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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Wenhan Luo Xiaoqin Zhang Yang Liu Xi Li Weiming Hu Wei Li National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China College of Mathematics & Information Science Wenzhou University Zhejiang China School of Computer Science University of Adelaide Australia

Tracking multiple objects under occlusion is one of the most challenging issues in computer vision. Occlusion results in mistaken match when finding the most similar candidate. Adapting to the change of objects is essential for tracking as objects often undergo intrinsic changes, but noise is unavoidably introduced during updating of the object, and this further confuses the tracker. In order to address these problems, a block-division appearance model is introduced to efficiently handle occlusion. In this model, spatial information is introduced to avoid the mistaken match between object and candidate. Based on this model, a selective updating strategy is proposed to incrementally learn the change of the object, avoiding introducing noise when updating. At the same time occlusion is deduced by monitoring the variation of each block. Experimental results in various videos validate the effectiveness of our algorithm in tracking multiple objects under occlusion.

关键词： Videos Covariance matrix Cognition Robustness Indexes Arrays Image reconstruction

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Vector field analysis for motion pattern identification in video

Vector field analysis for motion pattern identification in v...

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IEEE International Conference on Image Processing

作者： Nandita M. Nayak Ahmed Tashrif Kamal Amit K. Roy-Chowdhury University of California Riverside CA USA

Identification of motion patterns in video is an important problem because it is the first step towards analysis of complex multi-person behaviors to obtain long-term interaction models. In this paper, we will present a flow based technique to identify spatio-temporal motion patterns in a multi-object video. We use the Helmholtz decomposition of optical flow and compute singular points corresponding to component fields. We will show that the optical flow can be used to identify regions which correspond to different moving entities in the video. The singular points in these regions capture the characteristics of the field around them and can be used to identify these regions. This representation would provide us with a framework to analyze activities of individual entities in the scene as well as the global interactions between them. We demonstrate our algorithm on a dataset composed of multi-object videos recorded in a realistic environment.

关键词： Optical imaging Vectors Object recognition Adaptive optics Feature extraction computer vision Conferences

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Descriptive local feature groups for image classification

Descriptive local feature groups for image classification

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IEEE International Conference on Image Processing

作者： Lei Yu Jing Liu Changsheng Xu Institute of Automation Chinese Academy of Sciences Beijing China

In the conventional bag of visual words (BoW) based image representation, single visual word is not discriminative enough and the spatial contextual information among local image features is ignored. In this paper, descriptive local feature groups are proposed to address these two problems. First, local image features are refined by slightly transforming the original image. Then they are clustered and represented by visual words. Second, the candidate local feature groups are generated by searching the neighbors of every local image features. This kind of grouping shows more discriminative power than a single feature and the local spatial contexts can be catched. Third, we obtain the groups more descriptive to the object category by defining a significance score and the groups with high score are selected. Finally, the high order descriptive local feature groups are integrated to the vector based object categorization framework by a feature reweighting strategy. Experimental results on Scene-15 and Caltech 101 demonstrate the superior performance of our method.

关键词： Visualization Conferences Feature extraction Vocabulary computer vision pattern recognition Entropy

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现代光学成像技术在食品品质快速检测中的应用

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华南理工大学学报（自然科学版） 2012年第10期40卷 59-68页

作者：孙大文吴迪何鸿举冯耀泽爱尔兰国立都柏林大学食品冷冻与计算机化食品技术研究所华南理工大学轻工与食品学院广东广州510640

食品现代化生产、加工过程中需要对食品品质信息进行快速无损获取,以保证食品品质安全,满足消费者的需要.近年来国内频发的食品质量与安全问题也要求实现食品品质安全信息的客观准确检测.现代光学成像技术通过获取食品在不同光谱波段下... 详细信息

食品现代化生产、加工过程中需要对食品品质信息进行快速无损获取,以保证食品品质安全,满足消费者的需要.近年来国内频发的食品质量与安全问题也要求实现食品品质安全信息的客观准确检测.现代光学成像技术通过获取食品在不同光谱波段下的图像信息,然后采用数字图像处理算法进行特征信息提取,并通过模式识别算法建立食品品质定量关系模型,从而实现食品品质信息的快速、无损、高效、低成本检测,为食品现代化加工流程中的自动控制和分级管理与监控提供信息支持.孙大文院士领导的爱尔兰国立都柏林大学(University College Dublin,UCD)食品冷冻与计算机化食品技术(Food Refrige-ration&computerised Food Technology,FRCFT)研究所在应用包括计算机视觉技术和高光谱成像技术在内的多种现代光学成像技术对食品品质快速无损检测这一领域的研究工作一直处于世界领先水平,研究成果得到了国际同行的广泛认可和高度关注.本文综述了孙大文院士及其团队过去十余年间在相关领域取得的一系列原创性研究成果.

关键词：高光谱成像成像光谱计算机视觉机器视觉食品品质检测图像处理模式识别

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An outlier rejection scheme for optical flow tracking

An outlier rejection scheme for optical flow tracking

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IEEE Workshop on Machine Learning for Signal Processing

作者： Minghao Yang Jianhua Tao Lihui Shi Kaihui Mu Jianfeng Che National Laboratory of Pattern Recognition Institute of Automation Institute of Equipment Technology of Artillery and Air Defense Chinese Academy of Sciences (CAS) China

ISBN: (纸本)9781457716218

During tracking process in optical flow, some points are normally easy to be lost or become outliers, due to the fact that the object undergoes changes of illumination or becomes partially occluded. The paper presents a novel scheme, Heterogeneity Elimination Individually (HEI), for outlier rejections from the tracking results of optical flow. HEI determines the most poisonous element by the distance between the tracked point and the projected point which is determined by its complements set from the source to the target one. Then HEI eliminates the farthest point every time from the element heterogeneous sequence until all remaining points are within the error tolerance and ensures the accuracy of the optical flow tracking results. Finally, we make an experiment by comparing our method with a popularly used method, RANSAC. The comparison results show the validity and effectiveness of our proposed method for outlier rejection.

关键词： Adaptive optics Cameras Optical signal processing computer vision Target tracking Visualization Real time systems

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Line positioning based on HT error compensation

Line positioning based on HT error compensation

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IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： Chunling Tu Barend Jacobus van Wyk Karim Djouani Yskandar Hamam Shengzhi Du French South Africa Technology Institute(FSATI) Tshwane University of Technology Pretoria South Africa Electrical and Mining University of South Africa Pretoria South Africa

The discretization of a HT space results in the ρ value detection error of a line. This paper addresses the ρ-direction precision improvement by compensating for this error. The mage are vertically or horizontally shifted, and a series peak positions are obtained by applying the standard HT (SHT) on these shifted images. The change of ρ value of a line due to these shifts is studied. On one hand this change can be measured by detecting the peak shift in HT space, on the other hand it can be obtained by geometric analysis on the vertical or horizontal shift in image space. The difference between the unit change in HT space (i.e. the ρ-direction resolution Δρ) and the unit change in image space (i.e. ρ value change due to vertical or horizontal unit shift) is used to measure the SHT ρ value detection error. A high precision ρ value is obtained by compensating for this error. The experiments show the effectiveness of the proposed method.

关键词： Image segmentation Transforms Belts pattern recognition Accuracy computer vision Error compensation

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Subgraph Spotting through Explicit Graph Embedding: An Application to Content Spotting in Graphic Document Images

Subgraph Spotting through Explicit Graph Embedding: An Appli...

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International Conference on Document Analysis and recognition

作者： Muhammad Muzzamil Luqman Jean-Yves Ramel Josep Llados Thierry Brouard Laboratoire dInformatique Université François Rabelais de Tours France Computer Vision Center Universitat Autònòma de Barcelona Spain

ISBN: (纸本)9781457713507

We present a method for spotting a subgraph in a graph repository. Subgraph spotting is a very interesting research problem for various application domains where the use of a relational data structure is mandatory. Our proposed method accomplishes subgraph spotting through graph embedding. We achieve automatic indexation of a graph repository during off-line learning phase, where we (i) break the graphs into 2-node sub graphs (a.k.a. cliques of order 2), which are primitive building-blocks of a graph, (ii) embed the 2-node sub graphs into feature vectors by employing our recently proposed explicit graph embedding technique, (iii) cluster the feature vectors in classes by employing a classic agglomerative clustering technique, (iv) build an index for the graph repository and (v) learn a Bayesian network classifier. The subgraph spotting is achieved during the on-line querying phase, where we (i) break the query graph into 2-node sub graphs, (ii) embed them into feature vectors, (iii) employ the Bayesian network classifier for classifying the query 2-node sub graphs and (iv) retrieve the respective graphs by looking-up in the index of the graph repository. The graphs containing all query 2-node sub graphs form the set of result graphs for the query. Finally, we employ the adjacency matrix of each result graph along with a score function, for spotting the query graph in it. The proposed subgraph spotting method is equally applicable to a wide range of domains, offering ease of query by example (QBE) and granularity of focused retrieval. Experimental results are presented for graphs generated from two repositories of electronic and architectural document images.

关键词： Graphics Vectors Bayesian methods Image edge detection Indexes pattern recognition Equations

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Image denoising using non linear diffusion tensors

Image denoising using non linear diffusion tensors

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Benzarti Faouzi Hamid Amiri Signal Image Processing and Pattern Recognition TSIRF (ENIT) France

Image denoising is an important pre-processing step for many image analysis and computer vision system. It refers to the task of recovering a good estimate of the true image from a degraded observation without altering and changing useful structure in the image such as discontinuities and edges. In this paper, we propose a new approach for image denoising based on the combination of two non linear diffusion tensors. One allows diffusion along the orientation of greatest coherences, while the other allows diffusion along orthogonal directions. The idea is to track perfectly the local geometry of the degraded image and applying anisotropie diffusion mainly along the preferred structure direction. To illustrate the effective performance of our model, we present some experimental results on a test and real photographic color images.

关键词： Tensile stress Image edge detection Anisotropic magnetoresistance Image denoising Eigenvalues and eigenfunctions Noise reduction

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匹配与姿态估计的粒子群优化算法

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中国图象图形学报 2011年第4期16卷 640-646页

作者：谭志国鲁敏任戈刘顺发国防科技大学电子科学与工程学院ATR重点实验室长沙410073 中国科学院光束控制重点实验室成都610200

点模式匹配问题是机器视觉与模式识别领域中一个基础问题,在目标识别、医学图像配准、遥感图像匹配、姿态估计等方面都得到广泛应用。提出一种在仿射变换下利用粒子群优化算法进行图像点模式下的匹配与姿态估计的方法。算法首先把点集... 详细信息

点模式匹配问题是机器视觉与模式识别领域中一个基础问题,在目标识别、医学图像配准、遥感图像匹配、姿态估计等方面都得到广泛应用。提出一种在仿射变换下利用粒子群优化算法进行图像点模式下的匹配与姿态估计的方法。算法首先把点集匹配问题转化为解空间为仿射参数空间下的目标函数优化问题,然后运用粒子群算法对相应的变换参数进行搜索,获得问题最优解。本文贡献如下:1)给出一种仿射参数的初始估计方法,提高了后续算法搜索效率;2)引入阈值和次近点规则,改进了最近点匹配搜索方法,能较好地拒绝出格点(outliers),并提高算法有效性;3)从两方面对PSO方法进行了改进,加强了原PSO的全局和局部搜索能力。实验结果表明,算法具有有效性和鲁棒性。

关键词：计算机视觉姿态估计点模式匹配粒子群优化

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Person Re-Identification by Siamese Network

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INTELIGENCIA ARTIFICIAL-IBEROAMERICAL JOURNAL OF ARTIFICIAL INTELLIGENCE 2023年第71期26卷 25-33页

作者： Shebiah, R. Newlin Arivazhagan, S. Amrith, S. G. Adarsh, S. Mepco Schlenk Engn Coll Dept Elect & Commun Engn Ctr Image Proc & Pattern Recognit Sivakasi Tamilnadu India

The re-identification of individuals aims to retrieve persons across multiple non-overlapping cameras. With the advancement of deep learning features and the increase in the number of surveillance videos, the computer vision community has experienced significant progress. However, person re-identification is still faced with various challenges such as low resolution images and pose variations. To overcome these challenges, state-of-the-art algorithms for person re-identification are supported by convolutional neural networks. This paper proposes the use of a Siamese network, which is a neural architecture that takes a pair of images or videos as input and predicts the similarity or dissimilarity of a person across two cameras. The output includes the prediction of similar and dissimilar persons along with their prediction scores. The proposed method was evaluated using iLIDS-VID and PRID 2011 datasets, and achieved recognition accuracy of 79.52% and 85.82%, respectively. These results demonstrate the effectiveness of the Siamese network for person re-identification tasks. Overall, this study contributes to the ongoing research on improving the accuracy of person re-identification across multiple cameras in surveillance videos.

关键词： Siamese Network Person re-identification Surveillance videos Deep learning

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