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5th International Conference on Applications of Artificial Neural Networks

作者： MCCRAE, KA RUCK, DW ROGERS, SK OXLEY, ME USAF INST TECHNOLDEPT ELECT & COMP ENGNWRIGHT PATTERSON AFBOH 45433

ISBN: (纸本)0819415472

The most difficult stage of automated target recognition is segmentation. Current segmentation problems include faces and tactical targets; previous efforts to segment these objects have used intensity and motion cues. This paper develops a color preprocessing scheme to be used with the other segmentation techniques. A neural network is trained to identify the color of a desired object, eliminating all but that color from the scene. Gabor correlations and 2D wavelet transformations will be performed on stationary images; and 3D wavelet transforms on multispectral data will incorporate color and motion detection into the machine visual system. The paper will demonstrate that color and motion cues can enhance a computer segmentation system. Results from segmenting faces both from the AFIT data base and from video taped television are presented; results from tactical targets such as tanks and airplanes are also given. color preprocessing is shown to greatly improve the segmentation in most cases.

关键词： image segmentation Wavelets Neural networks Computing systems color image segmentation image filtering Sensors Visual system Motion detection Target recognition

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Linearized circular energy curve color image segmentation based on Tsallis entropy

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 273卷

作者： Wang, Shaoxun Fan, Jiulun Liu, Heng Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian 710121 Peoples R China Xian Univ Posts & Telecommun Ctr Image & Informat Proc Xian 710121 Peoples R China

image processing is a continuously evolving field, and due to the fact that color images contain more information than grayscale images and are the most encountered type of images in daily life, the analysis and processing of color images are particularly important. The use of the H component of the HSI color space model to construct a circular histogram for selecting the optimal threshold is one method in color image segmentation. However, traditional circular histograms only reflect the pixel distribution of the entire image by statistically counting the frequency of pixel occurrences in the entire image, without considering the spatial context information of the image for optimal threshold selection. To address this drawback, we propose the method of circular energy curves (CEC). This method involves calculating energy curves on the H component to construct CEC. The optimal threshold is then selected based on these curves. To reduce the complexity of threshold selection for CEC and enhance their generality, we combine cumulative distribution functions with information energy, introducing the method of cumulative distribution information energy (CDF-IE) to determine the optimal breakpoint. After linearizing the circular energy curves (L-CEC), we use the Tsallis entropy thresholding method to achieve the optimal segmentation threshold selection for the H component CEC. To improve the effectiveness of the algorithm, we present the Tsallis entropy recursive algorithm after L-CEC, which reduces the time complexity. The proposed method in this paper, compared to the circular histogram method, achieves better image segmentation results and exhibits stronger applicability.

关键词： color image segmentation HSI color model Circular energy curve Cumulative distribution function Information energy Tsallis entropy

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color image segmentation for bladder cancer diagnosis

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MATHEMATICAL AND COMPUTER MODELLING 1998年第2期27卷 103-120页

作者： Chen, CH Lee, JD Wang, JR Mao, CW Chang Gung Univ Dept Elect Engn Tao Yuan 333 Taiwan Natl Cheng Kung Univ Dept Elect Engn Tainan 701 Taiwan

In this paper, a new color image segmentation method is proposed to extract the region of bladder tumor from a color bladder image. In the past, the diagnosis of bladder tumors mainly relies upon cystoscopic examination with an in vivo staining technique. This manner heavily depends on the interpreter's experience and often results in errors in diagnosis. Instead of previous method, we use methylene blue in vivo staining combined with color segmentation techniques to improve the accuracy of the diagnosis of bladder tumors. The segmentation task can be decomposed into two stages. First, cluster detection combined with probabilistic relaxation is used to extract the clusters of specified colors from the HLS color space. Then, in order to refine the chromatic properties, the Bayesian algorithm is employed to reject the false region from the clusters obtained in the first stage. Experimental results show that the proposed method can segment the bladder tumor successfully. The technique could serve as an auxiliary tool for doctors or researchers in the diagnosis of bladder tumors.

关键词： color image segmentation bladder tumor diagnosis Probabilistic relaxation Bayesian algorithm

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color image segmentation using an enhanced Gradient Network Method

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PATTERN RECOGNITION LETTERS 2009年第15期30卷 1404-1412页

作者： Wangenheim, A. V. Bertoldi, R. F. Abdala, D. D. Sobieranski, A. Coser, L. Jiang, X. Richter, M. M. Priese, L. Schmitt, F. Univ Fed Santa Catarina Image Proc & Comp Graph Lab LAPIX Florianopolis SC Brazil Univ Munster CS Dept Munster Germany Univ Calgary CS Dept Calgary AB T2N 1N4 Canada Univ Koblenz CS Dept Koblenz Germany

The objective of this paper is to evaluate a new combined approach intended for reliable color image segmentation, in particular images presenting color structures with strong but continuous color or luminosity changes, such as commonly found in outdoors scenes. The approach combines an enhanced version of the Gradient Network 2, with common region-growing approaches used as pre-segmentation steps. The GNM2 is an post-segmentation procedure based on graph analysis of global color and luminosity gradients in conjunction with a segmentation algorithm to produce a reliable segmentation result. The approach was automatically evaluated using a close/open world approach. Two different region-growing segmentation methods, CSC and Mumford and Shah with and without the GNM post-processing were compared against ground truth images using segmentation evaluation indices Rand and Bipartite Graph Matching. These results were also confronted with other well established segmentation methods (RHSEG, Watershed, EDISON, JSEG and Blobworld). (C) 2009 Elsevier B.V. All rights reserved.

关键词： color image segmentation Region-growing Outdoors scenes Gradient Network Method

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color image segmentation using Hopfield networks

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image AND VISION COMPUTING 1997年第3期15卷 161-166页

作者： Campadelli, P Medici, D Schettini, R CNR IST TECNOL INFORMAT MULTIMEDIALINATL RES COUNCILI-20131 MILANITALY UNIV MILAN DIPARTIMENTO SCI INFORMAZMILANITALY

color image segmentation is frequently based on pixel classification, either supervised or unsupervised, without taking into account spatial information. This may generate noisy results. One technique proposed to solve this problem is the use of Hopfield neural networks. In this paper, we present two segmentation algorithms for color image segmentation based on Huang's idea of describing the segmentation problem as one of minimizing a suitable energy function for a Hopfield network. The first algorithm, which resembles Huang's algorithm for grey-level images, builds three different networks (one for each color feature considered), and then combines the results. The second builds a single network according to the number of clusters obtained by histogram analysis. We have changed the network initialization, its dynamic evolution, and the technique of histogram analysis employed in both with respect to the original proposition. The experimental results, heuristically and quantitatively evaluated, are encouraging.

关键词： color image segmentation Hopfield networks scale-space filtering

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color image segmentation USING COMPETITIVE LEARNING

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1994年第12期16卷 1197-1206页

作者： UCHIYAMA, T ARBIB, MA UNIV SO CALIF CTR NEURAL ENGNLOS ANGELESCA 90089

This paper presents a color image segmentation method which divides color space into clusters. Competitive learning is used as a tool for clustering color space based on the least sum of squares criterion. We show that competitive learning converges to approximate the optimum solution based on this criterion theoretically and experimentally. We applied this method to various color scenes and show its efficiency as a color image segmentation method. We also show the effects of using different color coordinates to be clustered by some experimental results.

关键词： image segmentation Multidimensional systems Shape Clustering algorithms Vector quantization image color analysis image converters Layout Computer performance Computer vision INSPEC: Controlled Indexing vector quantisation image colour analysis image segmentation unsupervised learning least squares approximations convergence of numerical methods INSPEC: Non-Controlled Indexing color coordinates color image segmentation competitive learning color space clustering least sum-of-squares criterion convergence optimum solution approximation color scenes efficiency

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color image segmentation using fuzzy integral and mountain clustering

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FUZZY SETS AND SYSTEMS 1999年第2期107卷 121-130页

作者： Pham, TD Yan, H Univ Sydney Dept Elect Engn Sydney NSW 2006 Australia

This paper presents a flexible model for the segmentation of color image data using the fuzzy integral and the mountain clustering. Fuzzy integral is used as a "distance" measure in the mountain clustering applied to find representative regions in the image. The proposed approach does not require an initial estimate of cluster centers for the segmentation process. segmentation results using the proposed method will depend on the griding of the image space, which specifies the degree of detail in the segmentation process. (C) 1999 Elsevier Science B.V. All right reserved.

关键词： color image segmentation fuzzy measure fuzzy integral mountain clustering

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color image segmentation for multimedia applications

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2000年第1-2期28卷 5-20页

作者： Ikonomakis, N Plataniotis, KN Venetsanopoulos, AN Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada Ryerson Polytech Inst Sch Comp Sci Toronto ON M5B 2K3 Canada

image segmentation is crucial for multimedia applications. Multimedia databases utilize segmentation for the storage and indexing of images and video. image segmentation is used for object tracking in the new MPEG-7 video compression standard. It is also used in video conferencing for compression and coding purposes. These are only some of the multimedia applications in image segmentation. It is usually the first task of any image analysis process, and thus, subsequent tasks rely heavily on the quality of segmentation. The proposed method of color image segmentation is very effective in segmenting a multimedia-type image into regions. Pixels are first classified as either chromatic or achromatic depending on their HSI color values. Next, a seed determination algorithm finds seed pixels that are in the center of regions. These seed pixels are used in the region growing step to grow regions by comparing these seed pixels to neighboring pixels using the cylindrical distance metric. Merging regions that are similar in color is a final means used for segmenting the image into even smaller regions.

关键词： color image segmentation region growing region merging cylindrical distance metric

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color image segmentation with genetic algorithm in a raisin sorting system based on machine vision in variable conditions

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第4期38卷 3671-3678页

作者： Abbasgholipour, M. Omid, M. Keyhani, A. Mohtasebi, S. S. Univ Tehran Fac Agr Engn & Technol Karaj Iran

This study was undertaken to develop machine vision-based raisin detection technology for various lighting conditions. Supervised color image segmentation using a permutation-coded genetic algorithm (GA) identifying regions in hue saturation intensity (HSI) color space (GAHSI) for desired and undesired raisin detection in various conditions was successfully implemented. images from two extreme intensity lighting and dense conditions: under weak lighting and high-density product and under suitable lighting and low-density product, were mosaicked to explore the possibility of using GAHSI to locate desired raisin and undesired raisin regions in color space when these two extremes were presented simultaneously. The GAHSI results provided evidence for the existence and separability of such regions. In the experiment, GAHSI performance was measured by comparing the GAHSI-segmented image with a corresponding hand-segmented reference image. When compared with cluster analysis-based segmentation results, the GAHSI method showed no significant difference. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Genetic algorithm Raisin sorting color image segmentation Lighting condition

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color image segmentation using mean shift and improved ant clustering

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Journal of Central South University 2012年第4期19卷 1040-1048页

作者：刘玲星谭冠政 M.Sami Soliman of Information Science and Engineering Central South University

To improve the segmentation quality and efficiency of color image,a novel approach which combines the advantages of the mean shift(MS) segmentation and improved ant clustering method is *** regions which can preserve the discontinuity characteristics of an image are segmented by MS algorithm,and then they are represented by a graph in which every region is represented by a *** order to solve the graph partition problem,an improved ant clustering algorithm,called similarity carrying ant model(SCAM-ant),is proposed,in which a new similarity calculation method is *** SCAM-ant,the maximum number of items that each ant can carry will increase,the clustering time will be effectively reduced,and globally optimized clustering can also be *** the graph is not based on the pixels of original image but on the segmentation result of MS algorithm,the computational complexity is greatly *** show that the proposed method can realize color image segmentation efficiently,and compared with the conventional methods based on the image pixels,it improves the image segmentation quality and the anti-interference ability.

关键词： color image segmentation improved ant clustering graph partition mean shift

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