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Local part chamfer matching for shape-based object detection

PATTERN RECOGNITION 2017年 65卷 82-96页

作者： Yu, Qian Wei, Hui Yang, Chengzhuan Fudan Univ Sch Comp Sci Lab Cognit Model & Algorithm 825 Zhangheng Rd Shanghai 201203 Peoples R China Fudan Univ Shanghai Key Lab Data Sci Sch Comp Sci 825 Zhangheng Rd Shanghai 201203 Peoples R China

Chamfer matching is one of the elegant and powerful tools for shape-based detection in cluttered images. However, the chamfer matching methods, including oriented chamfer matching (OCM) and directional chamfer matching (DCM), tend to produce bad detections due to deformation of object shapes and cluttering in the scene. To improve detection accuracy of these chamfer matching methods, we propose local part oriented chamfer matching (LPOCM) and local part directional chamfer matching (LPDCM). First, shape templates and discriminative contour fragments are learned, and then a shape representation is built using a Markov random field (MRF). Finally, the template detection in an input image is formulated as an inference in the MRF. Experimental results for benchmark datasets including ETHZ shape Classes, INRIA Horses and Weizmann Horses clearly demonstrate that the proposed LPOCM and LPDCM significantly improve the detection accuracy of OCM and DCM without sacrificing much time efficiency.

关键词： Chamfer matching shape matching shape detection shape-based object detection

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Efficient graph-based search for object detection

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INFORMATION SCIENCES 2017年 385卷 395-414页

作者： Wei, Hui Yang, Chengzhuan Yu, Qian Fudan Univ Sch Comp Sci Lab Cognit Model & Algorithm 825 Zhangheng Rd Shanghai 201203 Peoples R China Fudan Univ Shanghai Key Lab Data Sci Sch Comp Sci 825 Zhangheng Rd Shanghai 201203 Peoples R China

In this paper, we focus on the object localization problem in images given a single hand drawn example as the object model. We propose a novel framework for shape-based object detection and recognition, which we formulate as a graph-based search problem. In our method, we first propose five preprocessing procedures to reduce the irrelevant edge fragments in cluttered real images that often occur in edge detection. Then we build a graph to represent the edge images. Therefore, our goal is to find the group of adjacent nodes in the graph that match well to the model contours. Finally, we present a new evaluation method to verify the candidate hypotheses. We did experiments on the ETHZ shape classes dataset and the INRIA horses dataset. Experimental results demonstrate that the proposed method achieves not only accurate object detection but also precise contour localization in cluttered real images. Comparisons with other recent template-based matching methods further demonstrate the effectiveness and efficiency of the proposed method. (C) 2017 Elsevier Inc. All rights reserved.

关键词： shape-based object detection Graph-based search Contour matching

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Hybrid multi-GPU computing: accelerated kernels for segmentation and object detection with medical image processing applications

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2017年第1期13卷 227-244页

作者： Graca, Carlos Falcao, Gabriel Figueiredo, Isabel N. Kumar, Sunil Univ Coimbra Inst Telecomunicacoes Fac Sci & Technol Dept Elect & Comp Engn P-3030290 Coimbra Portugal Univ Coimbra Dept Math CMUC P-3001501 Coimbra Portugal Natl Inst Technol Delhi Dept Appl Sci Delhi 110040 India

In the last two decades, we have seen an amazing development of image processing techniques targeted for medical applications. We propose multi-GPU-based parallel real-time algorithms for segmentation and shape-based object detection, aiming at accelerating two medical image processing methods: automated blood detection in wireless capsule endoscopy (WCE) images and automated bright lesion detection in retinal fundus images. In the former method we identified segmentation and object detection as being responsible for consuming most of the global processing time. While in the latter, as segmentation was not used, shape-based object detection was the compute-intensive task identified. Experimental results show that the accelerated method running on multi-GPU systems for blood detection in WCE images is on average 265 times faster than the original CPU version and is able to process 344 frames per second. By applying the multi-GPU framework for bright lesion detection in fundus images we are able to process 62 frames per second with a speedup average 667 times faster than the equivalent CPU version.

关键词： Segmentation shape-based object detection Wireless capsule endoscopy Fundus images Automated diagnosis Parallel image processing Multi-GPU Systems

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Contour segment grouping for object detection

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2017年 48卷 292-309页

作者： Wei, Hui Yang, Chengzhuan Yu, Qian Fudan Univ Sch Comp Sci Lab Cognit Model & Algorithm 825 Zhangheng Rd Shanghai 201203 Peoples R China Fudan Univ Sch Comp Sci Shanghai Key Lab Data Sci 825 Zhangheng Rd Shanghai 201203 Peoples R China

In this paper, we propose a novel framework for object detection and recognition in cluttered images, given a single hand-drawn example as model. Compared with previous work, our contribution is threefold. (1) Three preprocessing procedures are proposed to reduce the number of irrelevant edge fragments that are often generated during edge detection in cluttered real images. (2) A novel shape descriptor is introduced for conducting partial matching between edge fragments and model contours. (3) An efficient search strategy is adopted to identify the location of target object hypotheses. In the hypotheses verification stage, an appearance-based (support vector machine on pyramid histogram of oriented gradients feature) method is adopted to verify the hypothesis, identify the object, and refine its location. We do extensive experiments on several benchmark datasets including ETHZ shape classes, INRIA horses, Weizmann horses, and the two classes (anchors and cups) from Caltech 101. Experimental results show that the proposed method can significantly improve the accuracy of object detection. Comparisons with other recent shape-based methods further demonstrate the effectiveness and robustness of the proposed method. (C) 2017 Elsevier Inc. All rights reserved.

关键词： shape-based object detection Contour grouping Depth-first search

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A probabilistic sparse skeleton based object detection

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PATTERN RECOGNITION LETTERS 2016年第Part3期83卷 243-250页

作者： Altinoklu, Burak Ulusoy, Ilkay Tari, Sibel Middle East Tech Univ Dept Elect & Elect Engn TR-06800 Ankara Turkey Middle East Tech Univ Dept Comp Engn TR-06800 Ankara Turkey

We present a Markov Random Field (MRF) based skeleton model for object shape and employ it in a probabilistic chamfer-matching framework for shape based object detection. Given an object category, shape hypotheses are generated from a set of sparse (coarse) skeletons guided by suitably defined unary and binary potentials at and between shape parts. The Markov framework assures that the generated samples properly reflect the observed or desired shape variability. As the model employs a sparsely sampled skeleton, the shape hypotheses are in the form of linear boundary segments;hence, matching can be performed using Directional Chamfer Matching. As the number of states that each MRF node can take is small, the matching process is efficient. Experiments with giraffe and swan categories of the ETHZ Dataset demonstrate that the method perform well in the case of articulated objects. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Markov random field Generative shape model shape skeletons shape-based object detection

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shape-based detection of Maya hieroglyphs using weighted bag representations

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PATTERN RECOGNITION 2015年第4期48卷 1161-1173页

作者： Roman-Rangel, Edgar Marchand-Maillet, Stephane Univ Geneva Dept Comp Sci CVMLab Viper Grp CH-1211 Geneva 4 Switzerland

This work addresses the problem of detecting individual visual patterns in binary images, and more precisely, individual syllabic signs in large inscriptions of Maya hieroglyphs with high levels of visual complexity. The data we use corresponds to a corpus that is of great interest for archaeologists, and it poses a difficult challenge in terms of visual complexity. We introduce a new weighting function, which helps constructing more robust bag-of-visual-words representations for detection purposes. This weighting function depends on the ratio of intersection of the local descriptors, and their respective distances to the center of the bounding box that is under evaluation. As shown by our results, the use of the proposed weighted bag representation improves the detection rate with respect to a traditional bag construction. Also, we validate the use of an ad hoc methodology to approach the detection scenario through a retrieval setup. Our results show that this approach achieves better detection performance than the traditional sliding-windows approach when only a few data is available for training, as is the case of the Maya hieroglyphs. To the best of our knowledge, our work is among the first contributions that addresses the problem of shape detection using binary images, since the previous attempts to detect shapes rely on the use of intensity images. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： shape-based object detection Binary images Bag-of-words Maya hieroglyphs

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AN ITERATIVE PARSING APPROACH FOR CONTOUR FRAGMENTS

AN ITERATIVE PARSING APPROACH FOR CONTOUR FRAGMENTS

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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Huang, Xiao Liu, Yuehu Su, Yuanqi Xi An Jiao Tong Univ IAIR Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9781479900152

This paper presents an approach for linking edge points into contour fragments, each of which is an ordered point sequence. In the problem of shape-based object detection, we investigate what characteristics of the contour fragments representation influence the detection performance. It is observed that if interest objects are represented by limited number of contour fragments, with moderate count of noisy points (not belonging to interest objects) brought in, the detection algorithm can reliably obtain interest objects. For this purpose, we propose to iteratively extract a pair of points which preserves the farthest geodesic distance in each connected component of edge map. We conduct experiments on the ETHZ dataset and compare with other typical methods for generating contour fragments. Experimental results demonstrate that the proposed approach outperforms some existing ones in object detection.

关键词： Contour fragments edge linking image shape features shape-based object detection

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An efficient omnidirectional vision system for soccer robots: From calibration to object detection

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MECHATRONICS 2011年第2期21卷 399-410页

作者： Neves, Antonio J. R. Pinho, Armando J. Martins, Daniel A. Cunha, Bernardo Univ Aveiro ATRI IEETA DETI P-3810193 Aveiro Portugal

Robotic soccer is nowadays a popular research domain in the area of multi-robot systems. In the context of RoboCup, the Middle Size League is one of the most challenging. This paper presents an efficient omnidirectional vision system for real-time object detection, developed for the robotic soccer team of the University of Aveiro, CAMBADA. The vision system is used to find the ball and white lines, which are used for self-localization, as well as to find the presence of obstacles. Algorithms for detecting these objects and also for calibrating most of the parameters of the vision system are presented in this paper. We also propose an efficient approach for detecting arbitrary FIFA balls, which is an important topic of research in the Middle Size League. The experimental results that we present show the effectiveness of our algorithms, both in terms of accuracy and processing time, as well as the results that the team has been achieving: 1st place in RoboCup 2008, 3rd place in 2009 and 1st place in the mandatory technical challenge in RoboCup 2009, where the robots have to play with an arbitrary standard FIFA ball. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Robotic vision Omnidirectional vision systems Color-based object detection shape-based object detection Vision system calibration

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shape based detection and Top-Down Delineation Using Image Segments

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2009年第3期83卷 211-232页

作者： Gorelick, Lena Basri, Ronen Weizmann Inst Sci Dept Comp Sci & Appl Math IL-76100 Rehovot Israel Toyota Technol Inst Chicago IL 60637 USA

We introduce a segmentation-based detection and top-down figure-ground delineation algorithm. Unlike common methods which use appearance for detection, our method relies primarily on the shape of objects as is reflected by their bottom-up segmentation. Our algorithm receives as input an image, along with its bottom-up hierarchical segmentation. The shape of each segment is then described both by its significant boundary sections and by regional, dense orientation information derived from the segment's shape using the Poisson equation. Our method then examines multiple, overlapping segmentation hypotheses, using their shape and color, in an attempt to find a "coherent whole," i.e., a collection of segments that consistently vote for an object at a single location in the image. Once an object is detected, we propose a novel pixel-level top-down figure-ground segmentation by "competitive coverage" process to accurately delineate the boundaries of the object. In this process, given a particular detection hypothesis, we let the voting segments compete for interpreting (covering) each of the semantic parts of an object. Incorporating competition in the process allows us to resolve ambiguities that arise when two different regions are matched to the same object part and to discard nearby false regions that participated in the voting process. We provide quantitative and qualitative experimental results on challenging datasets. These experiments demonstrate that our method can accurately detect and segment objects with complex shapes, obtaining results comparable to those of existing state of the art methods. Moreover, our method allows us to simultaneously detect multiple instances of class objects in images and to cope with challenging types of occlusions such as occlusions by a bar of varying size or by another object of the same class, that are difficult to handle with other existing class-specific top-down segmentation methods.

关键词： shape-based object detection Class-based top-down segmentation

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