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Auditory filter based broadband MUSIC algorithm for sound source localization

Chinese Journal of Acoustics 2013年第4期32卷 439-453页

作者： LIAO Fengchai LI Peng LIU Wenju Department of Math and Computer Science Sanming University Sanming Digital Content Technique and Service Research Center Institute of Automation Chinese Academy of Sciences National Key Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences

Based on the analysis of the shortcomings of broadband MUSIC algorithm with short-time Fourier transform （SF-MUSIC） for sound source localization, a broadband MUSIC algorithm with auditory filter （AF-MUSIC） was proposed. The proposed algorithm first em- ploys auditory filter bank to decompose the signals received on the microphone array, and then locates the sound source with MUSIC algorithm over every frequency channel. At last, by combining with the subinterval frequency estimation, the final localization result is gained. Evaluations on the proposed algorithm prove that comparing with the SF-MUSIC algorithm, the AF-MUSIC algorithm decreases the average error of the estimation results with 2.5479 de- gree in different source conditions. The accuracy of sound source DOA estimation is enhanced effectively.

关键词： MUSIC SF DOA

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Generalized stochastic tube model: tracking 3D blood vessels in MR images

Generalized stochastic tube model: tracking 3D blood vessels...

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International Conference on pattern recognition

作者： Qian Huang G.C. Stockman Almaden Research Center San Jose CA USA Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East Lansing MI USA

This paper addresses the issue of tracking tubular objects, particularly blood vessels from MR images. A model-based approach is adopted. The generalized stochastic tube (GST) model is developed which is an extension of our previously proposed (1993) generalized tube (GT) model. Transitions among adjacent tubes are explicitly parameterized. Integrated with a bivariate Gaussian density function adopted to model the blood flow within cross sections, the GST model is applied to tracking blood vessels in MRA volumetric data. Experimental results on both synthetic data with different degrees of Gaussian noise and real MRA data demonstrated that simultaneously utilizing both models yields robust performance under noisy conditions.

关键词： Stochastic processes Blood vessels Biomedical imaging Shape Blood flow Gaussian noise Image recognition Solid modeling pattern recognition Density functional theory

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Ordinal palmprint represention for personal identification [represention read representation]

Ordinal palmprint represention for personal identification [...

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Conference on computer vision and pattern recognition (CVPR)

作者： Zhenan Sun Tieniu Tan Yunhong Wang S.Z. Li Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China School of Computer Science and Engineering Beihang University China

Palmprint-based personal identification, as a new member in the biometrics family, has become an active research topic in recent years. Although great progress has been made, how to represent palmprint for effective classification is still an open problem. In this paper, we present a novel palmprint representation - ordinal measure, which unifies several major existing palmprint algorithms into a general framework. In this framework, a novel palmprint representation method, namely orthogonal line ordinal features, is proposed. The basic idea of this method is to qualitatively compare two elongated, line-like image regions, which are orthogonal in orientation and generate one bit feature code. A palmprint pattern is represented by thousands of ordinal feature codes. In contrast to the state-of-the-art algorithm reported in the literature, our method achieves higher accuracy, with the equal error rate reduced by 42% for a difficult set, while the complexity of feature extraction is halved.

关键词： Biometrics Fingerprint recognition pattern recognition Feature extraction Face recognition computer vision National security Laboratories Automation computer science

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Model-based automatic recognition of blood vessels from MR images and its 3D visualization

Model-based automatic recognition of blood vessels from MR i...

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IEEE International Conference on Image Processing

作者： Qian Huang G.C. Stockman IBM Research Division Almaden Research Center San Jose CA USA Pattern Recognition and Image Processing LaboratoryDepartment of Computer Science Michigan State University East Lansing MI USA

A model-based approach is used for recognizing arterial blood vessels from MRA volumetric data. The modeling includes (1) a generalized stochastic tube model characterizing the structural properties of the vessels, and (2) a bivariate Gaussian function, modeling the expected cross sectional blood flow. This integrated model renders the recognition problem as a parameter estimation problem which is subsequently solved in a hierarchical fashion. Due to the descriptive representation for objects, a new visualization scheme for blood vessels is proposed that allows the observation of the blood flow of each cross section along a recognized vessel. Some experimental results from both synthetic data and real MRA volumes are given. The visualization of interior blood flow patterns for some vessels are also shown.< >

关键词： Image recognition Blood vessels Biomedical imaging Object oriented modeling Blood flow Stochastic processes Solid modeling Data visualization Shape Image segmentation

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Unsupervised writer style adaptation for handwritten word spotting

Unsupervised writer style adaptation for handwritten word sp...

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International Conference on pattern recognition

作者： Jose A. Rodriguez Florent Perronnin Gemma Sanchez Josep Llados Xerox Research Centre Europe Textual and Visual Pattern Analysis (TVPA) France Computer Vision Center (CVC) Universitat Authnòma de Barcelona Spain

ISBN: (纸本)9781424421749

We propose a novel approach for writer adaptation in a word spotting task. The method exploits the fact that a semi-continuous hidden Markov model separates the word model parameters into (i) a shared codebook of shapes and (ii) a set of word-specific parameters. Our main contribution is to derive writer-specific word models by statistically adapting an initial universal codebook to each document. This process is unsupervised and does not even require the appearance of the keyword(s) in the searched document. Experimental results show an increase in performance when this adaptation technique is applied. To the best knowledge of the authors, this is the first work dealing with adaptation for word spotting.

关键词： Hidden Markov models Character recognition Handwriting recognition Shape Testing pattern analysis Europe computer vision pattern classification Degradation

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Explainable deep classification models for domain generalization

arXiv

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arXiv 2020年

作者： Zunino, Andrea Bargal, Sarah Adel Volpi, Riccardo Sameki, Mehrnoosh Zhang, Jianming Sclaroff, Stan Murino, Vittorio Saenko, Kate Huawei Ireland Research Center Department of Computer Science Boston University Naver Labs Europe Microsoft Adobe Research Pattern Analysis & Computer Vision Istituto Italiano di Tecnologia Università di Verona

Conventionally, AI models are thought to trade off explainability for lower accuracy. We develop a training strategy that not only leads to a more explainable AI system for object classification, but as a consequence, suffers no perceptible accuracy degradation. Explanations are defined as regions of visual evidence upon which a deep classification network makes a decision. This is represented in the form of a saliency map conveying how much each pixel contributed to the network's decision. Our training strategy enforces a periodic saliency-based feedback to encourage the model to focus on the image regions that directly correspond to the ground-truth object. We quantify explainability using an automated metric, and using human judgement. We propose explainability as a means for bridging the visual-semantic gap between different domains where model explanations are used as a means of disentagling domain specific information from otherwise relevant features. We demonstrate that this leads to improved generalization to new domains without hindering performance on the original domain. Copyright © 2020, The Authors. All rights reserved.

关键词： Conveying

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Mathematical modeling for corner saliency

Mathematical modeling for corner saliency

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International Conference on Software Technology and Engineering (ICSTE)

作者： Shao-hua Jiang Xue-jun Xu Hong-bo Zhu Xue-song Chen Institute of Image Recognition & Computer Vision Hunan Normal University Changsha China Digital Engineering & Simulation Research Center Huazhong University of Science and Technology Wuhan China

This paper proposed the definition of corner saliency firstly, which is an intrinsic characteristic of a corner and denote the contributiveness of a corner on the result of image processing. Then, we discussed the relation among the corner saliency, the out-in degree, the angle sequence and the ratio of line-length sequence in detail, and formulate a mathematic model to calculate the corner saliency. Finally, we introduced an experiment to illustrate the calculation of corner saliency. It shows that the proposed model is effective and has practical application value also.

关键词： Mathematical model Object recognition Image edge detection Software Image fusion Correlation

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EfficientFCN: Holistically-guided decoding for semantic segmentation

arXiv

引用

arXiv 2020年

作者： Liu, Jianbo He, Junjun Zhang, Jiawei Ren, Jimmy S. Li, Hongsheng CUHK-SenseTime Joint Laboratory Chinese University of Hong Kong Hong Kong Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China SenseTime Research China

Both performance and efficiency are important to semantic segmentation. State-of-the-art semantic segmentation algorithms are mostly based on dilated Fully Convolutional Networks (dilatedFCN), which adopt dilated convolutions in the backbone networks to extract high-resolution feature maps for achieving high-performance segmentation performance. However, due to many convolution operations are conducted on the high-resolution feature maps, such dilatedFCN-based methods result in large computational complexity and memory consumption. To balance the performance and efficiency, there also exist encoder-decoder structures that gradually recover the spatial information by combining multi-level feature maps from the encoder. However, the performances of existing encoder-decoder methods are far from comparable with the dilatedFCN-based methods. In this paper, we propose the EfficientFCN, whose backbone is a common ImageNet pretrained network without any dilated convolution. A holistically-guided decoder is introduced to obtain the high-resolution semantic-rich feature maps via the multi-scale features from the encoder. The decoding task is converted to novel codebook generation and codeword assembly task, which takes advantages of the high-level and low-level features from the encoder. Such a framework achieves comparable or even better performance than state-of-the-art methods with only 1/3 of the computational cost. Extensive experiments on PASCAL Context, PASCAL VOC, ADE20K validate the effectiveness of the proposed EfficientFCN. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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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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Semi-supervised deep generative modelling of incomplete multi-modality emotional data

arXiv

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arXiv 2018年

作者： Du, Changde Du, Changying Wang, Hao Li, Jinpeng Zheng, Wei-Long Lu, Bao-Liang He, Huiguang Research Center for Brain-inspired Intelligence National Laboratory of Pattern Recognition CASIA University of CAS Beijing China 360 Search Lab Beijing China Department of Computer Science and Engineering SJTU Shanghai China Research Center for Brain-inspired Intelligence National Laboratory of Pattern Recognition CASIA Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences University of Chinese Academy of Sciences

There are threefold challenges in emotion recognition. First, it is difficult to recognize human’s emotional states only considering a single modality. Second, it is expensive to manually annotate the emotional data. Third, emotional data often suffers from missing modalities due to unforeseeable sensor malfunction or configuration issues. In this paper, we address all these problems under a novel multi-view deep generative framework. Specifically, we propose to model the statistical relationships of multi-modality emotional data using multiple modality-specific generative networks with a shared latent space. By imposing a Gaussian mixture assumption on the posterior approximation of the shared latent variables, our framework can learn the joint deep representation from multiple modalities and evaluate the importance of each modality simultaneously. To solve the labeled-data-scarcity problem, we extend our multi-view model to semi-supervised learning scenario by casting the semi-supervised classification problem as a specialized missing data imputation task. To address the missing-modality problem, we further extend our semi-supervised multi-view model to deal with incomplete data, where a missing view is treated as a latent variable and integrated out during inference. This way, the proposed overall framework can utilize all available (both labeled and unlabeled, as well as both complete and incomplete) data to improve its generalization ability. The experiments conducted on two real multi-modal emotion datasets demonstrated the superiority of our framework. Copyright © 2018, The Authors. All rights reserved.

关键词： Supervised learning

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