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People/vehicle classification by recurrent motion of skeleton features

IET computer vision 2012年第5期6卷 442-450页

作者： Yogameena, B. Roomi, S. Md. Mansoor Priya, R. Jyothi Raju, S. Abhaikumar, V. Thiagarajar Coll Engn Madurai 625015 Tamil Nadu India

Object classification is a major application in video surveillance such as automatic vehicle detection and pedestrian detection, which is to monitor thousands of vehicles and people. In this study, an object classification algorithm is proposed to classify the objects into persons and vehicles despite the presence of shadow and partial occlusion in mid-field video using recurrent motion image (RMI) of skeleton features. In this framework, the background subtraction using a Gaussian mixture model is followed by Gabor filter based shadow removal in order to remove the shadow in the image. The star skeletonisation algorithm is performed on the segmented objects to obtain skeleton features. Then the RMI is computed and it is partitioned into two sections such as top and bottom. Based on the signatures derived from the bottom section of the partitioned RMI using skeleton features, the object is classified into people and vehicles.

关键词： Other topics in statistics Video signal processing Traffic engineering computing image segmentation star skeletonisation algorithm traffic engineering computing people-vehicle classification background subtraction Gaussian processes Gabor filter based shadow removal partial occlusion automatic vehicle detection road vehicles recurrent motion image object classification algorithm RMI pedestrian detection skeleton features Optical, image and video signal processing pedestrians image classification computer vision and image processing techniques Filtering methods in signal processing image motion analysis Gaussian mixture model midfield video video surveillance Gabor filters shadow occlusion video signal processing

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Multi-human tracking from sparse detection responses

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IET computer vision 2012年第6期6卷 590-602页

作者： Shen, Y. Miao, Z. Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China

In this study, the authors focus on the performance improvements of multi-human tracking from sparse detection responses. Many previous detection-based data association tracking methods used dense detection responses as input, but they did not take into account in the case of sparse detection responses. Dense detection responses are difficult to obtain in complex environments all the time. In order to achieve this goal, they propose a particle-filter-based triple threshold method to build reliable trajectories. Here, they apply topic model to represent human appearance. The appearance of each person can be considered as topic distribution. Then a cost function algorithm is used to associate these trajectories in a time sliding window for final tracking results. The cost function is composed of four parts: appearance cost, motion direction cost, object size cost and distance cost. These four parts are integrated into a unified formula to build this cost function. Finally, they use three challenging datasets to evaluate the performance of the author's approach in the case of dense and sparse detection responses, respectively, and compare with state-of-the-art approaches. The results show that their approach can obtain better tracking performance than that of previous methods in both cases.

关键词： topic model human appearance representation object size cost motion direction cost sparse detection response particle-filter-based triple threshold method time sliding window dense detection response object tracking distance cost detection-based data association tracking appearance cost object detection Optical, image and video signal processing computer vision and image processing techniques Filtering methods in signal processing image representation multihuman tracking particle filtering (numerical methods)

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Improved normalised difference vegetation index method based on discrete cosine transform and singular value decomposition for satellite image processing

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IET SIGNAL processing 2012年第7期6卷 617-625页

作者： Kumar, A. Bhandari, A. K. Padhy, P. PDPM Indian Inst Informat Technol Design & Mfg Ja Jabalpur 482005 MP India

In this study, an improved multi-band satellite contrast enhancement technique based on the singular value decomposition (SVD) and discrete cosine transform (DCT) has been proposed for the feature extraction of low-contrast satellite images using normalised difference vegetation index (NDVI) technique. The method employs multi-spectral remote sensing data technique to find the spectral signature of different objects such as the vegetation index and land cover classification presented in the satellite image. The proposed technique converts the image into the SVD-DCT domain and after normalising the singular value matrix;the enhanced image is reconstructed by using inverse DCT. The visual and quantitative results included in this study clearly show the increased efficiency and flexibility of the proposed method over the existing methods. The simulation results show that the enhancement-based NDVI using DCT-SVD technique is highly useful to detect the surface features of the visible area which are extremely beneficial for municipal planning and management.

关键词： computer vision and image processing techniques visible area Geography and cartography computing discrete cosine transforms singular value matrix low-contrast satellite image processing normalised difference vegetation index method image enhancement-based NDVI technique inverse DCT discrete cosine transform Optical, image and video signal processing Data and information matrix algebra multiband satellite contrast enhancement technique image reconstruction acquisition, processing, storage and dissemination in geophysics spectral signature feature extraction Algebra municipal planning remote sensing surface features detection geophysical image processing image enhancement Integral transforms multispectral remote sensing data technique singular value decomposition municipal management inverse transforms SVD-DCT domain

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image quality-augmented intramodal palmprint authentication

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IET image processing 2012年第6期6卷 668-676页

作者： Prasad, S. M. Govindan, V. K. Sathidevi, P. S. Natl Inst Technol Calicut Dept Comp Sci & Engn Calicut 673601 Kerala India Natl Inst Technol Calicut Dept Elect & Commun Engn Calicut 673601 Kerala India

This study serves dual objectives. First, as an efficient, in terms of speed and accuracy, wavelet-based intramodal palmprint authentication approach. Second, quantification of illumination, quality of palmprints and its incorporation in the score fusion for the classification performance enhancement. The later objective is realised using localised contrast measurement and quality-augmented fusion based on illumination sensitiveness of the features. The former objective is realised through intramodal feature extraction and fusion exploiting the multi-scale analysis of palmprint using wavelet transform. The intramodal features (energy, principal lines, dominant wrinkles and high-scale spatial patterns) are extracted in the wavelet domain thus significantly minimising the computational disadvantage of intramodal approach. Significant reduction in the equal error rate (EER) is observed upon match score fusion. Experimental results on PolyU-Online-Palmprint-Database (PolyU) of 386 classes show a relative improvement index of 71.75% with an overall EER of 0.14%;better than the state-of-the-art intramodal and wavelet-based approaches.

关键词： Sensor fusion multiscale analysis image enhancement wavelet-based intramodal palmprint authentication approach image recognition wavelet transform computer vision and image processing techniques image quality-augmented intramodal palmprint authentication image fusion intramodal feature extraction Integral transforms classification performance enhancement quality-augmented fusion palmprint recognition equal error rate localised contrast measurement wavelet transforms feature extraction PolyU-online-palmprint-database

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Fusion of visual cues of intensity and texture in Markov random fields image segmentation

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IET computer vision 2012年第6期6卷 603-609页

作者： Dawoud, A. Netchaev, A. Univ So Mississippi Sch Comp Hattiesburg MS 39406 USA

This study proposes an algorithm that fuses visual cues of intensity and texture in Markov random fields region growing texture image segmentation. The idea is to segment the image in a way that takes EdgeFlow edges into consideration, which provides a single framework for identifying objects boundaries based on texture and intensity descriptors. This is achieved by modifying the energy minimisation process, so that it would penalise merging regions that have EdgeFlow edges in the boundary between them. Experimental results confirm the hypothesis that the integration of edge information increases the precision of the segmentation by ensuring the conservation of the homogeneous objects contours during the region growing process.

关键词： energy minimisation process Optimisation techniques texture descriptor Markov processes region growing texture visual cues image texture EdgeFlow edges Markov random field objects boundary Optical, image and video signal processing computer vision and image processing techniques random processes minimisation image segmentation intensity descriptor

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Security evaluation of biometric authentication systems under real spoofing attacks

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IET BIOMETRICS 2012年第1期1卷 11-24页

作者： Biggio, B. Akhtar, Z. Fumera, G. Marcialis, G. L. Roli, F. Univ Cagliari Dept Elect & Elect Engn I-09123 Cagliari Italy

Multimodal biometric systems are commonly believed to be more robust to spoofing attacks than unimodal systems, as they combine information coming from different biometric traits. Recent work has shown that multimodal systems can be misled by an impostor even by spoofing only one biometric trait. This result was obtained under a 'worst-case' scenario, by assuming that the distribution of fake scores is identical to that of genuine scores (i.e. the attacker is assumed to be able to perfectly replicate a genuine biometric trait). This assumption also allows one to evaluate the robustness of score fusion rules against spoofing attacks, and to design robust fusion rules, without the need of actually fabricating spoofing attacks. However, whether and to what extent the 'worst-case' scenario is representative of real spoofing attacks is still an open issue. In this study, we address this issue by an experimental investigation carried out on several data sets including real spoofing attacks, related to a multimodal verification system based on face and fingerprint biometrics. On the one hand, our results confirm that multimodal systems are vulnerable to attacks against a single biometric trait. On the other hand, they show that the 'worst-case' scenario can be too pessimistic. This can lead to two conservative choices, if the 'worst-case' assumption is used for designing a robust multimodal system. Therefore developing methods for evaluating the robustness of multimodal systems against spoofing attacks, and for designing robust ones, remain a very relevant open issue.

关键词： security evaluation genuine scores fingerprint identification multimodal biometric system biometric traits image recognition biometric authentication system security of data fake scores real spoofing attacks robust fusion rules face biometrics face recognition unimodal system multimodal verification system Data security fingerprint biometrics computer vision and image processing techniques worst-case scenario score fusion rules

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Marker-based quadri-ocular tracking system for surgery

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IET computer vision 2012年第5期6卷 435-441页

作者： He, Q. Wei, N. Li, B. Hu, C. Meng, M. Q. -H. Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Zhejiang Univ Ningbo Inst Technol Coll Informat Ningbo Zhejiang Peoples R China Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

This study presents a quadri-ocular tracking system, which is based on PC and infrared reflective markers, for a spine surgical robot. The authors mainly focus on four tasks of the system. First of all, a two-step strategy for point correspondence of the multi-ocular system is introduced. The strategy enhances the traditional epipolar constrain for a bi-ocular system, and it decomposes the point correspondence of the multi-ocular system into several bi-ocular systems and corresponding steps to improve the speed of the system. Second, this paper proposes a fast algorithm of three-dimensional point reconstruction based on the perpendicular feet of back-projection lines. A marker constraint is also dug up to solve the combination problem of target recognition. Finally, this study uses a generalised inverse and singular value decomposition-based method to locate the pose of the target. The experiments show that the speed and accuracy of the system are satisfactory.

关键词： Robotics infrared reflective markers Biology and medical computing image recognition Biological and medical control systems target recognition perpendicular feet Biophysics of neurophysiological processes biomedical optical imaging medical robotics three-dimensional point reconstruction epipolar constrain image reconstruction PC multi-ocular system singular value decomposition-based method generalised inverse method infrared imaging computer vision and image processing techniques medical image processing back-projection lines neurophysiology image recognition singular value decomposition Patient care and treatment spine surgical robot marker-based quadri-ocular tracking system bi-ocular system surgery

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image thresholding framework based on two-dimensional digital fractional integration and Legendre moments'

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IET image processing 2012年第6期6卷 717-727页

作者： Nakib, A. Schulze, Y. Petit, E. Univ Paris EST Creteil LISSI EA 3956 F-94010 Creteil France

In this study, the authors present a new image segmentation algorithm based on two-dimensional digital fractional integration (2D-DFI) that was inspired from the properties of the fractional integration function. Although obtaining a good segmentation result corresponds to finding the optimal 2D-DFI order, the authors propose a new alternative based on Legendre moments. This framework, called two dimensional digital fractional integration and Legendre moments' (2D-DFILM), allows one to include contextual information such as the global object shape and exploits the properties of the 2D fractional integration. The efficiency of 2D-DFILM is shown by the comparison to other six competing methods recently published and it was tested on real-world problem.

关键词： integration fractional integration function two dimensional digital fractional integration-Legendre moments image segmentation 2D fractional integration image segmentation algorithm computer vision and image processing techniques 2D-DFILM global object shape optimal 2D-DFI order Optical, image and video signal processing Mathematical analysis image thresholding framework

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Matching parameter estimation by using the radial basis function interpolation

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IET image processing 2012年第4期6卷 407-416页

作者： Fuji, Y. Abe, Y. Iiguni, Y. Osaka Univ Grad Sch Engn Sci Toyonaka Osaka 5608531 Japan

A matching parameter estimation method with subpixel accuracy is derived by using the radial basis function (RBF) interpolation. This method reconstructs two analogue images from two given digital images by the RBF, and then minimises a non-linear cost function by the steepest-descent algorithm to estimate translation, rotation, scaling factor and intensity change between the two analogue images. The RBF provides accurate interpolation, resulting in accurate estimation. A Gaussian weighting function is introduced into the cost function to provide a local estimate within a region of interest (ROC). Then double integrals included in the cost function are analytically computed and the computational complexity is significantly reduced by exploiting the property that the Gaussian function decays rapidly. When the matching parameters are not constant over the whole image, or equivalently, the ROC is set to be small, the proposed method is better than the conventional phase correlation method in estimation accuracy.

关键词： Gaussian weighting function rotation estimation matching parameter estimation method image registration double-integrals image reconstruction scaling factor nonlinear cost function radial basis function interpolation computer vision and image processing techniques intensity change translation estimation Optical, image and video signal processing phase correlation method analogue image reconstruction image matching subpixel accuracy digital images Interpolation and function approximation (numerical analysis) RBF interpolation interpolation steepest-descent algorithm computational complexity estimation accuracy gradient methods

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Measuring empirical discrepancy in image segmentation results

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IET computer vision 2012年第3期6卷 224-230页

作者： Correa-Tome, F. E. Sanchez-Yanez, R. E. Ayala-Ramirez, V. Univ Guanajuato DICIS Salamanca Gto 36885 Mexico

A methodology for comparison of boundary and segmentation images based on Precision-Recall graphs is presented in this study. The proposed methodology compares the location of edge pixels between an image under test and an ideal reference, in order to obtain a precise normalised similarity measure. This approach also deals with the case when multiple references are available using a merging procedure. Small displacement errors in edge pixel location are handled using a tolerance radius, which introduces the problem of multiple matching between test and reference edge pixels. This problem is addressed as a bipartite graph, solved by using the Hopcroft-Karp algorithm to obtain the maximum number of unique matchings. Experiments have been carried out in order to determine the performance of this evaluation approach.

关键词： image segmentation graph theory edge pixels precision-recall graphs bipartite graph image matching displacement errors empirical discrepancy measurement Hopcroft-Karp algorithm Combinatorial mathematics Optical, image and video signal processing computer vision and image processing techniques merging procedure

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