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Automatic White Matter Fiber Clustering Using Dominant Sets

Automatic White Matter Fiber Clustering Using Dominant Sets

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International Workshop on pattern Recognition in NeuroImaging (PRNI)

作者： L. Dodero S. Vascon L. Giancardo A. Gozzi D. Sona V. Murino Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genoa Italy Center for Neuroscience and Cognitive Systems@UniTn Istituto Italiano di Tecnologia Rovereto Italy

We present an unsupervised approach based on the Dominant Sets framework to automatically segment the white matter fibers into bundles. This framework, rooted in the Game Theory, allows for the automatic determination of the number of clusters from the data itself, without any prior assumption. The clustered bundles are a key information for the generation of unbiased structural connectivity atlases. We have thoroughly validated our algorithm both quantitatively and qualitatively. Indeed, we used biologically plausible synthetic datasets to numerically validate the performance in terms of Precision, Recall and other measures employed in the literature. We also evaluated the algorithm on a real Diffusion Tensor Imaging tractography of a whole mouse brain obtaining promising results. In fact, some of the most prominent brain structures determined by the algorithm correspond to white matter expected anatomy.

关键词： Clustering algorithms Diffusion tensor imaging Mice Image segmentation Indexes

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Neuronal network structural connectivity estimation by probabilistic features and graph heat kernels

Neuronal network structural connectivity estimation by proba...

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IEEE International Symposium on Biomedical Imaging

作者： S. Ullo U. Castellani D. Sona A. Del Bue A. Maccione L. Berdondini V. Murino Pattern Analysis and Computer Vision (PAVIS) Istituto Italiano di Tecnologia Genova Italy Department of Computer Science University of Verona Verona Italy Neuroscience and Brain Technologies (NBT) Istituto Italiano di Tecnologia Genova Italy

ISBN: (纸本)9781467364560

It is well proven that the functional electrophysiological behavior of in-vitro neuronal networks is influenced by the structural connectivity. Thus, the automatic extraction of the topology in large assemblies of interconnected neurons can be a valuable tool for investigating the basic mechanisms underlying high-level cognitive functions. In this paper we propose a method for estimating the structural connectivity of neuronal networks from multimodal datasets combining high-resolution Multi-Electrode Arrays (MEA) and fluorescence microscopy. Probabilistic directional features are used in a graph heat kernel framework to identify the structural connectivity of the neuronal network. Electrode connectivity maps are computed as weighted graphs in which the edge weights represent the strength of the structural connection.

关键词： Electrodes Heating Biological neural networks Kernel Probabilistic logic Feature extraction Neurons

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Compressed sensing ensemble classifier for human detection

Compressed sensing ensemble classifier for human detection

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4th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2013

作者： Zhang, Baochang Liu, Juan Gao, Yongsheng Liu, Jianzhuang Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering BeiHang University Beijing 100191 China School of Engineering Griffith University Australia Shenzhen Key Lab for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Department of Information Engineering Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9783642420566

This paper proposes a novel Compressed Sensing Ensemble Classifier (CSEC) for human detection. The proposed CSEC employs the compressed sensing technique to get a more sparse model with a more reasonable selection of base classifiers. The major contributions of this paper are: 1) a novel principled framework for ensemble classifier design based on compressed sensing;2) a new concept of considering both the simplicity of ensemble classifier and irrelevance of base classifiers towards optimal classifier design;and 3) a quadratic function for CSEC optimization which includes a new optimizable positive semi-definite relevance matrix to simultaneously select appropriate base classifiers with minimized relevance. Experimental results on INRIA and SDL databases show that the performance of CSEC is better than two most popular classifiers SVM and AdaBoost, as well as a most recent method CLML. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Compressed sensing

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A fast matching algorithm for graph-based handwriting recognition

A fast matching algorithm for graph-based handwriting recogn...

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9th IAPR-TC-15 International Workshop on Graph-Based Representations in pattern Recognition, GbRPR 2013

作者： Fischer, Andreas Suen, Ching Y. Frinken, Volkmar Riesen, Kaspar Bunke, Horst Centre for Pattern Recognition and Machine Intelligence Concordia University 1455 de Maisonneuve Blvd West Montreal QC H3G 1M8 Canada Computer Vision Center Dept. of Computer Science Universitat Autònoma de Barcelona 08193 Bellaterra Spain Institute for Informations Systems University of Applied Sciences and Arts Northwestern Switzerland Riggenbachstrasse 16 4600 Olten Switzerland Institute of Computer Science and Applied Mathematics University of Bern Neubrückstrasse 10 3012 Bern Switzerland

ISBN: (纸本)9783642382208

The recognition of unconstrained handwriting images is usually based on vectorial representation and statistical classification. Despite their high representational power, graphs are rarely used in this field due to a lack of efficient graph-based recognition methods. Recently, graph similarity features have been proposed to bridge the gap between structural representation and statistical classification by means of vector space embedding. This approach has shown a high performance in terms of accuracy but had shortcomings in terms of computational speed. The time complexity of the Hungarian algorithm that is used to approximate the edit distance between two handwriting graphs is demanding for a real-world scenario. In this paper, we propose a faster graph matching algorithm which is derived from the Hausdorff distance. On the historical Parzival database it is demonstrated that the proposed method achieves a speedup factor of 12.9 without significant loss in recognition accuracy. © 2013 Springer-Verlag.

关键词： Vector spaces

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Signature segmentation and recognition from scanned documents

Signature segmentation and recognition from scanned document...

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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Ranju Mandal Partha Pratim Roy Umapada Pal Michael Blumenstein School of Information and Communication Technology Griffith University Queensland Australia Automation Engineering Department Synchromedia Lab Montreal Canada Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Signature as a query is important for content-based document image retrieval from a scanned document repository. This paper presents a two-stage approach towards automatic signature segmentation and recognition from scanned document images. In the first stage, signature blocks are segmented from the document using word-wise component extraction and classification. Gradient based features are extracted from each component at the word level to perform the classification task. In the 2nd stage, SIFT (Scale-Invariant Feature Transform) descriptors and Spatial Pyramid Matching (SPM)-based approaches are used for signature recognition. Support Vector Machines (SVMs) are employed as the classifier for both levels in this experiment. The experiments are performed on the publicly available “Tobacco-800” and GPDS [1] datasets and the results obtained from the experiments are promising.

关键词： Image segmentation Industries Vector quantization Hidden Markov models

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A colour image encryption based on DNA coding and chaotic sequences

A colour image encryption based on DNA coding and chaotic se...

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International Conference on Emerging Trends and Applications in computer Science (ICETACS)

作者： Sukalyan Som Atanu Kotal Ayantika Chatterjee Soumista Dey Sarbani Palit Department of Computer Science Barrackpore Rastraguru Surendranath College Kolkata West Bengal India Department of Computer Science and Engineering Techno India College of Technology Kolkata West Bengal India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata West Bengal India

In this communication, a Chaos Based Symmetric Key Encryption of RGB Color Images with DNA Coding and a Chaos based Pseudorandom Binary Number Generator (PRBNG) has been proposed. In the proposed algorithm, the plain image is first scrambled using generalized Arnold Cat Map to achieve confusion. The scrambled image pixel are converted to DNA codes and again reconverted to integers where the choice of DNA coding rule is made pseudorandom based on the binary sequences generated by chaos based pseudorandom binary number generator. The integers thus obtained are diffused by performing exclusive OR operation with the integer sequences generated by 1D Logistic map producing the cipher image. The experimental results depicts that the proposed algorithm can successfully encrypt and decrypt RGB color images with secret keys. The simulation analysis also exhibit that the proposed method is loss-less, secure and efficient measured in terms of statistical tests(like histogram analysis, correlation coefficient analysis, measures of central tendency and dispersion), key sensitivity test, key space analysis, information entropy test, encryption quality by MSE, PSNR, NPCR and UACI.

关键词： DNA Ciphers Encryption Chaotic communication Logistics

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ICDAR 2013 Handwriting Segmentation Contest

ICDAR 2013 Handwriting Segmentation Contest

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International Conference on Document Analysis and Recognition

作者： Nikolaos Stamatopoulos Basilis Gatos Georgios Louloudis Umapada Pal Alireza Alaei Computational Intelligence Laboratory Institute of Informatics and Telecommunications National Center for Scientific Research Demokritos Athens Greece Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Computer Science Laboratory Universite Francois Rabelais Tours France

ISBN: (纸本)9781479901937

This paper presents the results of the Handwriting Segmentation Contest that was organized in the context of the ICDAR2013. The general objective of the contest was to use well established evaluation practices and procedures to record recent advances in off-line handwriting segmentation. Two benchmarking datasets, one for text line and one for word segmentation, were created in order to test and compare all submitted algorithms as well as some state-of-the-art methods for handwritten document image segmentation in realistic circumstances. Handwritten document images were produced by many writers in two Latin based languages (English and Greek) and in one Indian language (Bangla, the second most popular language in India). These images were manually annotated in order to produce the ground truth which corresponds to the correct text line and word segmentation results. The datasets of previously organized contests (ICDAR2007, ICDAR2009 and ICFHR2010 Handwriting Segmentation Contests) along with a dataset of Bangla document images were used as training dataset. Eleven methods are submitted in this competition. A brief description of the submitted algorithms, the evaluation criteria and the segmentation results obtained from the submitted methods are also provided in this manuscript.

关键词： Image segmentation Educational institutions Handwriting recognition Benchmark testing Measurement Matched filters Text analysis

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Tamil Handwritten City Name Database Development and Recognition for Postal Automation

Tamil Handwritten City Name Database Development and Recogni...

引用

International Conference on Document Analysis and Recognition

作者： S. Thadchanamoorthy N.D. Kodikara H.L. Premaretne Umapada Pal Fumitaka Kimura Eastern University Sri Lanka School of Computing University of Colombo Sri Lanka Computer Vision and Pattern Recognition unit Indian Statistical Institute Kolkata India Graduate School of Engineering Mie University Japan

ISBN: (纸本)9781479901937

Although there are some reports on offline Tamil isolated handwritten character recognition, to our knowledge there is only two reports on Tamil off-line handwritten word recognition. Also no city name dataset is available for Tamil script. In this paper we present a Tamil offline city name dataset, we developed, and propose a scheme for recognition. Because of the different writing style of various individuals, some of the characters in a Tamil city name may touch and accurate segmentation of such touching into individual characters is a difficult task. Avoiding proper segmentation here, we consider a city name string as a word and the recognition problem is treated as lexicon driven word recognition. In the proposed method, binarized city names are pre-segmented into primitives (individual character or its parts). Primitive components of each city name are then merged into possible characters to get the best city name using dynamic programming. For merging, total likelihood of characters is used as the objective function and character likelihood is computed based on Modified Quadratic Discriminant Function (MQDF), where direction features are applied. A dataset of 265 Tamil city names is developed. and the database will be available freely to the researchers. From the experiment of the proposed scheme 96.89% city name accuracy is obtained from this dataset.

关键词： Cities and towns Handwriting recognition Accuracy Image segmentation Dynamic programming Feature extraction Cavity resonators

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Visual place recognition for multi-robots maps merging

Visual place recognition for multi-robots maps merging

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2012 IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2012

作者： Li, Zhao Un Nabi Jafri, Syed Riaz Chellali, Ryad Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Universita Degli Studi di Genova Italy Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Italy

ISBN: (纸本)9781479901654

This paper presents a new approach allowing to a group of robots team to merge their individual maps without any a priori knowledge about their relative positions. While moving, each robot acquires laser data and a video stream. The first data are used to create individual maps (SLAM), while images sequences are used to derive invariant visual descriptions of the visited places. The later are then exchanged between robots to determine a probability of being close to each other and sharing a common place in order to initiate a map merging process. In such a way, ambiguous situations that usually occur when a single sensor is used are reduced. We present the solution we developed in order to extract a compact visual description that remains constant regardless to the actual pose of the robots. Two or more robots having such description can verify if they are or not present at the same place, before merging their respective individual maps. This scheme, e.g. the fusion of laser-range data and visual information enhances and accelerates the construction of the global map and consequently disambiguate individual maps. The validation of the approach has been performed on various indoor environments mainly, office-like spaces and houses. © 2012 IEEE.

关键词： Robots

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ONLINE ADAPTIVE DICTIONARY LEARNING AND WEIGHTED SPARSE CODING FOR ABNORMALITY DETECTION

ONLINE ADAPTIVE DICTIONARY LEARNING AND WEIGHTED SPARSE CODI...

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IEEE International Conference on Image Processing

作者： Sheng Han Ruiqing Fu Suzhen Wang Xinyu Wu Shenzhen Key Lab for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences The Chinese University of Hong Kong

ISBN: (纸本)9781479923427

This paper focuses mainly on adaptive dictionary updating and abnormality detection via weighted space coding in video surveillance. Generally, abnormality analysis conducted on a large amount of video data is very complicated, time-consuming and time-variant. However, our dictionary is very efficient at following up on shifted contents in video and abandoning old inactive information in time. The adaptability characteristic also helps reduce the dictionary's size to a small scale, since it only needs to keep recent or active information. We also introduce a simple, but effective, judgement criterion for abnormal detection based on sparse coding over weighted bases. Because of the condensed dictionary and the simplified judgment criterion, our algorithm performs online learning and online detection with a high speed and a high accuracy in various scenes.

关键词： Dictionary Learning Adaptive Learning Sparse Coding Abnormality Detection Sparse coding anomaly detection video data Dictionaries as Topic video monitoring

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