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Graphical processing unit-based machine vision system for simultaneous measurement of shrinkage and soil release in fabrics

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JOURNAL OF ELECTRONIC IMAGING 2010年第2期19卷

作者： Kamalakannan, Sridharan Gururajan, Arunkumar Hill, Matthew Shahriar, Muneem Sari-Sarraf, Hamed Hequet, Eric F. Texas Tech Univ Dept Elect & Comp Engn Appl Vis Lab Lubbock TX 79409 USA Texas Tech Univ Fiber & Biopolymer Res Inst Lubbock TX 79403 USA

We present a machine vision system for simultaneous and objective evaluation of two important functional attributes of a fabric, namely, soil release and shrinkage. Soil release corresponds to the efficacy of the fabric in releasing stains after laundering and shrinkage essentially quantifies the dimensional changes in the fabric postlaundering. Within the framework of the proposed machine vision scheme, the samples are prepared using a prescribed procedure and subsequently digitized using a commercially available off-the-shelf scanner. Shrinkage measurements in the lengthwise and widthwise directions are obtained by detecting and measuring the distance between two pairs of appropriately placed markers. In addition, these shrinkage markers help in producing estimates of the location of the center of the stain on the fabric image. Using this information, a customized adaptive statistical snake is initialized, which evolves based on region statistics to segment the stain. Once the stain is localized, appropriate measurements can be extracted from the stain and the background image that can help in objectively quantifying stain release. In addition, the statistical snakes algorithm has been parallelized on a graphical processing unit, which allows for rapid evolution of multiple snakes. This, in turn, translates to the fact that multiple stains can be detected and segmented in a computationally efficient fashion. Finally, the aforementioned scheme is validated on a sizeable set of fabric images and the promising nature of the results help in establishing the efficacy of the proposed approach. (C) 2010 SPIE and IS&T. [DOI: 10.1117/1.3421977]

关键词： Image segmentation Machine vision Soil science Image processing Imaging systems Image processing algorithms and systems Image registration Scanners detection and tracking algorithms Computing systems

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Image registration based on subpixel localization and Cauchy-Schwarz divergence

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JOURNAL OF ELECTRONIC IMAGING 2010年第3期19卷

作者： Ge, Yongxin Yang, Dan Zhang, Xiaohong Lu, Jiwen Chongqing Univ Coll Comp Sci Chongqing 400030 Peoples R China Chongqing Univ Sch Software Engn Chongqing 400030 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

We define a new matching metric-corner Cauchy-Schwarz divergence (CCSD) and present a new approach based on the proposed CCSD and subpixel localization for image registration. First, we detect the corners in an image by a multiscale Harris operator and take them as initial interest points. And then, a subpixel localization technique is applied to determine the locations of the corners and eliminate the false and unstable corners. After that, CCSD is defined to obtain the initial matching corners. Finally, we use random sample consensus to robustly estimate the parameters based on the initial matching. The experimental results demonstrate that the proposed algorithm has a good performance in terms of both accuracy and efficiency. (C) 2010 SPIE and IS&T. [DOI: 10.1117/1.3455428]

关键词： Image registration Corner detection Data modeling Sensors Image processing Image sensors detection and tracking algorithms Information theory Mathematical modeling Statistical analysis

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Effects of hyperellipsoidal decision surfaces on image segmentation in artificial color

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JOURNAL OF ELECTRONIC IMAGING 2010年第2期19卷

作者： Fu, Jian Caulfield, H. John Wu, Dongsheng Montgomery, Trent Alabama A&M Univ Dept Comp Sci Normal AL 35762 USA Alabama A&M Univ Res Inst Normal AL 35762 USA Univ Alabama Dept Math Sci Huntsville AL 35899 USA Alabama A&M Univ Sch Engn & Technol Normal AL 35762 USA

Artificial color uses the projection of the spectrum into two or more broad, overlapping spectral bands to discriminate, pixel by pixel, among user-defined classes of objects. As initially practiced, it used a sequence of hyperspherical regions of the decision space to define class membership. Of course, a hypersphere is just a degenerate hyperellipsoid;thus, exploring the effect of loosening that degeneracy seemed appropriate. Initially, we use two-foci hyperellipsoids with a hyperellipsoidal distance metric to classify pixels with dramatic improvement in performance. We explore the work even further by allowing many foci and noting the effects of increased complexity of the decision surfaces. In the example case, three foci gave superior performance to one or two foci, but four added little improvement. (C) 2010 SPIE and IS&T. [DOI: 10.1117/1.3377146]

关键词： Image segmentation Prototyping Pattern recognition Distance measurement Lens design RGB color model Virtual colonoscopy detection and tracking algorithms Eye Stochastic processes

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detection of LSB±1 steganography based on co-occurrence matrix and bit plane clipping

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JOURNAL OF ELECTRONIC IMAGING 2010年第1期19卷

作者： Abolghasemi, Mojtaba Aghaeinia, Hassan Faez, Karim Mehrabi, Mohammad Ali Amirkabir Univ Technol Dept Elect Engn Tehran Iran

Spatial LSB+/-1 steganography changes smooth characteristics between adjoining pixels of the raw image. We present a novel steganalysis method for LSB+/-1 steganography based on feature vectors derived from the co-occurrence matrix in the spatial domain. We investigate how LSB+/-1 steganography affects the bit planes of an image and show that it changes more least significant bit (LSB) planes of it. The co-occurrence matrix is derived from an image in which some of its most significant bit planes are clipped. By this preprocessing, in addition to reducing the dimensions of the feature vector, the effects of embedding were also preserved. We compute the co-occurrence matrix in different directions and with different dependency and use the elements of the resulting co-occurrence matrix as features. This method is sensitive to the data embedding process. We use a Fisher linear discrimination (FLD) classifier and test our algorithm on different databases and embedding rates. We compare our scheme with the current LSB+/-1 steganalysis methods. It is shown that the proposed scheme outperforms the state-of-the-art methods in detecting the LSB+/-1 steganographic method for grayscale images. (C) 2010 SPIE and IS&T. [DOI: 10.1117/1.3295709]

关键词： Steganalysis Steganography Databases Focus stacking software Cameras detection and tracking algorithms Image compression Matrices Data processing Ferroelectric LCDs

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Field-programmable gate array-based hardware architecture for image processing with complementary metal-oxide-semiconductor image sensor

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JOURNAL OF ELECTRONIC IMAGING 2010年第3期19卷

作者： Ge, Zhiwei Yao, Suying Xu, Jiangtao Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

We present a field-programmable gate array (FPGA)-based hardware architecture for image processing as well as novel algorithms for fast autoexposure control and color filter array (CFA) demosaicing utilizing a CMOS image sensor (CIS). The proposed hardware architecture includes basic color processing functions of black-level correction, noise reduction, autoexposure control, auto-white-balance adjustment, CFA demosaicing, and gamma correction while applying advanced peripheral bus architecture to implement the hardware architecture. The speed of traditional autoexposure control algorithms to reach a proper exposure level is so slow that it is necessary to develop a fast autoexposure control method. Based on the optical-electrical characteristics of the CIS, we present a fast auto-exposure-control algorithm that can guarantee speed and accuracy. To ensure the peak SNR performance of the demosaiced images of the CIS and reduce the computational cost at the same time, the proposed demosaicing algorithm improves on the adaptive edge-sensitive algorithm and the fuzzy assignment algorithm. The experimental results show that the proposed hardware architecture works well on the FPGA development board and produces better quality images. (C) 2010 SPIE and IS&T. [DOI: 10.1117/1.3483904]

关键词： Image processing Image quality Algorithm development Copper indium disulfide Field programmable gate arrays detection and tracking algorithms Fuzzy logic Data processing Signal processing Image sensors

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Fuzzy variable-branch decision tree

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JOURNAL OF ELECTRONIC IMAGING 2010年第4期19卷

作者： Yang, Shiueng-Bien Wenzao Ursuline Coll Languages Dept Informat Management & Commun Kaohsiung 807 Taiwan

Decision trees and their variants recently have been pro-posed. All trees used are fixed M-ary tree structured, such that the training samples in each node must be artificially divided into a fixed number of branches. This work proposes a fuzzy variable-branch decision tree (FVBDT) based on the fuzzy genetic algorithm (FGA). The FGA automatically searches for the proper number of branches of each node according to the classification error rate and the classification time of FVBDT. Therefore, FGA reduces both the classification error rate and classification time, and then optimizes the FVBDT. In our experiments, FVBDT outperforms the traditional C-fuzzy decision tree (CFDT) based on the fuzzy C-means (FCM) algorithm. (C) 2010 SPIE and IS&T. [DOI: 10.1117/1.3504357]

关键词： Fuzzy logic Image segmentation Genetic algorithms Glasses Iris recognition Databases detection and tracking algorithms Distance measurement Quantization Roads

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Image-processing algorithms for detecting and counting vehicles waiting at a traffic light

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JOURNAL OF ELECTRONIC IMAGING 2010年第4期19卷

作者： de la Rocha, Ernesto Palacios, Rafael Univ Pontificia Comillas Inst Invest Tecnol Madrid 28015 Spain

Traffic lights at most road intersections operate on a fixed timing schedule that leads to suboptimal traffic management, with unnecessary delays, higher fuel consumption, and higher emissions. Traffic management can be improved by installing inductive loops;however, installation involves temporary road closures and high maintenance costs, especially if there is normally a lot of heavy traffic on the road. We present a vehicle detection and counting system based on digital image-processing techniques. These images can be taken by digital cameras installed at the top of existing traffic lights. By using the proposed approach, it is possible to detect the number of vehicles waiting on each side of the intersection, hence, providing the necessary information for optimal traffic management. Results achieved after testing this methodology on three real intersections are promising, attaining high accuracy during the day (98.8%) and the night (91.3%) while counting several vehicles at the same time. Hence, the system is equivalent to installing multiple inductive loops in all the streets of the intersection, but with lower installation and maintenance costs. After integrating the proposed algorithms into a traffic-management system, it was possible to reduce fuel and CO2 emissions by half compared to the standard fixed-time scheduler. (C) 2010 SPIE and IS&T. [DOI: 10.1117/1.3528465]

关键词： Cameras Roads detection and tracking algorithms Edge detection Headlamps Imaging systems Intelligence systems Video Algorithm development Sensors

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Comparative study of localization metrics for the evaluation of image interpretation systems

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JOURNAL OF ELECTRONIC IMAGING 2010年第2期19卷

作者： Hemery, Baptiste Laurent, Helene Emile, Bruno Rosenberger, Christophe Univ Caen Basse Normandie Ecole Natl Super Ingeneurs Caen Lab GREYC CNRS F-14000 Caen France Univ Orleans ENSI Bourges Inst PRISME F-18020 Bourges France

Image interpretation is particularly important in many real applications (video monitoring, biometrics, etc.). Due to the proliferation of image interpretation systems in the literature, their evaluation still remains a crucial stake. Among all the tasks in this field, the quality of object localization is often evaluated through an evaluation metric. We propose to review these techniques and study their reliability. We first propose a generic definition of a localization algorithm. Then, different state of the art techniques to evaluate image interpretation results are detailed. Secondly, we focus on metrics that enable us to evaluate localization results. We propose a general methodology to analyze the behavior of an evaluation metric, considered here as a black box (its definition is not even supposed to be known). We define the properties that these metrics should fulfill. We then perform a comparative study of 33 localization metrics from the state of the art. Experimental results conducted on a large and significant image database permit us to determine metrics that should be used in the future for the evaluation of object localization results. (C) 2010 SPIE and IS&T. [DOI: 10.1117/1.3446803]

关键词： Image processing Lab on a chip Signal to noise ratio Databases detection and tracking algorithms Image analysis Image segmentation Algorithm development Binary data Reliability

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Automatic target recognition performance losses in the presence of atmospheric and camera effects

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JOURNAL OF ELECTRONIC IMAGING 2010年第2期19卷

作者： Chen, Xiaohan Schmid, Natalia A. W Virginia Univ Lane Dept Comp Sci & Elect Engn Morgantown WV 26506 USA

The importance of networked automatic target recognition systems for surveillance applications is continuously increasing. Because of the requirement of a low cost and limited payload, these networks are traditionally equipped with lightweight, low-cost sensors such as electro-optical (EO) or infrared sensors. The quality of imagery acquired by these sensors critically depends on the environmental conditions, type and characteristics of sensors, and absence of occluding or concealing objects. In the past, a large number of efficient detection, tracking, and recognition algorithms have been designed to operate on imagery of good quality. However, detection and recognition limits under nonideal environmental and/or sensor-based distortions have not been carefully evaluated. We introduce a fully automatic target recognition system that involves a Haar-based detector to select potential regions of interest within images, performs adjustment of detected regions, segments potential targets using a region-based approach, identifies targets using Bessel K form-based encoding, and performs clutter rejection. We investigate the effects of environmental and camera conditions on target detection and recognition performance. Two databases are involved. One is a simulated database generated using a 3-D tool. The other database is formed by imaging 10 die-cast models of military vehicles from different elevation and orientation angles. The database contains imagery acquired both indoors and outdoors. The indoors data set is composed of clear and distorted images. The distortions include defocus blur, sided illumination, low contrast, shadows, and occlusions. All images in this database, however, have a uniform (blue) background. The indoors database is applied to evaluate the degradations of recognition performance due to camera and illumination effects. The database collected outdoors includes a real background and is much more complex to process. The numerical results demonstrat

关键词： Databases Automatic target recognition Sensors Target detection Cameras Image segmentation Image filtering Target recognition detection and tracking algorithms 3D acquisition

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Unusual event detection and prediction based on sectional contextual edit distance

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JOURNAL OF ELECTRONIC IMAGING 2010年第1期19卷

作者： Zhang, Yi Yang, Jie Liu, Kun Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200240 Peoples R China

We redefine the unusual event detection problem from a different point of view. Several fundamental event features are investigated and adopted. These features are redescribed in a uniform model. Thus, using this model, supervised/unsupervised unusual event detection algorithms can be designed to fit various situations. Trajectory is treated as the most important feature. To more accurately measure the similarity of different moving object trajectories, a novel distance measurement, the sectional contextual edit distance ( SCED), is developed. In the SCED, cost functions are designed according to contextual information and trajectories are segmented into subsections automatically, based on the relevant contexts. Velocity and orientation are also taken into account in cost functions to build an integrated distance similarity measurement. Experimental results demonstrate better performance using the newly proposed similarity measurement while being compared with the existing methods, and some cases of the unusual event detection problem are also demonstrated. (C) 2010 SPIE and IS&T. [DOI: 10.1117/1.3327951]

关键词： Distance measurement Distributed interactive simulations Motion measurement detection and tracking algorithms Machine learning Motion models Process modeling Integration Roads Surveillance systems

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