieve the MEF. The structure decomposition of the image patch and the appropriate exposure evaluation are integrated into the proposed solution. Both global and local exposures are optimized to improve the fusion performance. Compared with six existing MEF methods, the proposed FPM not only improves the robustness of ghost removal in a dynamic scene, but also performs well in color saturation, image sharpness, and local detail processing.
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Zanfir, Mihai, Elisabeta Oneata, Alin-Ionut Popa, Andrei Zanfir, and Cristian Sminchisescu. "Human Synthesis and Scene Compositing." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12749–56. http://dx.doi.org/10.1609/aaai.v34i07.6969.
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Generating good quality and geometrically plausible synthetic images of humans with the ability to control appearance, pose and shape parameters, has become increasingly important for a variety of tasks ranging from photo editing, fashion virtual try-on, to special effects and image compression. In this paper, we propose a HUSC (HUman Synthesis and Scene Compositing) framework for the realistic synthesis of humans with different appearance, in novel poses and scenes. Central to our formulation is 3d reasoning for both people and scenes, in order to produce realistic collages, by correctly modeling perspective effects and occlusion, by taking into account scene semantics and by adequately handling relative scales. Conceptually our framework consists of three components: (1) a human image synthesis model with controllable pose and appearance, based on a parametric representation, (2) a person insertion procedure that leverages the geometry and semantics of the 3d scene, and (3) an appearance compositing process to create a seamless blending between the colors of the scene and the generated human image, and avoid visual artifacts. The performance of our framework is supported by both qualitative and quantitative results, in particular state-of-the art synthesis scores for the DeepFashion dataset.
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TORRALBA, ANTONIO. "How many pixels make an image?" Visual Neuroscience 26, no. 1 (2009): 123–31. http://dx.doi.org/10.1017/s0952523808080930.
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AbstractThe human visual system is remarkably tolerant to degradation in image resolution: human performance in scene categorization remains high no matter whether low-resolution images or multimegapixel images are used. This observation raises the question of how many pixels are required to form a meaningful representation of an image and identify the objects it contains. In this article, we show that very small thumbnail images at the spatial resolution of 32 × 32 color pixels provide enough information to identify the semantic category of real-world scenes. Most strikingly, this low resolution permits observers to report, with 80% accuracy, four to five of the objects that the scene contains, despite the fact that some of these objects are unrecognizable in isolation. The robustness of the information available at very low resolution for describing semantic content of natural images could be an important asset to explain the speed and efficiently at which the human brain comprehends the gist of visual scenes.
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Liu, K., A. Wu, X. Wan, and S. Li. "MRSSC: A BENCHMARK DATASET FOR MULTIMODAL REMOTE SENSING SCENE CLASSIFICATION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (June 28, 2021): 785–92. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-785-2021.
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Abstract. Scene classification based on multi-source remote sensing image is important for image interpretation, and has many applications, such as change detection, visual navigation and image retrieval. Deep learning has become a research hotspot in the field of remote sensing scene classification, and dataset is an important driving force to promote its development. Most of the remote sensing scene classification datasets are optical images, and multimodal datasets are relatively rare. Existing datasets that contain both optical and SAR data, such as SARptical and WHU-SEN-City, which mainly focused on urban area without wide variety of scene categories. This largely limits the development of domain adaptive algorithms in remote sensing scene classification. In this paper, we proposed a multi-modal remote sensing scene classification dataset (MRSSC) based on Tiangong-2, a Chinese manned spacecraft which can acquire optical and SAR images at the same time. The dataset contains 12167 images (optical 6155 and 6012 for optical and SAR, resp.) of seven typical scenes, namely city, farmland, mountain, desert, coast, lake and river. Our dataset is evaluated by state-of-theart domain adaptation methods to establish a baseline with average classification accuracy of 79.2%. The MRSSC dataset will be released freely for the educational purpose and can be found at China Manned Space Engineering data service website (http://www.msadc.cn). This dataset will fill the gap between remote sensing scene classification between different image sources, and paves the way for a generalized image classification model for multi-modal earth observation data.
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Tanaka, Kanji, Yuuto Chokushi, and Masatoshi Ando. "Mining Visual Phrases for Visual Robot Localization." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 1 (2016): 57–65. http://dx.doi.org/10.20965/jaciii.2016.p0057.
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We propose a discriminative and compact scene descriptor for single-view place recognition that facilitates long-term visual SLAM in familiar, semi-dynamic, and partially changing environments. In contrast to popular bag-of-words scene descriptors, which rely on a library of vector quantized visual features, our proposed scene descriptor is based on a library of raw image data (such as an available visual experience, images shared by other colleague robots, and publicly available image data on the Web) and directly mine it to find visual phrases (VPs) that discriminatively and compactly explain an input query/database image. Our mining approach is motivated by recent success achieved in the field of common pattern discovery – specifically mining of common visual patterns among scenes – and requires only a single library of raw images that can be acquired at different times or on different days. Experimental results show that, although our scene descriptor is significantly more compact than conventional descriptors, its recognition performance is relatively high.
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Yuan, Sheng, Yuting Chen, Huihui Huo, and Li Zhu. "Analysis and Synthesis of Traffic Scenes from Road Image Sequences." Sensors 20, no. 23 (2020): 6939. http://dx.doi.org/10.3390/s20236939.
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Traffic scene construction and simulation has been a hot topic in the community of intelligent transportation systems. In this paper, we propose a novel framework for the analysis and synthesis of traffic elements from road image sequences. The proposed framework is composed of three stages: traffic elements detection, road scene inpainting, and road scene reconstruction. First, a new bidirectional single shot multi-box detector (BiSSD) method is designed with a global context attention mechanism for traffic elements detection. After the detection of traffic elements, an unsupervised CycleGAN is applied to inpaint the occlusion regions with optical flow. The high-quality inpainting images are then obtained by the proposed image inpainting algorithm. Finally, a traffic scene simulation method is developed by integrating the foreground and background elements of traffic scenes. The extensive experiments and comparisons demonstrate the effectiveness of the proposed framework.
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An, Shunmin, Xixia Huang, Linling Wang, Zhangjing Zheng, and Le Wang. "Unsupervised water scene dehazing network using multiple scattering model." PLOS ONE 16, no. 6 (2021): e0253214. http://dx.doi.org/10.1371/journal.pone.0253214.
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In water scenes, where hazy images are subject to multiple scattering and where ideal data sets are difficult to collect, many dehazing methods are not as effective as they could be. Therefore, an unsupervised water scene dehazing network using atmospheric multiple scattering model is proposed. Unlike previous image dehazing methods, our method uses the unsupervised neural network and the atmospheric multiple scattering model and solves the problem of difficult acquisition of ideal datasets and the effect of multiple scattering on the image. In our method, in order to embed the atmospheric multiple scattering model into the unsupervised dehazing network, the unsupervised dehazing network uses four branches to estimate the scene radiation layer, transmission map layer, blur kernel layer and atmospheric light layer, the hazy image is then synthesized from the four output layers, minimizing the input hazy image and the output hazy image, where the output scene radiation layer is the final dehazing image. In addition, we constructed unsupervised loss functions which applicable to image dehazing by prior knowledge, i.e., color attenuation energy loss and dark channel loss. The method has a wide range of applications, with haze being thick and variable in marine, river and lake scenes, the method can be used to assist ship vision for target detection or forward road recognition in hazy conditions. Through extensive experiments on synthetic and real-world images, the proposed method is able to recover the details, structure and texture of the water image better than five advanced dehazing methods.
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FOSTER, DAVID H., KINJIRO AMANO, and SÉRGIO M. C. NASCIMENTO. "Color constancy in natural scenes explained by global image statistics." Visual Neuroscience 23, no. 3-4 (2006): 341–49. http://dx.doi.org/10.1017/s0952523806233455.
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To what extent do observers' judgments of surface color with natural scenes depend on global image statistics? To address this question, a psychophysical experiment was performed in which images of natural scenes under two successive daylights were presented on a computer-controlled high-resolution color monitor. Observers reported whether there was a change in reflectance of a test surface in the scene. The scenes were obtained with a hyperspectral imaging system and included variously trees, shrubs, grasses, ferns, flowers, rocks, and buildings. Discrimination performance, quantified on a scale of 0 to 1 with a color-constancy index, varied from 0.69 to 0.97 over 21 scenes and two illuminant changes, from a correlated color temperature of 25,000 K to 6700 K and from 4000 K to 6700 K. The best account of these effects was provided by receptor-based rather than colorimetric properties of the images. Thus, in a linear regression, 43% of the variance in constancy index was explained by the log of the mean relative deviation in spatial cone-excitation ratios evaluated globally across the two images of a scene. A further 20% was explained by including the mean chroma of the first image and its difference from that of the second image and a further 7% by the mean difference in hue. Together, all four global color properties accounted for 70% of the variance and provided a good fit to the effects of scene and of illuminant change on color constancy, and, additionally, of changing test-surface position. By contrast, a spatial-frequency analysis of the images showed that the gradient of the luminance amplitude spectrum accounted for only 5% of the variance.
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Li, Linyi, Tingbao Xu, and Yun Chen. "Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features." Computational Intelligence and Neuroscience 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/9858531.
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In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images.
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Zhao, Zhicheng, Ze Luo, Jian Li, Can Chen, and Yingchao Piao. "When Self-Supervised Learning Meets Scene Classification: Remote Sensing Scene Classification Based on a Multitask Learning Framework." Remote Sensing 12, no. 20 (2020): 3276. http://dx.doi.org/10.3390/rs12203276.
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In recent years, the development of convolutional neural networks (CNNs) has promoted continuous progress in scene classification of remote sensing images. Compared with natural image datasets, however, the acquisition of remote sensing scene images is more difficult, and consequently the scale of remote sensing image datasets is generally small. In addition, many problems related to small objects and complex backgrounds arise in remote sensing image scenes, presenting great challenges for CNN-based recognition methods. In this article, to improve the feature extraction ability and generalization ability of such models and to enable better use of the information contained in the original remote sensing images, we introduce a multitask learning framework which combines the tasks of self-supervised learning and scene classification. Unlike previous multitask methods, we adopt a new mixup loss strategy to combine the two tasks with dynamic weight. The proposed multitask learning framework empowers a deep neural network to learn more discriminative features without increasing the amounts of parameters. Comprehensive experiments were conducted on four representative remote sensing scene classification datasets. We achieved state-of-the-art performance, with average accuracies of 94.21%, 96.89%, 99.11%, and 98.98% on the NWPU, AID, UC Merced, and WHU-RS19 datasets, respectively. The experimental results and visualizations show that our proposed method can learn more discriminative features and simultaneously encode orientation information while effectively improving the accuracy of remote sensing scene classification.
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Sieberth, T., R. Wackrow, V. Hofer, and V. Barrera. "LIGHT FIELD CAMERA AS TOOL FOR FORENSIC PHOTOGRAMMETRY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1 (September 26, 2018): 393–99. http://dx.doi.org/10.5194/isprs-archives-xlii-1-393-2018.
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<p><strong>Abstract.</strong> Light field cameras record both the light intensity received by the sensor and the direction in which the light rays are travelling through space. Recording the additional information of the direction of Light rays provides the opportunity to refocus an image after acquisition. Furthermore, a depth image can be created, providing 3D information for each image pixel. Both, focused images and 3D information are relevant for forensic investigations. Basic overview images are often acquired by photographic novices and under difficult conditions, which make refocusing of images a useful feature to enhance information for documentation purposes. Besides focused images, it can also be useful to have 3D data of an incident scene. Capital crime scenes such as homicide are usually documented in 3D using laser scanning. However, not every crime scene can be identified as capital crime scene straight away but only in the course of the investigation, making 3D data acquisition of the discovery situation impossible. If this is the case, light field images taken during the discovery of the scene can provide substantial 3D data. We will present how light field images are refocused and used to perform photogrammetric reconstruction of a scene and compare the generated 3D model to standard photogrammetry and laser scanning data. The results show that refocused light field images used for photogrammetry can improve the photogrammetry result and aid photogrammetric processing.</p>
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Liu, Tao, Zhixiang Fang, Qingzhou Mao, Qingquan Li, and Xing Zhang. "A cube-based saliency detection method using integrated visual and spatial features." Sensor Review 36, no. 2 (2016): 148–57. http://dx.doi.org/10.1108/sr-07-2015-0110.
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Purpose The spatial feature is important for scene saliency detection. Scene-based visual saliency detection methods fail to incorporate 3D scene spatial aspects. This paper aims to propose a cube-based method to improve saliency detection through integrating visual and spatial features in 3D scenes. Design/methodology/approach In the presented approach, a multiscale cube pyramid is used to organize the 3D image scene and mesh model. Each 3D cube in this pyramid represents a space unit similar to a pixel in the image saliency model multiscale image pyramid. In each 3D cube color, intensity and orientation features are extracted from the image and a quantitative concave–convex descriptor is extracted from the 3D space. A Gaussian filter is then used on this pyramid of cubes with an extended center-surround difference introduced to compute the cube-based 3D scene saliency. Findings The precision-recall rate and receiver operating characteristic curve is used to evaluate the method and other state-of-art methods. The results show that the method used is better than traditional image-based methods, especially for 3D scenes. Originality/value This paper presents a method that improves the image-based visual saliency model.
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Madhuanand, L., F. Nex, and M. Y. Yang. "DEEP LEARNING FOR MONOCULAR DEPTH ESTIMATION FROM UAV IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 451–58. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-451-2020.
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Abstract. Depth is an essential component for various scene understanding tasks and for reconstructing the 3D geometry of the scene. Estimating depth from stereo images requires multiple views of the same scene to be captured which is often not possible when exploring new environments with a UAV. To overcome this monocular depth estimation has been a topic of interest with the recent advancements in computer vision and deep learning techniques. This research has been widely focused on indoor scenes or outdoor scenes captured at ground level. Single image depth estimation from aerial images has been limited due to additional complexities arising from increased camera distance, wider area coverage with lots of occlusions. A new aerial image dataset is prepared specifically for this purpose combining Unmanned Aerial Vehicles (UAV) images covering different regions, features and point of views. The single image depth estimation is based on image reconstruction techniques which uses stereo images for learning to estimate depth from single images. Among the various available models for ground-level single image depth estimation, two models, 1) a Convolutional Neural Network (CNN) and 2) a Generative Adversarial model (GAN) are used to learn depth from aerial images from UAVs. These models generate pixel-wise disparity images which could be converted into depth information. The generated disparity maps from these models are evaluated for its internal quality using various error metrics. The results show higher disparity ranges with smoother images generated by CNN model and sharper images with lesser disparity range generated by GAN model. The produced disparity images are converted to depth information and compared with point clouds obtained using Pix4D. It is found that the CNN model performs better than GAN and produces depth similar to that of Pix4D. This comparison helps in streamlining the efforts to produce depth from a single aerial image.
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Wu, Li Ming, Shi Long Yang, Fu Jian Li, Xin Luo, and Bing Jing Li. "Adaptive Video Image Enhancement Algorithm Based on FPGA Design." Key Engineering Materials 620 (August 2014): 516–21. http://dx.doi.org/10.4028/www.scientific.net/kem.620.516.
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Aiming at the problem of image quality degradation due to scenes change rapidly, an image enhancement algorithm based on scene intelligent identification is proposed. The algorithm sharpen the image detail by using Laplace operator. Determines the change image scene according to the gray value, constructs different gray mapping function, and adjusts gray value range of image adaptively to improve the contrast ratio of image enhancement. By using parallel processing, the algorithm has high execution efficiency, so it can meet the real-time processing of HD video. Experimental result shows that the proposed algorithm has satisfying performance in the rapidly change scene.
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Chlubna, T., T. Milet, and P. Zemčík. "Real-time per-pixel focusing method for light field rendering." Computational Visual Media 7, no. 3 (2021): 319–33. http://dx.doi.org/10.1007/s41095-021-0205-0.
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AbstractLight field rendering is an image-based rendering method that does not use 3D models but only images of the scene as input to render new views. Light field approximation, represented as a set of images, suffers from so-called refocusing artifacts due to different depth values of the pixels in the scene. Without information about depths in the scene, proper focusing of the light field scene is limited to a single focusing distance. The correct focusing method is addressed in this work and a real-time solution is proposed for focusing of light field scenes, based on statistical analysis of the pixel values contributing to the final image. Unlike existing techniques, this method does not need precomputed or acquired depth information. Memory requirements and streaming bandwidth are reduced and real-time rendering is possible even for high resolution light field data, yielding visually satisfactory results. Experimental evaluation of the proposed method, implemented on a GPU, is presented in this paper.
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Bao, Yongtang, Pengfei Lin, Yao Li, et al. "Parallel Structure from Motion for Sparse Point Cloud Generation in Large-Scale Scenes." Sensors 21, no. 11 (2021): 3939. http://dx.doi.org/10.3390/s21113939.
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Scene reconstruction uses images or videos as input to reconstruct a 3D model of a real scene and has important applications in smart cities, surveying and mapping, military, and other fields. Structure from motion (SFM) is a key step in scene reconstruction, which recovers sparse point clouds from image sequences. However, large-scale scenes cannot be reconstructed using a single compute node. Image matching and geometric filtering take up a lot of time in the traditional SFM problem. In this paper, we propose a novel divide-and-conquer framework to solve the distributed SFM problem. First, we use the global navigation satellite system (GNSS) information from images to calculate the GNSS neighborhood. The number of images matched is greatly reduced by matching each image to only valid GNSS neighbors. This way, a robust matching relationship can be obtained. Second, the calculated matching relationship is used as the initial camera graph, which is divided into multiple subgraphs by the clustering algorithm. The local SFM is executed on several computing nodes to register the local cameras. Finally, all of the local camera poses are integrated and optimized to complete the global camera registration. Experiments show that our system can accurately and efficiently solve the structure from motion problem in large-scale scenes.
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Zhang, Wenfang, and Chi Xu. "Noise Removal Algorithm for Out-of-focus Blurred Images Based on Bilateral Filtering." International Journal of Circuits, Systems and Signal Processing 15 (September 6, 2021): 1314–23. http://dx.doi.org/10.46300/9106.2021.15.142.
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The feature resolution of traditional methods for fuzzy image denoising is low, for the sake of improve the strepitus removal and investigation ability of defocused blurred night images, a strepitus removal algorithm based on bilateral filtering is suggested. The method include the following steps of: Building an out-of-focus blurred night scene image acquisition model with grid block feature matching of the out-of-focus blurred night scene image; Carrying out information enhancement processing of the out-of-focus blurred night scene image by adopting a high-resolution image detail feature enhancement technology; Collecting edge contour feature quantity of the out-of-focus blurred night scene image; Carrying out grid block feature matching design of the out-of-focus blurred night scene image by adopting a bilateral filtering information reconstruction technology; And building the gray-level histogram information location model of the out-of-focus blurred night scene image. Fuzzy pixel information fusion investigation method is used to collect gray features of defocused blurred night images. According to the feature collection results, bilateral filtering algorithm is used to automatically optimize the strepitus removal of defocused blurred night images. The simulation results show that the out-of-focus blurred night scene image using this method for machine learning has better strepitus removal performance, shorter time cost and higher export peak signal-to-strepitus proportion.
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Bondzulic, Boban, and Vladimir Petrovic. "Same scene image fusion." Vojnotehnicki glasnik 55, no. 4 (2007): 414–28. http://dx.doi.org/10.5937/vojtehg0704414b.
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Gu, Zhenfei, Mingye Ju, and Dengyin Zhang. "A Single Image Dehazing Method Using Average Saturation Prior." Mathematical Problems in Engineering 2017 (2017): 1–17. http://dx.doi.org/10.1155/2017/6851301.
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Outdoor images captured in bad weather are prone to yield poor visibility, which is a fatal problem for most computer vision applications. The majority of existing dehazing methods rely on an atmospheric scattering model and therefore share a common limitation; that is, the model is only valid when the atmosphere is homogeneous. In this paper, we propose an improved atmospheric scattering model to overcome this inherent limitation. By adopting the proposed model, a corresponding dehazing method is also presented. In this method, we first create a haze density distribution map of a hazy image, which enables us to segment the hazy image into scenes according to the haze density similarity. Then, in order to improve the atmospheric light estimation accuracy, we define an effective weight assignment function to locate a candidate scene based on the scene segmentation results and therefore avoid most potential errors. Next, we propose a simple but powerful prior named the average saturation prior (ASP), which is a statistic of extensive high-definition outdoor images. Using this prior combined with the improved atmospheric scattering model, we can directly estimate the scene atmospheric scattering coefficient and restore the scene albedo. The experimental results verify that our model is physically valid, and the proposed method outperforms several state-of-the-art single image dehazing methods in terms of both robustness and effectiveness.
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Liu, Zhi Yuan, Jin He, Jin Long Wang, and Fei Zhao. "Scene Classification Based on Improved Spatial Partition Model." Applied Mechanics and Materials 527 (February 2014): 339–42. http://dx.doi.org/10.4028/www.scientific.net/amm.527.339.
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In order to make full use of the spatial information of images in the classification of natural scene, we use the spatial partition model. But mechanically space division caused the abuse of spatial information. So spatial partition model must be properly improved to make the different categories of images were more diversity, so that the classification performance is improved. In addition, to further improve the performance, we use FAN-SIFT as local image features. Experiments made on 8 scenes image dataset and Caltech101 dataset show that the improved model can obtain better classification performance.
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Ye, Minchao, Yongqiu Xu, Chenxi Ji, Hong Chen, Huijuan Lu, and Yuntao Qian. "Feature selection for cross-scene hyperspectral image classification using cross-domain ReliefF." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 05 (2019): 1950039. http://dx.doi.org/10.1142/s0219691319500395.
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Hyperspectral images (HSIs) have hundreds of narrow and adjacent spectral bands, which will result in feature redundancy, decreasing the classification accuracy. Feature (band) selection helps to remove the noisy or redundant features. Most traditional feature selection algorithms can be only performed on a single HSI scene. However, appearance of massive HSIs has placed a need for joint feature selection across different HSI scenes. Cross-scene feature selection is not a simple problem, since spectral shift exists between different HSI scenes, even though the scenes are captured by the same sensor. The spectral shift makes traditional single-dataset-based feature selection algorithms no longer applicable. To solve this problem, we extend the traditional ReliefF to a cross-domain version, namely, cross-domain ReliefF (CDRF). The proposed method can make full use of both source and target domains and increase the similarity of samples belonging to the same class in both domains. In the cross-scene classification problem, it is necessary to consider the class-separability of spectral features and the consistency of features between different scenes. The CDRF takes into account these two factors using a cross-domain updating rule of the feature weights. Experimental results on two cross-scene HSI datasets show the superiority of the proposed CDRF in cross-scene feature selection problems.
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Yao, Y., H. Zhao, D. Huang, and Q. Tan. "REMOTE SENSING SCENE CLASSIFICATION USING MULTIPLE PYRAMID POOLING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W16 (September 17, 2019): 279–84. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w16-279-2019.
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<p><strong>Abstract.</strong> Remote sensing image scene classification has gained remarkable attention, due to its versatile use in different applications like geospatial object detection, ground object information extraction, environment monitoring and etc. The scene not only contains the information of the ground objects, but also includes the spatial relationship between the ground objects and the environment. With rapid growth of the amount of remote sensing image data, the need for automatic annotation methods for image scenes is more urgent. This paper proposes a new framework for high resolution remote sensing images scene classification based on convolutional neural network. To eliminate the requirement of fixed-size input image, multiple pyramid pooling strategy is equipped between convolutional layers and fully connected layers. Then, the fixed-size features generated by multiple pyramid pooling layer was extended to one-dimension fixed-length vector and fed into fully connected layers. Our method could generate a fixed-length representation regardless of image size, at the same time get higher classification accuracy. On UC-Merced and NWPU-RESISC45 datasets, our framework achieved satisfying accuracies, which is 93.24% and 88.62% respectively.</p>
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Gu, Yating, Yantian Wang, and Yansheng Li. "A Survey on Deep Learning-Driven Remote Sensing Image Scene Understanding: Scene Classification, Scene Retrieval and Scene-Guided Object Detection." Applied Sciences 9, no. 10 (2019): 2110. http://dx.doi.org/10.3390/app9102110.
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As a fundamental and important task in remote sensing, remote sensing image scene understanding (RSISU) has attracted tremendous research interest in recent years. RSISU includes the following sub-tasks: remote sensing image scene classification, remote sensing image scene retrieval, and scene-driven remote sensing image object detection. Although these sub-tasks have different goals, they share some communal hints. Hence, this paper tries to discuss them as a whole. Similar to other domains (e.g., speech recognition and natural image recognition), deep learning has also become the state-of-the-art technique in RSISU. To facilitate the sustainable progress of RSISU, this paper presents a comprehensive review of deep-learning-based RSISU methods, and points out some future research directions and potential applications of RSISU.
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Bai, Shuang. "Scene Categorization Through Using Objects Represented by Deep Features." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 09 (2017): 1755013. http://dx.doi.org/10.1142/s0218001417550138.
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Objects in scenes are thought to be important for scene recognition. In this paper, we propose to utilize scene-specific objects represented by deep features for scene categorization. Our approach combines benefits of deep learning and Latent Support Vector Machine (LSVM) to train a set of scene-specific object models for each scene category. Specifically, we first use deep Convolutional Neural Networks (CNNs) pre-trained on the large-scale object-centric image database ImageNet to learn rich object features and a large number of general object concepts. Then, the pre-trained CNNs is adopted to extract features from images in the target dataset, and initialize the learning of scene-specific object models for each scene category. After initialization, the scene-specific object models are obtained by alternating between searching over the most representative and discriminative regions of images in the target dataset and training linear SVM classifiers based on obtained region features. As a result, for each scene category a set of object models that are representative and discriminative can be acquired. We use them to perform scene categorization. In addition, to utilize global structure information of scenes, we use another CNNs pre-trained on the large-scale scene-centric database Places to capture structure information of scene images. By combining objects and structure information for scene categorization, we show superior performances to state-of-the-art approaches on three public datasets, i.e. MIT-indoor, UIUC-sports and SUN. Experiment results demonstrated the effectiveness of the proposed method.
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Hughes, L. H., S. Auer, and M. Schmitt. "INVESTIGATION OF JOINT VISIBILITY BETWEEN SAR AND OPTICAL IMAGES OF URBAN ENVIRONMENTS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2 (May 28, 2018): 129–36. http://dx.doi.org/10.5194/isprs-annals-iv-2-129-2018.
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In this paper, we present a work-flow to investigate the joint visibility between very-high-resolution SAR and optical images of urban scenes. For this task, we extend the simulation framework SimGeoI to enable a simulation of individual pixels rather than complete images. Using the extended SimGeoI simulator, we carry out a case study using a TerraSAR-X staring spotlight image and a Worldview-2 panchromatic image acquired over the city of Munich, Germany. The results of this study indicate that about 55&amp;thinsp;% of the scene are visible in both images and are thus suitable for matching and data fusion endeavours, while about 25&amp;thinsp;% of the scene are affected by either radar shadow or optical occlusion. Taking the image acquisition parameters into account, our findings can provide support regarding the definition of upper bounds for image fusion tasks, as well as help to improve acquisition planning with respect to different application goals.
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Peng, Jian, Xiaoming Mei, Wenbo Li, Liang Hong, Bingyu Sun, and Haifeng Li. "Scene Complexity: A New Perspective on Understanding the Scene Semantics of Remote Sensing and Designing Image-Adaptive Convolutional Neural Networks." Remote Sensing 13, no. 4 (2021): 742. http://dx.doi.org/10.3390/rs13040742.
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Scene understanding of remote sensing images is of great significance in various applications. Its fundamental problem is how to construct representative features. Various convolutional neural network architectures have been proposed for automatically learning features from images. However, is the current way of configuring the same architecture to learn all the data while ignoring the differences between images the right one? It seems to be contrary to our intuition: it is clear that some images are easier to recognize, and some are harder to recognize. This problem is the gap between the characteristics of the images and the learning features corresponding to specific network structures. Unfortunately, the literature so far lacks an analysis of the two. In this paper, we explore this problem from three aspects: we first build a visual-based evaluation pipeline of scene complexity to characterize the intrinsic differences between images; then, we analyze the relationship between semantic concepts and feature representations, i.e., the scalability and hierarchy of features which the essential elements in CNNs of different architectures, for remote sensing scenes of different complexity; thirdly, we introduce CAM, a visualization method that explains feature learning within neural networks, to analyze the relationship between scenes with different complexity and semantic feature representations. The experimental results show that a complex scene would need deeper and multi-scale features, whereas a simpler scene would need lower and single-scale features. Besides, the complex scene concept is more dependent on the joint semantic representation of multiple objects. Furthermore, we propose the framework of scene complexity prediction for an image and utilize it to design a depth and scale-adaptive model. It achieves higher performance but with fewer parameters than the original model, demonstrating the potential significance of scene complexity.
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Morrison, H. Boyd. "Depth and Image Quality of Three-Dimensional, Lenticular-Sheet Images." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 41, no. 2 (1997): 1338–42. http://dx.doi.org/10.1177/1071181397041002135.
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This study investigated the inherent tradeoff between depth and image quality in lenticular-sheet (LS) imaging. Four different scenes were generated as experimental stimuli to represent a range of typical LS images. The overall amount of depth in each image, as well as the degree of foreground and background disparity, were varied, and the images were rated by subjects using the free-modulus magnitude estimation procedure. Generally, subjects preferred images which had smaller amounts of overall depth and tended to dislike excessive amounts of foreground or background disparity. The most preferred image was also determined for each scene by selecting the image with the highest mean rating. In a second experiment, these most preferred LS images for each scene were shown to subjects along with the analogous two-dimensional (2D) photographic versions. Results indicate that observers from the general population looked at the LS images longer than they did at the 2D versions and rated them higher on the attributes of quality of depth and attention-getting ability, although the LS images were rated lower on sharpness. No difference was found in overall quality or likeability.
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Fry, Edward W. S., Sophie Triantaphillidou, Robin B. Jenkin, Ralph E. Jacobson, and John R. Jarvis. "Noise Power Spectrum Scene-Dependency in Simulated Image Capture Systems." Electronic Imaging 2020, no. 9 (2020): 345–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.9.iqsp-345.
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The Noise Power Spectrum (NPS) is a standard measure for image capture system noise. It is derived traditionally from captured uniform luminance patches that are unrepresentative of pictorial scene signals. Many contemporary capture systems apply nonlinear content-aware signal processing, which renders their noise scene-dependent. For scene-dependent systems, measuring the NPS with respect to uniform patch signals fails to characterize with accuracy: i) system noise concerning a given input scene, ii) the average system noise power in real-world applications. The sceneand- process-dependent NPS (SPD-NPS) framework addresses these limitations by measuring temporally varying system noise with respect to any given input signal. In this paper, we examine the scene-dependency of simulated camera pipelines in-depth by deriving SPD-NPSs from fifty test scenes. The pipelines apply either linear or non-linear denoising and sharpening, tuned to optimize output image quality at various opacity levels and exposures. Further, we present the integrated area under the mean of SPD-NPS curves over a representative scene set as an objective system noise metric, and their relative standard deviation area (RSDA) as a metric for system noise scene-dependency. We close by discussing how these metrics can also be computed using scene-and-processdependent Modulation Transfer Functions (SPD-MTF).
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Wei, Di, Yuang Du, Lan Du, and Lu Li. "Target Detection Network for SAR Images Based on Semi-Supervised Learning and Attention Mechanism." Remote Sensing 13, no. 14 (2021): 2686. http://dx.doi.org/10.3390/rs13142686.
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The existing Synthetic Aperture Radar (SAR) image target detection methods based on convolutional neural networks (CNNs) have achieved remarkable performance, but these methods require a large number of target-level labeled training samples to train the network. Moreover, some clutter is very similar to targets in SAR images with complex scenes, making the target detection task very difficult. Therefore, a SAR target detection network based on a semi-supervised learning and attention mechanism is proposed in this paper. Since the image-level label simply marks whether the image contains the target of interest or not, which is easier to be labeled than the target-level label, the proposed method uses a small number of target-level labeled training samples and a large number of image-level labeled training samples to train the network with a semi-supervised learning algorithm. The proposed network consists of a detection branch and a scene recognition branch with a feature extraction module and an attention module shared between these two branches. The feature extraction module can extract the deep features of the input SAR images, and the attention module can guide the network to focus on the target of interest while suppressing the clutter. During the semi-supervised learning process, the target-level labeled training samples will pass through the detection branch, while the image-level labeled training samples will pass through the scene recognition branch. During the test process, considering the help of global scene information in SAR images for detection, a novel coarse-to-fine detection procedure is proposed. After the coarse scene recognition determining whether the input SAR image contains the target of interest or not, the fine target detection is performed on the image that may contain the target. The experimental results based on the measured SAR dataset demonstrate that the proposed method can achieve better performance than the existing methods.
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Anbarasu, B., and G. Anitha. "Indoor Scene Recognition for Micro Aerial Vehicles Navigation using Enhanced-GIST Descriptors." Defence Science Journal 68, no. 2 (2018): 129. http://dx.doi.org/10.14429/dsj.68.10504.
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<p>An indoor scene recognition algorithm combining histogram of horizontal and vertical directional morphological gradient features and GIST features is proposed in this paper. New visual descriptor is called enhanced-GIST. Three different classifiers, k-nearest neighbour classifier, Naïve Bayes classifier and support vector machine, are employed for the classification of indoor scenes into corridor, staircase or room. The evaluation was performed on two indoor scene datasets. The scene recognition algorithm consists of training phase and a testing phase. In the training phase, GIST, CENTRIST, LBP, HODMG and enhanced-GIST feature vectors are extracted for all the training images in the datasets and classifiers are trained for these image feature vectors and image labels (corridor-1, staircase-2 and room-3). In the test phase, GIST, CENTRIST, LBP, HODMG and enhanced-GIST feature vectors are extracted for each unknown test image sample and classification is performed using a trained scene recognition model. The experimental results show that indoor scene recognition algorithm employing SVM with enhanced GIST descriptors produces very high recognition rates of 97.22 per cent and 99.33 per cent for dataset-1 and dataset-2, compared to kNN and Naïve Bayes classifiers. In addition to its accuracy and robustness, the algorithm is suitable for real-time operations.</p>
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Yang, Xieliu, Chenyu Yin, Ziyu Zhang, et al. "Robust Chromatic Adaptation Based Color Correction Technology for Underwater Images." Applied Sciences 10, no. 18 (2020): 6392. http://dx.doi.org/10.3390/app10186392.
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Recovering correct or at least realistic colors of underwater scenes is a challenging issue for image processing due to the unknown imaging conditions including the optical water type, scene location, illumination, and camera settings. With the assumption that the illumination of the scene is uniform, a chromatic adaptation-based color correction technology is proposed in this paper to remove the color cast using a single underwater image without any other information. First, the underwater RGB image is first linearized to make its pixel values proportional to the light intensities arrived at the pixels. Second, the illumination is estimated in a uniform chromatic space based on the white-patch hypothesis. Third, the chromatic adaptation transform is implemented in the device-independent XYZ color space. Qualitative and quantitative evaluations both show that the proposed method outperforms the other test methods in terms of color restoration, especially for the images with severe color cast. The proposed method is simple yet effective and robust, which is helpful in obtaining the in-air images of underwater scenes.
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Ye, Minchao, Wenbin Zheng, Huijuan Lu, Xianting Zeng, and Yuntao Qian. "Cross-scene hyperspectral image classification based on DWT and manifold-constrained subspace learning." International Journal of Wavelets, Multiresolution and Information Processing 15, no. 06 (2017): 1750062. http://dx.doi.org/10.1142/s021969131750062x.
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Hyperspectral image (HSI) classification draws a lot of attentions in the past decades. The classical problem of HSI classification mainly focuses on a single HSI scene. In recent years, cross-scene classification becomes a new problem, which deals with the classification models that can be applied across different but highly related HSI scenes sharing common land cover classes. This paper presents a cross-scene classification framework combining spectral–spatial feature extraction and manifold-constrained feature subspace learning. In this framework, spectral–spatial feature extraction is completed using three-dimensional (3D) wavelet transform while manifold-constrained feature subspace learning is implemented via multitask nonnegative matrix factorization (MTNMF) with manifold regularization. In 3D wavelet transform, we drop some coefficients corresponding to high frequency in order to avoid data noise. In feature subspace learning, a common dictionary (basis) matrix is shared by different scenes during the nonnegative matrix factorization, indicating that the highly related scenes should share than same low-dimensional feature subspace. Furthermore, manifold regularization is applied to force the consistency across the scenes, i.e. all pixels representing the same land cover class should be similar in the low-dimensional feature subspace, though they may be drawn from different scenes. The experimental results show that the proposed method performs well in cross-scene HSI datasets.
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Wang, Ruize, Zhongyu Wei, Piji Li, Qi Zhang, and Xuanjing Huang. "Storytelling from an Image Stream Using Scene Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 9185–92. http://dx.doi.org/10.1609/aaai.v34i05.6455.
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Visual storytelling aims at generating a story from an image stream. Most existing methods tend to represent images directly with the extracted high-level features, which is not intuitive and difficult to interpret. We argue that translating each image into a graph-based semantic representation, i.e., scene graph, which explicitly encodes the objects and relationships detected within image, would benefit representing and describing images. To this end, we propose a novel graph-based architecture for visual storytelling by modeling the two-level relationships on scene graphs. In particular, on the within-image level, we employ a Graph Convolution Network (GCN) to enrich local fine-grained region representations of objects on scene graphs. To further model the interaction among images, on the cross-images level, a Temporal Convolution Network (TCN) is utilized to refine the region representations along the temporal dimension. Then the relation-aware representations are fed into the Gated Recurrent Unit (GRU) with attention mechanism for story generation. Experiments are conducted on the public visual storytelling dataset. Automatic and human evaluation results indicate that our method achieves state-of-the-art.
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Sajko, Robert, and Zeljka Mihajlovic. "Fast Image-Based Ambient Occlusion IBAO." International Journal of Virtual Reality 10, no. 4 (2011): 61–65. http://dx.doi.org/10.20870/ijvr.2011.10.4.2830.
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The quality of computer rendering and perception of realism greatly depend on the shading method used to implement the interaction of light with the surfaces of objects in a scene. Ambient occlusion (AO) enhances the realistic impression of rendered objects and scenes. Properties that make Screen Space Ambient Occlusion (SSAO) interesting for real-time graphics are scene complexity independence, and support for fully dynamic scenes. However, there are also important issues with current approaches: poor texture cache use, introduction of noise, and performance swings. In this paper, a straightforward solution is presented. Instead of a traditional, geometry-based sampling method, a novel, image-based sampling method is developed, coupled with a revised heuristic function for computing occlusion. Proposed algorithm harnessing GPU power improves texture cache use and reduces aliasing artifacts. Two implementations are developed, traditional and novel, and their comparison reveals improved performance and quality of the proposed algorithm.
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Duh, Henry Been-Lirn, James J. W. Lin, Robert V. Kenyon, Donald E. Parker, and Thomas A. Furness. "Effects of Characteristics of Image Quality in an Immersive Environment." Presence: Teleoperators and Virtual Environments 11, no. 3 (2002): 324–32. http://dx.doi.org/10.1162/105474602317473259.
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Image quality issues such as field of view (FOV) and resolution are important for evaluating “presence” and simulator sickness (SS) in virtual environments (VEs). This research examined effects on postural stability of varying FOV, image resolution, and scene content in an immersive visual display. Two different scenes (a photograph of a fountain and a simple radial pattern) at two different resolutions were tested using six FOVs (30, 60, 90, 120, 150, and 180 deg.). Both postural stability, recorded by force plates, and subjective difficulty ratings varied as a function of FOV, scene content, and image resolution. Subjects exhibited more balance disturbance and reported more difficulty in maintaining posture in the wide-FOV, highresolution, and natural scene conditions.
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Sial, Hassan A., Ramon Baldrich, Maria Vanrell, and Dimitris Samaras. "Light Direction and Color Estimation from Single Image with Deep Regression." London Imaging Meeting 2020, no. 1 (2020): 139–43. http://dx.doi.org/10.2352/issn.2694-118x.2020.lim-25.
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We present a method to estimate the direction and color of a scene light source from a single image. Our method is based on two main ideas: (a) we use a new synthetic dataset with strong shadow effects with similar constraints to SID dataset; (b) we define a deep architecture trained on the mentioned dataset to estimate direction and color of the scene light source. Apart from showing a good performance on synthetic images, we additionally propose a preliminary procedure to obtain light positions of the Multi-Illumination dataset, and, in this way, we also prove that our trained model achieves a good performance when it is applied to real scenes.
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Belov, A. M., and A. Y. Denisova. "Scene distortion detection algorithm using multitemporal remote sensing images." Computer Optics 43, no. 5 (2019): 869–85. http://dx.doi.org/10.18287/2412-6179-2019-43-5-869-885.
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Multitemporal remote sensing images of a particular territory might include accidental scene distortions. Scene distortion is a significant local brightness change caused by the scene overlap with some opaque object or a natural phenomenon coincident with the moment of image capture, for example, clouds and shadows. The fact that different images of the scene are obtained at different instants of time makes the appearance, location and shape of scene distortions accidental. In this article we propose an algorithm for detecting accidental scene distortions using a dataset of multitemporal remote sensing images. The algorithm applies superpixel segmentation and anomaly detection methods to get binary images of scene distortion location for each image in the dataset. The algorithm is adapted to handle images with different spectral and spatial sampling parameters, which makes it more multipurpose than the existing solutions. The algorithm's quality was assessed using model images with scene distortions for two remote sensing systems. The experiments showed that the proposed algorithm with the optimal settings can reach a detection accuracy of about 90% and a false detection error of about 10%.
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Zhang, Yongxin, Deguang Li, and WenPeng Zhu. "Infrared and Visible Image Fusion with Hybrid Image Filtering." Mathematical Problems in Engineering 2020 (July 29, 2020): 1–17. http://dx.doi.org/10.1155/2020/1757214.
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Image fusion is an important technique aiming to generate a composite image from multiple images of the same scene. Infrared and visible images can provide the same scene information from different aspects, which is useful for target recognition. But the existing fusion methods cannot well preserve the thermal radiation and appearance information simultaneously. Thus, we propose an infrared and visible image fusion method by hybrid image filtering. We represent the fusion problem with a divide and conquer strategy. A Gaussian filter is used to decompose the source images into base layers and detail layers. An improved co-occurrence filter fuses the detail layers for preserving the thermal radiation of the source images. A guided filter fuses the base layers for retaining the background appearance information of the source images. Superposition of the fused base layer and fused detail layer generates the final fusion image. Subjective visual and objective quantitative evaluations comparing with other fusion algorithms demonstrate the better performance of the proposed method.
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Cheng, H. D., and Rutvik Desai. "Scene Classification by Fuzzy Local Moments." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 07 (1998): 921–38. http://dx.doi.org/10.1142/s0218001498000506.
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The identification of images irrespective of their location, size and orientation is one of the important tasks in pattern analysis. The use of global moment features has been one of the most popular techniques for this purpose. We present a simple and effective method for gray-level image representation and identification which utilizes fuzzy radial moments of image segments (local moments) as features as opposed to global features. A multilayer perceptron neural network is employed for classification. Fuzzy entropy measure is applied to optimize the parameters of the membership function. The technique does not require translation, scaling or rotation of the image. Furthermore, it is suitable for parallel implementation which is an advantage for real-time applications. The classification capability and robustness of the technique are demonstrated by experiments on scaled, rotated and noisy gray-level images of uppercase and lowercase characters and digits of English alphabet, as well as the images of a set of tools. The proposed approach can handle rotation, scale and translation invariance, noise and fuzziness simultaneously.
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Hussain, Md Arafat, and Emon Kumar Dey. "Remote Sensing Image Scene Classification." International Journal of Engineering and Manufacturing 8, no. 4 (2018): 13–20. http://dx.doi.org/10.5815/ijem.2018.04.02.
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Li, Zhihui, Yuyou Sun, Lei Xu, et al. "Explosion Scene Forensic Image Interpretation." Journal of Forensic Sciences 64, no. 4 (2019): 1221–29. http://dx.doi.org/10.1111/1556-4029.13996.
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