Journal articles: 'Contour extraction'

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Chen chen and Daohui Bi. "A Motion Image Pose Contour Extraction Method Based on B-Spline Wavelet." International Journal of Antennas and Propagation 2021 (October 26, 2021): 1–8. http://dx.doi.org/10.1155/2021/4553143.

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In order to improve the accuracy of traditional motion image pose contour extraction and shorten the extraction time, a motion image pose contour extraction method based on B-spline wavelet is proposed. Moving images are acquired through the visual system, the information fusion process is used to perform statistical analysis on the images containing motion information, the location of the motion area is determined, convolutional neural network technology is used to preprocess the initial motion image pose contour, and B-spline wavelet theory is used. The preprocessed motion image pose contour is detected, combined with the heuristic search method to obtain the pose contour points, and the motion image pose contour extraction is completed. The simulation results show that the proposed method has higher accuracy and shorter extraction time in extracting motion image pose contours.

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Liu, Guang Shuai, and Bai Lin Li. "Extraction of Optimal Contour Dominant Points Based on ICT Images in Reverse Engineering." Applied Mechanics and Materials 423-426 (September 2013): 2570–75. http://dx.doi.org/10.4028/www.scientific.net/amm.423-426.2570.

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How to effectively extract contour dominant points is one of key problems in process of industrial CT image, second extraction method was put forward. Second extraction method included two steps: rough extraction and accurate extraction. Firstly, discrete circular curvatures of contour points are calculated. Secondly, through rough extraction step, bad points and points which arent correlated with contour features were removed. At last, through accurate extraction step, contour dominant points were extracted by levels of detail. Experimental results show that contour dominant points can describe contours shape and redundant data are removed, the proposed method is simple and efficient.

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Fang, Fang, Kaishun Wu, Yuanyuan Liu, Shengwen Li, Bo Wan, Yanling Chen, and Daoyuan Zheng. "A Coarse-to-Fine Contour Optimization Network for Extracting Building Instances from High-Resolution Remote Sensing Imagery." Remote Sensing 13, no. 19 (September 23, 2021): 3814. http://dx.doi.org/10.3390/rs13193814.

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Building instances extraction is an essential task for surveying and mapping. Challenges still exist in extracting building instances from high-resolution remote sensing imagery mainly because of complex structures, variety of scales, and interconnected buildings. This study proposes a coarse-to-fine contour optimization network to improve the performance of building instance extraction. Specifically, the network contains two special sub-networks: attention-based feature pyramid sub-network (AFPN) and coarse-to-fine contour sub-network. The former sub-network introduces channel attention into each layer of the original feature pyramid network (FPN) to improve the identification of small buildings, and the latter is designed to accurately extract building contours via two cascaded contour optimization learning. Furthermore, the whole network is jointly optimized by multiple losses, that is, a contour loss, a classification loss, a box regression loss and a general mask loss. Experimental results on three challenging building extraction datasets demonstrated that the proposed method outperformed the state-of-the-art methods’ accuracy and quality of building contours.

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ZHAO, JIAN, and JIAN AN. "AN ITERATIVE CONVEX HULL APPROACH FOR IMAGE SEGMENTATION AND CONTOUR EXTRACTION." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 07 (November 2012): 1255013. http://dx.doi.org/10.1142/s0218001412550130.

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The contours and segments of objects in digital images have many important applications. Contour extractions of gray images can be converted into contour extractions of binary images. This paper presents a novel contour-extraction algorithm for binary images and provides a deduction theory for this algorithm. First, we discuss the method used to construct convex hulls of regions of objects. The contour of an object evolves from a convex polygon until the exact boundary is obtained. Second, the projection methods from lines to objects are studied, in which, a polygon iteration method is presented using linear projection. The result of the iteration is the contour of the object region. Lastly, addressing the problem that direct projections probably cannot find correct projection points, an effective discrete ray-projection method is presented. Comparisons with other contour deformation algorithms show that the algorithm in the present paper is very robust with respect to the shapes of the object regions. Numerical tests show that time consumption is primarily concentrated on convex hull computation, and the implementation efficiency of the program can satisfy the requirement of interactive operations.

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Wu, Shaofei. "A Traffic Motion Object Extraction Algorithm." International Journal of Bifurcation and Chaos 25, no. 14 (December 30, 2015): 1540039. http://dx.doi.org/10.1142/s0218127415400398.

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A motion object extraction algorithm based on the active contour model is proposed. Firstly, moving areas involving shadows are segmented with the classical background difference algorithm. Secondly, performing shadow detection and coarse removal, then a grid method is used to extract initial contours. Finally, the active contour model approach is adopted to compute the contour of the real object by iteratively tuning the parameter of the model. Experiments show the algorithm can remove the shadow and keep the integrity of a moving object.

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Yang, Shudi, Jiaxiong Wu, and Zhipeng Feng. "Dual-Fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images." Applied Sciences 12, no. 5 (February 28, 2022): 2515. http://dx.doi.org/10.3390/app12052515.

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Underwater vision research is the foundation of marine-related disciplines. The target contour extraction is significant for target tracking and visual information mining. Aiming to resolve the problem that conventional active contour models cannot effectively extract the contours of salient targets in underwater images, we propose a dual-fusion active contour model with semantic information. First, the saliency images are introduced as semantic information and salient target contours are extracted by fusing Chan–Vese and local binary fitting models. Then, the original underwater images are used to supplement the missing contour information by using the local image fitting. Compared with state-of-the-art contour extraction methods, our dual-fusion active contour model can effectively filter out background information and accurately extract salient target contours. Moreover, the proposed model achieves the best results in the quantitative comparison of MAE (mean absolute error), ER (error rate), and DR (detection rate) indicators and provides reliable prior knowledge for target tracking and visual information mining.

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Matsumoto, Sumiaki, Reinin Asato, Tomohisa Okada, and Junji Konishi. "Intracranial contour extraction with active contour models." Journal of Magnetic Resonance Imaging 7, no. 2 (March 1997): 353–60. http://dx.doi.org/10.1002/jmri.1880070216.

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Tsuji, Hiroyuki, Shinji Tokumasu, Hiroki Takahashi, and Masayuki Nakajima. "Extracting Objects Using Contour Evolutions in Edge-Based Object Tracking." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 3 (May 20, 2006): 362–71. http://dx.doi.org/10.20965/jaciii.2006.p0362.

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We propose edge-based object extraction targeting automatic video object plane (VOP) generation in MPEG-4 content-based video coding. In an edge-based VOP generation framework proposed by Meier, the object is represented as a binary edge image that does not generally form a closed contour and that also contains many extra edges, making extracting the object contour accurately less straightforward in such situations. To solve this problem, we adopt a PDE-based contour evolution approach to evolve initial multiple contours contained inside the object toward its boundary based on evolution equations, and to finally merge them into a single contour that accurately represents the object’s shape. Our experimental results using an MPEG standard image sequence show that object contours obtained as we propose appear subjectively more natural in shape compared with those obtained by two conventional methods, especially when the binary object model is not in good condition.

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Lederman, Susan J., Roberta L. Klatzky, and J. D. Balakrishnan. "Task-Driven Extraction of Object Contour by Human Haptics: Part 2." Robotica 9, no. 2 (April 1991): 179–88. http://dx.doi.org/10.1017/s0263574700010225.

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SUMMARYThe extraction of contour information from subjects is essential for purposes of grasping and manipulation. We proposed that human haptic exploration of contours, in the absence of vision, would reveal specialized patterns, or “contour exploration procedures,” that are directly related to task goals and intrinsic system capacities. Our general assumptions, method, and initial results were described in Part 1. Part 2 provides an analysis of the relation between contour extraction procedures and processing constraints. These theoretical assumptions are supported by empirical findings, and implications are discussed for issues of importance to robotic exploration and manipulation.

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Wang, Bei, and Jin Guo He. "Contour Reconstruction Based on Non-Closed Contours." Applied Mechanics and Materials 220-223 (November 2012): 2313–18. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2313.

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There are a lot of developments focused on 3D surface reconstruction based on 2D contours in recent decades, mostly based on closed contours. Due to the limitation of imperfect technology on material classification and edge extraction, it’s difficult to extract closed contours automatically, and this situation limits the application of 3D surface reconstruction based on contour. This paper designs a reconstruction algorithm based on non-closed contour, which not only provides a new viewpoint for research on 3D reconstruction, but also make 3D reconstruction based on contour more applicable.

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Zhang, De Jiang, Na Na Dong, and Xiao Mei Lin. "Contour Extraction of Cerebrovascular Based on Maximum Optimization Cost." Advanced Materials Research 204-210 (February 2011): 1415–18. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.1415.

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By studying the conventional algorithm of contour extraction, a new method of contour extraction in blood vessel of brain is proposed based on the MOC maximum optimization cost. First of all, the theory computes the gray differential of the image by conventional differential method to build the cost space. Then, by using dynamic programming theory, the maximum optimization cost curve in the space is extracted to serve as the specific cerebrovascular profile. The experiments show that this method ensures high efficiency in extracting cerebrovascular contour and a high accuracy in positioning cerebrovascular contour, and it diminishes the target image ambiguity caused by noise to improve the anti-interference ability of Contour extraction.

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Liu, Guoqi, and Jinjin Wei. "Active Contour Models Based on Block Similarity for Multiple Objects Segmentation." Complexity 2019 (November 6, 2019): 1–17. http://dx.doi.org/10.1155/2019/5465289.

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For the model of active contours with group similarity (ACGS), a rank constraint for a group of evolving contours is defined to keep the shape similarity. ACGS obtains robust results in extracting a single object with missing or misleading features. However, with one initial contour, it could not extent to multiple objects segmentation because low-rank property will not hold in some image sequences. Besides, ACGS is affected by nontarget objects. In this paper, an active contour model based on block similarity of shapes is proposed to extend the ACGS model to realize multiple objects extraction. For a sequence of image with multiple objects, a model for multiple objects extraction is constructed by combining sparse decomposition and ACGS; second, a block low-rank constraint is proposed to constrain the similarity of these evolving contours in every block; finally, segmentation results are obtained through iterative evolutions. Experimental results show the proposed method could segment images with multiple targets, and it improves the robust segmentation performance of sequence of image when the features of multiobjects are missing or misleading.

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Zhang, Hao, Zhi Jing Liu, and Hai Yong Zhao. "Contour Extraction of Human with Single-Pixel Width." Applied Mechanics and Materials 20-23 (January 2010): 376–81. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.376.

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A novel algorithm is presented to acquire accurate human contour. For current algorithms extracting contour with multi-pixels width, it is difficulty in obtaining accurate distance between centroid and any point on human contour for gait recognition. For connective human contour, we use candidate regions and vectors to calculate and compare the angles of adjoining vectors, so that we get point set of human contour and describe human contour of single-pixel width. For disjoint contour, image pre-processing is implemented to fuse disjoint silhouettes with their centroids. Subsequently, we go on performing the algorithm of complete contour to get accurate contour. This algorithm solves the problem that any point on human contour locates accurately. It takes the advantages in image processing of human silhouette, particularly in accurate extraction of human contour for gait or posture recognition.

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WANG, WEI, and CHI-KIT RONALD CHUNG. "IMAGE SEGMENTATION WITH COMPLEMENTARY USE OF EDGE AND REGION INFORMATION." International Journal of Image and Graphics 11, no. 04 (October 2011): 549–70. http://dx.doi.org/10.1142/s0219467811004275.

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Two familiar approaches to image segmentation are the salient contour extraction approach and the closed-contour deformation approach. The former uses Gestalt laws to link individual edge elements and construct segmentation boundaries. However, it is often difficult to have both closure and precision of the boundary addressed at the same time. The latter starts with a closed contour and deforms the contour to localize the segmentation boundary more precisely whilst maintaining the closure. The approach does not have the closure problem, but how to assign a proper initial contour for it remains an open issue. In this work, we propose a scheme that puts together the two approaches to let them work complementarily. Specifically, we design a salient contour extraction process that extracts a proper initialization of the closed contours; the process looks into edge evidence and proximity to the desired segmentation boundaries. Then, a region-based active contour in a level set formulation is adopted to refine the contour position to locate the segmentation boundaries more precisely. The scheme requires neither manual input on contour initialization nor prior knowledge about the imaged scene. Experiments on extensive benchmarking image-sets are presented to illustrate the performance of the scheme.

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Xu, Huipu, Wenjie Lu, and Meng Joo Er. "An Integrated Strategy toward the Extraction of Contour and Region of Sonar Images." Journal of Marine Science and Engineering 8, no. 8 (August 10, 2020): 595. http://dx.doi.org/10.3390/jmse8080595.

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In this paper, an integrated underwater sonar image extraction strategy, which combines two improved methods, namely the level set method (LSM) and the Lattice Boltzmann Method (LBM), is proposed. First, sonar images are processed by a clustering method and a connected domain analysis to generate the target minimum rectangle frame. Next, the segmentation task is decomposed into two subtasks, namely a coarse segmentation task to obtain the initial contour and a fine segmentation task after embedding the initial contour. Finally, the improved LSM is used to obtain the target contour, and the coarse contour of the segment is embedded into the LBM to obtain the region segmentation of the target in the sonar images. The main contributions of the paper are as follows: (1) The contours and regions of the sonar images are extracted simultaneously. (2) The original LBM method is enhanced to solve the level set iteration problem. (3) The region segmentation with the original image background is extracted, and a more intuitive region segmentation result than that of directly extracting the contour of the level set is achieved. Experimental results based on four evaluation indices of image segmentation show that our method is effective, accurate, and superior to other existing methods.

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N S., Aruna, and Dr Hariharan S. "Detection of Sickle Cell Anemia Through Contour Evidence Extraction and Estimation." International Journal of Engineering and Advanced Technology 10, no. 6 (August 30, 2021): 182–91. http://dx.doi.org/10.35940/ijeat.f3076.0810621.

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Diagnosis of sickle cell anemia by manual visual inspection through microscope is time consuming and causes human errors. Observational errors occur mostly due to overlapping of cells in blood smear image. Here, an automatic segmentation approach is introduced which isolates sickle cells from all other cells within a blood sample. The proposed system is an approach to find the elliptically shaped sickle cells through geometric feature extraction and contour based segmentation to isolate sickle cells. This technique is a method of isolating sickle cells from other cells within blood sample using cell morphology. A combined approach of extraction of seed points, contour extraction and estimation of contours is used for separation of sickle cells from red blood cells. The methods used for the extraction of seed points are by Ultimate Erosion for Convex Sets and Fast Radial Symmetry transform. The contour evidence is extracted by associating edges of the cells to the seed points. The overlapping and clustered cells in image are identified using ellipse fitting method for contour estimation. Using the seed points and the contour extraction, the edges of the cells are estimated. The lines joining the shape of cells are drawn through estimation of shape of contour. This eliminates cells other than elliptical shaped cells. The proposed system can successfully isolate sickle cells from healthy blood cells within the blood smear image. This automated system has a better accuracy and faster computation speed compared to the existing methods for the detection of sickle cells. This identification methodology helps the health professionals for faster diagnosis.

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Wang, Xiaoqi, and Jian Zhang. "An Improved Automatic Shape Feature Extraction Method Based on Template Matching." Journal of Physics: Conference Series 2095, no. 1 (November 1, 2021): 012053. http://dx.doi.org/10.1088/1742-6596/2095/1/012053.

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Abstract Image shape extraction is an important step in the image analysis, AI electronic industry and automation, as well as a significant part of content-based image retrieval(CBIR), which cannot be separated from contour extraction. However, traditional approach of the border following algorithm is susceptible to noise interference, thus the shape extracted is always complex in real images and cannot express feature of the target image well. Therefore, an improved shape feature extraction method is proposed, which converts color space into HSV model when preprocessing, filters contour by area size, merges adjacent contours by drawing convex hull and filters with template shapes. Lastly, this method is tested on UAV123 and YCB_Video dataset, which showed that the proportion of valid contour improved from less than 10% to 87.7% based on border following algorithm. In the experiment of OPenCV open source library in Visual Studio environment, we hope to improve the extraction efficiency of shape features.

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Wang, Jucui, Mingzhi Li, Anton Dziatkovskii, Uladzimir Hryneuski, and Aleksandra Krylova. "Research on contour feature extraction method of multiple sports images based on nonlinear mechanics." Nonlinear Engineering 11, no. 1 (January 1, 2022): 347–54. http://dx.doi.org/10.1515/nleng-2022-0037.

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Abstract This article solves the issue of long extraction time and low extraction accuracy in traditional moving image contour feature extraction methods. Here authors have explored deformable active contour model to research the image processing technology in scientific research and the application of multiple sports and the method. A B-spline active contour model based on dynamic programming method is proposed in this article. This article proposes a method of using it to face image processing and extracting computed tomography (CT) image data to establish a three-dimensional model. The Lyapunov exponent, correlation dimension and approximate entropy of the nonlinear dynamics algorithm were used to extract the features of eight types of motor imagination electroencephalogram (EEG) signals. The results show that the success rate of pose reconstruction is more than 97% when the contour extraction quality is relatively ideal. The method is also robust to image noise, and the success rate of pose reconstruction can reach 94% when the video image has large noise. The execution efficiency is sub-linear, which can basically meet the requirements of real-time processing in video-based human posture reconstruction. The proposed method has a low error rate in the calculation of curvature features, effectively reduces the time for extracting contour features of moving images, and improves the accuracy of feature information extraction.

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Junhong Xu, Feng Yang, Rui Chang, and Yan Xu. "A Gait Contour Extraction Algorithm Using Improved Active Contour." International Journal of Advancements in Computing Technology 5, no. 9 (May 31, 2013): 933–41. http://dx.doi.org/10.4156/ijact.vol5.issue9.111.

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Tsujino, Junpei, Hiroaki Kudo, Tetsuya Matsumoto, Yoshinori Takeuchi, and Noboru Ohnishi. "Contour Extraction of Overlapped Objects." IEEJ Transactions on Electronics, Information and Systems 130, no. 3 (2010): 483–89. http://dx.doi.org/10.1541/ieejeiss.130.483.

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Qi Li and C. Kambhamettu. "Contour Extraction of Drosophila Embryos." IEEE/ACM Transactions on Computational Biology and Bioinformatics 8, no. 6 (November 2011): 1509–21. http://dx.doi.org/10.1109/tcbb.2011.37.

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Zhang, Fan, Rui Li, and Shuai Liu. "Contour extraction of gait recognition." Procedia Engineering 7 (2010): 275–79. http://dx.doi.org/10.1016/j.proeng.2010.11.044.

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Bandyopadhyay, Sanghamitra. "Contour Extraction using Genetic Algorithms." IETE Journal of Research 48, no. 5 (September 2002): 369–76. http://dx.doi.org/10.1080/03772063.2002.11416298.

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Kang, L., Q. Wang, and H. W. Yan. "BUILDING EXTRACTION BASED ON OPENSTREETMAP TAGS AND VERY HIGH SPATIAL RESOLUTION IMAGE IN URBAN AREA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 715–18. http://dx.doi.org/10.5194/isprs-archives-xlii-3-715-2018.

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How to derive contour of buildings from VHR images is the essential problem for automatic building extraction in urban area. To solve this problem, OSM data is introduced to offer vector contour information of buildings which is hard to get from VHR images. First, we import OSM data into database. The line string data of OSM with tags of building, amenity, office etc. are selected and combined into completed contours; Second, the accuracy of contours of buildings is confirmed by comparing with the real buildings in Google Earth; Third, maximum likelihood classification is conducted with the confirmed building contours, and the result demonstrates that the proposed approach is effective and accurate. The approach offers a new way for automatic interpretation of VHR images.

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Klatzky, Roberta L., Susan J. Lederman, and J. D. Balakrishnan. "Task–Driven Extraction of Object Contour by Human Haptics: Part 1." Robotica 9, no. 1 (January 1991): 43–51. http://dx.doi.org/10.1017/s0263574700015551.

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SUMMARYThe extraction of contour information from objects is essential for purposes of grasping and manipulation. We proposed that human haptic exploration of contours, in the absence of vision, would reveal specialized patterns. Task goals and intrinsic system capacities were assumed to constrain the breadth of processing and the precision with which contour is encoded, thus determining parameters of exploration and ultimately producing movement synergies or “contour exploration procedures.” A methodology for testing these assumptions is described, and the most frequently observed procedures are documented in Part 1. Part 2 will further analyze the procedures, test predictions, and develop implications of the research. The paper (2 parts) is novel in its study of human manipulative behavior from a robotic standpoint; it is thus of interest to robotics research workers interested in the long-term goals of robot manipulation and those interested in an anthropomorphic approach to robotics studies.

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Liao, Cheng, Han Hu, Haifeng Li, Xuming Ge, Min Chen, Chuangnong Li, and Qing Zhu. "Joint Learning of Contour and Structure for Boundary-Preserved Building Extraction." Remote Sensing 13, no. 6 (March 10, 2021): 1049. http://dx.doi.org/10.3390/rs13061049.

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Most of the existing approaches to the extraction of buildings from high-resolution orthoimages consider the problem as semantic segmentation, which extracts a pixel-wise mask for buildings and trains end-to-end with manually labeled building maps. However, as buildings are highly structured, such a strategy suffers several problems, such as blurred boundaries and the adhesion to close objects. To alleviate the above problems, we proposed a new strategy that also considers the contours of the buildings. Both the contours and structures of the buildings are jointly learned in the same network. The contours are learnable because the boundary of the mask labels of buildings implicitly represents the contours of buildings. We utilized the building contour information embedded in the labels to optimize the representation of building boundaries, then combined the contour information with multi-scale semantic features to enhance the robustness to image spatial resolution. The experimental results showed that the proposed method achieved 91.64%, 81.34%, and 74.51% intersection over union (IoU) on the WHU, Aerial, and Massachusetts building datasets, and outperformed the state-of-the-art (SOTA) methods. It significantly improved the accuracy of building boundaries, especially for the edges of adjacent buildings. The code is made publicly available.

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LOURENS, TINO, and ROLF P. WÜRTZ. "EXTRACTION AND MATCHING OF SYMBOLIC CONTOUR GRAPHS." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 07 (November 2003): 1279–302. http://dx.doi.org/10.1142/s0218001403002848.

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We describe an object recognition system based on symbolic contour graphs. The image to be analyzed is transformed into a graph with object corners as vertices and connecting contours as edges. Image corners are determined using a robust multiscale corner detector. Edges are constructed by line-following between corners based on evidence from the multiscale Gabor wavelet transform. Model matching is done by finding subgraph isomorphisms in the image graph. The complexity of the algorithm is reduced by labeling vertices and edges, whereby the choice of labels also makes the recognition system invariant under translation, rotation and scaling. We provide experimental evidence and theoretical arguments that the matching complexity is below O(#V3), and show that the system is competitive with other graph-based matching systems.

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McDermott, Josh H., Andriana J. Lehr, and Andrew J. Oxenham. "Is Relative Pitch Specific to Pitch?" Psychological Science 19, no. 12 (December 2008): 1263–71. http://dx.doi.org/10.1111/j.1467-9280.2008.02235.x.

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Melodies, speech, and other stimuli that vary in pitch are processed largely in terms of the relative pitch differences between sounds. Relative representations permit recognition of pitch patterns despite variations in overall pitch level between instruments or speakers. A key component of relative pitch is the sequence of pitch increases and decreases from note to note, known as the melodic contour. Here we report that contour representations are also produced by patterns in loudness and brightness (an aspect of timbre). The representations of contours in different dimensions evidently have much in common, as contours in one dimension can be readily recognized in other dimensions. Moreover, contours in loudness and brightness are nearly as useful as pitch contours for recognizing familiar melodies that are normally conveyed via pitch. Our results indicate that relative representations via contour extraction are a general feature of the auditory system, and may have a common central locus.

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Nishiura, Masahide, Mayumi Yuasa, and Mutsumi Watanabe. "Active contour extraction method using partial shape constraint contour model." Systems and Computers in Japan 31, no. 14 (2000): 20–28. http://dx.doi.org/10.1002/1520-684x(200012)31:14<20::aid-scj3>3.0.co;2-l.

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UEDA, NAONORI, and KENJI MASE. "TRACKING MOVING CONTOURS USING ENERGY-MINIMIZING ELASTIC CONTOUR MODELS." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 03 (June 1995): 465–84. http://dx.doi.org/10.1142/s0218001495000481.

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This paper proposes a robust method for tracking an object contour in a sequence of images. In this method, both object extraction and tracking problems can be solved simultaneously. Furthermore, it is applicable to the tracking of arbitrary shapes since it does not need a priori knowledge about the object shapes. In the contour tracking, energy-minimizing elastic contour models are utilized, which is newly presented in this paper. The contour tracking is formulated as an optimization problem to find the position that minimizes both the elastic energy of its model and the potential energy derived from the edge potential image that includes a target object contour. We also present an algorithm which efficiently solves energy minimization problems within a dynamic programming framework. The algorithm enables us to obtain optimal solution even when the variables to be optimized are not ordered. We show the validity and usefulness of the proposed method with some experimental results.

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Faridah, Faridah, Balza Achmad, and Binar Listyana S. "Lip Image Feature Extraction Utilizing Snake’s Control Points for Lip Reading Applications." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 4 (August 1, 2015): 720. http://dx.doi.org/10.11591/ijece.v5i4.pp720-728.

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Snake is an active contour model that catches and locks image edges, then localizes them accurately. The simplest Snake consists of a set of control points that are connected by straight lines to form a closed loop. This paper discusses the application of Snake to find the visual feature of lip shapes. In most previous papers, visual feature of lip shapes is represented by Snake’s contour. In this paper, the feature of lip shapes is represented by six control points on lip Snake’s contours. By simply utilizing six control points representing one lip Snake’s contour, it is expected to reduce the burden on pattern recognition stage. To demonstrate the performance of this method, some analysis has been conducted on the effect of lip conditions and illumination. The results shows that the overall lip feature extraction using the proposed method is better for lips that have more contrast to the surrounding skin, optimum room illumination that gives the best result is in the range of 330-340 lux.

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Liu, Na, Yanyan Ma, Linyi Shao, and Hao Wang. "Rapid Extraction of Clothing Sample Profile Based on the Improved Canny Algorithm." Advances in Multimedia 2022 (April 11, 2022): 1–6. http://dx.doi.org/10.1155/2022/7554652.

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In order to reduce the difficulty of clothing diversity on the edge extraction link, this study proposes an improved Canny algorithm that segments the pattern, styles, and contours of the clothing image after creating a sample library of a women’s image. Extract, improve the accuracy of the edge, and reduce the noise formed by texture and clothing. The results show that in the simulation experiment, the contour extraction of multiple categories of clothing is carried out, and compared with the differential operator algorithm and Canny algorithm, the experimental results show that the improved algorithm can more accurately segment the edge of clothing, extract the style contour, and express the characteristics of clothing.

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Li, Shuai, Yuelei Xu, Wei Cong, Shiping Ma, Mingming Zhu, and Min Qi. "Biologically Inspired Hierarchical Contour Detection with Surround Modulation and Neural Connection." Sensors 18, no. 8 (August 4, 2018): 2559. http://dx.doi.org/10.3390/s18082559.

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Contour is a very important feature in biological visual cognition and has been extensively investigated as a fundamental vision problem. In connection with the limitations of conventional models in detecting image contours in complex scenes, a hierarchical image contour extraction method is proposed based on the biological vision mechanism that draws on the perceptual characteristics of the early vision for features such as edges, shapes, and colours. By simulating the information processing mechanisms of the cells’ receptive fields in the early stages of the biological visual system, we put forward a computational model that combines feedforward, lateral, and feedback neural connections to decode and obtain the image contours. Our model simulations and their results show that the established hierarchical contour detection model can adequately fit the characteristics of the biological experiment, quickly and effectively detect the salient contours in complex scenes, and better suppress the unwanted textures.

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Jaiswal, Rachana, and Srikant Satarkar. "Role of Hybrid Level Set in Fetal Contour Extraction." Signal & Image Processing : An International Journal 12, no. 1 (February 28, 2021): 39–52. http://dx.doi.org/10.5121/sipij.2021.12104.

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Image processing technologies may be employed for quicker and accurate diagnosis in analysis and feature extraction of medical images. Here, existing level set algorithm is modified and it is employed for extracting contour of fetus in an image. In traditional approach, fetal parameters are extracted manually from ultrasound images. An automatic technique is highly desirable to obtain fetal biometric measurements due to some problems in traditional approach such as lack of consistency and accuracy. The proposed approach utilizes global & local region information for fetal contour extraction from ultrasonic images. The main goal of this research is to develop a new methodology to aid the analysis and feature extraction.

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Yan, Yun Yang, Shang Bing Gao, Hong Yan Wang, and Zhi Bo Guo. "Contour Extraction of Flame for Fire Detection." Advanced Materials Research 383-390 (November 2011): 1106–10. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.1106.

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Fire detection based on sequences of images is more suitable for the need in big room or badly environment. Color and contour are both the important features of a flame image. The method to extract the contour feature of a flame image is developed based on threshold of flame area. The edges of the burning flames jitter continuously, but their contour are similar each other. The method to detect flames in video sequences is proposed here based on flame’s dynamic contour. Many experiments show that the system is able to work well and get high detection rate with a low false positive rate.

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Cao, Jun. "Image-fusion-based contour extraction scheme." Optical Engineering 44, no. 12 (December 1, 2005): 127005. http://dx.doi.org/10.1117/1.2149308.

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Wiseman, Yair, and Erick Fredj. "Contour Extraction of Compressed JPEG Images." Journal of Graphics Tools 6, no. 3 (January 2001): 37–43. http://dx.doi.org/10.1080/10867651.2001.10487544.

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Bergevin, R., and A. Bubel. "Object-level structured contour map extraction." Computer Vision and Image Understanding 91, no. 3 (September 2003): 302–34. http://dx.doi.org/10.1016/s1077-3142(03)00099-7.

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Capson, David W. "Performance comparisons of contour extraction algorithms." IEEE Transactions on Instrumentation and Measurement IM-35, no. 4 (December 1986): 409–17. http://dx.doi.org/10.1109/tim.1986.6499107.

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Liu, Songlin, Li Zhang, Wei Liu, Jun Hu, Hui Gong, Xin Zhou, and Danchao Gong. "RERB: A Dataset for Residential Area Extraction with Regularized Boundary in Remote Sensing Imagery for Mapping Application." Electronics 11, no. 17 (September 5, 2022): 2790. http://dx.doi.org/10.3390/electronics11172790.

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Due to the high automaticity and efficiency of image-based residential area extraction, it has become one of the research hotspots in surveying, mapping, and computer vision, etc. For the application of mapping residential area, the extracted contour is required to be regular. However, the contour results of existing deep-learning-based residential area extraction methods are assigned accurately according to the actual range of residential areas in imagery, which are difficult to directly apply to mapping due to the extractions being messy and irregular. Most of the existing ground object extraction datasets based on optical satellite images mainly promote the research of semantic segmentation, thereby ignoring the requirements of mapping applications. In this paper, we introduce an optical satellite images dataset named RERB (Residential area Extraction with Regularized Boundary) to support and advance end-to-end learning of residential area mapping. The characteristic of RERB is that it embeds the prior knowledge of regularized contour in the dataset. In detail, the RERB dataset contains 13,892 high-quality satellite images with a spatial resolution of 2 m acquired from different cities in China, and the size of each image is approximately 256 × 256 pixels, which covers an area of more than 3640 square kilometers. The novel published RERB dataset encompasses four superiorities: (1) Large-scale and high-resolution; (2) well annotated and regular label contour; (3) rich background; and (4) class imbalance. Therefore, the RERB dataset is suitable for both semantic segmentation and mapping application tasks. Furthermore, to validate the effectiveness of the RERB, a novel end-to-end regularization extraction algorithm of residential areas based on contour cross-entropy constraints is designed and implemented, which can significantly improve the regularization degree of extraction for the mapping of residential areas. The comparative experimental results demonstrate the preponderance and practicability of our public dataset and can further facilitate future research.

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Zhou, Xu, and Jian-min Chang. "Basic study on contour tracing method in softwood cell contour extraction." Forestry Studies in China 11, no. 2 (April 28, 2009): 127–31. http://dx.doi.org/10.1007/s11632-009-0022-5.

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Ai, Tinghua. "The drainage network extraction from contour lines for contour line generalization." ISPRS Journal of Photogrammetry and Remote Sensing 62, no. 2 (June 2007): 93–103. http://dx.doi.org/10.1016/j.isprsjprs.2007.04.002.

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Wei, Xueyun, Wei Zheng, Caiping Xi, and Shang Shang. "Shoreline Extraction in SAR Image Based on Advanced Geometric Active Contour Model." Remote Sensing 13, no. 4 (February 10, 2021): 642. http://dx.doi.org/10.3390/rs13040642.

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Rapid and accurate extraction of shoreline is of great significance for the use and management of sea area. Remote sensing has a strong ability to obtain data and has obvious advantages in shoreline survey. Compared with visible-light remote sensing, synthetic aperture radar (SAR) has the characteristics of all-weather and all-day working. It has been well-applied in shoreline extraction. However, due to the influence of natural conditions there is a problem of weak boundary in extracting shoreline from SAR images. In addition, the complex micro topography near the shoreline makes it difficult for traditional visual interpretation and image edge detection methods based on edge information to obtain a continuous and complete shoreline in SAR images. In order to solve these problems, this paper proposes a method to detect the land–sea boundary based on a geometric active contour model. In this method, a new symbolic pressure function is used to improve the geometric active-contour model, and the global regional smooth information is used as the convergence condition of curve evolution. Then, the influence of different initial contours on the number and time of iterations is studied. The experimental results show that this method has the advantages of fewer iteration times, good stability and high accuracy.

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Cheng, Fang Xiao, Xiao Mei Lin, and Na Na Dong. "Vascular Contour Extraction of MRS Regional Morphology Algorithm Based on Sobel Operator." Advanced Materials Research 424-425 (January 2012): 676–79. http://dx.doi.org/10.4028/www.scientific.net/amr.424-425.676.

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Through the study of the traditional vascular contour extraction algorithm, in this paper the means that vascular contour extraction of MRS regional morphology algorithm based on Sobel Operator is proposed. This method comprehensively applying regional growth algorithm and morphologic detection algorithm, and combining with Sobel operator through the correction and contrast enhanced, the result of vascular contour is more accurate and more continuous on this basis, which plays a decisive role. Experiments show that this method not only can effectively extract vascular outline and vascular contour positioning accuracy is higher, but also can effectively reduce the interference of noise to the image , Thereby this method improve the anti-interference ability of contour extraction for image

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Ma, Xianmin, and Xiaofeng Li. "Dynamic Gesture Contour Feature Extraction Method Using Residual Network Transfer Learning." Wireless Communications and Mobile Computing 2021 (October 13, 2021): 1–11. http://dx.doi.org/10.1155/2021/1503325.

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The current dynamic gesture contour feature extraction method has the problems that the recognition rate of dynamic gesture contour feature and the recognition accuracy of dynamic gesture type are low, the recognition time is long, and comprehensive is poor. Therefore, we propose a dynamic gesture contour feature extraction method using residual network transfer learning. Sensors are used to integrate dynamic gesture information. The distance between the dynamic gesture and the acquisition device is detected by transfer learning, the dynamic gesture image is segmented, and the characteristic contour image is initialized. The residual network method is used to accurately identify the contour and texture features of dynamic gestures. Fusion processing weights are used to trace the contour features of dynamic gestures frame by frame, and the contour area of dynamic gestures is processed by gray and binarization to realize the extraction of contour features of dynamic gestures. The results show that the dynamic gesture contour feature recognition rate of the proposed method is 91%, the recognition time is 11.6 s, and the dynamic gesture type recognition accuracy rate is 92%. Therefore, this method can effectively improve the recognition rate and type recognition accuracy of dynamic gesture contour features and shorten the time for dynamic gesture contour feature recognition, and the F value is 0.92, with good comprehensive performance.

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Yang, Xuan, Zhengchao Chen, Bing Zhang, Baipeng Li, Yongqing Bai, and Pan Chen. "A Block Shuffle Network with Superpixel Optimization for Landsat Image Semantic Segmentation." Remote Sensing 14, no. 6 (March 16, 2022): 1432. http://dx.doi.org/10.3390/rs14061432.

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In recent years, with the development of deep learning in remotely sensed big data, semantic segmentation has been widely used in large-scale landcover classification. Landsat imagery has the advantages of wide coverage, easy acquisition, and good quality. However, there are two significant challenges for the semantic segmentation of mid-resolution remote sensing images: the insufficient feature extraction capability of deep convolutional neural network (DCNN); low edge contour accuracy. In this paper, we propose a block shuffle module to enhance the feature extraction capability of DCNN, a differentiable superpixel branch to optimize the feature of small objects and the accuracy of edge contours, and a self-boosting method to fuse semantic information and edge contour information to further optimize the fine-grained edge contour. We label three sets of Landsat landcover classification datasets, and achieved an overall accuracy of 86.3%, 83.2%, and 73.4% on the three datasets, respectively. Compared with other mainstream semantic segmentation networks, our proposed block shuffle network achieves state-of-the-art performance, and has good generalization ability.

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Liang Jinxin, 梁晋欣, 张乐 Zhang Le, 孟余耀 Meng Yuyao, 滕杰 Teng Jie, 何全令 He Quanling, 傅雷扬 Fu Leiyang, and 李绍稳 Li Shaowen. "改进的玉米植株轮廓提取方法." Laser & Optoelectronics Progress 60, no. 12 (2023): 1210004. http://dx.doi.org/10.3788/lop220561.

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Osawa, Fumiaki, and Kazunori Kano. "Contour Tracking of Soft Sheet Materials Using Local Contour Image Data." International Journal of Automation Technology 6, no. 5 (September 5, 2012): 654–61. http://dx.doi.org/10.20965/ijat.2012.p0654.

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Technologies related to the handling of cloth products still rely on human hands in the apparel industry and in the fields of medicine, welfare, and nursing care; hence, the automation of these technologies is strongly desired. However, the cloth material in question is characteristically non-linear and anisotropic, so it is not easy to predict its dynamic deformation. Therefore, no guidelines for integrated methods of using robots to handle cloth material have been established. In this paper, we propose a method of tracking the contour of a piece of cloth that has curved edges. The aim of the method is to spread soft, flat objects of different shapes, such as clothes. We show contour extraction performed by a camera mounted on a fingertip of a robot hand and operational plan of a manipulator on the basis of the extracted information. The effectiveness of the method is demonstrated by extracting the contour of a piece of patterned cloth, controlling the hand direction and the depth of grasp, and tracking the contour of a piece of cloth with partly curved edges.

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Liu, Shu Min, Ying Ping Huang, and Ren Jie Zhang. "Contour Extraction Based on Improved Snake Model and its Application in Vehicle Identification." Applied Mechanics and Materials 391 (September 2013): 441–47. http://dx.doi.org/10.4028/www.scientific.net/amm.391.441.

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Contour curve is an important shape feature for vehicle recognition and it is a hard work to extraction it from complex dynamic traffic video for in-vehicle detection system. Snake Model is used to automatically extract the object contour curve proposed by Kass et al, but it is inability for traffic objects. Presented here is a novel approach for extracting vehicle contour curve by combining stereo vision with Snake Model. In this paper, Stereo vision is first used to segment vehicle from traffic background, then Snake Model is adopted to obtain complete contour curve. In view of classical Snake model is easily affected by noise, here we propose a improved Snake model by combining corner detection technology with Distance Potential Snake Model. Moreover, a vehicle identification method based on contour curve is presented. The method presented here was tested on complex traffic scenes and the corresponding results prove the efficiency of our proposed method.

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Li, Ronghua, Jiaru Fu, Fengxiang Zhai, and Zikang Huang. "Recognition and Pose Estimation Method for Stacked Sheet Metal Parts." Applied Sciences 13, no. 7 (March 26, 2023): 4212. http://dx.doi.org/10.3390/app13074212.

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To address issues such as detection failure and the difficulty in locating gripping points caused by the stacked placement of irregular parts in the automated sheet metal production process, a highly robust method for the recognition and pose estimation of parts is proposed. First, a decoding framework for parts of a two-dimensional code is established. The morphological closed operation and topology of contours are used to locate the two-dimensional code, and the type of the part is decoded according to the structure of the two-dimensional code extracted by the projection method. Second, the recognition model of the occluded part type is constructed. The edge information of parts is extracted. The contour convex hull is used to split the part contours, and the similarity of segmented contours is calculated based on the Fourier transform. Finally, the occluded parts are located. The corner points of the metal parts are extracted by the adjacency factor of the differential chain code sequence and the contour radius of curvature. The transformation matrix between the part and the standard template is calculated using similar contour segments and contour corner points. A stereo vision system is built to detect and localize the irregular sheet metal parts for experiments, including detection and information extraction experiments of the two-dimensional laser-generated code and detection and positioning experiments of parts under different occlusion rates. The experimental results show that the decoding framework can accurately decode the two-dimensional code made by a laser under low-contrast conditions, the average recognition rate can reach 93% at multiple occlusion rates, the geometric feature extraction algorithm is more accurate than common algorithms and no pseudo-corner points, the localization error is less than 0.8 mm, and the pose angle error is less than 0.6°. The methods proposed in this paper have high accuracy and robustness.

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Journal articles on the topic 'Contour extraction'

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