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Journal articles on the topic 'Image partition'

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Author: Grafiati

Published: 10 March 2023

Last updated: 11 March 2023

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1

Wang, Lei, Bo Yu, Fang Chen, Congrong Li, Bin Li, and Ning Wang. "A Cluster-Based Partition Method of Remote Sensing Data for Efficient Distributed Image Processing." Remote Sensing 14, no. 19 (October 5, 2022): 4964. http://dx.doi.org/10.3390/rs14194964.

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Data stream partitioning is a fundamental and important mechanism for distributed systems. However, use of an inappropriate partition scheme may generate a data skew problem, which can influence the execution efficiency of many application tasks. Processing of skewed partitions usually takes a longer time, need more computational resources to complete the task and can even become a performance bottleneck. To solve such data skew issues, this paper proposes a novel partition method to divide on demand the image tiles uniformly into partitions. The partitioning problem is then transformed into a uniform and compact clustering problem whereby the image tiles are regarded as image pixels without spectrum and texture information. First, the equal area conversion principle was used to select the seed points of the partitions and then the image tiles were aggregated in an image layout, thus achieving an initial partition scheme. Second, the image tiles of the initial partition were finely adjusted in the vertical and horizontal directions in separate steps to achieve a uniform distribution among the partitions. Two traditional partition methods were adopted to evaluate the efficiency of the proposed method in terms of the image segmentation testing, data shuffle testing, and image clipping testing. The results demonstrated that the proposed partition method solved the data skew problem observed in the hash partition method. In addition, this method is designed specifically for processing of image tiles and makes the related processing operations for large-scale images faster and more efficient.

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Jung, Ho Yub, and Kyoung Mu Lee. "Image Segmentation by Edge Partitioning over a Nonsubmodular Markov Random Field." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/683176.

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Edge weight-based segmentation methods, such as normalized cut or minimum cut, require a partition number specification for their energy formulation. The number of partitions plays an important role in the segmentation overall quality. However, finding a suitable partition number is a nontrivial problem, and the numbers are ordinarily manually assigned. This is an aspect of the general partition problem, where finding the partition number is an important and difficult issue. In this paper, the edge weights instead of the pixels are partitioned to segment the images. By partitioning the edge weights into two disjoints sets, that is, cut and connect, an image can be partitioned into all possible disjointed segments. The proposed energy function is independent of the number of segments. The energy is minimized by iterating the QPBO-α-expansion algorithm over the pairwise Markov random field and the mean estimation of the cut and connected edges. Experiments using the Berkeley database show that the proposed segmentation method can obtain equivalently accurate segmentation results without designating the segmentation numbers.

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Zuo, Yong Xia, Guo Qiang Wang, and Chun Cheng Zuo. "The Segmentation Algorithm for Pavement Cracking Images Based on the Improved Fuzzy Clustering." Applied Mechanics and Materials 319 (May 2013): 362–66. http://dx.doi.org/10.4028/www.scientific.net/amm.319.362.

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The segmentation technology of pavement cracking image is critical for identifying, quantifying and classifying pavement cracks. An improved fuzzy clustering algorithm is introduced to segment pavement cracking images. The algorithm makes no assumptions the initial position of clusters. For each value of the multiscale parameter, it obtains a corresponding hard partition. The different partitions values of the multiscale parameter indicate the structure of the image in different partitional scales. The algorithm was tested on actual pavement cracking images. We compared the results with FCM and OTSU to show that the improved fuzzy clustering algorithm can provide better crack edges.

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Liang, Dong, Rui Wang, Xiaowei Tian, and Cong Zou. "PCGAN: Partition-Controlled Human Image Generation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8698–705. http://dx.doi.org/10.1609/aaai.v33i01.33018698.

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Human image generation is a very challenging task since it is affected by many factors. Many human image generation methods focus on generating human images conditioned on a given pose, while the generated backgrounds are often blurred. In this paper, we propose a novel Partition-Controlled GAN to generate human images according to target pose and background. Firstly, human poses in the given images are extracted, and foreground/background are partitioned for further use. Secondly, we extract and fuse appearance features, pose features and background features to generate the desired images. Experiments on Market-1501 and DeepFashion datasets show that our model not only generates realistic human images but also produce the human pose and background as we want. Extensive experiments on COCO and LIP datasets indicate the potential of our method.

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Alameddine, Jihan, Kacem Chehdi, and Claude Cariou. "Hierarchical Unsupervised Partitioning of Large Size Data and Its Application to Hyperspectral Images." Remote Sensing 13, no. 23 (November 30, 2021): 4874. http://dx.doi.org/10.3390/rs13234874.

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In this paper, we propose a true unsupervised method to partition large-size images, where the number of classes, training samples, and other a priori information is not known. Thus, partitioning an image without any knowledge is a great challenge. This novel adaptive and hierarchical classification method is based on affinity propagation, where all criteria and parameters are adaptively calculated from the image to be partitioned. It is reliable to objectively discover classes of an image without user intervention and therefore satisfies all the objectives of an unsupervised method. Hierarchical partitioning adopted allows the user to analyze and interpret the data very finely. The optimal partition maximizing an objective criterion provides the number of classes and the exemplar of each class. The efficiency of the proposed method is demonstrated through experimental results on hyperspectral images. The obtained results show its superiority over the most widely used unsupervised and semi-supervised methods. The developed method can be used in several application domains to partition large-size images or data. It allows the user to consider all or part of the obtained classes and gives the possibility to select the samples in an objective way during a learning process.

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Yao, Hongtai, Xianpei Wang, Le Zhao, Meng Tian, Zini Jian, Li Gong, and Bowen Li. "An Object-Based Markov Random Field with Partition-Global Alternately Updated for Semantic Segmentation of High Spatial Resolution Remote Sensing Image." Remote Sensing 14, no. 1 (December 29, 2021): 127. http://dx.doi.org/10.3390/rs14010127.

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The Markov random field (MRF) method is widely used in remote sensing image semantic segmentation because of its excellent spatial (relationship description) ability. However, there are some targets that are relatively small and sparsely distributed in the entire image, which makes it easy to misclassify these pixels into different classes. To solve this problem, this paper proposes an object-based Markov random field method with partition-global alternately updated (OMRF-PGAU). First, four partition images are constructed based on the original image, they overlap with each other and can be reconstructed into the original image; the number of categories and region granularity for these partition images are set. Then, the MRF model is built on the partition images and the original image, their segmentations are alternately updated. The update path adopts a circular path, and the correlation assumption is adopted to establish the connection between the label fields of partition images and the original image. Finally, the relationship between each label field is constantly updated, and the final segmentation result is output after the segmentation has converged. Experiments on texture images and different remote sensing image datasets show that the proposed OMRF-PGAU algorithm has a better segmentation performance than other selected state-of-the-art MRF-based methods.

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Lin, Cheng-Shian, Chien-Chang Chen, and Yu-Cheng Chen. "XOR-Based Progressively Secret Image Sharing." Mathematics 9, no. 6 (March 12, 2021): 612. http://dx.doi.org/10.3390/math9060612.

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Secret image sharing technology is a strategy for jointly protecting secret images. The (n, n) secret image sharing problem can be solved by conventional Boolean calculation easily. However, how to recover secret images with progressive steps is not addressed. In this study, we proposed an XOR-based (m, t, Ti) multi-secret image sharing scheme that shares m secret images among m participants and recovers m shared images progressively with t thresholds. The proposed secret images partition strategy (SIPS) partitions m secret images to generate intermediate images for different thresholds in the sharing procedure. Based on progressive recovery property, the proposed recovery method recovers parts of the secret images by gathering consecutive shared images. Moreover, gathering all shared images can perfectly recover all secret images. The experimental results show that the proposed XOR-based multi-secret image sharing method has high security and efficiency.

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Gondimalla, Ashish, Jianqiao Liu, Mithuna Thottethodi, and T. N. Vijaykumar. "Occam: Optimal Data Reuse for Convolutional Neural Networks." ACM Transactions on Architecture and Code Optimization 20, no. 1 (December 16, 2022): 1–25. http://dx.doi.org/10.1145/3566052.

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Convolutional neural networks (CNNs) are emerging as powerful tools for image processing in important commercial applications. We focus on the important problem of improving the latency of image recognition. While CNNs are highly amenable to prefetching and multithreading to avoid memory latency issues, CNNs’ large data – each layer’s input, filters, and output – poses a memory bandwidth problem. While previous work captures only some of the enormous data reuse, full reuse implies that the initial input image and filters are read once from off-chip and the final output is written once off-chip without spilling the intermediate layers’ data to off-chip. We propose Occam to capture full reuse via four contributions. First, we identify the necessary conditions for full reuse. Second, we identify the dependence closure as the sufficient condition to capture full reuse using the least on-chip memory. Third, because the dependence closure is often too large to fit in on-chip memory, we propose a dynamic programming algorithm that optimally partitions a given CNN to guarantee the least off-chip traffic at the partition boundaries for a given on-chip capacity. While tiling is well-known, our contribution determines the optimal cross-layer tiles. Occam’s partitions reside on different chips, forming a pipeline so that a partition’s filters and dependence closure remain on-chip as different images pass through (i.e., each partition incurs off-chip traffic only for its inputs and outputs). Finally, because the optimal partitions may result in an unbalanced pipeline, we propose staggered asynchronous pipelines (STAPs) that replicate bottleneck stages to improve throughput by staggering mini-batches across replicas. Importantly, STAPs achieve balanced pipelines without changing Occam’s optimal partitioning. Our simulations show that, on average, Occam cuts off-chip transfers by 21× and achieves 2.04× and 1.21× better performance, and 33% better energy than the base case, respectively. Using a field-programmable gate array (FPGA) implementation, Occam performs 6.1× and 1.5× better, on average, than the base case and Layer Fusion, respectively.

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Wei, Yuan, and Kin Tak U. "A Novel Non-Uniform BTC and its Application." Applied Mechanics and Materials 590 (June 2014): 789–94. http://dx.doi.org/10.4028/www.scientific.net/amm.590.789.

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In this paper, a novel image representation coding based on non-uniform Block Truncation Coding (BTC) is proposed. A given image can automatically be partitioned into different regions with different sizes and the bivariate piecewise polynomials are used to do the Least Square Approximation for the pixel values in each sub-region based on BTC bitmap. When the approximation error and initial partition are specified, a specific image partition result is obtained. The image re-construction quality of the proposed algorithm is better than that of the traditional BTC. Based on this algorithm, an effective denoising scheme of image is implemented and some of the experimental examples are illustrated to prove that the quality of the re-constructed image, the denoising effect are all satisfactory and can be referenced by other researchers.

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10

Hindman, Neil, and Imre Leader. "Image Partition Regularity of Matrices." Combinatorics, Probability and Computing 2, no. 4 (December 1993): 437–63. http://dx.doi.org/10.1017/s0963548300000821.

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Many of the classical results of Ramsey Theory, including those of Hilbert, Schur, and van der Waerden, are naturally stated as instances of the following problem: given a u × ν matrix A with rational entries, is it true, that whenever the set ℕ of positive integers is finitely coloured, there must exist some x∈ℕν such that all entries of Ax are the same colour? While the theorems cited are all consequences of Rado's theorem, the general problem had remained open. We provide here several solutions for the alternate problem, which asks that x∈ℕν. Based on this, we solve the general problem, giving various equivalent characterizations.

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De, Dibyendu, and Neil Hindman. "Image partition regularity near zero." Discrete Mathematics 309, no. 10 (May 2009): 3219–32. http://dx.doi.org/10.1016/j.disc.2008.09.023.

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Mohan, C. Rama, Kiran S, Vasu deva, and A. Ashok Kumar. "Advanced Multispectral Image Fusion based on Frequency Partition and Normalization." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11-SPECIAL ISSUE (February 20, 2019): 546–55. http://dx.doi.org/10.5373/jardcs/v11sp11/20193065.

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13

MIGUET, SERGE, and JEAN-MARC PIERSON. "QUALITY AND COMPLEXITY BOUNDS OF LOAD BALANCING ALGORITHMS FOR PARALLEL IMAGE PROCESSING." International Journal of Pattern Recognition and Artificial Intelligence 14, no. 04 (June 2000): 463–76. http://dx.doi.org/10.1142/s0218001400000301.

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The parallel implementation of image processing algorithms implies an important choice of data distribution strategy. In order to handle the specific constraints associated with images, data distribution must take into account not only the locality of the data and its geometrical regularity but also the possible irregular computation costs associated with different image elements. A widely studied field to tackle this problem is the family of methods related to rectilinear partitioning. We introduce two fully parallel heuristics that compute suboptimal partitions, with a better complexity than the best known algorithms that compute optimal partitions. In this paper, we compare our heuristics to an optimal partitioning, both in terms of execution time and accuracy of the partition. We give some theoretical bounds on the quality of these heuristics that are corroborated by results of random numerical experiments and real applications.

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14

Shi, Cheng, Zhiyong Lv, Xiuhong Yang, Pengfei Xu, and Irfana Bibi. "Hierarchical Multi-View Semi-Supervised Learning for Very High-Resolution Remote Sensing Image Classification." Remote Sensing 12, no. 6 (March 21, 2020): 1012. http://dx.doi.org/10.3390/rs12061012.

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Traditional classification methods used for very high-resolution (VHR) remote sensing images require a large number of labeled samples to obtain higher classification accuracy. Labeled samples are difficult to obtain and costly. Therefore, semi-supervised learning becomes an effective paradigm that combines the labeled and unlabeled samples for classification. In semi-supervised learning, the key issue is to enlarge the training set by selecting highly-reliable unlabeled samples. Observing the samples from multiple views is helpful to improving the accuracy of label prediction for unlabeled samples. Hence, the reasonable view partition is very important for improving the classification performance. In this paper, a hierarchical multi-view semi-supervised learning framework with CNNs (HMVSSL) is proposed for VHR remote sensing image classification. Firstly, a superpixel-based sample enlargement method is proposed to increase the number of training samples in each view. Secondly, a view partition method is designed to partition the training set into two independent views, and the partitioned subsets are characterized by being inter-distinctive and intra-compact. Finally, a collaborative classification strategy is proposed for the final classification. Experiments are conducted on three VHR remote sensing images, and the results show that the proposed method performs better than several state-of-the-art methods.

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Zhao, Shuhuan, and Zheng-ping Hu. "Occluded Face Recognition Based on Double Layers Module Sparsity Difference." Advances in Electronics 2014 (August 18, 2014): 1–6. http://dx.doi.org/10.1155/2014/687827.

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Image recognition with occlusion is one of the popular problems in pattern recognition. This paper partitions the images into some modules in two layers and the sparsity difference is used to evaluate the occluded modules. The final identification is processed on the unoccluded modules by sparse representation. Firstly, we partition the images into four blocks and sparse representation is performed on each block, so the sparsity of each block can be obtained; secondly, each block is partitioned again into two modules. Sparsity of each small module is calculated as the first step. Finally, the sparsity difference of small module with the corresponding block is used to detect the occluded modules; in this paper, the small modules with negative sparsity differences are considered as occluded modules. The identification is performed on the selected unoccluded modules by sparse representation. Experiments on the AR and Yale B database verify the robustness and effectiveness of the proposed method.

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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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Assas, Ouarda. "Improvement of 2-Partition Entropy Approach Using Type-2 Fuzzy Sets for Image Thresholding." International Journal of Applied Evolutionary Computation 6, no. 3 (July 2015): 33–48. http://dx.doi.org/10.4018/ijaec.2015070103.

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Thresholding is a fundamental task and a challenge for many image analysis and pre-processing process. However, the automatic selection of an optimum threshold has remained a challenge in image segmentation. The fuzzy 2-partition entropy approach for threshold selection is one of the best image thresholding techniques. In this work, an improvement of the later method using type-2 fuzzy sets is proposed to represent the imprecision or lack of knowledge of the expert in the choice of the membership function associated with the image. Two databases are used to evaluate its effectiveness: dataset of standard grayscale test images and MR Brain images. Experiment results show that the type-2 Fuzzy 2-partition entropy algorithm performs equally well in terms of the quality of image segmentation and leads to a good visual result.

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Yu, Hai Yan. "An Image Adaptive Watermarking Algorithm Based on Ridgelet Transform and Two-Dimensional Fuzzy Partition." Advanced Materials Research 301-303 (July 2011): 1299–304. http://dx.doi.org/10.4028/www.scientific.net/amr.301-303.1299.

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An image adaptive watermarking algorithm based on ridgelet transform and two- dimensional(2-D) fuzzy partition classification is proposed. In order to obtain a sparse representation of straight edge singularity, the image is first partitioned into small pieces and the ridgelet transform is applied for each piece. After analyzing texture distribution in ridgelet coefficients of each piece, two feature vectors are selected to make up for the ‘wrap around’ effect for FRIT on representation of the image texture. Then the image is classifed into frat regions and texture regions by applying 2-D fuzzy partition classification algorithm with the two feature vectors prepocessed. An watermark sequence is embedded into texture regions with the embedding strength adaptively adjusted by ridgelet coefficients based on the feature of luminance masking and texture masking. Experimental results prove robustness and transparency of the proposed watermarking scheme.

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Abdul-Kareem Abdul-Azeez, Bushra. "Fast Image Retrieval Prototype using Color Descriptor." Journal of Education College Wasit University 1, no. 22 (January 18, 2016): 745–58. http://dx.doi.org/10.31185/eduj.vol1.iss22.233.

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In recent years, image retrieval prototypes become important and increased noticeably. Color feature is one of the most significant features to represent image. In this paper, we use a Dominant Color (DC) feature to represent images where each image represented by 8-DCs as maximum. Based on DCs values, image database is indexed using 3-D RGB partitioning color space. This is to reduce searching process where once a query image is given to the prototype; it will not search the whole database. Proposed technique will identify the partition and search the image within this partition only. According to the proposed method, extensive experiments were conducted on Corel databases. As a result, the retrieval time is reduced significantly without degradation to precision of retrieval.

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Liang, Ye, Jian Yu, Hongzhe Liu, and Zhifeng Xiao. "Multi-annulus partition based image representation for image classification." International Journal of Sensor Networks 13, no. 1 (2013): 57. http://dx.doi.org/10.1504/ijsnet.2013.052733.

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Yuchi Huang, Qingshan Liu, Fengjun Lv, Yihong Gong, and Dimitris N. Metaxas. "Unsupervised Image Categorization by Hypergraph Partition." IEEE Transactions on Pattern Analysis and Machine Intelligence 33, no. 6 (June 2011): 1266–73. http://dx.doi.org/10.1109/tpami.2011.25.

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Lu, H., J. C. Woods, and M. Ghanbari. "Image segmentation by binary partition tree." Electronics Letters 42, no. 17 (2006): 966. http://dx.doi.org/10.1049/el:20061398.

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23

Li, Minxian, Chunxia Zhao, and Jinhui Tang. "Hybrid image summarization by hypergraph partition." Neurocomputing 119 (November 2013): 41–48. http://dx.doi.org/10.1016/j.neucom.2012.02.050.

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24

Efstratiadis, S. N., D. Tzovaras, and M. G. Strintzis. "Hierarchical partition priority wavelet image compression." IEEE Transactions on Image Processing 5, no. 7 (July 1996): 1111–23. http://dx.doi.org/10.1109/83.502391.

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Hindman, Neil, and Irene Moshesh. "Image partition regularity of affine transformations." Journal of Combinatorial Theory, Series A 114, no. 8 (November 2007): 1375–93. http://dx.doi.org/10.1016/j.jcta.2007.02.002.

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Nathalie Diane, Wandji Nanda, Sun Xingming, and Fah Kue Moise. "A Survey of Partition-Based Techniques for Copy-Move Forgery Detection." Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/975456.

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A copy-move forged image results from a specific type of image tampering procedure carried out by copying a part of an image and pasting it on one or more parts of the same image generally to maliciously hide unwanted objects/regions or clone an object. Therefore, detecting such forgeries mainly consists in devising ways of exposing identical or relatively similar areas in images. This survey attempts to cover existing partition-based copy-move forgery detection techniques.

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Xue, Dan, and Weiqi Yuan. "Dynamic Partition Gaussian Crack Detection Algorithm Based on Projection Curve Distribution." Sensors 20, no. 14 (July 17, 2020): 3973. http://dx.doi.org/10.3390/s20143973.

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When detecting the cracks in the tunnel lining image, due to uneven illumination, there are generally differences in brightness and contrast between the cracked pixels and the surrounding background pixels as well as differences in the widths of the cracked pixels, which bring difficulty in detecting and extracting cracks. Therefore, this paper proposes a dynamic partitioned Gaussian crack detection algorithm based on the projection curve distribution. First, according to the distribution of the image projection curve, the background pixels are dynamically partitioned. Second, a new dynamic partitioned Gaussian (DPG) model was established, and the set rules of partition boundary conditions, partition number, and partition corresponding threshold were defined. Then, the threshold and multi-scale Gaussian factors corresponding to different crack widths were substituted into the Gaussian model to detect cracks. Finally, crack morphology and the breakpoint connection algorithm were combined to complete the crack extraction. The algorithm was tested on the lining gallery captured on the site of the Tang-Ling-Shan Tunnel in Liaoning Province, China. The optimal parameters in the algorithm were estimated through the Recall, Precision, and Time curves. From two aspects of qualitative and quantitative analysis, the experimental results demonstrate that this algorithm could effectively eliminate the effect of uneven illumination on crack detection. After detection, Recall could reach more than 96%, and after extraction, Precision was increased by more than 70%.

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Nenashev, Vadim, and Igor Khanykov. "Formation of a fused image of the land surface based on pixel clustering of location images in a multi-position onboard system." Informatics and Automation 20, no. 2 (March 30, 2021): 302–40. http://dx.doi.org/10.15622/ia.2021.20.2.3.

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The paper proposes a method for fusioning multi-angle images implementing the algorithm for quasi-optimal clustering of pixels to the original images of the land surface. The original multi-angle images formed by the onboard equipment of multi-positional location systems are docked into a single composite image and, using a high-speed algorithm for quasi-optimal pixel clustering, are reduced to several colors while maintaining characteristic boundaries. A feature of the algorithm of quasi-optimal pixel clustering is the generation of a series of partitions with gradually increasing detail due to a variable number of clusters. This feature allows you to choose an appropriate partition of a pair of docked images from the generated series. The search for reference points of the isolated contours is performed on a pair of images from the selected partition of the docked image. A functional transformation is determined for these points. And after it has been applied to the original images, the degree of correlation of the fused image is estimated. Both the position of the reference points of the contour and the desired functional transformation itself are refined until the evaluation of the fusion quality is acceptable. The type of functional transformation is selected according to the images reduced in color, which later is applied to the original images. This process is repeated for clustered images with greater detail in the event that the assessment of the fusion quality is not acceptable. The purpose of present study is to develop a method that allows synthesizing fused image of the land surface from heteromorphic and heterogeneous images. The paper presents the following features of the fusing method. The first feature is the processing of a single composite image from a pair of docked source images by the pixel clustering algorithm, what makes it possible to isolate the same areas in its different parts in a similar way. The second feature consists in determining the functional transformation by the isolated reference points of the contour on the processed pair of clustered images, which is later applied to the original images to combine them. The paper presents the results on the synthesis of a fused image both from homogeneous (optical) images and from heterogeneous (radar and optical) images. A distinctive feature of the developed method is to improve the quality of synthesis, increase the accuracy and information content of the final fused image of the land surface.

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Zhang, Xiaoli, Xiongfei Li, Zhaojun Liu, and Yuncong Feng. "Multi-focus image fusion using image-partition-based focus detection." Signal Processing 102 (September 2014): 64–76. http://dx.doi.org/10.1016/j.sigpro.2014.02.024.

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Lv, Xuan, Zezhong Ma, and Qing Liu. "A Subblock Partition Of Multi-Layer Pattern Based Image Classification Approach." MATEC Web of Conferences 246 (2018): 03043. http://dx.doi.org/10.1051/matecconf/201824603043.

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Since traditional partition approach may construct very different image representation because of the changed locations of objects in the same image, a subblock partition of multi-layer pattern method for image representation is proposed. The saliency windows straddled by superpixels are utilized to partition the image into multi-layer pattern subblocks. Then all the subblocks are combined to a three order tensor. Comparing to the results of image classification item of Pascal Voc 2007 Challenge，it indicates that the proposed representation method is robust to the varied object locations and achieves better performance than other approaches. CCS Concepts Computing methodologies➝Computer vision • Computing methodologies➝ Machine learning

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Suroso Putro, Tunjung Atmadi, Rizky Dinata, and Ali Ramadhan. "STUDY OF APPLICATION "BATIK" IN THE INTERIOR OFFICE PARTITION." Journal of Urban Society's Arts 6, no. 2 (October 27, 2019): 123–27. http://dx.doi.org/10.24821/jousa.v6i2.3187.

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Office partitions are part of office furniture that serves as a dividers of office space. Office partitions provide solutions to organize work space in the office to make it more tidy and efficient. The use of office partition is a modern concept of office space in maximizing office space to make it more comfortable. By applying knock down system to the office partition, wall panels can be dismantled and installed back to be installed or applied with certain batik pattern. The office interior now became an important part to represent clients to increase the look of an office. The method used in this research is descriptive qualitative method, starting from the initial concept of the product, product development and prototype application of batik motif design on the partition. The results of this study are expected to be able to complement the space image with a certain style as a supporting element of interior design, as well as add to the appearance of the office interior to be more dynamic and increase working productivity.

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Shi, Jiao, Jiaji Wu, Anand Paul, Licheng Jiao, and Maoguo Gong. "A Partition-Based Active Contour Model Incorporating Local Information for Image Segmentation." Scientific World Journal 2014 (2014): 1–19. http://dx.doi.org/10.1155/2014/840305.

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Active contour models are always designed on the assumption that images are approximated by regions with piecewise-constant intensities. This assumption, however, cannot be satisfied when describing intensity inhomogeneous images which frequently occur in real world images and induced considerable difficulties in image segmentation. A milder assumption that the image is statistically homogeneous within different local regions may better suit real world images. By taking local image information into consideration, an enhanced active contour model is proposed to overcome difficulties caused by intensity inhomogeneity. In addition, according to curve evolution theory, only the region near contour boundaries is supposed to be evolved in each iteration. We try to detect the regions near contour boundaries adaptively for satisfying the requirement of curve evolution theory. In the proposed method, pixels within a selected region near contour boundaries have the opportunity to be updated in each iteration, which enables the contour to be evolved gradually. Experimental results on synthetic and real world images demonstrate the advantages of the proposed model when dealing with intensity inhomogeneity images.

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Rezaee, Alireza. "Partition Fuzzy Median Filter for Image Restoration." Fuzzy Information and Engineering 13, no. 2 (April 3, 2021): 199–210. http://dx.doi.org/10.1080/16168658.2021.1921377.

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Davoine, Franck, Etienne Bertin, and Jean-Marc Chassery. "An Adaptive Partition for Fractal Image Coding." Fractals 05, supp01 (April 1997): 243–56. http://dx.doi.org/10.1142/s0218348x97000796.

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In this paper we present a flexible partitioning scheme for fractal image compression, based on the Delaunay triangles. The aim is to have the advantage of triangular blocks over squares, in terms of adaptivity to the image content. In a first step, the triangulation is computed so that the triangles are more densely distributed in regions containing interesting features such as corners and edges, or so that they tend to run along the strong edges in the image. In a second step we merge adjacent triangles into quadrilaterals, in order to decrease the number of blocks. Quadrilaterals permit a reduction of the number of local contractive affine transformations composing the fractal transform, and thus to increase the compression ratio, while preserving the visual quality of the decoded image.

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Accame, Marco, Francesco G. B. De Natale, and Fabrizio Granelli. "Efficient labeling procedures for image partition encoding." Signal Processing 80, no. 6 (June 2000): 1127–31. http://dx.doi.org/10.1016/s0165-1684(00)00080-3.

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Liu, Bingyuan, Jing Liu, and Hanqing Lu. "Adaptive spatial partition learning for image classification." Neurocomputing 142 (October 2014): 282–90. http://dx.doi.org/10.1016/j.neucom.2014.03.057.

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Xing, Jiangwa, Pei Yang, and Letu Qingge. "Robust 2D Otsu’s Algorithm for Uneven Illumination Image Segmentation." Computational Intelligence and Neuroscience 2020 (August 11, 2020): 1–14. http://dx.doi.org/10.1155/2020/5047976.

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Otsu’s algorithm is one of the most well-known methods for automatic image thresholding. 2D Otsu’s method is more robust compared to 1D Otsu’s method. However, it still has limitations on salt-and-pepper noise corrupted images and uneven illumination images. To alleviate these limitations and improve the overall performance, here we propose an improved 2D Otsu’s algorithm to increase the robustness to salt-and-pepper noise together with an adaptive energy based image partition technology for uneven illumination image segmentation. Based on the partition method, two schemes for automatic thresholding are adopted to find the best segmentation result. Experiments are conducted on both synthetic and real world uneven illumination images as well as real world regular illumination cell images. Original 2D Otsu’s method, MAOTSU_2D, and two latest 1D Otsu’s methods (Cao’s method and DVE) are included for comparisons. Both qualitative and quantitative evaluations are introduced to verify the effectiveness of the proposed method. Results show that the proposed method is more robust to salt-and-pepper noise and acquires better segmentation results on uneven illumination images in general without compromising its performance on regular illumination images. For a test group of seven real world uneven illumination images, the proposed method could lower the ME value by 15% and increase the DSC value by 10%.

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Peng, Wei Fu, Shu Du, and Fu Xiang Li. "Unsupervised Image Segmentation via Affinity Propagation." Applied Mechanics and Materials 610 (August 2014): 464–70. http://dx.doi.org/10.4028/www.scientific.net/amm.610.464.

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Image segmentation is an important research subject in the area of image processing. Most of the existing image segmentation methods partition the image based on the single cue of the image, the color, which brings a serious limitation when the complex scenes involve in the natural images. In this paper, we introduce a novel unsupervised image segmentation method via affinity propagation which takes into local texture and color features with superpixel map. The new method fuses color and texture information as local feature of each superpixel. The experimental results show that the proposed method performs better and steadier when partitioning various complex nature images, comparing to the existing methods.

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Sharma, Dr Kamlesh, and Nidhi Garg. "An Extensive Review on Image Segmentation Techniques." Indian Journal of Image Processing and Recognition 1, no. 2 (June 10, 2021): 1–5. http://dx.doi.org/10.35940/ijipr.b1002.061221.

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Image processing is the use of algorithms to perform various operations on digital images. The techniques that are explained further are image segmentation and image enhancement. Image Segmentation is a method to partition an image into multiple segments, to change the presentation of an image into something more meaningful and easier to analyze. The current image segmentation techniques include region-based segmentation and edge detection segmentation. Image Enhancement is the process of improving the quality of image. Under this section there are two broad divisions- Spatial Domain Technique and Frequency Domain Technique.

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Sharma, Dr Kamlesh, and Nidhi Garg. "An Extensive Review on Image Segmentation Techniques." Indian Journal of Image Processing and Recognition 1, no. 2 (June 10, 2021): 1–5. http://dx.doi.org/10.54105/ijipr.b1002.061221.

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Image processing is the use of algorithms to perform various operations on digital images. The techniques that are explained further are image segmentation and image enhancement. Image Segmentation is a method to partition an image into multiple segments, to change the presentation of an image into something more meaningful and easier to analyze. The current image segmentation techniques include region-based segmentation and edge detection segmentation. Image Enhancement is the process of improving the quality of image. Under this section there are two broad divisions- Spatial Domain Technique and Frequency Domain Technique.

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Yu, Huai, Tianheng Yan, Wen Yang, and Hong Zheng. "AN INTEGRATIVE OBJECT-BASED IMAGE ANALYSIS WORKFLOW FOR UAV IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 7, 2016): 1085–91. http://dx.doi.org/10.5194/isprsarchives-xli-b1-1085-2016.

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In this work, we propose an integrative framework to process UAV images. The overall process can be viewed as a pipeline consisting of the geometric and radiometric corrections, subsequent panoramic mosaicking and hierarchical image segmentation for later Object Based Image Analysis (OBIA). More precisely, we first introduce an efficient image stitching algorithm after the geometric calibration and radiometric correction, which employs a fast feature extraction and matching by combining the local difference binary descriptor and the local sensitive hashing. We then use a Binary Partition Tree (BPT) representation for the large mosaicked panoramic image, which starts by the definition of an initial partition obtained by an over-segmentation algorithm, i.e., the simple linear iterative clustering (SLIC). Finally, we build an object-based hierarchical structure by fully considering the spectral and spatial information of the super-pixels and their topological relationships. Moreover, an optimal segmentation is obtained by filtering the complex hierarchies into simpler ones according to some criterions, such as the uniform homogeneity and semantic consistency. Experimental results on processing the post-seismic UAV images of the 2013 Ya’an earthquake demonstrate the effectiveness and efficiency of our proposed method.

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Yu, Huai, Tianheng Yan, Wen Yang, and Hong Zheng. "AN INTEGRATIVE OBJECT-BASED IMAGE ANALYSIS WORKFLOW FOR UAV IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 7, 2016): 1085–91. http://dx.doi.org/10.5194/isprs-archives-xli-b1-1085-2016.

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In this work, we propose an integrative framework to process UAV images. The overall process can be viewed as a pipeline consisting of the geometric and radiometric corrections, subsequent panoramic mosaicking and hierarchical image segmentation for later Object Based Image Analysis (OBIA). More precisely, we first introduce an efficient image stitching algorithm after the geometric calibration and radiometric correction, which employs a fast feature extraction and matching by combining the local difference binary descriptor and the local sensitive hashing. We then use a Binary Partition Tree (BPT) representation for the large mosaicked panoramic image, which starts by the definition of an initial partition obtained by an over-segmentation algorithm, i.e., the simple linear iterative clustering (SLIC). Finally, we build an object-based hierarchical structure by fully considering the spectral and spatial information of the super-pixels and their topological relationships. Moreover, an optimal segmentation is obtained by filtering the complex hierarchies into simpler ones according to some criterions, such as the uniform homogeneity and semantic consistency. Experimental results on processing the post-seismic UAV images of the 2013 Ya’an earthquake demonstrate the effectiveness and efficiency of our proposed method.

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Breton, V., I. E. Magnin, and J. Montagnat. "Partitioning Medical Image Databases for Content-based Queries on a Grid." Methods of Information in Medicine 44, no. 02 (2005): 154–60. http://dx.doi.org/10.1055/s-0038-1633937.

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Summary Objectives: In this paper we study the impact of executing a medical image database query application on the grid. For lowering the total computation time, the image database is partitioned into subsets to be processed on different grid nodes. Methods: A theoretical model of the application complexity and estimates of the grid execution overhead are used to efficiently partition the database. Results: We show results demonstrating that smart partitioning of the database can lead to significant improvements in terms of total computation time. Conclusions: Grids are promising for content-based image retrieval in medical databases.

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Arora, Jyoti, and Meena Tushir. "Intuitionistic Level Set Segmentation for Medical Image Segmentation." Recent Advances in Computer Science and Communications 13, no. 5 (November 5, 2020): 1039–46. http://dx.doi.org/10.2174/2213275912666190218150045.

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Introduction: Image segmentation is one of the basic practices that involve dividing an image into mutually exclusive partitions. Learning how to partition an image into different segments is considered as one of the most critical and crucial step in the area of medical image analysis. Objective: The primary objective of the work is to design an integrated approach for automating the process of level set segmentation for medical image segmentation. This method will help to overcome the problem of manual initialization of parameters. Methods: In the proposed method, input image is simplified by the process of intuitionistic fuzzification of an image. Further segmentation is done by intuitionistic based clustering technique incorporated with local spatial information (S-IFCM). The controlling parameters of level set method are automated by S-IFCM, for defining anatomical boundaries. Results: Experimental results were carried out on MRI and CT-scan images of brain and liver. The results are compared with existing Fuzzy Level set segmentation; Spatial Fuzzy Level set segmentation using MSE, PSNR and Segmentation Accuracy. Qualitatively results achieved after proposed segmentation technique shows more clear definition of boundaries. The attain PSNR and MSE value of propose algorithm proves the robustness of algorithm. Segmentation accuracy is calculated for the segmentation results of the T-1 weighted axial slice of MRI image with 0.909 value. Conclusion: The proposed method shows good accuracy for the segmentation of medical images. This method is a good substitute for the segmentation of different clinical images with different modalities and proves to give better result than fuzzy technique.

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CAI, D., T. ARISAWA, N. ASAI, Y. IKEBE, and T. ITOH. "FRACTAL IMAGE COMPRESSION USING LOCALLY REFINED PARTITIONS." Fractals 02, no. 03 (September 1994): 405–8. http://dx.doi.org/10.1142/s0218348x94000533.

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In the present report, we show a practical fractal image compression method using locally refined partition which is generated automatically and controlled by the values of gradients in images, The method is similar to the ones by Barnsley and Hurd. In our method, using the quad-tree scheme, before the compression processes of the image, we locally refine the domain regions recursively until the size of the regions become smaller than the scale lengths of the gradients in the image or until the predefined minimum refinement size is reached. Based on our method, it is possible to assess the optimum-like set of domain regions for the desired file size.

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Xiao, Xiao, De Wen Zhuang, and Shou Jue Wang. "Content-Based Image Retrieval through Region Uniformly Partition." Key Engineering Materials 500 (January 2012): 471–74. http://dx.doi.org/10.4028/www.scientific.net/kem.500.471.

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It has been demonstrated that accurate image segmentation is still an open problem. For avoiding this difficulties in content-based image retrieval, an region uniform partition approaching was proposed. Based on fusing regional color features using smooth slide histogram and texture features extracted using Gabor wavelet, we provided the corresponding similarity measure. The image retrieval performance on a subset of the COREL database are better than SIMPLIcity system showed the effectiveness of the proposed method.

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Pearlman, William A., and Amir Said. "Set Partition Coding: Part I of Set Partition Coding and Image Wavelet Coding Systems." Foundations and Trends® in Signal Processing 2, no. 2 (2007): 95–180. http://dx.doi.org/10.1561/2000000013.

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Liang, Fa Yun, Jin Xia Niu, Jing Wang, Jie Cheng, Zhi Cheng Tie, Fa Zhou Liang, Hua Zhang, Xiao Ping Liu, and Hai Chu Chen. "Auto-Multi-View Stereoscopic Technology." Applied Mechanics and Materials 103 (September 2011): 147–51. http://dx.doi.org/10.4028/www.scientific.net/amm.103.147.

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The auto-multi-view technology enables us to perceive multi-angle stereo vision. The image resolution reduced lowly because of multi-partition. A two-partition method is promoted in this paper, detecting the eyes’ position by CCD sensor and comparing with individual view-area boundary , then adjusting the stereo image on the sub-screen. So, the method can improve the resolution of stereo image greatly by dividing the screen into two sub-screens no matter how many viewpoints.

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H.Al.Ghuraify, Huda, Ali A.Al-Bakry, and Ahmad T. Al-Jayashi. "Dual Security Using Image Steganography Based Matrix Partition." International Journal of Network Security & Its Applications 11, no. 02 (March 31, 2019): 13–31. http://dx.doi.org/10.5121/ijnsa.2019.11202.

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Pan, Yuetao, Shishuai Xing, and Danfeng Liu. "Partition Optimal Band Selection Method for Hyperspectral Image." Journal of Physics: Conference Series 2005, no. 1 (August 1, 2021): 012054. http://dx.doi.org/10.1088/1742-6596/2005/1/012054.

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