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Special section guest editorial: Machine vision: Processing, components, and systems

Optical Engineering 2014年第10期53卷

作者： Kleihorst, Richard Saito, Hideo Xetal NV Boeimeerstraat 1A Bonheiden Belgium Ghent University Krijgslaan 281 Gent Belgium Keio University Department of Information and Computer Science 3-14-1 Hiyoshi Kohoku-ku Yokohama Kanagawa Japan

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Traffic sign recognition based on a context-aware scale-invariant feature transform approach

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JOURNAL OF ELECTRONIC IMAGING 2013年第4期22卷 041105-041105页

作者： Yuan, Xue Hao, Xiaoli Chen, Houjin Wei, Xueye Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Chinese Acad Surveying & Mapping Beijing 100830 Peoples R China

A new context-aware scale-invariant feature transform (CASIFT) approach is proposed, which is designed for the use in traffic sign recognition (TSR) systems. The following issues remain in previous works in which SIFT is used for matching or recognition: (1) SIFT is unable to provide color information;(2) SIFT only focuses on local features while ignoring the distribution of global shapes;(3) the template with the maximum number of matching points selected as the final result is instable, especially for images with simple patterns;and (4) SIFT is liable to result in errors when different images share the same local features. In order to resolve these problems, a new CASIFT approach is proposed. The contributions of the work are as follows: (1) color angular patterns are used to provide the color distinguishing information;(2) a CASIFT which effectively combines local and global information is proposed;and (3) a method for computing the similarity between two images is proposed, which focuses on the distribution of the matching points, rather than using the traditional SIFT approach of selecting the template with maximum number of matching points as the final result. The proposed approach is particularly effective in dealing with traffic signs which have rich colors and varied global shape distribution. Experiments are performed to validate the effectiveness of the proposed approach in TSR systems, and the experimental results are satisfying even for images containing traffic signs that have been rotated, damaged, altered in color, have undergone affine transformations, or images which were photographed under different weather or illumination conditions. (C) 2013 SPIE and IS&T

关键词： Databases Detection and tracking algorithms Feature extraction Algorithms Roads Sensors Safety Schools and universities Artificial neural networks Cameras computer vision technology Computing systems Genetic algorithms Hough transforms Image registration Image retrieval Information technology MATLAB Matrices Modeling Principal component analysis

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AKITA™: Application Knowledge Interface To Algorithms

AKITA™: Application Knowledge Interface To Algorithms

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Conference on Automatic Target Recognition XXIII

作者： Barros, Paul Mathis, Allison Newman, Kevin Wilder, Steven Lockheed Martin Corp Bethesda MD 20817 USA

ISBN: (纸本)9780819495358

We propose a methodology for using sensor metadata and targeted preprocessing to optimize which selection from a large suite of algorithms are most appropriate for a given data set. Rather than applying several general purpose algorithms or requiring a human operator to oversee the analysis of the data, our method allows the most effective algorithm to be automatically chosen, conserving both computational, network and human resources. For example, the amount of video data being produced daily is far greater than can ever be analyzed. computer vision algorithms can help sift for the relevant data, but not every algorithm is suited to every data type nor is it efficient to run them all. A full body detector won't work well when the camera is zoomed in or when it is raining and all the people are occluded by foul weather gear. However, leveraging metadata knowledge of the camera settings and the conditions under which the data was collected (generated by automatic preprocessing), face or umbrella detectors could be applied instead, increasing the likelihood of a correct reading. The Lockheed Martin AKITA system is a modular knowledge layer which uses knowledge of the system and environment to determine how to most efficiently and usefully process whatever data it is given.

关键词： Detection and tracking algorithms Video Sensors Video processing Image processing computer vision technology Machine vision Algorithm development Cameras Data modeling

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One-class support vector machine-assisted robust tracking

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JOURNAL OF ELECTRONIC IMAGING 2013年第2期22卷 023002-023002页

作者： Fu, Keren Gong, Chen Qiao, Yu Yang, Jie Gu, Irene Yu-Hua Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200030 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Chalmers Univ Technol Dept Signals & Syst Signal Proc Grp S-41296 Gothenburg Sweden

Recently, tracking is regarded as a binary classification problem by discriminative tracking methods. However, such binary classification may not fully handle the outliers, which may cause drifting. We argue that tracking may be regarded as one-class problem, which avoids gathering limited negative samples for background description. Inspired by the fact the positive feature space generated by one-class support vector machine (SVM) is bounded by a closed hyper sphere, we propose a tracking method utilizing one-class SVMs that adopt histograms of oriented gradient and 2bit binary patterns as features. Thus, it is called the one-class SVM tracker (OCST). Simultaneously, an efficient initialization and online updating scheme is proposed. Extensive experimental results prove that OCST outperforms some state-of-the-art discriminative tracking methods that tackle the problem using binary classifiers on providing accurate tracking and alleviating serious drifting. (c) 2013 SPIE and IS&T

关键词： Feature extraction Video Detection and tracking algorithms Diamond Feature selection Sensors computer vision technology

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computer vision in roadway transportation systems: a survey

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JOURNAL OF ELECTRONIC IMAGING 2013年第4期22卷 041121-041121页

作者： Loce, Robert P. Bernal, Edgar A. Wu, Wencheng Bala, Raja Xerox Corp Xerox Res Ctr Webster Webster NY 14580 USA

There is a worldwide effort to apply 21st century intelligence to evolving our transportation networks. The goals of smart transportation networks are quite noble and manifold, including safety, efficiency, law enforcement, energy conservation, and emission reduction. computer vision is playing a key role in this transportation evolution. Video imaging scientists are providing intelligent sensing and processing technologies for a wide variety of applications and services. There are many interesting technical challenges including imaging under a variety of environmental and illumination conditions, data overload, recognition and tracking of objects at high speed, distributed network sensing and processing, energy sources, as well as legal concerns. This paper presents a survey of computer vision techniques related to three key problems in the transportation domain: safety, efficiency, and security and law enforcement. A broad review of the literature is complemented by detailed treatment of a few selected algorithms and systems that the authors believe represent the state-of-the-art. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

关键词： Cameras computer vision technology Roads Video Surveillance Safety vision Sensors

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ARTIFICIAL NEURAL NETWORK DOES BETTER SPATIOTEMPORAL COMPRESSIVE SAMPLING

ARTIFICIAL NEURAL NETWORK DOES BETTER SPATIOTEMPORAL COMPRES...

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Conference on Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X

作者： Lee, Soo-Young Hsu, Charles Szu, Harold Korea Adv Inst Sci & Technol Seoul South Korea CUA Washington DC USA

ISBN: (纸本)9780819490797

Spatiotemporal sparseness is generated naturally by human visual system based on artificial neural network modeling of associative memory. Sparseness means nothing more and nothing less than the compressive sensing achieves merely the information concentration. To concentrate the information, one uses the spatial correlation or spatial FFT or DWT or the best of all adaptive wavelet transform (cf. NUS, Shen Shawei). However, higher dimensional spatiotemporal information concentration, the mathematics can not do as flexible as a living human sensory system. The reason is obviously for survival reasons. The rest of the story is given in the paper.

关键词： Optical flow Artificial neural networks computer vision technology Machine vision Visual process modeling Wavelets Mathematics Neurons Sensors Visual system

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Lens distortion calibration using homography

Lens distortion calibration using homography

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4th International Conference on Digital Image Processing (ICDIP)

作者： Xu, X. Q. Xia, J. Y. Zhang, Q. Xiong, J. L. Natl Univ Def Technol Coll Mech Elect Engn & Automatizat Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9780819489913

Undistortion methods based on perspective invariants play an important role in computer vision. The key of these methods is how to choose an proper measure to describe the perspective invariance of undistorted image features. We propose a new measure based on the homography between the control points in undistorted image and the pattern. The less the distortion is, the less the mapping error is. A new lens distortion calibration method is also put forward which uses this measure to search for accurate distortion parameters by iterative optimization. Comparing with other proposed measures based on perspective invariants, our measure is both concise and comprehensive. Both synthetic and real experiments show that our method performs well in accuracy and runtime.

关键词： Distortion Calibration Cameras computer vision technology Image segmentation Machine vision 3D modeling Image analysis Defense technologies Electronics engineering

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Toward Automatic Subpixel Registration of Unmanned Airborne Vehicle Images

Toward Automatic Subpixel Registration of Unmanned Airborne ...

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Conference on Visual Information Processing XXI

作者： Yousef, Amr Hussein Li, Jiang Karim, Mohammad Old Dominion Univ Dept Elect & Comp Engn Norfolk VA 23529 USA

ISBN: (纸本)9780819490773

Many applications require to register images within subpixel accuracy like computer vision especially super-resolution (SR) where the estimated subpixel shifts are very crucial in the reconstruction and restoration of SR images. In our work we have an optical sensor that is mounted on an unmanned airborne vehicle (UAV) and captures a set of images that contain sufficient overlapped area required to reconstruct a SR image. Due to the wind, The UAV may encounter rotational effects such as yaw, pitch and roll which can distort the acquired as well as processed images with shear, tilt or perspective distortions. In this paper we propose a hybrid algorithm to register these UAV images within subpixel accuracy to feed them in a SR reconstruction step. Our algorithm consists of two steps. The first step uses scale invariant feature transform (SIFT) to correct the distorted images. Because the resultant images are not registered to a subpixel precision, the second step registers the images using a fast Fourier transform (FFT) based method that is both efficient and robust to moderate noise and lens optical blur. Our FFT based method reduces the dimensionality of the Fourier matrix of the cross correlation and uses a forward and backward search in order to obtain an accurate estimation of the subpixel shifts. We discuss the relation between the dimensionality reduction factors and the image shifts as well as propose criteria that can be used to optimally select these factors. Finally, we compare the results of our approach to other subpixel techniques in terms of their efficiency and computational speed.

关键词： Unmanned aerial vehicles Image registration Fourier transforms Reconstruction algorithms Error analysis Image processing Matrix multiplication computer vision technology Lithium Optical sensors

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Real-time computational attention model for dynamic scenes analysis: from implementation to evaluation

Real-time computational attention model for dynamic scenes a...

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Conference on Optics, Photonics and Digital Technologies for Multimedia Applications II

作者： Courboulay, Vincent Da Silva, Matthieu Perreira Univ La Rochelle L3i Lab La Rochelle France

ISBN: (纸本)9780819491282

Providing time analysis of the huge amount of data generated by computer vision algorithms in interactive applications is still an open problem. It promises great advances across a wide variety of fields. When using dynamics scene analysis algorithms for computer vision, a trade-off must be found between the quality of the results expected, and the amount of computer resources allocated for each task. It is usually a design time decision, implemented through the choice of pre-defined algorithms and parameters. However, this way of doing limits the generality of Hie system. Using an adaptive vision system provides a more flexible solution as its analysis strategy can be changed according to the new information available. As a consequence, such a system requires some kind of guiding mechanism to explore the scene faster and more efficiently. We propose a visual attention system that it adapts its processing according to the interest (or salience) of each element of the dynamic scene. Somewhere in between hierarchical salience based and competitive distributed, we propose a hierarchical yet competitive and non salience based model. Our original approach allows the generation of attentional focus points without the need of neither saliency map nor explicit inhibition of return mechanism. This new real-time computational model is based on a preys / predators system. The use of this kind of dynamical system is justified by an adjustable trade-off between nondeterministic attentional behavior and properties of stability, reproducibility and reactiveness.

关键词： Visual process modeling Dynamical systems Eye models Visualization Machine vision Computing systems computer vision technology Data modeling computer simulations Systems modeling

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Haar-like compact local binary pattern for illumination-robust feature matching

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JOURNAL OF ELECTRONIC IMAGING 2012年第4期21卷 043014-043014页

作者： Kim, Bongjoe Choi, Jihoon Joo, Sanghyun Sohn, Kwanghoon Yonsei Univ Dept Elect & Elect Engn Seoul 120749 South Korea Agcy Def Dev Taejon 305152 South Korea

A novel method for local image descriptor called a Haar-like compact local binary pattern (HC-LBP) is presented, which is robust to Gaussian noise and illumination changes and reduces the dimension of features by modifying the local binary pattern (LBP) method in two ways. First, the binary Haar-like feature method is applied to calculate region-based rank order. The binary Haar-like feature method maintains only the ordinal relationship and not the difference of intensity. Using a region-based binary operation, the proposed local image descriptor becomes more robust to Gaussian noise and illumination changes. Second, a HC-LBP feature is generated by applying the binary Haar-like feature according to the edge class in order to reduce feature dimensions and increase the discriminating power of each feature. In the feature matching experiment, the proposed method outperforms the popular scale invariant feature transform, center-symmetric local binary pattern and center-symmetric local ternary pattern in the presence of illumination changes, Gaussian noise, image blurring, and viewpoint change. Compared with popular local feature descriptors, the proposed method could get more than 0.1 increases in recall. Also, the HC-LBP descriptor could be used for many computer vision applications such as image retrieval, face recognition, and texture recognition. (C) 2012 SPIE and IS&T. [DOI: 10.1117/***.21.4.043014]

关键词： computer vision technology Facial recognition systems Image retrieval Sensors

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