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Journal articles on the topic 'Image Merging'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Ushakov, K. D., and O. N. Kaneva. "COMPARATIVE ANALYSIS OF IMAGE MERGING ALGORITHMS." Applied Mathematics and Fundamental Informatics 9, no. 3 (2022): 53–59. http://dx.doi.org/10.25206/2311-4908-2022-9-3-53-59.

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The paper discusses algorithms for merging images by machine learning. Both statistical algorithms and machine learning algorithms are used in image merging. Fusion algorithms based on machine learning give a clearer and sharper image while preserving details. Pairs of images with different blurred areas were used to train the algorithm. The comparison of the developed algorithms by quality metrics is carried out.

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GU, JIANPING, LI ZHANG, and CUN CHENG. "DYNAMIC GRAPH MERGING FOR IMAGE SEGMENTATION." International Journal of Wavelets, Multiresolution and Information Processing 11, no. 06 (2013): 1350051. http://dx.doi.org/10.1142/s0219691313500513.

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A new algorithm named dynamic graph merging (DGM) for automatic image segmentation is explored. Firstly a novel variational model for multi-section cut is introduced by decomposing the traditional cut into two parts, the harmonic cut and the elastic energy of the boundary. The new energy is called the continuous combined cut. Secondly a new algorithm that removes those edges with higher energy and synchronously merges their starting and ending vertices in an ordered manner is proposed. The continual merging process would iteratively contract the graph, merge those homogeneous vertices into big

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S, Sadagopan. "Image Segmentation and Hiding Using Statistical Region Merging." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (2019): 1010–15. http://dx.doi.org/10.5373/jardcs/v11/20192665.

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Wang, Haoyu, Zhanfeng Shen, Zihan Zhang, et al. "Improvement of Region-Merging Image Segmentation Accuracy Using Multiple Merging Criteria." Remote Sensing 13, no. 14 (2021): 2782. http://dx.doi.org/10.3390/rs13142782.

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Image segmentation plays a significant role in remote sensing image processing. Among numerous segmentation algorithms, the region-merging segmentation algorithm is widely used due to its well-organized structure and outstanding results. Many merging criteria (MC) were designed to improve the accuracy of region-merging segmentation, but each MC has its own shortcomings, which can cause segmentation errors. Segmentation accuracy can be improved by referring to the segmentation results. To achieve this, an approach for detecting and correcting region-merging image segmentation errors is proposed

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Andersone, Ilze. "Map Merging in the Context of Image Processing." Scientific Journal of Riga Technical University. Computer Sciences 44, no. 1 (2011): 124–30. http://dx.doi.org/10.2478/v10143-011-0030-5.

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Map Merging in the Context of Image ProcessingThe area of map merging is tightly connected to the area of image processing. Usually metric maps created by robots are represented as occupancy grids. It is easy to apply algorithms used in image processing to this kind of map representation. The image processing subfield that is closest to the map merging is the image registration. It can be assumed that metric map merging methods similarly to the image registration methods consist of three components: feature space, search strategy and similarity metric. Algorithms from image processing can also

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Fagel, Sascha. "Merging methods of speech visualization." ZAS Papers in Linguistics 40 (January 1, 2005): 19–32. http://dx.doi.org/10.21248/zaspil.40.2005.255.

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The author presents MASSY, the MODULAR AUDIOVISUAL SPEECH SYNTHESIZER. The system combines two approaches of visual speech synthesis. Two control models are implemented: a (data based) di-viseme model and a (rule based) dominance model where both produce control commands in a parameterized articulation space. Analogously two visualization methods are implemented: an image based (video-realistic) face model and a 3D synthetic head. Both face models can be driven by both the data based and the rule based articulation model.  The high-level visual speech synthesis generates a sequence

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Lor, Kuo-Lung, and Chung-Ming Chen. "FAST INTERACTIVE REGIONAL PATTERN MERGING FOR GENERIC TISSUE SEGMENTATION IN HISTOPATHOLOGY IMAGES." Biomedical Engineering: Applications, Basis and Communications 33, no. 02 (2021): 2150012. http://dx.doi.org/10.4015/s1016237221500125.

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The image segmentation of histopathological tissue images has always been a challenge due to the overlapping of tissue color distributions, the complexity of extracellular texture and the large image size. In this paper, we introduce a new region-merging algorithm, namely, the Regional Pattern Merging (RPM) for interactive color image segmentation and annotation, by efficiently retrieving and applying the user’s prior knowledge of stroke-based interaction. Low-level color/texture features of each region are used to compose a regional pattern adapted to differentiating a foreground object from

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Lukmana Sardi, Indra, Fazmah Arif Yulianto, and Febryanti Sthevanie. "Analysis and Implementation of a Fast Corner Detector on Image Stitching in the Formation of Aerial Photogrammetry Images." International Journal on Information and Communication Technology (IJoICT) 10, no. 2 (2025): 259–69. https://doi.org/10.21108/ijoict.v10i2.1031.

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Aerial Photogrammetry is one of the products from the fields of geography in taking the object, area, or phenomenon on the surface of the earth. Using kamera with a photographic recording process and by the help of a detector in the form of film. In the application, required proper technique in merging images of aerial photographs in order to gain a broader perspective. In this final project, stitching method used in order to merging the images. Image stitching is a method for combining multiple images with overlapping fields of view to produce a panoramic image or picture, the image has a wid

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Panić, Branislav, Marko Nagode, Jernej Klemenc, and Simon Oman. "On Methods for Merging Mixture Model Components Suitable for Unsupervised Image Segmentation Tasks." Mathematics 10, no. 22 (2022): 4301. http://dx.doi.org/10.3390/math10224301.

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Unsupervised image segmentation is one of the most important and fundamental tasks in many computer vision systems. Mixture model is a compelling framework for unsupervised image segmentation. A segmented image is obtained by clustering the pixel color values of the image with an estimated mixture model. Problems arise when the selected optimal mixture model contains a large number of mixture components. Then, multiple components of the estimated mixture model are better suited to describe individual segments of the image. We investigate methods for merging the components of the mixture model

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Gonet, Michal, Boris Epel, Howard J. Halpern, and Martyna Elas. "Merging Preclinical EPR Tomography with other Imaging Techniques." Cell Biochemistry and Biophysics 77, no. 3 (2019): 187–96. http://dx.doi.org/10.1007/s12013-019-00880-7.

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Abstract This paper presents a survey of electron paramagnetic resonance (EPR) image registration. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints and/or different techniques. EPR-imaging (EPRI) techniques belong to the functional-imaging modalities and therefore suffer from a lack of anatomical reference which is mandatory in preclinical imaging. For this reason, it is necessary to merging EPR images with other modalities which allow for obtaining anatomy images. Methodological analysis and review of th

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Yocky, David A. "Artifacts in wavelet image merging." Optical Engineering 35, no. 7 (1996): 2094. http://dx.doi.org/10.1117/1.600765.

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Wawan, Gunawan, Zainal Arifin Agus, Indraswari Rarasmaya, and Adni Navastara Dini. "Fuzzy Region Merging Using Fuzzy Similarity Measurement on Image Segmentation." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (2017): 3402–10. https://doi.org/10.11591/ijece.v7i6.pp3402-3410.

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Some image’s regions have unbalance information, such as blurred contour, shade, and uneven brightness. Those regions are called as ambiguous regions. Ambiguous region cause problem during region merging process in interactive image segmentation because that region has double information, both as object and background. We proposed a new region merging strategy using fuzzy similarity measurement for image segmentation. The proposed method has four steps; the first step is initial segmentation using mean-shift algorithm. The second step is giving markers manually to indicate the object and

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Cui, Jian, Dong Ling Ma, Ming Yang Yu, and Ying Zhou. "Research of Remote Sensing Image Segmentation Based on Mean Shift and Region Merging." Applied Mechanics and Materials 90-93 (September 2011): 2836–39. http://dx.doi.org/10.4028/www.scientific.net/amm.90-93.2836.

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In order to extract ground information more accurately, it is important to find an image segmentation method to make the segmented features match the ground objects. We proposed an image segmentation method based on mean shift and region merging. With this method, we first segmented the image by using mean shift method and small-scale parameters. According to the region merging homogeneity rule, image features were merged and large-scale image layers were generated. What’s more, Multi-level image object layers were created through scaling method. The test of segmenting remote sensing images sh

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Stawiaski, Jean, and Etienne Decenciére. "REGION MERGING VIA GRAPH-CUTS." Image Analysis & Stereology 27, no. 1 (2011): 39. http://dx.doi.org/10.5566/ias.v27.p39-45.

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In this paper, we discuss the use of graph-cuts to merge the regions of the watershed transform optimally. Watershed is a simple, intuitive and efficient way of segmenting an image. Unfortunately it presents a few limitations such as over-segmentation and poor detection of low boundaries. Our segmentation process merges regions of the watershed over-segmentation by minimizing a specific criterion using graph-cuts optimization. Two methods will be introduced in this paper. The first is based on regions histogram and dissimilarity measures between adjacent regions. The second method deals with e

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Gong, Xiu Li, and Zhi Ming Wang. "Remote Sensing Image Segmentation Based on Improved Statistical Region Merging." Applied Mechanics and Materials 667 (October 2014): 226–29. http://dx.doi.org/10.4028/www.scientific.net/amm.667.226.

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Statistical Region Merging (SRM) is an efficient image segmentation algorithm for images with noise and partial occlusion. However, due to the complexity of remote sensing image, SRM can’t give satisfactory results. This paper proposes an improved image segmentation algorithm for remote sensing image based on SRM. Firstly, 8-connexity gradient estimation models are used to obtain more precisely edges. Secondly, the dissimilarity criterion between regions is replaced by a normalized distance standard. Finally, it dynamically updates and sorts dissimilarity between regions during region merging.

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Yi, Huiguo, Jie Yang, Pingxiang Li, Lei Shi, and Fengkai Lang. "A PolSAR Image Segmentation Algorithm Based on Scattering Characteristics and the Revised Wishart Distance." Sensors 18, no. 7 (2018): 2262. http://dx.doi.org/10.3390/s18072262.

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A novel segmentation algorithm for polarimetric synthetic aperture radar (PolSAR) images is proposed in this paper. The method is composed of two essential components: a merging order and a merging predicate. The similarity measured by the complex-kind Hotelling–Lawley trace (HLT) statistic is used to decide the merging order. The merging predicate is determined by the scattering characteristics and the revised Wishart distance between adjacent pixels, which can greatly improve the performance in speckle suppression and detail preservation. A postprocessing step is applied to obtain a satisfac

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Priya, J. Shirsath* Neeta Pingle. "IMAGE ENHANCEMENT USING AUTOMATIC SEGMENTATION TECHNIQUES." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 10 (2016): 459–66. https://doi.org/10.5281/zenodo.160880.

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This paper provides automatic image segmentation using dynamic region merging algorithm. Image segmentation is to partition the image into meaningful regions with respect to particular application. Image segmentation is basically to partition image into certain number of pieces that have coherent features, and again group the meaningful pieces for the convenience of perceiving. Image segmentation usually serves as the pre-processing before image pattern recognition. Initially the image is clustered and oversegmented using K-means and Watershed Algorithm. With an initially oversegmented image i

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Gunawan, Wawan, Agus Zainal Arifin, Rarasmaya Indraswari, and Dini Adni Navastara. "Fuzzy Region Merging using Fuzzy Similarity Measurement on Image Segmentation." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (2017): 3402. http://dx.doi.org/10.11591/ijece.v7i6.pp3402-3410.

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Some image’s regions have unbalance information, such as blurred contour, shade, and uneven brightness. Those regions are called as ambiguous regions. Ambiguous region cause problem during region merging process in interactive image segmentation because that region has double information, both as object and background. We proposed a new region merging strategy using fuzzy similarity measurement for image segmentation. The proposed method has four steps; the first step is initial segmentation using mean-shift algorithm. The second step is giving markers manually to indicate the object and backg

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Thomas, Irene M., and Michael F. Schmid. "A Cross-Correlation Method for Merging Electron Crystallographic Image Data." Microscopy and Microanalysis 1, no. 4 (1995): 167–73. http://dx.doi.org/10.1017/s1431927695111678.

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An efficient method of merging images from electron crystallographic data has been developed that is several orders of magnitude faster than the method of phase shift origin search. The results of this new cross-correlation method are compared with the phase residual in terms of speed, consistency with the phase residual method, and the ease of incorporating symmetry constraints along with the image-to-image merging. The program is further accelerated by a more efficient sorting procedure.

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Song, Yu Ting, Xiu Hua Ji, and Shi Lin Zhao. "An Improved Color Image Enhancement Algorithm Based on 3-D Color Histogram Equalization." Applied Mechanics and Materials 321-324 (June 2013): 1133–37. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1133.

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This paper proposes an improved color image enhancement algorithm based on 3-D color histogram equalization algorithm. When the existed 3-D color histogram equalization algorithms in the literatures are applied in processing dim color images, the processed color images often turn pale due to the decrease of color-saturations and have false contours due to gray-scale merging phenomenon in the histogram equalization algorithm. In this paper, the proposed algorithm can make more pixels of the processed color images keep their color-saturations and reduce the gray-scale merging with Logarithmic hi

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Le, Thanh Dat, Seong Young Kwon, and Changho Lee. "Performance Comparison of Feature Generation Algorithms for Mosaic Photoacoustic Microscopy." Photonics 8, no. 9 (2021): 352. http://dx.doi.org/10.3390/photonics8090352.

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Mosaic imaging is a computer vision process that is used for merging multiple overlapping imaging patches into a wide-field-of-view image. To achieve a wide-field-of-view photoacoustic microscopy (PAM) image, the limitations of the scan range of PAM require a merging process, such as marking the location of patches or merging overlapping areas between adjacent images. By using the mosaic imaging process, PAM shows a larger field view of targets and preserves the quality of the spatial resolution. As an essential process in mosaic imaging, various feature generation methods have been used to es

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Wang, Ping, Zheng Wei, Weihong Cui, and Zhiyong Lin. "A MINIMUM SPANNING TREE BASED METHOD FOR UAV IMAGE SEGMENTATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 111–17. http://dx.doi.org/10.5194/isprsannals-iii-7-111-2016.

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This paper proposes a Minimum Span Tree (MST) based image segmentation method for UAV images in coastal area. An edge weight based optimal criterion (merging predicate) is defined, which based on statistical learning theory (SLT). And we used a scale control parameter to control the segmentation scale. Experiments based on the high resolution UAV images in coastal area show that the proposed merging predicate can keep the integrity of the objects and prevent results from over segmentation. The segmentation results proves its efficiency in segmenting the rich texture images with good boundary o

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Wang, Ping, Zheng Wei, Weihong Cui, and Zhiyong Lin. "A MINIMUM SPANNING TREE BASED METHOD FOR UAV IMAGE SEGMENTATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 111–17. http://dx.doi.org/10.5194/isprs-annals-iii-7-111-2016.

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This paper proposes a Minimum Span Tree (MST) based image segmentation method for UAV images in coastal area. An edge weight based optimal criterion (merging predicate) is defined, which based on statistical learning theory (SLT). And we used a scale control parameter to control the segmentation scale. Experiments based on the high resolution UAV images in coastal area show that the proposed merging predicate can keep the integrity of the objects and prevent results from over segmentation. The segmentation results proves its efficiency in segmenting the rich texture images with good boundary o

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Searcy, Craig, Ken Dean, and William Stringer. "Merging remotely sensed data with geophysical models." Polar Record 31, no. 178 (1995): 297–304. http://dx.doi.org/10.1017/s003224740001384x.

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AbstractGeophysical models are usually derived from the idealistic viewpoint that all required external parameters are, in principle, measurable. The models are then driven with the best available data for those parameters. In some cases, there are few measurements available, because of factors such as the location of the phenomena modeled. Satellite imagery provides a synoptic overview of a particular environment, supplying spatial and temporal variability as well as spectral data, making this an ideal source of data for some models. In other cases, although frequent satellite-image observati

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GUNAWAN, Wawan. "FUZZY REGION MERGING WITH HIERARCHICAL CLUSTERING TO FIND OPTIMAL INITIALIZATION OF FUZZY REGION IN IMAGE SEGMENTATION." Applied Computer Science 20, no. 4 (2024): 211–20. https://doi.org/10.35784/acs-2024-48.

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One of the most important goals in image segmentation is the process of separating the object parts from the image background. Image segmentation is also a fundamental stage in the development of other image applications such as object recognition, target tracking, computer vision, and biomedical image processing. Interactive image segmentation methods with additional user interaction are still popular in research. Interactive image segmentation aims to provide additional information through simple interactions, especially in images with complex objects. Interactive image segmentation with reg

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Li, Qiang Qiang, and Wei Li. "An Improved Watershed Segmentation Algorithm for Bridge Image." Applied Mechanics and Materials 513-517 (February 2014): 3691–94. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3691.

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In order to solve the problem of over-segmentation of traditional watershed algorithm, an improved watershed segmentation algorithm of the bridge image was proposed in this paper. First, the input image was filtered by top-hat transformation and bottom-hat transformation, and then, a multiscale algorithm for computing morphological gradient images is proposed, and the threshold for marker-extraction is automatically calculated according to the statistics of local extreme points in the gradient map. The watershed algorithm is applied on the modified gradient map to segment the image. Then, the

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Sima, Haifeng, Aizhong Mi, Zhiheng Wang, and Youfeng Zou. "Objectness Supervised Merging Algorithm for Color Image Segmentation." Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/3180357.

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Ideal color image segmentation needs both low-level cues and high-level semantic features. This paper proposes a two-hierarchy segmentation model based on merging homogeneous superpixels. First, a region growing strategy is designed for producing homogenous and compact superpixels in different partitions. Total variation smoothing features are adopted in the growing procedure for locating real boundaries. Before merging, we define a combined color-texture histogram feature for superpixels description and, meanwhile, a novel objectness feature is proposed to supervise the region merging procedu

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Kumar, Umesh, Neha Gopaliya, Uma Sharma, and Sandeep Gupta. "Discrete Transform Based Image Fusion." International Journal of Multimedia Data Engineering and Management 8, no. 2 (2017): 43–49. http://dx.doi.org/10.4018/ijmdem.2017040105.

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With the advancement of image processing, the distinct area of image fusion has been explored. The word fusion represents a way of obtaining data acquired in several domains. A technique of merging useful data from input images is defined as image fusion. It improves features and performance. Fused image includes all the important features of input images without introducing any artifacts. This paper depicts the basic of image fusion and fusion techniques. Paper mainly focuses on frequency domain techniques. Image fusion widely used in surveillance, medical diagnosis, biometric, enhanced visio

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Umam, Khoirul, Fidi Wincoko Putro, and Gulpi Qorik Oktagalu Pratamasunu. "Segmentasi pada Citra Panoramik Gigi dengan Metode Two-Stage SOM dan T-CLUSTER." Jurnal ULTIMA Computing 6, no. 1 (2014): 7–13. http://dx.doi.org/10.31937/sk.v6i1.289.

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Segmentation on medical image requires good quality due to affect the interpretation and diagnosis of medical experts. On medical image segmentation, there is merging phase to increase the quality of the segmentation result. However, stopping criteria on merging phase was determined manually by medical experts. It implied the subjectivity of segmentation result. To increase the objectivity of segmentation result, a method to automate merging phase on medical image segmentation is required. Therefore, we propose a novel method on medical image segmentation which combine two-stage SOM and T-clus

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Naidu, A. Rajesh, and D. Bhavana. "Multimodal Medical Image Fusion using Hybrid Domains." Scalable Computing: Practice and Experience 23, no. 4 (2022): 225–32. http://dx.doi.org/10.12694/scpe.v23i4.2022.

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In a variety of clinical applications, image fusion is critical for merging data from multiple sources into a single, more understandable outcome. The use of medical image fusion technologies to assist the physician in executing combination procedures can be advantageous. The diagnostic process includes preoperative planning, intra operative supervision, an interventional treatment. In this thesis, a technique for image fusion was suggested that used a combination model of PCA and CNN. A method of real-time image fusion that employs pre-trained neural networks to synthesize a single image from

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Xu, Zhi Yuan, Yong Kai Wang, Xiao Hong Su, and Yi Liu. "A Defogging Algorithm Based on Statistical Region Merging." Applied Mechanics and Materials 644-650 (September 2014): 2189–93. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2189.

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Different regions in Foggy images have different contrast attenuations. This paper presented a defogging algorithm based on Statistical Region Merging. Firstly, we segmented the image by Mean Shift; Secondly, we merged these divided regions through Statistical Region Merging; Finally, we enhanced the contrast of different regions by Dynamic Segmentation Histogram Equalization contrast enhancement. The experimental results show that our defogging method is more effective than other methods.

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Javid, Tariq, Muhammad Faris, and Pervez Akhtar. "Integrated representation for discrete Fourier and wavelet transforms using vector notation." Mehran University Research Journal of Engineering and Technology 41, no. 3 (2022): 175–84. http://dx.doi.org/10.22581/muet1982.2203.18.

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Many mathematical operations are implemented easily through transform domain operations. Multiple transform domain operations are used independently in large and complex applications. There is a need to develop integrated representations for multiple transform domain operations. This paper presents an integrated mathematical representation for the discrete Fourier transformation and the discrete wavelet transformation. The proposed combined representation utilizes the powerful vector notation. A mathematical operator, called the star operator, is formulated that merges coefficients from differ

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Cheng, Cun, and Li Zhang. "An efficient segmentation method based on dynamic graph merging." International Journal of Wavelets, Multiresolution and Information Processing 14, no. 06 (2016): 1650052. http://dx.doi.org/10.1142/s0219691316500521.

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A novel energy functional based on the Mumford–Shah model is established for performing automatic image segmentation. And in order to optimize the global model using graph-based methods, we develop a localized formula. Then, we propose a merging predicate for determining whether an edge connecting two neighboring pixels or regions merge. The dynamic graph merging (DGM) method is applied based on this merging predicate. That is, those edges with large energy merge and the edges with low energy are remained, such that the energy functional is minimized. Compared with other graph-based segmentati

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Haiyan Yu, Haiyan Yu, and Jihong Wang Haiyan Yu. "Infrared Image Segmentation for Power Equipment Using Linear Spectral Clustering and Maximal Similarity-based Region Merging." 電腦學刊 33, no. 1 (2022): 043–53. http://dx.doi.org/10.53106/199115992022023301005.

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<p>The diagnostic method of power equipment based on infrared images is widely used because it has the advantages of non-contact and does not affect the online operation of power equipment. However, in actual using, the power equipment diagnosis method based on infrared image still relies on manual judgment, that is, the detection personnel can judge the fault according to the obtained infrared image of power equipment by experience. This process consumes a lot of time, and the subjectivity is strong, misjudgment rate is higher, which cannot meet the requirements of modern smart grid dev

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Panguluri, Sumanth Kumar, and Laavanya Mohan. "Discrete Wavelet Transform Based Image Fusion Using Unsharp Masking." Periodica Polytechnica Electrical Engineering and Computer Science 64, no. 2 (2019): 211–20. http://dx.doi.org/10.3311/ppee.14702.

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Nowadays the result of infrared and visible image fusion has been utilized in significant applications like military, surveillance, remote sensing and medical imaging applications. Discrete wavelet transform based image fusion using unsharp masking is presented. DWT is used for decomposing input images (infrared, visible). Approximation and detailed coefficients are generated. For improving contrast unsharp masking has been applied on approximation coefficients. Then for merging approximation coefficients produced after unsharp masking average fusion rule is used. The rule that is used for mer

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ChiMan Pun, and NingYu An. "Image Segmentation using Effective Region Merging Strategy." International Journal of Digital Content Technology and its Applications 5, no. 8 (2011): 59–69. http://dx.doi.org/10.4156/jdcta.vol5.issue8.8.

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Chongbo Zhou, and Chuancai Liu. "Interactive Image Semantic Segmentation using Region Merging." International Journal of Digital Content Technology and its Applications 6, no. 15 (2012): 35–44. http://dx.doi.org/10.4156/jdcta.vol6.issue15.5.

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Bo Peng, Lei Zhang, and D. Zhang. "Automatic Image Segmentation by Dynamic Region Merging." IEEE Transactions on Image Processing 20, no. 12 (2011): 3592–605. http://dx.doi.org/10.1109/tip.2011.2157512.

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Archer, Jesse, Geoff Leach, and Ron van Schyndel. "GPU based techniques for deep image merging." Computational Visual Media 4, no. 3 (2018): 277–85. http://dx.doi.org/10.1007/s41095-018-0118-8.

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Jian~, WANG. "High Resolution Image Merging Based on EMD." National Remote Sensing Bulletin, no. 1 (2007): 55–61. http://dx.doi.org/10.11834/jrs.20070108.

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Farouk, Esraa. "Gender Recognition from Selfie Images by Merging Convolutional Neural Networks with Genetic Algorithms." International Journal of Engineering and Computer Science 13, no. 08 (2024): 26338–50. http://dx.doi.org/10.18535/ijecs/v13i08.4876.

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Human gender plays an imperative role as social construct and an essential form of an individual’s personality. Gender recognition is a fundamental task for human beings. It is highly reflected in social communication, forensic science, surveillance, and target marketing. Gender recognition previously depended only on standard face images. Using the term “standard” means that the image was taken in a standard light without any background variations and without any cropped parts. But this type of image is not found in real-world. These images are called non-standard images, as they have a lot o

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Xiao, Yifan, Peter Veelaert, and Wilfried Philips. "Deep HDR Deghosting by Motion-Attention Fusion Network." Sensors 22, no. 20 (2022): 7853. http://dx.doi.org/10.3390/s22207853.

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Multi-exposure image fusion (MEF) methods for high dynamic range (HDR) imaging suffer from ghosting artifacts when dealing with moving objects in dynamic scenes. The state-of-the-art methods use optical flow to align low dynamic range (LDR) images before merging, introducing distortion into the aligned LDR images from inaccurate motion estimation due to large motion and occlusion. In place of pre-alignment, attention-based methods calculate the correlation between the reference LDR image and non-reference LDR images, thus excluding misaligned regions in LDR images. Nevertheless, they also excl

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Juang, Ying-Shen, Hsi-Chin Hsin, Tze-Yun Sung, Yaw-Shih Shieh, and Carlo Cattani. "A Rate-Distortion-Based Merging Algorithm for Compressed Image Segmentation." Computational and Mathematical Methods in Medicine 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/648320.

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Original images are often compressed for the communication applications. In order to avoid the burden of decompressing computations, it is thus desirable to segment images in the compressed domain directly. This paper presents a simple rate-distortion-based scheme to segment images in the JPEG2000 domain. It is based on a binary arithmetic code table used in the JPEG2000 standard, which is available at both encoder and decoder; thus, there is no need to transmit the segmentation result. Experimental results on the Berkeley image database show that the proposed algorithm is preferable in terms

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TANTI, MARC, ALBERT GATT, and KENNETH P. CAMILLERI. "Where to put the image in an image caption generator." Natural Language Engineering 24, no. 3 (2018): 467–89. http://dx.doi.org/10.1017/s1351324918000098.

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AbstractWhen a recurrent neural network (RNN) language model is used for caption generation, the image information can be fed to the neural network either by directly incorporating it in the RNN – conditioning the language model by ‘injecting’ image features – or in a layer following the RNN – conditioning the language model by ‘merging’ image features. While both options are attested in the literature, there is as yet no systematic comparison between the two. In this paper, we empirically show that it is not especially detrimental to performance whether one architecture is used or another. Th

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Swaroopa, H. Narayan, N. Jagadale Basavaraj, and Abdullah Murshed Farhan Alnaggar Omar. "A Novel Image Denoising and Segmentation Using Machine Learning with SRM Strategy." Indian Journal of Science and Technology 15, no. 17 (2022): 778–89. https://doi.org/10.17485/IJST/v15i17.2203.

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Abstract <strong>Objectives:</strong> In the last decade, artificial intelligence (AI) and machine learning (ML) have a significant impact on image analysis and segmentation. The most demanding aspect in digital image processing is efficient segmentation to extract the desired features or recognize the hidden patterns of the digital images, which are one of the major challenges. Even in existing segmentation techniques, due to under segmentation issues, particularly in remote or arial image analysis, necessary object features and residual details are not properly segmented. Therefore, the

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Xiong, Ding, and Lu Yan. "Early smoke detection of forest fires based on SVM image segmentation." Journal of Forest Science 65, No. 4 (2019): 150–59. http://dx.doi.org/10.17221/82/2018-jfs.

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A smoke detection method is proposed in single-frame video sequence images for forest fire detection in large space and complex scenes. A new superpixel merging algorithm is further studied to improve the existing horizon detection algorithm. This method performs Simple Linear Iterative Clustering (SLIC) superpixel segmentation on the image, and the over-segmentation problem is solved with a new superpixel merging algorithm. The improved sky horizon line segmentation algorithm is used to eliminate the interference of clouds in the sky for smoke detection. According to the spectral features, th

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Baqer Qazzaz, Ali Abdulazeez Mohammed, and ABDULHUSSEIN ABDULMOHSON. "A NOVEL PROPOSED FUSION METHOD." International Research Journal of Computer Science 8, no. 4 (2021): 74–83. http://dx.doi.org/10.26562/irjcs.2021.v0804.002.

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In this paper, two proposed fusion methods will be proposed for merging important information from more than one source for the purpose of either making final image with high quality compared to the input images or making final image holding more information that captured in the input images depending on some important extracted features from the input images. The first method merge images depending on edges information in each pixel by applying one order prewitt method for edge detection then calculating the ratio related to each image for this position then calculating new pixel value for th

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Ali, Abdulazeez Mohammed Baqer Qazzaz, and ABDULMOHSON ABDULHUSSEIN. "A NOVEL PROPOSED FUSION METHOD." International Research Journal of Computer Science VIII, no. IV (2021): 73–82. https://doi.org/10.26562/irjcs.2021.v0804.002.

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In this paper, two proposed fusion methods will be proposed for merging important information from more than one source for the purpose of either making final image with high quality compared to the input images or making final image holding more information that captured in the input images depending on some important extracted features from the input images. The first method merge images depending on edges information in each pixel by applying one order prewitt method for edge detection then calculating the ratio related to each image for this position then calculating new pixel value for th

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Ciupala, Laura, Adrian DEACONU, and Delia SPRIDON. "ALGORITHM FOR MERGING AND INTERPOLATING CLUSTERS IN OVERLAPPING IMAGES." SERIES III - MATEMATICS, INFORMATICS, PHYSICS 13(62), no. 2 (2021): 697–704. http://dx.doi.org/10.31926/but.mif.2020.13.62.2.25.

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An image overlapping algorithm, taking into account certain properties of objects identified in the images (average intensity, movement speed, etc) is proposed. The algorithm minimizes both memory and time complexity and it can be used in various applications, especially in medical imaging analysis. The idea behind the proposed algorithm is surface merging and interpolation.

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Wu, Wen Huan, Ying Jun Zhao, Dong Hua Lu, and Dong Hui Zhang. "The Extraction Research of Urban Road Information Based on the High Resolution QuickBird Image." Advanced Materials Research 718-720 (July 2013): 2136–41. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.2136.

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This paper studied the key technology of the extraction of urban road information using high-resolution QuickBird image. And this paper discussed the road information extraction algorithms, K-mean image clustering and segmentation algorithm, lambda-schedule image merging algorithm and the construction of knowledge database using spectral and shape features, from three angles, image segmentation algorithm, image merging algorithm and specific road information extraction algorithm. Studies showed that, after the introduction of the spectral and texture information, road extraction method reached

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