Relevant bibliographies by topics / Object contour detection method

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Object contour detection method'

Author: Grafiati
Published: 28 May 2022

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Journal articles on the topic "Object contour detection method"

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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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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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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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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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Dissertations / Theses on the topic "Object contour detection method"

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Vylíčil, Radek. "Detektor objektů v obrazech založený na metodě C4." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-220400.

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This thesis deals with the image object detection using Contour cues. The text describes methods how to train and test object detector. The main contribution of this thesis consists in creation Feature extractor for creation object detector in Java programming. The functionality of object detector was demonstrated on medical images.
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Berjass, Hisham. "Hardware Implementation Of An Object Contour Detector Using Morphological Operators." Thesis, Linköpings universitet, Institutionen för systemteknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-66353.

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The purpose of this study was the hardware implementation of a real time moving object contour extraction.Segmentation of image frames to isolate moving objects followed by contour extraction using digitalmorphology was carried out in this work. Segmentation using temporal difference with median thresholdingapproach was implemented, experimental methods were used to determine the suitable morphological operatorsalong with their structuring elements dimensions to provide the optimum contour extraction.The detector with image resolution of 1280 x1024 pixels and frame rate of 60 Hz was successfully implemented,the results indicate the effect of proper use of morphological operators for post processing and contourextraction on the overall efficiency of the system. An alternative segmentation method based on Stauffer & Grimson algorithm was investigated and proposed which promises better system performance at the expense ofimage resolution and frame rate
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Голінка, А. Ю. "Інтелектуальна система керування автомобільною стоянкою." Master's thesis, Сумський державний університет, 2020. https://essuir.sumdu.edu.ua/handle/123456789/79551.

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Проведено аналіз типів автоматичних систем автостоянок, порівняльний аналіз існуючих методів виявлення контуру об’єкту, розроблений функціонал автоматичного виявлення заповнення автомобільної парковки, проведено тестування реалізації, зроблені висновки по результатам тестування, дана оцінка актуальності та ефективності даної системи.
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Che, Peining. "ZERO-SHOT OBJECT DETECTION METHOD COMPARISON AND ANALYSIS." Miami University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=miami1567160037757546.

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Furundzic, Bojan, and Fabian Mathisson. "Dataset Evaluation Method for Vehicle Detection Using TensorFlow Object Detection API." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43345.

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Recent developments in the field of object detection have highlighted a significant variation in quality between visual datasets. As a result, there is a need for a standardized approach of validating visual dataset features and their performance contribution. With a focus on vehicle detection, this thesis aims to develop an evaluation method utilized for comparing visual datasets. This method was utilized to determine the dataset that contributed to the detection model with the greatest ability to detect vehicles. The visual datasets compared in this research were BDD100K, KITTI and Udacity, each one being trained on individual models. Applying the developed evaluation method, a strong indication of BDD100K's performance superiority was determined. Further analysis and feature extraction of dataset size, label distribution and average labels per image was conducted. In addition, real-world experimental conduction was performed in order to validate the developed evaluation method. It could be determined that all features and experimental results pointed to BDD100K's superiority over the other datasets, validating the developed evaluation method. Furthermore, the TensorFlow Object Detection API's ability to improve performance gain from a visual dataset was studied. Through the use of augmentations, it was concluded that the TensorFlow Object Detection API serves as a great tool to increase performance gain for visual datasets.
Inom fältet av objektdetektering har ny utveckling demonstrerat stor kvalitetsvariation mellan visuella dataset. Till följd av detta finns det ett behov av standardiserade valideringsmetoder för att jämföra visuella dataset och deras prestationsförmåga. Detta examensarbete har, med ett fokus på fordonsigenkänning, som syfte att utveckla en pålitlig valideringsmetod som kan användas för att jämföra visuella dataset. Denna valideringsmetod användes därefter för att fastställa det dataset som bidrog till systemet med bäst förmåga att detektera fordon. De dataset som användes i denna studien var BDD100K, KITTI och Udacity, som tränades på individuella igenkänningsmodeller. Genom att applicera denna valideringsmetod, fastställdes det att BDD100K var det dataset som bidrog till systemet med bäst presterande igenkänningsförmåga. En analys av dataset storlek, etikettdistribution och genomsnittliga antalet etiketter per bild var även genomförd. Tillsammans med ett experiment som genomfördes för att testa modellerna i verkliga sammanhang, kunde det avgöras att valideringsmetoden stämde överens med de fastställda resultaten. Slutligen studerades TensorFlow Object Detection APIs förmåga att förbättra prestandan som erhålls av ett visuellt dataset. Genom användning av ett modifierat dataset, kunde det fastställas att TensorFlow Object Detection API är ett lämpligt modifieringsverktyg som kan användas för att öka prestandan av ett visuellt dataset.
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Yiu, Wai-sing Boris, and 姚維勝. "A fast probabilistic method for vehicle detection and tracking with anexplicit contour model." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B35057178.

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MOREIRA, GUSTAVO COSTA GOMES. "A METHOD FOR REAL-TIME OBJECT DETECTION IN HD VIDEOS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=24507@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
A detecção e o subsequente rastreamento de objetos em sequencias de vídeo é um desafio no que tange o processamento de vídeos em tempo real. Nesta tese propomos um método de detecção em tempo real adequado para o processamento de vídeos de alta definição. Neste método utilizamos um procedimento de segmentação de quadros usando as imagens integrais de frente, o que permite o rápido descarte de várias partes da imagem a cada quadro, desta maneira atingindo uma alta taxa de quadros processados por segundo. Estendemos ainda o algoritmo proposto para que seja possível detectar múltiplos objetos em paralelo. Além disto, através da utilização de uma GPU e técnicas que podem ter seu desempenho aumentado por meio de paralelismo, como o operador prefix sum, conseguimos atingir um desempenho ainda melhor do algoritmo, tanto para a detecção do objeto, como na etapa de treinamento de novas classes de objetos.
The detection and subsequent tracking of objects in video sequences is a challenge in terms of video processing in real time. In this thesis we propose an detection method suitable for processing high-definition video in real-time. In this method we use a segmentation procedure through integral image of the foreground, which allows a very quick disposal of various parts of the image in each frame, thus achieving a high rate of processed frames per second. Further we extend the proposed method to be able to detect multiple objects in parallel. Furthermore, by using a GPU and techniques that can have its performance enhanced through parallelism, as the operator prefix sum, we can achieve an even better performance of the algorithm, both for the detection of the object, as in the training stage of new classes of objects.
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Yiu, Wai-sing Boris. "A fast probabilistic method for vehicle detection and tracking with an explicit contour model." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B35057178.

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HE, LEI. "A COMPARISON OF DEFORMABLE CONTOUR METHODS AND MODEL BASED APPROACH USING SKELETON FOR SHAPE RECOVERY FROM IMAGES." University of Cincinnati / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1059746287.

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Ye, Fanjie. "A Method of Combining GANs to Improve the Accuracy of Object Detection on Autonomous Vehicles." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1752364/.

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As the technology in the field of computer vision becomes more and more mature, the autonomous vehicles have achieved rapid developments in recent years. However, the object detection and classification tasks of autonomous vehicles which are based on cameras may face problems when the vehicle is driving at a relatively high speed. One is that the camera will collect blurred photos when driving at high speed which may affect the accuracy of deep neural networks. The other is that small objects far away from the vehicle are difficult to be recognized by networks. In this paper, we present a method to combine two kinds of GANs to solve these problems. We choose DeblurGAN as the base model to remove blur in images. SRGAN is another GAN we choose for solving small object detection problems. Due to the total time of these two are too long, we still do the model compression on it to make it lighter. Then we use the Yolov4 to do the object detection. Finally we do the evaluation of the whole model architecture and proposed a model version 2 based on DeblurGAN and ESPCN which is faster than previous one but the accuracy may be lower.
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Book chapters on the topic "Object contour detection method"

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Cao, Yuan-yuan, Guang-you Xu, and Thomas Riegel. "Moving Object Contour Detection Based on S-T Characteristics in Surveillance." In Human Interface and the Management of Information. Methods, Techniques and Tools in Information Design, 575–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73345-4_66.

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Ferrari, Vittorio, Tinne Tuytelaars, and Luc Van Gool. "Object Detection by Contour Segment Networks." In Computer Vision – ECCV 2006, 14–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11744078_2.

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Li, Xin, Fan Yang, Hong Cheng, Wei Liu, and Dinggang Shen. "Contour Knowledge Transfer for Salient Object Detection." In Computer Vision – ECCV 2018, 370–85. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01267-0_22.

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Kelm, André Peter, Vijesh Soorya Rao, and Udo Zölzer. "Object Contour and Edge Detection with RefineContourNet." In Computer Analysis of Images and Patterns, 246–58. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29888-3_20.

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Weiler, Daniel, Volker Willert, and Julian Eggert. "A Probabilistic Prediction Method for Object Contour Tracking." In Artificial Neural Networks - ICANN 2008, 1011–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87536-9_103.

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Memar, Sara, Karen Jin, and Boubakeur Boufama. "Object Detection Using Active Contour Model with Depth Clue." In Lecture Notes in Computer Science, 640–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39094-4_73.

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Shi, Xin, Tao Xue, and Xueqing Zhao. "Moving Object Detection Based on Self-adaptive Contour Extraction." In Lecture Notes in Computer Science, 126–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87355-4_11.

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Zhu, Qihui, Liming Wang, Yang Wu, and Jianbo Shi. "Contour Context Selection for Object Detection: A Set-to-Set Contour Matching Approach." In Lecture Notes in Computer Science, 774–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88688-4_57.

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Ghadiri, Farnoosh, Robert Bergevin, and Guillaume-Alexandre Bilodeau. "Carried Object Detection Based on an Ensemble of Contour Exemplars." In Computer Vision – ECCV 2016, 852–66. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46478-7_52.

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Mohanty, Mihir Narayan, and Subhashree Rout. "An Intelligent Method for Moving Object Detection." In Advances in Intelligent Systems and Computing, 343–51. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2009-1_39.

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Conference papers on the topic "Object contour detection method"

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Kim, Shin-Hyoung, and Jong Whan Jang. "An Improved Snake-Based Method for Object Contour Detection." In 2007 IEEE International Conference on Image Processing. IEEE, 2007. http://dx.doi.org/10.1109/icip.2007.4378938.

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Zirakchi, Armaan, Cody Lee Lundberg, and Hakki Erhan Sevil. "Omni Directional Moving Object Detection and Tracking With Virtual Reality Feedback." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5352.

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Computer vision methods are commonly used to detect and track motion using conventional cameras, however, that is limited with the field of view (FOV) of the camera. This study is to attempt to overcome this challenge by using a 360 degree camera. Our approach utilizes background subtracter from OpenCV Library which creates a continuously updating background model for the motion detection. The model is subtracted from the current frame leaving contours symbolizing the movement observed in the camera view. These contours are then analyzed and processed so that the system can track the largest contour. The tracked movement is outlined and directed to the user via Virtual Reality (VR) headset. The VR headset only displays a 60 degree portion of the camera view to the user which provides more realistic situational awareness of the surroundings for the user. These activities are a part of a larger effort to establish a foundation for autonomous unmanned vehicle systems with situational awareness capabilities.
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Theodoracatos, Vassilios E., and Ranganath R. Katti. "An Automated and Interactive Approach for Fitting B-Spline Surfaces Through 3D Planar Visual Data." In ASME 1991 Design Technical Conferences. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/detc1991-0102.

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Abstract The work reported here is an integral part of a system developed for the automated reconstruction of arbitrary-shaped physical objects using vision systems, three dimensional computer graphics, and B-Spline surface approximation techniques. Digitized planar contour points are automatically fitted in the image and world space to define a minimum number of B-Spline control points using least-squares approximation techniques. The final set of points represent the B-Spline control net of the entire surface of the object. The resulting curves and surfaces can be further interactively modified until a satisfactory fit is obtained. Three parametrization techniques, viz., uniform, chord length, and affine invariant angle method are implemented and adjusted to their local minima using the Newton-Raphson iteration method. The effect of each method on the accuracy of the reconstructed surface is discussed. The techniques were tested using a clay model of a human face. The uniform parametrization performed better with the highest speed of convergence and best least-squares error characteristics. On the other hand, it was less effective in detecting sharp corners as compared to the other two methods. The results also show that there is a minimum number of control points for every surface beyond which there is no error improvement. This is useful in several industrial applications when checking surface accuracy of manufactured parts using Coordinate Measuring Machines (CMM).
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Schlecht, Joseph, and Björn Ommer. "Contour-based object detection." In British Machine Vision Conference 2011. British Machine Vision Association, 2011. http://dx.doi.org/10.5244/c.25.50.

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Hwang, Kao-Shing, Ming-Dar Tsai, and Ming-Yi Ju. "3D Collision-Free Trajectory Planning for Mobile Robot Based on Quadric Modeling." In ASME 1998 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/detc98/mech-5995.

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Abstract A 3-D collision-free trajectory planning method for a mobile robot is proposed in the paper. The geometric shapes of the objects in the workspace are modeled as ellipsoids of 3-D quadric model for simple mathematical representation and easy geometric approximation. By a series of coordinate transformations between the mobile robot and obstacles, the collision detection problem in trajectory planning is reduced to test a point falling outside or inside the transformed ellipsoids, which models obstacles geometrically. Finally, the collision probability, which is defined by projecting the quadric ellipsoid onto a 3-D Gaussian distribution contour, plays a very significant role in search the optimal path through the defined objective function.
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Loke, Kar Seng. "Wedgelets-based automatic object contour detection." In 2010 Sixth International Conference on Natural Computation (ICNC). IEEE, 2010. http://dx.doi.org/10.1109/icnc.2010.5583731.

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Bi, Wei, Yongping Zhang, Weiguo Huang, and Guanqi Gao. "Salient Contour Matching for Object Detection." In 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). IEEE, 2016. http://dx.doi.org/10.1109/ihmsc.2016.37.

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Shotton, J., A. Blake, and R. Cipolla. "Contour-based learning for object detection." In Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1. IEEE, 2005. http://dx.doi.org/10.1109/iccv.2005.63.

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Xu, Changhai, and Benjamin Kuipers. "Object Detection Using Principal Contour Fragments." In 2011 Canadian Conference on Computer and Robot Vision (CRV). IEEE, 2011. http://dx.doi.org/10.1109/crv.2011.55.

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Kezheng, Lin, and Li Xinyuan. "Improved Contour-Based Object Detection and Segmentation." In 2008 International Multi-symposiums on Computer and Computational Sciences (IMSCCS). IEEE, 2008. http://dx.doi.org/10.1109/imsccs.2008.48.

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Reports on the topic "Object contour detection method"

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Christie, Benjamin, Osama Ennasr, and Garry Glaspell. ROS integrated object detection for SLAM in unknown, low-visibility environments. Engineer Research and Development Center (U.S.), November 2021. http://dx.doi.org/10.21079/11681/42385.

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Integrating thermal (or infrared) imagery on a robotics platform allows Unmanned Ground Vehicles (UGV) to function in low-visibility environments, such as pure darkness or low-density smoke. To maximize the effectiveness of this approach we discuss the modifications required to integrate our low-visibility object detection model on a Robot Operating System (ROS). Furthermore, we introduce a method for reporting detected objects while performing Simultaneous Localization and Mapping (SLAM) by generating bounding boxes and their respective transforms in visually challenging environments.
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Clausen, Jay, Christopher Felt, Michael Musty, Vuong Truong, Susan Frankenstein, Anna Wagner, Rosa Affleck, Steven Peckham, and Christopher Williams. Modernizing environmental signature physics for target detection—Phase 3. Engineer Research and Development Center (U.S.), March 2022. http://dx.doi.org/10.21079/11681/43442.

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The present effort (Phase 3) builds on our previously published prior efforts (Phases 1 and 2), which examined methods of determining the probability of detection and false alarm rates using thermal infrared for buried object detection. Environmental phenomenological effects are often represented in weather forecasts in a relatively coarse, hourly resolution, which introduces concerns such as exclusion or misrepresentation of ephemera or lags in timing when using this data as an input for the Army’s Tactical Assault Kit software system. Additionally, the direct application of observed temperature data with weather model data may not be the best approach because metadata associated with the observations are not included. As a result, there is a need to explore mathematical methods such as Bayesian statistics to incorporate observations into models. To better address this concern, the initial analysis in Phase 2 data is expanded in this report to include (1) multivariate analyses for detecting objects in soil, (2) a moving box analysis of object visibility with alternative methods for converting FLIR radiance values to thermal temperature values, (3) a calibrated thermal model of soil temperature using thermal IR imagery, and (4) a simple classifier method for automating buried object detection.
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Yan, Yujie, and Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, May 2021. http://dx.doi.org/10.17760/d20410114.

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Recent advances in visual sensing technology have gained much attention in the field of bridge inspection and management. Coupled with advanced robotic systems, state-of-the-art visual sensors can be used to obtain accurate documentation of bridges without the need for any special equipment or traffic closure. The captured visual sensor data can be post-processed to gather meaningful information for the bridge structures and hence to support bridge inspection and management. However, state-of-the-practice data postprocessing approaches require substantial manual operations, which can be time-consuming and expensive. The main objective of this study is to develop methods and algorithms to automate the post-processing of the visual sensor data towards the extraction of three main categories of information: 1) object information such as object identity, shapes, and spatial relationships - a novel heuristic-based method is proposed to automate the detection and recognition of main structural elements of steel girder bridges in both terrestrial and unmanned aerial vehicle (UAV)-based laser scanning data. Domain knowledge on the geometric and topological constraints of the structural elements is modeled and utilized as heuristics to guide the search as well as to reject erroneous detection results. 2) structural damage information, such as damage locations and quantities - to support the assessment of damage associated with small deformations, an advanced crack assessment method is proposed to enable automated detection and quantification of concrete cracks in critical structural elements based on UAV-based visual sensor data. In terms of damage associated with large deformations, based on the surface normal-based method proposed in Guldur et al. (2014), a new algorithm is developed to enhance the robustness of damage assessment for structural elements with curved surfaces. 3) three-dimensional volumetric models - the object information extracted from the laser scanning data is exploited to create a complete geometric representation for each structural element. In addition, mesh generation algorithms are developed to automatically convert the geometric representations into conformal all-hexahedron finite element meshes, which can be finally assembled to create a finite element model of the entire bridge. To validate the effectiveness of the developed methods and algorithms, several field data collections have been conducted to collect both the visual sensor data and the physical measurements from experimental specimens and in-service bridges. The data were collected using both terrestrial laser scanners combined with images, and laser scanners and cameras mounted to unmanned aerial vehicles.
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Kirichek, Galina, Vladyslav Harkusha, Artur Timenko, and Nataliia Kulykovska. System for detecting network anomalies using a hybrid of an uncontrolled and controlled neural network. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3743.

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In this article realization method of attacks and anomalies detection with the use of training of ordinary and attacking packages, respectively. The method that was used to teach an attack on is a combination of an uncontrollable and controlled neural network. In an uncontrolled network, attacks are classified in smaller categories, taking into account their features and using the self- organized map. To manage clusters, a neural network based on back-propagation method used. We use PyBrain as the main framework for designing, developing and learning perceptron data. This framework has a sufficient number of solutions and algorithms for training, designing and testing various types of neural networks. Software architecture is presented using a procedural-object approach. Because there is no need to save intermediate result of the program (after learning entire perceptron is stored in the file), all the progress of learning is stored in the normal files on hard disk.
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