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Journal articles on the topic 'Image structure representation'

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1

Chen, Yuhao, Alexander Wong, Yuan Fang, Yifan Wu, and Linlin Xu. "Deep Residual Transform for Multi-scale Image Decomposition." Journal of Computational Vision and Imaging Systems 6, no. 1 (2021): 1–5. http://dx.doi.org/10.15353/jcvis.v6i1.3537.

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Multi-scale image decomposition (MID) is a fundamental task in computer vision and image processing that involves the transformation of an image into a hierarchical representation comprising of different levels of visual granularity from coarse structures to fine details. A well-engineered MID disentangles the image signal into meaningful components which can be used in a variety of applications such as image denoising, image compression, and object classification. Traditional MID approaches such as wavelet transforms tackle the problem through carefully designed basis functions under rigid de
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RIZO-RODRÍGUEZ, DAYRON, HEYDI MÉNDEZ-VAZQUEZ, and EDEL GARCÍA-REYES. "ILLUMINATION INVARIANT FACE RECOGNITION IN QUATERNION DOMAIN." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 03 (2013): 1360004. http://dx.doi.org/10.1142/s0218001413600045.

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The performance of face recognition systems tends to decrease when images are affected by illumination. Feature extraction is one of the main steps of a face recognition process, where it is possible to alleviate the illumination effects on face images. In order to increase the accuracy of recognition tasks, different methods for obtaining illumination invariant features have been developed. The aim of this work is to compare two different ways to represent face image descriptions in terms of their illumination invariant properties for face recognition. The first representation is constructed
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Fu, Y., Y. Ye, G. Liu, B. Zhang, and R. Zhang. "ROBUST MULTIMODAL IMAGE MATCHING BASED ON MAIN STRUCTURE FEATURE REPRESENTATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 583–89. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-583-2020.

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Abstract. Image matching is a crucial procedure for multimodal remote sensing image processing. However, the performance of conventional methods is often degraded in matching multimodal images due to significant nonlinear intensity differences. To address this problem, this letter proposes a novel image feature representation named Main Structure with Histogram of Orientated Phase Congruency (M-HOPC). M-HOPC is able to precisely capture similar structure properties between multimodal images by reinforcing the main structure information for the construction of the phase congruency feature descr
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WANG, ZHIYONG, ZHERU CHI, DAGAN FENG, and AH CHUNG TSOI. "CONTENT-BASED IMAGE RETRIEVAL WITH RELEVANCE FEEDBACK USING ADAPTIVE PROCESSING OF TREE-STRUCTURE IMAGE REPRESENTATION." International Journal of Image and Graphics 03, no. 01 (2003): 119–43. http://dx.doi.org/10.1142/s0219467803000944.

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Content-based image retrieval has become an essential technique in multimedia data management. However, due to the difficulties and complications involved in the various image processing tasks, a robust semantic representation of image content is still very difficult (if not impossible) to achieve. In this paper, we propose a novel content-based image retrieval approach with relevance feedback using adaptive processing of tree-structure image representation. In our approach, each image is first represented with a quad-tree, which is segmentation free. Then a neural network model with the Back-
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Yu, Siquan, Jiaxin Liu, Zhi Han, Yong Li, Yandong Tang, and Chengdong Wu. "Representation Learning Based on Autoencoder and Deep Adaptive Clustering for Image Clustering." Mathematical Problems in Engineering 2021 (January 9, 2021): 1–11. http://dx.doi.org/10.1155/2021/3742536.

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Image clustering is a complex procedure, which is significantly affected by the choice of image representation. Most of the existing image clustering methods treat representation learning and clustering separately, which usually bring two problems. On the one hand, image representations are difficult to select and the learned representations are not suitable for clustering. On the other hand, they inevitably involve some clustering step, which may bring some error and hurt the clustering results. To tackle these problems, we present a new clustering method that efficiently builds an image repr
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CHEN, XIAOWU, BIN ZHOU, FANG XU, and QINPING ZHAO. "AUTOMATIC IMAGE COMPLETION WITH STRUCTURE PROPAGATION AND TEXTURE SYNTHESIS." International Journal of Software Engineering and Knowledge Engineering 20, no. 08 (2010): 1097–117. http://dx.doi.org/10.1142/s0218194010005055.

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In this paper, we present a novel automatic image completion solution in a greedy manner inspired by a primal sketch representation model. Firstly, an image is divided into structure (sketchable) components and texture (non-sketchable) components, and the missing structures, such as curves and corners, are predicted by tensor voting. Secondly, the textures along structural sketches are synthesized with the sampled patches of some known structure components. Then, using the texture completion priorities decided by the confidence term, data term and distance term, the similar image patches of so
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Li, Wei, Yuxiang Zhang, Na Liu, Qian Du, and Ran Tao. "Structure-Aware Collaborative Representation for Hyperspectral Image Classification." IEEE Transactions on Geoscience and Remote Sensing 57, no. 9 (2019): 7246–61. http://dx.doi.org/10.1109/tgrs.2019.2912507.

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Li, Zhao, Le Wang, Tao Yu, and Bing Liang Hu. "Image Super-Resolution via Low-Rank Representation." Applied Mechanics and Materials 568-570 (June 2014): 652–55. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.652.

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This paper presents a novel method for solving single-image super-resolution problems, based upon low-rank representation (LRR). Given a set of a low-resolution image patches, LRR seeks the lowest-rank representation among all the candidates that represent all patches as the linear combination of the patches in a low-resolution dictionary. By jointly training two dictionaries for the low-resolution and high-resolution images, we can enforce the similarity of LLRs between the low-resolution and high-resolution image pair with respect to their own dictionaries. Therefore, the LRR of a low-resolu
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Dong, Bin, Songlei Jian, and Kai Lu. "Learning Multimodal Representations by Symmetrically Transferring Local Structures." Symmetry 12, no. 9 (2020): 1504. http://dx.doi.org/10.3390/sym12091504.

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Multimodal representations play an important role in multimodal learning tasks, including cross-modal retrieval and intra-modal clustering. However, existing multimodal representation learning approaches focus on building one common space by aligning different modalities and ignore the complementary information across the modalities, such as the intra-modal local structures. In other words, they only focus on the object-level alignment and ignore structure-level alignment. To tackle the problem, we propose a novel symmetric multimodal representation learning framework by transferring local str
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Berg, A. P., and W. B. Mikhael. "An efficient structure and algorithm for image representation using nonorthogonal basis images." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 44, no. 10 (1997): 818–28. http://dx.doi.org/10.1109/82.633439.

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Guru, D. S., K. B. Nagasundara, S. Manjunath, and R. Dinesh. "An Approach for Hand Vein Representation and Indexing." International Journal of Digital Crime and Forensics 3, no. 2 (2011): 1–15. http://dx.doi.org/10.4018/jdcf.2011040101.

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This paper proposes a model for representing and indexing of hand vein images. The proposed representation model identifies the junction points and perceives the spatial relationships existing among all junction points in hand vein images by the use of triangular spatial relationship (TSR). The model preserves the TSR among the junction points in a symbolic hand vein image by the use of quadruples and for each quadruple, a unique TSR key is generated. A novel methodology to label the junction points based on graph properties of junction points is also proposed. A Symbolic Hand Vein Image Datab
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Fei, Yin, Gao Wei, and Song Zongxi. "Medical Image Fusion Based on Feature Extraction and Sparse Representation." International Journal of Biomedical Imaging 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/3020461.

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As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM) and energy information map (EM) as well as structure and energy map (SEM) to make the
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Zhang, Yongqin, Jiaying Liu, Wenhan Yang, and Zongming Guo. "Image Super-Resolution Based on Structure-Modulated Sparse Representation." IEEE Transactions on Image Processing 24, no. 9 (2015): 2797–810. http://dx.doi.org/10.1109/tip.2015.2431435.

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Xu, Peng, Man Guo, Lei Chen, Weifeng Hu, Qingshan Chen, and Yujun Li. "No-Reference Stereoscopic Image Quality Assessment Based on Binocular Statistical Features and Machine Learning." Complexity 2021 (January 28, 2021): 1–14. http://dx.doi.org/10.1155/2021/8834652.

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Learning a deep structure representation for complex information networks is a vital research area, and assessing the quality of stereoscopic images or videos is challenging due to complex 3D quality factors. In this paper, we explore how to extract effective features to enhance the prediction accuracy of perceptual quality assessment. Inspired by the structure representation of the human visual system and the machine learning technique, we propose a no-reference quality assessment scheme for stereoscopic images. More specifically, the statistical features of the gradient magnitude and Laplaci
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Cadieu, Charles F., and Bruno A. Olshausen. "Learning Intermediate-Level Representations of Form and Motion from Natural Movies." Neural Computation 24, no. 4 (2012): 827–66. http://dx.doi.org/10.1162/neco_a_00247.

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We present a model of intermediate-level visual representation that is based on learning invariances from movies of the natural environment. The model is composed of two stages of processing: an early feature representation layer and a second layer in which invariances are explicitly represented. Invariances are learned as the result of factoring apart the temporally stable and dynamic components embedded in the early feature representation. The structure contained in these components is made explicit in the activities of second-layer units that capture invariances in both form and motion. Whe
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Thiedmann, Ralf, Henrik Hassfeld, Ole Stenzel, et al. "A MULTISCALE APPROACH TO THE REPRESENTATION OF 3D IMAGES, WITH APPLICATION TO POLYMER SOLAR CELLS." Image Analysis & Stereology 30, no. 1 (2011): 19. http://dx.doi.org/10.5566/ias.v30.p19-30.

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A multiscale approach to the description of geometrically complex 3D image data is proposed which distinguishes between morphological features on a ‘macro-scale’ and a ‘micro-scale’. Since our method is mainly tailored to nanostructures observed in composite materials consisting of two different phases, an appropriate binarization of grayscale images is required first. Then, a morphological smoothing is applied to extract the structural information from binarized image data on the ‘macro-scale’. A stochastic algorithm is developed for the morphologically smoothed images whose goal is to find a
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Carlson, Eric S. "Representation and Structure Conflict in the Digital Age." Advances in Archaeological Practice 2, no. 4 (2014): 269–84. http://dx.doi.org/10.7183/2326-3768.2.4.269.

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AbstractDigital imaging technologies have enhanced archaeological research and profoundly expanded the scale of the discipline’s potentialities. As illustrators and archaeologists move away from using hand-drawn images (of hand-held, real-life objects) to depict artifacts and other archaeological information, certain capabilities of the traditional illustrative process are lost. One such loss is the ability to present a complete and informed representation of an artifact free of the distortions and visual limitations that single-perspective (i.e., digital or photographic) imagery produces. Thi
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Dhaya, R. "Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain." September 2021 3, no. 3 (2021): 218–33. http://dx.doi.org/10.36548/jscp.2021.3.007.

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In recent years, there has been an increasing research interest in image de-noising due to an emphasis on sparse representation. When sparse representation theory is compared to transform domain-based image de-noising, the former indicates that the images have more information. It contains structural characteristics that are quite similar to the structure of dictionary-based atoms. This structure and the dictionary-based method is highly unsuccessful. However, image representation assumes that the noise lack such a feature. The dual-tree complex wavelet transform incorporates an increase in tr
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Zhu, Fuzhen, Yue Liu, Xin Huang, and Haitao Zhu. "Remote Sensing Image Super-resolution Based on Sparse Representation." MATEC Web of Conferences 232 (2018): 02037. http://dx.doi.org/10.1051/matecconf/201823202037.

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In order to obtain higher resolution remote sensing images with more details, an improved sparse representation remote sensing image super-resolution reconstruction(SRR) algorithm is proposed. First, remote sensing image is preprocessed to obtain the required training sample image; then, the KSVD algorithm is used for dictionary training to obtain the high-low resolution dictionary pairs; finally, the image feature extraction block is represented, which is improved by using adaptive filtering method. At the same time, the mean value filtering method is used to improve the super-resolution reco
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Yuan, Xiaobin, Jingping Zhu, and Xiaobin Li. "Blur Kernel Estimation by Structure Sparse Prior." Applied Sciences 10, no. 2 (2020): 657. http://dx.doi.org/10.3390/app10020657.

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Blind image deblurring tries to recover a sharp version from a blurred image, where blur kernel is usually unknown. Recently, sparse representation has been successfully applied to estimate the blur kernel. However, the sparse representation has not considered the structure relationships among original pixels. In this paper, a blur kernel estimation method is proposed by introducing the locality constraint into sparse representation framework. Both the sparsity regularization and the locality constraint are incorporated to exploit the structure relationships among pixels. The proposed method w
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Hecht, Helge, Mhd Hasan Sarhan, and Vlad Popovici. "Disentangled Autoencoder for Cross-Stain Feature Extraction in Pathology Image Analysis." Applied Sciences 10, no. 18 (2020): 6427. http://dx.doi.org/10.3390/app10186427.

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A novel deep autoencoder architecture is proposed for the analysis of histopathology images. Its purpose is to produce a disentangled latent representation in which the structure and colour information are confined to different subspaces so that stain-independent models may be learned. For this, we introduce two constraints on the representation which are implemented as a classifier and an adversarial discriminator. We show how they can be used for learning a latent representation across haematoxylin-eosin and a number of immune stains. Finally, we demonstrate the utility of the proposed repre
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Fu, Lingli, Chao Ren, Xiaohai He, Xiaohong Wu, and Zhengyong Wang. "Single Remote Sensing Image Super-Resolution with an Adaptive Joint Constraint Model." Sensors 20, no. 5 (2020): 1276. http://dx.doi.org/10.3390/s20051276.

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Remote sensing images have been widely used in many applications. However, the resolution of the obtained remote sensing images may not meet the increasing demands for some applications. In general, the sparse representation-based super-resolution (SR) method is one of the most popular methods to solve this issue. However, traditional sparse representation SR methods do not fully exploit the complementary constraints of images. Therefore, they cannot accurately reconstruct the unknown HR images. To address this issue, we propose a novel adaptive joint constraint (AJC) based on sparse represent
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Aghbari, Zaher Al. "Effective Image Mining by Representing Color Histograms as Time Series." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 2 (2009): 109–14. http://dx.doi.org/10.20965/jaciii.2009.p0109.

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Due to the wide spread of digital libraries, digital cameras, and the increase access to WWW by individuals, the number of digital images that exist pose a great challenge. Easy access to such collections requires an index structure to facilitate random access to individual images and ease navigation of these images. As these images are not annotated or associated with descriptions, existing systems represent the images by their extracted low level features.In this paper, we demonstrate two image mining tasks, namely image classification and image clustering, which are preliminary steps in fac
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Liao, Liang, Jing Xiao, Yating Li, Mi Wang, and Ruimin Hu. "Learned Representation of Satellite Image Series for Data Compression." Remote Sensing 12, no. 3 (2020): 497. http://dx.doi.org/10.3390/rs12030497.

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Real-time transmission of satellite video data is one of the fundamentals in the applications of video satellite. Making use of the historical information to eliminate the long-term background redundancy (LBR) is considered to be a crucial way to bridge the gap between the compressed data rate and the bandwidth between the satellite and the Earth. The main challenge lies in how to deal with the variant image pixel values caused by the change of shooting conditions while keeping the structure of the same landscape unchanged. In this paper, we propose a representation learning based method to mo
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Wen, Kui, Zhaojian Zhang, Xinpeng Jiang, Jie He, and Junbo Yang. "Image representation of structure color based on edge detection algorithm." Results in Physics 19 (December 2020): 103441. http://dx.doi.org/10.1016/j.rinp.2020.103441.

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Tao, Gao, Xiangmo Zhao, Ting Chen, Zhanwen Liu, and Si Li. "Image feature representation with orthogonal symmetric local weber graph structure." Neurocomputing 240 (May 2017): 70–83. http://dx.doi.org/10.1016/j.neucom.2017.02.047.

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Jiang, Bo, Jin Tang, Aihua Zheng, and Bin Luo. "Image representation and matching with geometric-edge random structure graph." Pattern Recognition Letters 87 (February 2017): 20–28. http://dx.doi.org/10.1016/j.patrec.2016.07.007.

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Movshon, J. Anthony, and Eero P. Simoncelli. "Representation of Naturalistic Image Structure in the Primate Visual Cortex." Cold Spring Harbor Symposia on Quantitative Biology 79 (2014): 115–22. http://dx.doi.org/10.1101/sqb.2014.79.024844.

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Ahuja, Narendra. "On detection and representation of multiscale low-level image structure." ACM Computing Surveys 27, no. 3 (1995): 304–6. http://dx.doi.org/10.1145/212094.212099.

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Qian, Jiansheng, Dong Wu, Leida Li, Deqiang Cheng, and Xuesong Wang. "Image quality assessment based on multi-scale representation of structure." Digital Signal Processing 33 (October 2014): 125–33. http://dx.doi.org/10.1016/j.dsp.2014.06.009.

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Liu, Guichi, Lei Gao, and Lin Qi. "Hyperspectral Image Classification via Multi-Feature-Based Correlation Adaptive Representation." Remote Sensing 13, no. 7 (2021): 1253. http://dx.doi.org/10.3390/rs13071253.

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In recent years, representation-based methods have attracted more attention in the hyperspectral image (HSI) classification. Among them, sparse representation-based classifier (SRC) and collaborative representation-based classifier (CRC) are the two representative methods. However, SRC only focuses on sparsity but ignores the data correlation information. While CRC encourages grouping correlated variables together but lacks the ability of variable selection. As a result, SRC and CRC are incapable of producing satisfied performance. To address these issues, in this work, a correlation adaptive
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Nawaz Jadoon, Rab, Waqas Jadoon, Ahmad Khan, et al. "Linear Discriminative Learning for Image Classification." Mathematical Problems in Engineering 2019 (October 20, 2019): 1–12. http://dx.doi.org/10.1155/2019/4760614.

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In this paper, we propose a linear discriminative learning model called adaptive locality-based weighted collaborative representation (ALWCR) that formulates the image classification task as an optimization problem to reduce the reconstruction error between the query sample and its computed linear representation. The optimal linear representation for a query image is obtained by using the weighted regularized linear regression approach which incorporates intrinsic locality structure and feature variance between data into representation. The resultant representation increases the discrimination
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Wallace, Luke, Bryan Hally, Samuel Hillman, Simon D. Jones, and Karin Reinke. "Terrestrial Image-Based Point Clouds for Mapping Near-Ground Vegetation Structure: Potential and Limitations." Fire 3, no. 4 (2020): 59. http://dx.doi.org/10.3390/fire3040059.

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Site-specific information concerning fuel hazard characteristics is needed to support wildfire management interventions and fuel hazard reduction programs. Currently, routine visual assessments provide subjective information, with the resulting estimate of fuel hazard varying due to observer experience and the rigor applied in making assessments. Terrestrial remote sensing techniques have been demonstrated to be capable of capturing quantitative information on the spatial distribution of biomass to inform fuel hazard assessments. This paper explores the use of image-based point clouds generate
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He, Zhouyan, Yang Song, Caiming Zhong, and Li Li. "Curvature and Entropy Statistics-Based Blind Multi-Exposure Fusion Image Quality Assessment." Symmetry 13, no. 8 (2021): 1446. http://dx.doi.org/10.3390/sym13081446.

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The multi-exposure fusion (MEF) technique provides humans a new opportunity for natural scene representation, and the related quality assessment issues are urgent to be considered for validating the effectiveness of these techniques. In this paper, a curvature and entropy statistics-based blind MEF image quality assessment (CE-BMIQA) method is proposed to perceive the quality degradation objectively. The transformation process from multiple images with different exposure levels to the final MEF image leads to the loss of structure and detail information, so that the related curvature statistic
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Cheng, Xi, Xiang Li, and Jian Yang. "Triple-Attention Mixed-Link Network for Single-Image Super-Resolution." Applied Sciences 9, no. 15 (2019): 2992. http://dx.doi.org/10.3390/app9152992.

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Single-image super-resolution is of great importance as a low-level computer-vision task. Recent approaches with deep convolutional neural networks have achieved impressive performance. However, existing architectures have limitations due to the less sophisticated structure along with less strong representational power. In this work, to significantly enhance the feature representation, we proposed triple-attention mixed-link network (TAN), which consists of (1) three different aspects (i.e., kernel, spatial, and channel) of attention mechanisms and (2) fusion of both powerful residual and dens
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Peng, Yong, Wanzeng Kong, Feiwei Qin, and Feiping Nie. "Manifold Adaptive Kernelized Low-Rank Representation for Semisupervised Image Classification." Complexity 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/2857594.

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Constructing a powerful graph that can effectively depict the intrinsic connection of data points is the critical step to make the graph-based semisupervised learning algorithms achieve promising performance. Among popular graph construction algorithms, low-rank representation (LRR) is a very competitive one that can simultaneously explore the global structure of data and recover the data from noisy environments. Therefore, the learned low-rank coefficient matrix in LRR can be used to construct the data affinity matrix. Consider the existing problems such as the following: (1) the essentially
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Wang, Qi, Xiang He, and Xuelong Li. "Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification." IEEE Transactions on Geoscience and Remote Sensing 57, no. 2 (2019): 911–23. http://dx.doi.org/10.1109/tgrs.2018.2862899.

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Christiansen, Mads-Peter V., Henrik Lund Mortensen, Søren Ager Meldgaard, and Bjørk Hammer. "Gaussian representation for image recognition and reinforcement learning of atomistic structure." Journal of Chemical Physics 153, no. 4 (2020): 044107. http://dx.doi.org/10.1063/5.0015571.

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Guo, Qin Zhen, Zhi Zeng, Shu Wu Zhang, Xiao Feng, and Hu Guan. "Simhash for Large Scale Image Retrieval." Applied Mechanics and Materials 651-653 (September 2014): 2197–200. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.2197.

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Due to its fast query speed and reduced storage cost, hashing, which tries to learn binary code representation for data with the expectation of preserving the neighborhood structure in the original data space, has been widely used in a large variety of applications like image retrieval. For most existing image retrieval methods with hashing, there are two main steps: describe images with feature vectors, and then use hashing methods to encode the feature vectors. In this paper, we make two research contributions. First, we creatively propose to use simhash which can be intrinsically combined w
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Bilquees, Samina, Hassan Dawood, Hussain Dawood, Nadeem Majeed, Ali Javed, and Muhammad Tariq Mahmood. "Noise Resilient Local Gradient Orientation for Content-Based Image Retrieval." International Journal of Optics 2021 (July 14, 2021): 1–19. http://dx.doi.org/10.1155/2021/4151482.

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In a world of multimedia information, where users seek accurate results against search query and demand relevant multimedia content retrieval, developing an accurate content-based image retrieval (CBIR) system is difficult due to the presence of noise in the image. The performance of the CBIR system is impaired by this noise. To estimate the distance between the query and database images, CBIR systems use image feature representation. The noise or artifacts present within the visual data might confuse the CBIR when retrieving relevant results. Therefore, we propose Noise Resilient Local Gradie
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ZHAO, YU, and YAN QIU CHEN. "CONNECTED EQUI-LENGTH LINE SEGMENTS FOR CURVE AND STRUCTURE MATCHING." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 06 (2004): 1019–37. http://dx.doi.org/10.1142/s0218001404003563.

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This paper deals with the problem of matching curves and structures extracted from 2D images that are subject to translation, rotation, scaling and other geometric transformations. We present in this paper a novel approach, Connected Equi-Length Line Segments (CELLS), for curve representation and matching. In our framework, a curve is represented by a number of connected equi-length line segments and a new matrix called Orientation Difference Matrix (ODM) is constructed for the curve, which reflects the distribution of the rest of the line segments with respect to the current one using orienta
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Fang, Jing, Shaohai Hu, and Xiaole Ma. "A Boosting SAR Image Despeckling Method Based on Non-Local Weighted Group Low-Rank Representation." Sensors 18, no. 10 (2018): 3448. http://dx.doi.org/10.3390/s18103448.

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In this paper, we propose a boosting synthetic aperture radar (SAR) image despeckling method based on non-local weighted group low-rank representation (WGLRR). The spatial structure information of SAR images leads to the similarity of the patches. Furthermore, the data matrix grouped by the similar patches within the noise-free SAR image is often low-rank. Based on this, we use low-rank representation (LRR) to recover the noise-free group data matrix. To maintain the fidelity of the recovered image, we integrate the corrupted probability of each pixel into the group LRR model as a weight to co
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Vida, Mark D., Adrian Nestor, David C. Plaut, and Marlene Behrmann. "Spatiotemporal dynamics of similarity-based neural representations of facial identity." Proceedings of the National Academy of Sciences 114, no. 2 (2016): 388–93. http://dx.doi.org/10.1073/pnas.1614763114.

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Humans’ remarkable ability to quickly and accurately discriminate among thousands of highly similar complex objects demands rapid and precise neural computations. To elucidate the process by which this is achieved, we used magnetoencephalography to measure spatiotemporal patterns of neural activity with high temporal resolution during visual discrimination among a large and carefully controlled set of faces. We also compared these neural data to lower level “image-based” and higher level “identity-based” model-based representations of our stimuli and to behavioral similarity judgments of our s
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Bouarara, Hadj Ahmed, and Yasmin Bouarara. "Swarm Intelligence Methods for Unsupervised Images Classification." International Journal of Organizational and Collective Intelligence 6, no. 2 (2016): 50–74. http://dx.doi.org/10.4018/ijoci.2016040104.

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Nowadays, Google estimates that more than 1000 billion the number of images on the internet where the classification of this type of data represents a big problem in the scientific community. Several techniques have been proposed belonging to the world of image-mining. The substance of our work is the application of swarm intelligence methods for the unsupervised image classification (UIC) problem following four steps: image digitalization by developing a new representation approach in order to transform each image into a set of term (set of pixels); image clustering using three methods: first
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Tong, Zhe, Wei Li, Fan Jiang, Zhencai Zhu, and Gongbo Zhou. "Bearing fault diagnosis based on spectrum image sparse representation of vibration signal." Advances in Mechanical Engineering 10, no. 9 (2018): 168781401879778. http://dx.doi.org/10.1177/1687814018797788.

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Bearings are crucial for industrial production and susceptible to malfunction in rotating machines. Image analysis can give a comprehensive description of vibration signal, thus, it has achieved much more attention recently in fault diagnosis field. However, it brings lots of redundant information from a single spectrum image matrix behind rich fault information, and massive spectrum image samples lead to exacerbation of this situation, which readily results in the accuracy-dropping problem of multiple local defective bearings diagnosis. To solve this issue, a novel feature extraction method b
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Lin, Tiffany Ying-Yu, and I.-Hsuan Chen. "How Semantics is Embodied through Visual Representation: Image Schemas in the Art of Chinese Calligraphy." Annual Meeting of the Berkeley Linguistics Society 38 (September 25, 2012): 328. http://dx.doi.org/10.3765/bls.v38i0.3338.

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<p>This study aims to investigate abstract reasoning and embodied cognition through the analysis of image schemas and conceptual metaphors in the interplay of art and language. Chinese calligraphy is noteworthy due to its unique embodied characteristics and image-schematic representations of visual art and language. The art of Chinese calligraphy not only represents the visual forms of Chinese characters but also conveys meanings, emotion, and style, demonstrating the aesthetics of language and art. By analyzing image schemas and metaphors in classical works of art, this paper shows how
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Ma, Changxia, Heng Zhang, and Bing Keong Li. "Shadow Separation of Pavement Images Based on Morphological Component Analysis." Journal of Control Science and Engineering 2021 (January 15, 2021): 1–10. http://dx.doi.org/10.1155/2021/8828635.

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The shadow of pavement images will affect the accuracy of road crack recognition and increase the rate of error detection. A shadow separation algorithm based on morphological component analysis (MCA) is proposed herein to solve the shadow problem of road imaging. The main assumption of MCA is that the image geometric structure and texture structure components are sparse within a class under a specific base or overcomplete dictionary, while the base or overcomplete dictionaries of each sparse representation of morphological components are incoherent. Thereafter, the corresponding image signal
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TANG, XIN, PATRICK S. WANG, and GUOCAN FENG. "A NOVEL SUPERVISED STRUCTURE DICTIONARY LEARNING FOR CLASSIFICATION BASED ON SPARSE REPRESENTATION." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 07 (2012): 1255012. http://dx.doi.org/10.1142/s0218001412550129.

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Sparse representation based classification has led to interesting image recognition results, while the dictionary used for sparse coding plays a key role in it. This paper presents a novel supervised structure dictionary learning (SSDL) algorithm to learn a discriminative and block structure dictionary. We associate label information with each dictionary item and make each class-specific sub-dictionary in the whole structured dictionary have good representation ability to the training samples from the associated class. More specifically, we learn a structured dictionary and a multiclass classi
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Lu, Shichen, Ruimin Hu, Jing Liu, Longteng Guo, and Fei Zheng. "Structure Preserving Convolutional Attention for Image Captioning." Applied Sciences 9, no. 14 (2019): 2888. http://dx.doi.org/10.3390/app9142888.

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In the task of image captioning, learning the attentive image regions is necessary to adaptively and precisely focus on the object semantics relevant to each decoded word. In this paper, we propose a convolutional attention module that can preserve the spatial structure of the image by performing the convolution operation directly on the 2D feature maps. The proposed attention mechanism contains two components: convolutional spatial attention and cross-channel attention, aiming to determine the intended regions to describe the image along the spatial and channel dimensions, respectively. Both
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MA, MATTHEW Y., JINHONG K. GUO, and PATRICK S. P. WANG. "FROM PIXELS TO TRUE XML STRUCTURES IN DIGITAL DOCUMENT IMAGES." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 06 (2004): 1057–69. http://dx.doi.org/10.1142/s0218001404003575.

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XML has been widely used as metadata for image retrieval. As a standard, it makes it easier to index and retrieve information across different platforms. However, how to automatically convert an image into XML format remains a challenge. In this paper, a system for generating structured document in XML from digitally captured document images is presented. The system is aimed at providing an easy to use tool for average users without requiring depth of knowledge in the document processing areas. Further, a XML/XSL generator is developed to accurately represent a document in a XML structure, yet
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