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Journal articles on the topic 'Object contour detection method'

Author: Grafiati
Published: 28 May 2022

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1

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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Zhang, Jianhua, Sheng Liu, Y. F. Li, and Jianwei Zhang. "Target Contour Recovering for Tracking People in Complex Environments." Computational and Mathematical Methods in Medicine 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/506908.

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Recovering people contours from partial occlusion is a challenging problem in a visual tracking system. Partial occlusions would bring about unreasonable contour changes of the target object. In this paper, a novel method is presented to detect partial occlusion on people contours and recover occluded portions. Unlike other occlusion detection methods, the proposed method is only based on contours, which makes itself more flexible to be extended for further applications. Experiments with synthetic images demonstrate the accuracy of the method for detecting partial occlusions, and experiments on real-world video sequence are also carried out to prove that the method is also good enough to be used to recover target contours.
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Wang, Bo, Kun Zhang, Liang Shi, and Hui Hui Zhong. "An Edge Detection Algorithm of Moving Object Based on Background Modeling and Active Contour Model." Advanced Materials Research 765-767 (September 2013): 2393–98. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2393.

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A novel algorithm based on background modelling and active contour model is proposed for moving object edge detection. Firstly, it uses the background modeling to complete moving object detection, then it uses quad-tree decomposition method to contain the corresponding to the foreground image, through the data distribution density of the sparse matrix, calculates the seed points corresponding to the regions which are containing the moving object. Finally, starting from these seed points, it executes the active contour model in parallel to complete the multiple moving objects edge detection. Experimental results show that the proposed algorithm can effectively obtain the object outlines of multi-moving objects and the edge detection results are close to the judgment of the human visual, parallel contour extraction makes our algorithm has good real-time.
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Khan, Umer Sadiq, Xingjun Zhang, and Yuanqi Su. "Active Contour Model Using Fast Fourier Transformation for Salient Object Detection." Electronics 10, no. 2 (January 15, 2021): 192. http://dx.doi.org/10.3390/electronics10020192.

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The active contour model is a comprehensive research technique used for salient object detection. Most active contour models of saliency detection are developed in the context of natural scenes, and their role with synthetic and medical images is not well investigated. Existing active contour models perform efficiently in many complexities but facing challenges on synthetic and medical images due to the limited time like, precise automatic fitted contour and expensive initialization computational cost. Our intention is detecting automatic boundary of the object without re-initialization which further in evolution drive to extract salient object. For this, we propose a simple novel derivative of a numerical solution scheme, using fast Fourier transformation (FFT) in active contour (Snake) differential equations that has two major enhancements, namely it completely avoids the approximation of expansive spatial derivatives finite differences, and the regularization scheme can be generally extended more. Second, FFT is significantly faster compared to the traditional solution in spatial domain. Finally, this model practiced Fourier-force function to fit curves naturally and extract salient objects from the background. Compared with the state-of-the-art methods, the proposed method achieves at least a 3% increase of accuracy on three diverse set of images. Moreover, it runs very fast, and the average running time of the proposed methods is about one twelfth of the baseline.
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Hermawan, Hendra. "Experimental Vision Robot for General Working Application using Raspberry Pi and Single Camera with Python-OpenCV." ACMIT Proceedings 3, no. 1 (March 18, 2019): 231–38. http://dx.doi.org/10.33555/acmit.v3i1.50.

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Development a visual-guided autonomous arm robot for general working application in service workshop require some preliminary works/research to ensure the quality and reliability of robot mainly on object detection/recognition and object pose estimation. We have experimented robot vision for this purpose using Raspberry Pi and single web camera supported by Python-OpenCV programming using color-base and contour-base detection algorithm for object recognition and Triangulation similarity method for object pose estimation. Experiment results showed that color-base detection is 22% faster than contour-based object detection for colorful tooling object without disturbance same color from environment. However, contour-base detection is more effective for target working object detection than color-base. Light illumination and disturbance from environment should be managed for successful object detection. Triangulation linearity method is simple and fastest method for tooling object position estimation when tooling object is a known sized object. Experiment result showed error only 2% for distance estimation using this method compared with actual.
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Xiang, Jinhai, Heng Fan, Honghong Liao, Jun Xu, Weiping Sun, and Shengsheng Yu. "Moving Object Detection and Shadow Removing under Changing Illumination Condition." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/827461.

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Moving object detection is a fundamental step in video surveillance system. To eliminate the influence of illumination change and shadow associated with the moving objects, we proposed a local intensity ratio model (LIRM) which is robust to illumination change. Based on the analysis of the illumination and shadow model, we discussed the distribution of local intensity ratio. And the moving objects are segmented without shadow using normalized local intensity ratio via Gaussian mixture model (GMM). Then erosion is used to get the moving objects contours and erase the scatter shadow patches and noises. After that, we get the enhanced moving objects contours by a new contour enhancement method, in which foreground ratio and spatial relation are considered. At last, a new method is used to fill foreground with holes. Experimental results demonstrate that the proposed approach can get moving objects without cast shadow and shows excellent performance under various illumination change conditions.
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7

Park, Hyun Jun, and Kwang Baek Kim. "Estimation of object location probability for object detection using brightness feature only." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (December 1, 2019): 5227. http://dx.doi.org/10.11591/ijece.v9i6.pp5227-5234.

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Most existing object detection methods use features such as color, shape, and contour. If there are no consistent features can be used, we need a new object detection method. Therefore, in this paper, we propose a new method for estimating the probability that an object can be located for object detection and generating an object location probability map using only brightness in a gray image. To evaluate the performance of the proposed method, we applied it to gallbladder detection. Experimental results showed 98.02% success rate for gallbladder detection in ultrasonogram. Therefore, the proposed method accurately estimates the object location probability and effectively detected gallbladder.
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Xu, Yang, Cheng Dong Wu, Ying Zhao, Ji Zhao, and Xue Dong Zhang. "Moving Object Detection Based on Improved Variational GAC Model." Advanced Materials Research 562-564 (August 2012): 1309–14. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.1309.

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In view of the detection of moving object in video sequence, the traditional moving object detection algorithms are researched. The paper presents a new algorithm for object detection based on initial contour and improved variational GAC model. First, this method built up background model utilizing Gaussian mixture model and background subtraction to extract initial contour of the object; taking initial contour as initial value of curve evolution. Then, an improved restriction item is introduced into variational GAC vector model, the proposed restriction item that is a nonlinear hear equation with normalized diffusion rate, therefore re-initialization procedure of level set function is completely eliminated. Iteration number of curve evolution and run time is reduced. The experimental show that accurate contour of moving object is got and this algorithm is effective and feasible in real video environment.
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9

Васильева, Ирина Карловна, and Анатолий Владиславович Попов. "МЕТОД СИНТЕЗА МНОГОКОМПОНЕНТНОЙ МОДЕЛИ АТРИБУТИВНЫХ ПРИЗНАКОВ ОБЪЕКТОВ." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 2 (October 8, 2018): 13–25. http://dx.doi.org/10.32620/reks.2018.2.02.

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The subject matter of the article are the processes of forming of objects’ attribute features analytical descriptions for solving applied problems of statistical recognition of objects’ images on multi-channel images. The goal is to develop a multicomponent mathematical model for representing statistical information about the summation of geometric, colour and structural parameters of observational objects. The tasks to be solved are: to formalize the procedure of statistical image segmentation in conditions of incomplete a priori information about objects classes and unknown distribution densities of classification characteristics; to build effective algorithms for detection and linking contour points; to choose a universal mathematical model for describing the geometric shape of both the object and its structural components and to develop a robust method for estimating the model parameters. The methods used are: statistical methods of pattern recognition, methods of probability theory and mathematical statistics, methods of contour analysis, numerical methods for conditional optimization. The following results were obtained. The method of multicomponent model synthesis for describing colour, geometric and structural attributes of object images on multichannel images is proposed. In the model terms, the object is represented by a hierarchical set of nested contours, for the selection of which information about the colour characteristics of statistically homogeneous regions of the image is used. Methods for detecting and linking contour points have been developed, which make it possible to obtain the coordinates of the boundaries circular sweep for both convex and concave geometric objects. As a universal basis for describing the model components, the Johnson SB distribution is adopted, which allows us to describe practically any unimodal and wide class of bimodal distributions. A method for Johnson distribution parameters’ estimation from sample data, based on the method of moments and using optimization procedures for a non-linear objective function with constraints is given. Conclusions. The scientific novelty of the results obtained is as follows: the methods for describing the objects’ images in the form of a combination of several bright-geometric elements and structural connections between them have been further developed, which makes it possible to comprehensively take into account the attribute features of objects in the procedures for analyzing and interpreting images, automatically detecting and locating objects with specified characteristics
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Zhu, Xin, Xin Xu, and Nan Mu. "Saliency Detection Based on the Combination of High-Level Knowledge and Low-Level Cues in Foggy Images." Entropy 21, no. 4 (April 6, 2019): 374. http://dx.doi.org/10.3390/e21040374.

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A key issue in saliency detection of the foggy images in the wild for human tracking is how to effectively define the less obvious salient objects, and the leading cause is that the contrast and resolution is reduced by the light scattering through fog particles. In this paper, to suppress the interference of the fog and acquire boundaries of salient objects more precisely, we present a novel saliency detection method for human tracking in the wild. In our method, a combination of object contour detection and salient object detection is introduced. The proposed model can not only maintain the object edge more precisely via object contour detection, but also ensure the integrity of salient objects, and finally obtain accurate saliency maps of objects. Firstly, the input image is transformed into HSV color space, and the amplitude spectrum (AS) of each color channel is adjusted to obtain the frequency domain (FD) saliency map. Then, the contrast of the local-global superpixel is calculated, and the saliency map of the spatial domain (SD) is obtained. We use Discrete Stationary Wavelet Transform (DSWT) to fuse the cues of the FD and SD. Finally, a fully convolutional encoder–decoder model is utilized to refine the contour of the salient objects. Experimental results demonstrate that the presented model can remove the influence of fog efficiently, and the performance is better than 16 state-of-the-art saliency models.
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11

Zhang, Jun. "Study on Object Contour Extraction Based on Hölder Exponent and Multifractal Spectrum." Advanced Materials Research 429 (January 2012): 267–70. http://dx.doi.org/10.4028/www.scientific.net/amr.429.267.

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Object contour extraction is one of the most important steps for object segmentation and recognition. The edge detection method based on gradient detection have some disadvantages if there are some noise in image, the edge errors occur in the gradient detection stage will be transmitted to the edge connection stage and the errors can’t be modified during the procedure. Multifractal spectrum gives a complete statistical description of the singularity in the image. In this paper, a new method based on the combination of the singularity and multifractal analysis is proposed to extract object contour. The experiments show that the contour extraction results are encouraging.
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12

Antoshchuk, Svitlana G., Sergey B. Kondratyev, Galyna Yu Shcherbakova, and Mykola A. Hodovychenko. "Depth map generation for mobile navigation systems based on objects localization in images." Herald of Advanced Information Technology 5, no. 1 (April 18, 2022): 11–18. http://dx.doi.org/10.15276/hait.05.2022.1.

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This paper is aimed to develop a method for a depth map generation based on objects localization in images, obtained through a stereopair. The proposed solution describes the objects by the following informative elements: contours, interest points (points of the greatest curvature of the contour), center of mass of the object. Moreover, to describe the contour of the image, it is proposed to use methods with adjustable detailing, based on the wavelet transform, which has frequency-selective properties. The novelty of this method is the possibility of obtaining an approximate depth map by simplifying the calculation of stereo image difference values, which is traditionally used to generate a depth map. Software was developed based on the proposed solutions. Modeling confirmed the effectiveness of the proposed approach. The proposed method makes it possible to significantly reduce the number of computational operations and, consequently, improve depth map generation performance and recommend the proposed method for mobile navigation systems operating in conditions of limited computing and energy resources. The method provides object detection and spatial positioning, makes it possible to obtain reliable information about the distance to objects for other subsystems that use technical vision in their operation, for example, navigation systems for visually impaired people, robotic devices, etc.
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Liu, Wei, Xue Jun Xu, Bi Tao Fu, and Xi Zhu. "Moving Object Detection Based on Edge Difference and Contour Matching." Applied Mechanics and Materials 182-183 (June 2012): 1863–67. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1863.

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This paper presents an improved method to detect moving object and obtain the relative accurate location. First we detect the edge difference of continuous frames. Then we utilize the contour matching to find the edge pairs in order to reach a good detection of the moving object and location. The extensive experiments show that our method is robust and efficient to the moving object detection.
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Zhang, Ming Jun, Yuan Yuan Wan, and Zhen Zhong Chu. "Research on Underwater Target Tracking Based on Contour Detection." Advanced Materials Research 317-319 (August 2011): 890–96. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.890.

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The traditional centroid tracking method over-relies on the accuracy of segment, which easily lead to loss of underwater moving target. This paper presents an object tracking method based on circular contour extraction, combining region feature and contour feature. Through the correction to circle features, the problem of multiple solutions causing by Hough transform circle detection is avoided. A new motion prediction model is constructed to make up the deficiency that three-order motion prediction model has disadvantage of high dimension and large calculation. The predicted position of object centroid is updated and corrected by circle contour, forming prediction-measurement-updating closed-loop target tracking system. To reduce system processing time, on the premise of the tracking accuracy, a dynamic detection method based on target state prediction model is proposed. The results of contour extraction and underwater moving target experiments demonstrate the effectiveness of the proposed method.
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15

LIU, ZHI-YONG, HONG QIAO, and LEI XU. "INVESTIGATION ON MULTISETS MIXTURE LEARNING BASED OBJECT DETECTION." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 08 (December 2007): 1339–51. http://dx.doi.org/10.1142/s0218001407005971.

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By minimizing the mean square reconstruction error, multisets mixture learning (MML) provides a general approach for object detection in image. To calculate each sample reconstruction error, as the object template is represented by a set of contour points, the MML needs to inefficiently enumerate the distances between the sample and all the contour points. In this paper, we develop the line segment approximation (LSA) algorithm to calculate the reconstruction error, which is shown theoretically and experimentally to be more efficient than the enumeration method. It is also experimentally illustrated that the MML based algorithm has a better noise resistance ability than the generalized Hough transform (GHT) based counterpart.
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Weng, Li Yuan, Min Li, and Zhen Bang Gong. "On Sonar Image Processing Techniques for Detection and Localization of Underwater Objects." Applied Mechanics and Materials 236-237 (November 2012): 509–14. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.509.

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This paper presents an underwater object detection and localization system based on multi-beam sonar image processing techniques. Firstly, sonar data flow collected by multi-beam sonar is processed by median filter to reduce noise. Secondly, an improved adaptive thresholding method based on Otsu method is proposed to extract foreground objects from sonar image. Finally, the object’s contour is calculated by Moore-Neighbor Tracing algorithm to locate the object. Experiments show that the proposed system can detect underwater objects quickly and the figure out the position of objects accurately.
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You, Xu Chen, Yu Hui Li, Bo Li, and Xian Biao Liu. "The Research on the Fusion of Sobel Evaluation Function about Vehicle Contour Clearness Algorithm." Applied Mechanics and Materials 556-562 (May 2014): 3818–21. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3818.

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In order to improve the controllability and rationality of vehicle object contour repair process, this paper proposes a fusion sobel evaluation function of vehicle contour clearness algorithm. Using the compensation method of image sequence inter-frame motion to restore vehicle object contour, to establish evaluation function based on sobel edge detection function, statistical characterization of the edge information of the number of pixels to restore judge, by constantly sequence of inter-frame motion compensation and evaluation of the iterative calculation, realize vehicle object contour clear. The experimental results show that this method is simple, effective、feasible and has good real-time performance and robustness.
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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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Shieh, I., and K. F. Gill. "A Moving Object Detection Process for Computer Vision Application." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 206, no. 1 (February 1992): 33–39. http://dx.doi.org/10.1243/pime_proc_1992_206_053_02.

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The aim of this paper is to present a novel method for processing a digitized image that will allow pertinent information to be extracted on object movement in a scene. A frame difference method locates the moving candidates in a region which is evaluated by a hypothesis testing procedure to identify accretion and deletion regions. Accretion regions are selected and used as seeds to search for moving objects in the current frame. Contour tracing is applied to establish the boundary of an accretion region which is then used to help recognize the moving object. The results of this work reveal that motion can be used as an effective cue for object detection from an image sequence.
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Pang, Lu Lu, Yao Qin Cao, Wen Bo Zhang, and Wei Long Ren. "Method for Extraction of Object Contour of near Space SAR Image Based on Level Set Algorithm." Applied Mechanics and Materials 347-350 (August 2013): 3581–85. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3581.

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Its a research hotspot recently to conduct image analysis and target detection using the object image acquired by the Synthetic Aperture Radar (SAR) in military survey, military mapping and ocean monitoring. To be able to quickly and accurately extract the object contour of SAR image real sequence captured by the aircraft in the low dynamic near space, the level set method is introduced in this article based on separating object from background by Difference Image Method. Then, the level set method is improved with the change of object image time sequence to accelerate contour extraction. The simulation experiment proves this method is efficient.
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Kashyap, Ramgopal. "Object boundary detection through robust active contour based method with global information." International Journal of Image Mining 3, no. 1 (2018): 22. http://dx.doi.org/10.1504/ijim.2018.093008.

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Kashyap, Ramgopal. "Object boundary detection through robust active contour based method with global information." International Journal of Image Mining 3, no. 1 (2018): 22. http://dx.doi.org/10.1504/ijim.2018.10014063.

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23

Chang, Jingxin, Xianjun Gao, Yuanwei Yang, and Nan Wang. "Object-Oriented Building Contour Optimization Methodology for Image Classification Results via Generalized Gradient Vector Flow Snake Model." Remote Sensing 13, no. 12 (June 19, 2021): 2406. http://dx.doi.org/10.3390/rs13122406.

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Building boundary optimization is an essential post-process step for building extraction (by image classification). However, current boundary optimization methods through smoothing or line fitting principles are unable to optimize complex buildings. In response to this limitation, this paper proposes an object-oriented building contour optimization method via an improved generalized gradient vector flow (GGVF) snake model and based on the initial building contour results obtained by a classification method. First, to reduce interference from the adjacent non-building object, each building object is clipped via their extended minimum bounding rectangles (MBR). Second, an adaptive threshold Canny edge detection is applied to each building image to detect the edges, and the progressive probabilistic Hough transform (PPHT) is applied to the edge result to extract the line segments. For those cases with missing or wrong line segments in some edges, a hierarchical line segments reconstruction method is designed to obtain complete contour constraint segments. Third, accurate contour constraint segments for the GGVF snake model are designed to quickly find the target contour. With the help of the initial contour and constraint edge map for GGVF, a GGVF force field computation is executed, and the related optimization principle can be applied to complex buildings. Experimental results validate the robustness and effectiveness of the proposed method, whose contour optimization has higher accuracy and comprehensive value compared with that of the reference methods. This method can be used for effective post-processing to strengthen the accuracy of building extraction results.
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Mudassar, Asloob Ahmad, and Saira Butt. "Snakes with Coordinate Regeneration Technique: An Application to Retinal Disc Boundary Detection." Journal of Medical Engineering 2013 (October 7, 2013): 1–11. http://dx.doi.org/10.1155/2013/852613.

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A modified snake method based on the novel idea of coordinate regeneration is presented and is tested on an object with complex concavities and on retinal images for locating the boundaries of optic discs, where the conventional snake methods fail. We have demonstrated that the use of conventional snake method with our proposed coordinate regeneration technique gives ultimate solution for finding the boundaries of complex objects. The proposed method requires a Gaussian blur of the object with a large kernel so that the snake can be initialised away from the object boundaries. In the second and third steps the blurring kernel size is reduced so that exact boundaries can be located. Coordinate regeneration is applied at each step which ultimately converges the snake (active contour) to exact boundaries. For complex objects like optic discs in retinal images, vessels act as snake distracters and some preimage processing is required before the proposed technique is applied. We are demonstrating this technique to find the boundary of optic discs in retinal images. In principle, this technique can be extended to find the boundary of any object in other modalities of medical imaging. Simulation results are presented to support the idea.
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Kowaleczko, Piotr, and Przemysław Rokita. "Improving Detection Results of Differential Background Subtraction Method by Merging it with C-HDC Algorithm." Research Works of Air Force Institute of Technology 38, no. 1 (August 1, 2016): 109–20. http://dx.doi.org/10.1515/afit-2016-0010.

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AbstractA method which allows to improve object detection abilities of conventional difference-based background subtraction algorithm has been presented in this paper. The algorithm is a hybrid method consisting of the background subtraction method and the Contour Histogram Displacement Calculation (C-HDC) algorithm. Background subtraction method processes a greyscale image, while C-HDC calculates displacement values basing on its contour histograms. Tracking ratios before, and after merging with C-HDC have been measured and presented.
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Savicheva, S. V. "RECOGNITION OF SINGLE AND OVERLAY OF OBJECTS ON A CONVEYOR BELT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5/W6 (May 18, 2015): 95–99. http://dx.doi.org/10.5194/isprsarchives-xl-5-w6-95-2015.

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Proposed a method for detection of flat objects when they overlap condition. The method is based on two separate recognition algorithms flat objects. The first algorithm is based on a binary image of the signature of the object plane. The second algorithm is based on the values of the discrete points in the curvature contour of a binary image. The results of experimental studies of algorithms and a method of recognition of individual superimposed flat objects.
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Miao, Peng, Shi Han Feng, Qi Zhang, and Yuan Yuan Ji. "Real-Time Boundary Detection of Fast Moving Object in Dark Surrounds." Applied Mechanics and Materials 397-400 (September 2013): 2231–34. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.2231.

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Dark surrounds make detection of moving target more difficult based on traditional methods. A real time identification of fast moving object under weak illumination is critical for some special applications. Traditional blob, contour and kernel-based tracking methods either need high computational loads or require normal illumination which limit their application. In this paper, we propose a new method trying to settle such difficulty based on temporal standard deviation. The performance of new method was evaluated with simulation data and real video data recorded by a simple imaging system. Combining hardware acceleration, a real time detection and visualization of fast moving boundary in dark environment can be achieved.
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Wei, A. Hui, and B. Yang Chen. "Robotic object recognition and grasping with a natural background." International Journal of Advanced Robotic Systems 17, no. 2 (March 1, 2020): 172988142092110. http://dx.doi.org/10.1177/1729881420921102.

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In this article, a novel, efficient grasp synthesis method is introduced that can be used for closed-loop robotic grasping. Using only a single monocular camera, the proposed approach can detect contour information from an image in real time and then determine the precise position of an object to be grasped by matching its contour with a given template. This approach is much lighter than the currently prevailing methods, especially vision-based deep-learning techniques, in that it requires no prior training. With the use of the state-of-the-art techniques of edge detection, superpixel segmentation, and shape matching, our visual servoing method does not rely on accurate camera calibration or position control and is able to adapt to dynamic environments. Experiments show that the approach provides high levels of compliance, performance, and robustness under diverse experiment environments.
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Mokshin, Vladimir, Ildar Sayfudinov, Svetlana Yudina, and Leonid Sharnin. "Object Detection in The Image Using the Method of Selecting Significant Structures." International Journal of Engineering & Technology 7, no. 4.38 (December 3, 2018): 1187. http://dx.doi.org/10.14419/ijet.v7i4.38.27759.

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The approach to image segmentation is reviewed in the article. The method of highlighting significant contours in the image is reviewed. Some structures in the image attract attention more than others due to certain distinctive properties. The article reviews the approach of highlighting significant structures in the image representing the areas of candidates identifying the object in the video frame for mobile platforms. For example, such shapes can be smoother, longer and closed. Such structures are called significant. It would be expedient to use only these significant structures to increase the speed of image recognition by computer vision methods focused on the contour selection. This approach allocates the computing resources only to significant structures, thus reducing the total computation time. Since the image consists of many pixels and links between them, which are called edges, significant structures can be measured. The article presents an approach to measuring the structure significance that largely coincides with human perception. Some image structures attract our attention without the need for a systematic scan of the entire image. In most cases, this significance represents the structure properties as a whole, i.e. parts of the structure cannot be isolated. This article presents a measure of significance based on the measurement of length and curvature. The measure highlights structures characteristic of human perception, and they often correspond to objects of interest in the image. A method is presented for calculating significance using an iterative scheme combined into a single local network for processing elements. The optimization approach to represent a processed image highlighting significant locations is used in the network.
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Shen, Jie, Tanghuai Fan, Min Tang, Qian Zhang, Zhen Sun, and Fengchen Huang. "A Biological Hierarchical Model Based Underwater Moving Object Detection." Computational and Mathematical Methods in Medicine 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/609801.

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Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results.
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31

Hyun, Ji Yeon, Seungeon Ha, Jongmin Baek, Junghun Han, Honggi An, Sung-Hun Woo, Yoon Suk Kim, Sang Woo Lee, Sejung Yang, and Sei Young Lee. "Analysis of Random Dynamics of Cell Segmented by a Modified Active Contour Method." Applied Sciences 10, no. 19 (September 28, 2020): 6806. http://dx.doi.org/10.3390/app10196806.

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To understand the dynamics of a living system, the analysis of particular and/or cellular dynamics has been performed based on shape-based center point detection. After collecting sequential time-lapse images of cellular dynamics, the trajectory of a moving object is determined from the set of center points of the cell analyzed from each image. The accuracy of trajectory is significant in understanding the stochastic nature of the dynamics of biological objects. In this study, to localize a cellular object in time-lapse images, three different localization methods, namely radial symmetry, circular Hough transform, and modified active contour, were considered. To analyze the accuracy of cellular dynamics, several statistical parameters such as mean square displacement and velocity autocorrelation function were employed, and localization error derived from these was reported for each localization method. In particular, through denoising using a Poisson noise filter, improved localization characteristics could be achieved. The modified active contour with denoising reduced localization error significantly, and thus allowed for accurate estimation of the statistical parameters of cellular dynamics.
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32

Qiu, Wen Hua, Zhen Zhen Qiu, and Liang Wang. "Detection and Extraction of Moving Object in Intelligent Surveillance System." Applied Mechanics and Materials 401-403 (September 2013): 1027–30. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1027.

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To solve the low automatic degree and low accurate alarm rate in the traditional intelligent surveillance system, a motion object segmentation algorithm based on level set method is proposed and implemented. The algorithm gains the primal contour curve by luminance difference between the video frames, and sets the curve as the primal zero level set. Then the narrow band level set algorithm is used to evolve the curve until achieving the segmentation result. Experimental results show that this method can greatly save the level set segmentation time and increase the detecting efficiency.
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Chen, Shou-Cih, and Chung-Cheng Chiu. "Texture Construction Edge Detection Algorithm." Applied Sciences 9, no. 5 (March 3, 2019): 897. http://dx.doi.org/10.3390/app9050897.

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The edge detection algorithm is the cornerstone of image processing; a good edge detection result can further extract the required information through rich texture information and achieve object detection, segmentation, and identification. To obtain a rich texture edge detection technology, this paper proposes using edge texture change for edge construction and constructs the edge contour through constructing an edge texture extension between the blocks to reduce the missing edge problem caused by the threshold setting. Finally, through verification of the experimental results, the proposed method can effectively overcome the problem caused by unsuitable threshold setting and detect rich object edge information compared to the adaptive edge detection method.
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Tang, Chao, Huosheng Hu, Miaohui Zhang, Wen-Jian Wang, Xiao-Feng Wang, Feng Cao, and Wei Li. "Real-time detection of moving objects in a video sequence by using data fusion algorithm." Transactions of the Institute of Measurement and Control 41, no. 3 (June 6, 2018): 793–804. http://dx.doi.org/10.1177/0142331218773550.

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The moving object detection and tracking technology has been widely deployed in visual surveillance for security, which is, however, an extremely challenge to achieve real-time performance owing to environmental noise, background complexity and illumination variation. This paper proposes a novel data fusion approach to attack this problem, which combines an entropy-based Canny (EC) operator with the local and global optical flow (LGOF) method, namely EC-LGOF. Its operation contains four steps. The EC operator firstly computes the contour of moving objects in a video sequence, and the LGOF method then establishes the motion vector field. Thirdly, the minimum error threshold selection (METS) method is employed to distinguish the moving object from the background. Finally, edge information fuses temporal information concerning the optic flow to label the moving objects. Experiments are conducted and the results are given to show the feasibility and effectiveness of the proposed method.
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Nemer Pelliza, K. A., and M. A. Pucheta. "ANALYSIS OF THE EFFICIENCY OF THE ADAPTIVE CANNY METHOD FOR THE DETECTION OF ICEBERGS AT OPEN SEA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12-2020 (November 6, 2020): 459–64. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-459-2020.

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Abstract. The detection of icebergs in the open sea, as well as its evolution in displacement and shrinking, is vital for navigation, the study of the evolution of Polar regions, and the Earth climate change, among others. In order to carry out these studies, it is necessary to delimit accurately the icebergs in satellite images, mainly of the Synthetic Aperture Radar (SAR) type. The Adaptive Canny method has shown to be efficient for the detection of edges of objects in SAR images, according to recent publications and conferences. These studies were only carried out for images that had approximately half of each backscatter, without considering that the dimension of the objects can affect the edge detection process. Here, we present the results of the efficiency of the Adaptive Canny method as the size of the object, from which it is intended to extract the contour, decreases. A systematic analysis of the behavior of the method has been performed with objects of variated dimensions, through a Monte Carlo type experiment with synthetic images, where the contours of the figures were extracted with the Adaptive Canny method and compared with the Ground Truth (GT). Then, the method was tested on real images of the Antarctic Ocean, with blocks of ice of different sizes to contrast the results with those obtained with synthetic images.
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36

Xu, Zheng, Haibo Luo, Bin Hui, and Zheng Chang. "Siamese Tracking from Single Point Initialization." Sensors 19, no. 3 (January 26, 2019): 514. http://dx.doi.org/10.3390/s19030514.

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Recently, we have been concerned with locating and tracking vehicles in aerial videos. Vehicles in aerial videos usually have small sizes due to use of cameras from a remote distance. However, most of the current methods use a fixed bounding box region as the input of tracking. For the purpose of target locating and tracking in our system, detecting the contour of the target is utilized and can help with improving the accuracy of target tracking, because a shape-adaptive template segmented by object contour contains the most useful information and the least background for object tracking. In this paper, we propose a new start-up of tracking by clicking on the target, and implement the whole tracking process by modifying and combining a contour detection network and a fully convolutional Siamese tracking network. The experimental results show that our algorithm has significantly improved tracking accuracy compared to the state-of-the-art regarding vehicle images in both OTB100 and DARPA datasets. We propose utilizing our method in real time tracking and guidance systems.
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37

Khunteta, Ajay, and D. Ghosh. "Object Boundary Detection Using Active Contour Model via Multiswarm PSO with Fuzzy-Rule Based Adaptation of Inertia Factor." Advances in Fuzzy Systems 2016 (2016): 1–20. http://dx.doi.org/10.1155/2016/6179576.

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Active contour models, colloquially known as snakes, are quite popular for several applications such as object boundary detection, image segmentation, object tracking, and classification via energy minimization. While energy minimization may be accomplished using traditional optimization methods, approaches based on nature-inspired evolutionary algorithms have been developed in recent years. One such evolutionary algorithm that has been used extensively in active contours is the particle swarm optimization (PSO). However, conventional PSO converges slowly and gets trapped in local minimum easily which results in inaccurate detection of concavities in the object boundary. This is taken care of by using proposed multiswarm PSO in which a swarm is set for every control point in the snake and then all the swarms search for their best points simultaneously through information sharing among them. The performance of the multiswarm PSO-based search process is further enhanced by using dynamic adaptation of the inertia factor. In this paper, we propose using a set of fuzzy rules to adjust the inertia weight on the basis of the current normalized snake energy and the current value of inertia. Experimental results demonstrate the effectiveness of the proposed method compared to conventional approaches.
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38

Peng, Baochai, Dong Ren, Cheng Zheng, and Anxiang Lu. "TRDet: Two-Stage Rotated Detection of Rural Buildings in Remote Sensing Images." Remote Sensing 14, no. 3 (January 22, 2022): 522. http://dx.doi.org/10.3390/rs14030522.

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Fast and accurate acquisition of the outline of rural buildings on remote sensing images is an efficient method to monitor illegal rural buildings. The traditional object detection method produces useless background information when detecting rural buildings; the semantic segmentation method cannot accurately segment the contours between buildings; the instance segmentation method cannot obtain regular building contours. The rotated object detection methods can effectively solve the problem that the traditional artificial intelligence method cannot accurately extract the outline of buildings. However, the rotated object detection methods are easy to lose location information of small objects in advanced feature maps and are sensitive to noise. To resolve these problems, this paper proposes a two-stage rotated object detection network for rural buildings (TRDet) by using a deep feature fusion network (DFF-Net) and a pixel attention module (PAM). Specifically, TRDet first fuses low-level location and high-level semantic information through the DFF-Net and then reduces the interference of noise information to the network through the PAM. The experimental results show that the mean average precession (mAP), precision, recall rate, and F1 score of the proposed TRDet are 83.57%, 91.11%, 86.5%, and 88.74%, respectively, which outperform the R2CNN model by 15%, 15.54%, 4.01%, and 9.87%. The results demonstrate that the TRDet can achieve better detection in small rural buildings and dense rural buildings.
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Wei, Hui, and Lei Wu. "A Line-Context Based Object Recognition Method." International Journal on Artificial Intelligence Tools 23, no. 06 (December 2014): 1460029. http://dx.doi.org/10.1142/s021821301460029x.

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The shape or contour of an object is usually stable and persistent, so it is a good basis for invariant recognition. For this purpose, two problems must be handled. The first is obtaining clean edges and the other is organizing those edges into a structured form so that they can be manipulated easily. We apply a bio-inspired orientation detection algorithm because it can output a fairly clean set of lines, and all lines are in the form of vectors instead of pixels. This line representation is efficient. We decompose them into several slope-depended layers and then create a hierarchical partition tree to record their geometric distribution. Based on the similarity of trees, a rough classification of objects can be realized. However, for an accuracy recognition, we design a moment-based measure to describe the detail layout of lines in a layer and then re-describe image by Hu's moment invariants. The experimental results suggest that the representation efficiency enabled by simple cell's neural mechanism and application of multi-layered representation schema can simplify the complexity of the algorithm. This proves that line-context representation greatly eases subsequent shape-oriented recognition.
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40

Baek, Ji-Won, and Kyungyong Chung. "Pothole Classification Model Using Edge Detection in Road Image." Applied Sciences 10, no. 19 (September 23, 2020): 6662. http://dx.doi.org/10.3390/app10196662.

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Since the image related to road damage includes objects such as potholes, cracks, shadows, and lanes, there is a problem that it is difficult to detect a specific object. In this paper, we propose a pothole classification model using edge detection in road image. The proposed method converts RGB (red green and blue) image data, including potholes and other objects, to gray-scale to reduce the amount of computation. It detects all objects except potholes using an object detection algorithm. The detected object is removed, and a pixel value of 255 is assigned to process it as a background. In addition, to extract the characteristics of a pothole, the contour of the pothole is extracted through edge detection. Finally, potholes are detected and classified based by the (you only look once) YOLO algorithm. The performance evaluation evaluates the distortion rate and restoration rate of the image, and the validity of the model and accuracy of the classification. The result of the evaluation shows that the mean square error (MSE) of the distortion rate and restoration rate of the proposed method has errors of 0.2–0.44. The peak signal to noise ratio (PSNR) is evaluated as 50 db or higher. The structural similarity index map (SSIM) is evaluated as 0.71–0.82. In addition, the result of the pothole classification shows that the area under curve (AUC) is evaluated as 0.9.
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41

Jia, Gui Hong. "Target Location of Industrial Robot Based on Vision." Advanced Materials Research 945-949 (June 2014): 1478–81. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.1478.

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Vision is the most important way to obtain information from the word. This paper collected the target image using industrial robot vision system, and We get Black and white images using binary image segmentation method, then the contour of each object in the image can be obtained with edge detection and contour extraction, The centroid position was confirmed using minimum enclosing rectangle method after gaining the outline of target. The experimental results show that this method can quickly and accurately obtain multiple target centroid position.
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42

Xu, Yin, and Qiang Fan. "A lightweight convolutional network for infrared object detection and tracking." Journal of Physics: Conference Series 2234, no. 1 (April 1, 2022): 012004. http://dx.doi.org/10.1088/1742-6596/2234/1/012004.

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Abstract As an important application of computer vision, visual tracking has being a fundamental topic. Compared with visible image, infrared image has the characteristic of low resolution, blurred contour and single color feature. Thus, it is still a challenge for infrared object tracking. Further, it is difficult to balance the real-time performance and accuracy. This paper proposed a method for target detection and tracking, with a deeper and lightweight MobileNet V2 structure as the backbone network. In the end, the tracker is tested on various datasets. Result shows that the tracker can get a balance between tracking accuracy and inference speed, which is crucial for deployment on mobile devices.
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43

Ikhsan, Ismawan Noor, and Son Ali Akbar. "Aplikasi Machine Vision pada Hexacopter untuk Deteksi Survival Kits di Bidang Mitigasi Bencana." Jurnal Teknik Elektro 12, no. 2 (December 20, 2020): 72–79. http://dx.doi.org/10.15294/jte.v12i2.26676.

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Hexacopter belongs to one of flying robots that is used to carry out a special mission such as retrieving and delivering survival kits object. Thus, it should be built by smart system to determine the object accurately. However, there was an interference from other object that made it difficult to recognize the survival kits object. Therefore, the development of machine vision with the integration of the hexacopter control system is expected to improve the object recognition process. This study intends to develop a survival kit detection using the image processing method, which involved 1) segmentation on the Hue, Saturation, Value (HSV) color space, 2) contour detection, and 3) Region of Interest (ROI) selected detection. The evaluation of the segmentation method performances was done through the three-part experiments (i.e., the similar shape, variety of a color object, and an object shape). The result of survival kits object detection evaluation obtained an accuracy of 90.33%, precision of 99.63%, and recall of 91.24%. According to the performances obtained in this study, the development of machine vision systems on Unmanned Aerial Vehicle (UAV) has a high accuracy for the object survival kits detection even with another object interference.
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44

Honeycutt, Wesley T., and Eli S. Bridge. "UnCanny: Exploiting Reversed Edge Detection as a Basis for Object Tracking in Video." Journal of Imaging 7, no. 5 (April 23, 2021): 77. http://dx.doi.org/10.3390/jimaging7050077.

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Few object detection methods exist which can resolve small objects (<20 pixels) from complex static backgrounds without significant computational expense. A framework capable of meeting these needs which reverses the steps in classic edge detection methods using the Canny filter for edge detection is presented here. Sample images taken from sequential frames of video footage were processed by subtraction, thresholding, Sobel edge detection, Gaussian blurring, and Zhang–Suen edge thinning to identify objects which have moved between the two frames. The results of this method show distinct contours applicable to object tracking algorithms with minimal “false positive” noise. This framework may be used with other edge detection methods to produce robust, low-overhead object tracking methods.
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45

Zhang, Yingjie, Jianxing Xu, and H. D. Cheng. "A Novel Fuzzy Level Set Approach for Image Contour Detection." Mathematical Problems in Engineering 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/2602647.

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The level set methods have provided powerful frameworks for image segmentation. However, to obtain accurate boundaries of the objects, especially when they have weak edges or inhomogeneous intensities, is still a very challenging task. Actually, we have studied the popular existing level set approaches and discovered that they failed to segment the images with weak edges or inhomogeneous intensities in many cases. The weak/blurry edges and inhomogeneous intensities cause uncertainty and fuzziness for segmentation. In this paper, a novel fuzzy level set approach is proposed. At first,S-function based on the maximum fuzzy entropy principle (MEP) is used to map the image from space domain to fuzzy domain. Then, an energy function is formulated according to the differences between the actual and estimated probability densities of the intensities in different regions. A partial differential equation is derived for finding the minimum of the energy function. The proposed approach has been tested on both synthetic images and real images and evaluated by several popular metrics. The experimental results demonstrate that the proposed approach can locate the true object boundaries, even for objects with blurry boundaries, low contrast, and inhomogeneous intensities.
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46

Han, Pengcheng, Junping Du, and Ming Fang. "Spatial Object Tracking Using an Enhanced Mean Shift Method Based on Perceptual Spatial-Space Generation Model." Journal of Applied Mathematics 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/420286.

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Object tracking is one of the fundamental problems in computer vision, but existing efficient methods may not be suitable for spatial object tracking. Therefore, it is necessary to propose a more intelligent mathematical model. In this paper, we present an intelligent modeling method using an enhanced mean shift method based on a perceptual spatial-space generation model. We use a series of basic and composite graphic operators to complete signal perceptual transformation. The Monte Carlo contour detection method could overcome the dimensions problem of existing local filters. We also propose the enhanced mean shift method with estimation of spatial shape parameters. This method could adaptively adjust tracking areas and eliminate spatial background interference. Extensive experiments on a variety of spatial video sequences with comparison to several state-of-the-art methods demonstrate that our method could achieve reliable and accurate spatial object tracking.
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47

Song, Kechen, and Yunhui Yan. "Micro Surface Defect Detection Method for Silicon Steel Strip Based on Saliency Convex Active Contour Model." Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/429094.

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Accurate detection of surface defect is an indispensable section in steel surface inspection system. In order to detect the micro surface defect of silicon steel strip, a new detection method based on saliency convex active contour model is proposed. In the proposed method, visual saliency extraction is employed to suppress the clutter background for the purpose of highlighting the potential objects. The extracted saliency map is then exploited as a feature, which is fused into a convex energy minimization function of local-based active contour. Meanwhile, a numerical minimization algorithm is introduced to separate the micro surface defects from cluttered background. Experimental results demonstrate that the proposed method presents good performance for detecting micro surface defects including spot-defect and steel-pit-defect. Even in the cluttered background, the proposed method detects almost all of the microdefects without any false objects.
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48

Mao, Kefeng, Xi Chen, Kelan Zhu, Dong Hu, and Yan Li. "A Method to Extract Essential Information from Meteorological Facsimile Charts." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 01 (October 11, 2018): 1954001. http://dx.doi.org/10.1142/s0218001419540016.

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Using image processing technology to extract important information, such as isoline and weather system of the meteorological facsimile chart, is conducive to integration with other information, and has important practical value in navigation operations, marine weather forecasting, target recognition, and image retrieval. In meteorological facsimile charts, there are many types of medium-value lines, dense lines in some areas, superimposition and presence of multiple information, such as isolines and isoline characters, intersection of specific weather system symbols, etc. For different types of contours, numeric characters, weather system symbols and other object characteristics, the corresponding object extraction and recognition methods are proposed: Remove the latitude and longitude lines and coastline in the meteorological facsimile map by basemap matching; According to the position and shape features of the figure box, extract the meteorological fax figure box, separate and remove the different character tagging information; On the basis of identifying triangles and semicircles in weather symbols of the frontal system, the frontal symbols are extracted based on the circumscribed triangles and template matching. First the contour character on the fax image is expanded into a block connected region. Determine the position of the character information by judging the number of pixels in the connected region, and then use rotation and template matching to identify the numeric character. Using the meteorological facsimile maps of the US Meteorological Center and the Japan Meteorological Center for the main information extraction, experiments show that the method of this paper has a good effect on the complete and accurate symbol extraction of frontal weather systems, and reduces the computational complexity of contour detection, isoline extraction and numerical recognition. The methods can detect some information from weather charts properly and the error rate is very low.
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ZOU, QI, XIANG-LIN HUANG, and SI-WEI LUO. "MULTIRESOLUTION IMAGE PERCEPTUAL GROUPING USING TOPOLOGICAL STRUCTURE EMBEDDED IN MANIFOLD." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 01 (January 2007): 39–49. http://dx.doi.org/10.1142/s0219691307001641.

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Contour grouping is the precondition for high-level visual tasks such as region-of-interest detection and object recognition. Those methods adopting local geometric features and simple photometric attributes always lead to unreliable result or bad robustness. In this paper, we propose a grouping method using multiresolution analysis and one-dimensional manifold. By using multiresolution analysis, grouping seeds and initial clusters are detected as precondition for describing topological structure. Then one-dimensional manifold is applied to discovering intrinsic order of topological structure, which corresponds to coordinates of edges in a closed contour. These two improvements enhance our method's robustness relative to those using local features or linear combinations of them. Experiments on different classes of real images show competence of our method.
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Li, Yi Bing, He Jiang Jia, and Ao Li. "A Moving Objects Detection Method Base on Improved GMM." Advanced Materials Research 225-226 (April 2011): 637–41. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.637.

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Motion detection is the first and important step in many computer vision applications. Gaussian mixture model is an effective way for moving objects detection, but there are some shortcomings of this model such as slow updating rate and false detection in complex background. In this paper, we proposed an improved Gaussian mixture model method. A matching distance is defined to compute the learning rate when updating the models, and we also use dual threshold to improve the matching mechanism. Experimental results show that this method can get a faster adaptation to background and better contour of the moving objects.
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