Key Laboratory of Industrial Image Processing and Analysis of Anhui Province

Hefei, China

Key Laboratory of Industrial Image Processing and Analysis of Anhui Province

Hefei, China
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Tu Z.,Anhui Science and Technology University | Tu Z.,Key Laboratory of Industrial Image Processing and Analysis of Anhui Province | Zheng A.,Anhui Science and Technology University | Yang E.,University of Stirling | And 3 more authors.
Cognitive Computation | Year: 2015

In the human brain, independent components of optical flows from the medial superior temporal area are speculated for motion cognition. Inspired by this hypothesis, a novel approach combining independent component analysis (ICA) with principal component analysis (PCA) is proposed in this paper for multiple moving objects detection in complex scenes—a major real-time challenge as bad weather or dynamic background can seriously influence the results of motion detection. In the proposed approach, by taking advantage of ICA’s capability of separating the statistically independent features from signals, the ICA algorithm is initially employed to analyze the optical flows of consecutive visual image frames. As a result, the optical flows of background and foreground can be approximately separated. Since there are still many disturbances in the foreground optical flows in the complex scene, PCA is then applied to the optical flows of foreground components so that major optical flows corresponding to multiple moving objects can be enhanced effectively and the motions resulted from the changing background and small disturbances are relatively suppressed at the same time. Comparative experimental results with existing popular motion detection methods for challenging imaging sequences demonstrate that our proposed biologically inspired vision-based approach can extract multiple moving objects effectively in a complex scene. © 2015, Springer Science+Business Media New York.


Li T.T.,Anhui Science and Technology University | Jiang B.,Anhui Science and Technology University | Tu Z.Z.,Anhui Science and Technology University | Luo B.,Anhui Science and Technology University | And 3 more authors.
Communications in Computer and Information Science | Year: 2015

Though weighted voting matching is one of most successful image matching methods, each candidate correspondence receives voting score from all other candidates, which can not apparently distinguish correct matches and incorrect matches using voting scores. In this paper, a new image matching method based on mutual k-nearest neighbor (k-nn) graph is proposed. Firstly, the mutual k-nn graph is constructed according to similarity between candidate correspondences. Then, each candidate only receives voting score from its mutual k nearest neighbors. Finally, based on voting scores, the matching correspondences are computed by a greedy ranking technique. Experimental results demonstrate the effectiveness of the proposed method. © Springer-Verlag Berlin Heidelberg 2015.


Tang J.,Anhui Science and Technology University | Tang J.,Key Laboratory of Industrial Image Processing and Analysis of Anhui Province | Ding Z.,Anhui Science and Technology University | Ding Z.,Key Laboratory of Industrial Image Processing and Analysis of Anhui Province | And 4 more authors.
Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering | Year: 2013

Point set matching is one of the classical NP problems in computer vision and pattern recognition. Membrane computing is an emergent branch of natural computing, which aims to abstract innovative computing models or computing ideas from the structure and function of a single cell or from complexes of cells, such as tissues and organs. On the basis of membrane optimization algorithms with hierarchical structure and the feature of the point set matching problem, a novel point set matching algorithm was proposed. In this algorithm, three new heuristic search rules were introduced, by which matching rate increased to some extent. Compared to the traditional optimization algorithms, the algorithm exhibited a better global search capability, thus a better solution for point set matching problem was obtained. Experimental results illustrate that the proposed algorithm is effective on both matching rate and stability.


Wang T.,Key Laboratory of Industrial Image Processing and Analysis of Anhui Province | Wang T.,Anhui Science and Technology University | Tang J.,Key Laboratory of Industrial Image Processing and Analysis of Anhui Province | Tang J.,Anhui Science and Technology University | And 6 more authors.
Communications in Computer and Information Science | Year: 2012

With regard to the Copy-Move forgery of image region, this paper proposes one blind detection algorithm based on Multi-Scale Autoconvolution. The eigenvectors of image are generated by extracting the MSA invariants of each image block, and sorted by dictionary ordering. Then the similarity of image blocks are computed by using correlation coefficients in order to detect and locate the tampered image regions. The experimental results show that the algorithm can not only effectively detect and locate the duplication regions, but also resist part of post-processing operations including rotation attack, Gaussian noises attack and JPEG compression attack. It demonstrates that our proposed algorithm has advantages of low time complexity and high robustness. © 2012 Springer-Verlag.


Wang T.,Anhui Science and Technology University | Wang T.,Key Laboratory of Industrial Image Processing and Analysis of Anhui Province | Tang J.,Anhui Science and Technology University | Tang J.,Key Laboratory of Industrial Image Processing and Analysis of Anhui Province | And 3 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

A robust image watermarking scheme combined with the human visual characteristics is proposed. Berkeley wavelet transform (BWT) which is used in watermarking embedding procedure simulates physiology characteristics of the mammalian primary visual cortex (V1). Independent Component Analysis (ICA) which is blind separation technology will be adapted to the watermarking extracting procedure. By combining the advantages of BWT and ICA, a robust image watermarking scheme is proposed and a simulation of the scheme is designed. Experimental results demonstrate that the proposed watermarking technique combines the imperceptibility, robustness, real-time and high capacity of digital watermarking algorithms. © 2013 Springer-Verlag.


Chen Y.,Anhui Science and Technology University | Tang J.,Anhui Science and Technology University | Tang J.,Key Laboratory of Industrial Image Processing and Analysis of Anhui Province | Luo B.,Anhui Science and Technology University | Luo B.,Key Laboratory of Industrial Image Processing and Analysis of Anhui Province
Advances in Intelligent Systems and Computing | Year: 2013

Image structure representation is a vital technique in the image recognition. A novel image representation and recognition method based on directed complex network is proposed in this paper. Firstly, the key points are extracted from an image as the nodes to construct an initial complete undirected complex network. Then, the k-nearest neighbor evolution method is designed to form a series of directed networks. At last, the feature descriptor of the image is constructed by concatenating the structure features of each directed network to finally achieve image recognition. Experimental results demonstrate that the proposed method outperforms the traditional methods in image recognition and can describe the structure of images more effectively. © Springer-Verlag Berlin Heidelberg 2013.


Huang L.-L.,Anhui Science and Technology University | Huang L.-L.,Key Laboratory of Industrial Image Processing and Analysis of Anhui Province | Tang J.,Anhui Science and Technology University | Tang J.,Key Laboratory of Industrial Image Processing and Analysis of Anhui Province | And 5 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

Feature selection is an important component of many machine learning applications. In this paper, we propose a new robust feature selection method for multi-class multi-label learning. In particular, feature correlation is added into the sparse learning of feature selection so that we can learn the feature correlation and do feature selection simultaneously. An efficient algorithm is introduced with rapid convergence. Our regression based objective makes the feature selection process more efficient. Experiments on benchmark data sets illustrate that the proposed method outperforms many state-of-the-art feature selection methods. © Springer-Verlag 2013.


Tu Z.,Anhui University | Tu Z.,Anhui Science and Technology University | Tu Z.,Key Laboratory of Industrial Image Processing and Analysis of Anhui Province | Sun D.,Anhui Science and Technology University | And 3 more authors.
Advances in Intelligent Systems and Computing | Year: 2013

Video summarization provides condensed and succinct representations of the content of a video stream. A static storyboard summarization approach based on robust low-rank subspace segmentation is proposed in this paper. Firstly, video frames are represented as multi-dimensional vectors, and then embedded into a group of affine subspaces using low-rank representation according to the content similarity of the frames in the same subspace. Secondly, a series of subspaces are segmented based on the Normalized Cuts algorithm. The video summary is finally generated by choosing key frames from the significant subspaces and ranking these key frames in temporal order. The experimental results demonstrate that the proposed summarization algorithm can produce crucial key frames and effectively reduce the visual content redundancy in summary comparing with the conventional approaches. © Springer-Verlag Berlin Heidelberg 2013.


Ding Z.,Anhui Science and Technology University | Tang J.,Anhui Science and Technology University | Tang J.,Key Laboratory of Industrial Image Processing and Analysis of Anhui Province | Zhang X.,Anhui Science and Technology University | And 3 more authors.
Advances in Intelligent Systems and Computing | Year: 2013

Point pattern matching is a fundamental problem in computer vision and pattern recognition. Membrane computing is an emergent branch of bio-inspired computing, which provides a novel idea to solve computationally hard problems. In this paper, a new point pattern matching algorithm with local elitism strategy is proposed based on membrane computing models. Local elitism strategy is used to keep good correspondences of point pattern matching found during the search, so the matching rate and the convergence speed are improved. Five heuristic mutation rules are introduced to avoid the local optimum. Experiment results on both synthetic data and real world data illustrate that the proposed algorithm is of higher matching rate and better stability. © Springer-Verlag Berlin Heidelberg 2013.


Zhao H.,Anhui University | Zhao H.,Key Laboratory of Intelligent Computing and Signal Processing of MOE | Jiang B.,Anhui University | Tang J.,Anhui University | And 3 more authors.
Neurocomputing | Year: 2015

In this paper, an efficient image matching algorithm for finding the consistent correspondences between two sets of image feature points has been presented. Correct assignments are usually compatible with each other, and thus likely to form a strong cluster. The main idea of the proposed algorithm is to detect this cluster using a local distribution based outlier detection technique. Based on neighbor similarity (or affinity), we first define an inlier score for each assignment in candidate assignment set. Then, we iteratively detect the correct assignments from the candidate assignment set by exploiting the inlier score. Experimental results on several real-world image matching tasks demonstrate the effectiveness and robustness of the proposed algorithm. © 2014 Elsevier B.V.

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