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Automatic crack detection in heritage site images for image inpainting 12

Automatic crack detection in heritage site images for image ...

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Turakhia, Naman Shah, Ruchin Joshi, Manjunath Dhirubhai Ambani Institute of Information and Communication Technology Gandhinagar India

ISBN: (纸本)9781450316606

Heritage structures are very important to mankind as they help in studying and understanding the growth of civilization and are sources of inspiration to modern craftsmen. However, many structures get degraded / vandalized due to various reasons like aging, material degradation, human interventions, etc. With the use of image inpainting techniques, one can digitally restore these structures. In almost all the available inpainting techniques, one has to manually select the target region. In this paper, we propose a novel approach to automatically identify cracks in the heritage structures. The method exploits the use of order-statistics filtering and the tensor voting to detect crack regions which are given as input to inpainting algorithms. The order-statistics filters enhance the contrast of the crack regions, which then act as input to a Canny edge detector. A 2D tensor voting is applied on the edge output for enhancing the curveness of the probable crack regions. Finally, image dilation is used for crack filling, generating the final target regions for image inpainting. We show the effectiveness of the method by conducting experiments on the images captured from the world Heritage site Hampi, Karnataka. © 2012 ACM.

关键词： Crack detection

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Human gait recognition using depth camera: A covariance based approach 12

Human gait recognition using depth camera: A covariance base...

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Kumar, M. S. Naresh Babu, R. Venkatesh Video Analytics Lab. SERC Indian Institute of Science Bangalore India

ISBN: (纸本)9781450316606

Gait is an important biometric modality for recognizing humans. Unlike other biometrics, human gait can be captured at a distance which makes it an unobtrusive method for recognition. In this paper, an unrestricted gait recognition algorithm is proposed which uses 3D skeleton information and trajectory covariance of joint points. 3-D skeleton is generated from the depth images that are captured using Kinect sensor. The temporal tracking of skeleton points is used for gait analysis. The covariance measure between these skeleton point trajectories are computed and the covariance matrices form the gait model. The gait is recognized by computing the minimum dissimilarity measure between the gait models of the training data and the testing data. Recognition accuracy of over 90% has been achieved for a data set consisting of fixed and moving camera scenarios of 20 subjects. © 2012 ACM.

关键词： Biometrics

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Wavelet-based contrast enhancement of dark images using dynamic stochastic resonance 12

Wavelet-based contrast enhancement of dark images using dyna...

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Chouhan, Rajlaxmi Jha, Rajib Kumar Biswas, Prabir Kumar Indian Institute of Technology Kharagpur India Indian Institute of Information Technology Design and Manufacturing Jabalpur India

ISBN: (纸本)9781450316606

In this paper, a dynamic stochastic resonance (DSR)-based technique has been proposed for contrast enhancement of dark and low contrast images in discrete wavelet transform (DWT) domain. Traditionally, the performance of a stochastic resonance (SR)-based system is improved by addition of external noise. However, in the proposed DSR-based approach, the internal noise of an image has been utilized for the purpose of contrast enhancement. The degradation due to inadequate illumination is treated as noise, and is used to produce a noise-induced transition of the image from a low-contrast state to a high-contrast state. Stochastic resonance is induced in the approximation and detail coefficients in an iterative fashion, producing an increase in variance and mean of the coefficient distribution. Optimal output response is ensured by selection of optimal of bistable system parameters. An iterative algorithm is followed to achieve target value of performance metrics, such as relative contrast enhancement factor (F), perceptual quality measures (PQM), and color enhancement factor (CEF), at minimum iteration count. When compared with the existing SR-based and non SR-based enhancement techniques in spatial and frequency domains, the proposed technique is found to give noteworthy performance in terms of contrast enhancement, perceptual quality, as well as colorfulness. © 2012 ACM.

关键词： Discrete wavelet transforms

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ISIS and NISIS: New bilingual dual-channel speech corpora for robust speaker recognition

ISIS and NISIS: New bilingual dual-channel speech corpora fo...

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2012 International conference on image processing, computer vision, and Pattern Recognition, IPCV 2012

作者： Pal, Amita Bose, Smarajit Mitra, Mandar Roy, Sandipan BIRU Indian Statistical Institute Kolkata India CVPRU Indian Statistical Institute Kolkata India Department of Statistics University of Michigan Ann Arbor MI United States

ISBN: (纸本)9781601322258

It is standard practice to use benchmark datasets for comparing meaningfully the performance of a number of competing speaker identification systems. Generally, such datasets consist of speech recordings from different speakers made at a single point of time, typically in the same language. That is, the training and test sets both consist of speech recorded at the same point of time in the same language over the same recording channel. This is generally not the case in real-life applications. In this paper, we introduce a new database consisting of speech recordings of 105 speakers, made over four sessions, in two languages and simultaneously over two channels. This database provides scope for experimentation regarding loss in efficiency due to possible mismatch in language, channel and recording session. Results of experiments with MFCC-based GMM speaker models are presented to highlight the need of such benchmark datasets for identifying robust speaker identification systems.

关键词： Speech recognition

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Automatic extraction of road segments from high resolution satellite images - A region and boundary based approach 12

Automatic extraction of road segments from high resolution s...

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Banerjee, Biplab Buddhiraju, Siddharth Mohan, B. Krishna Satellite Image Analysis Lab. CSRE IIT Bombay Powai Mumbai India Electrical Engineering Dept. IIT Bombay Powai Mumbai India

ISBN: (纸本)9781450316606

The problem of road segment extraction from high resolution satellite or aerial images has been considered in this paper. Efficient extraction of road segments is a difficult task due to the problems regarding image acquisition, local road width and orientation etc. A novel method of road region extraction using local spectral and geometrical properties has been proposed here. The image is first segmented using mean-shift based approach. Edge preserving smoothing is performed beforehand to preserve the sharpness of the object boundaries. Then a rejection stage using chord distribution technique is considered to remove dominant non-road regions. It is followed by a cost optimization step based on a region following grid graph to efficiently extract the road segments. A further orientation based region linking is performed to overcome problems due to occlusion. Experiments on several high resolution satellite images have established the capability of the proposed method. © 2012 ACM.

关键词： Extraction

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A finite mixture model based on pair-copula construction of multivariate distributions and its application to color image segmentation 12

A finite mixture model based on pair-copula construction of ...

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Roy, Anandarup Parui, Swapan K. Roy, Utpal CVPR Unit Indian Statistical Institute 203 B. T. Road Kolkata - 108 India Dept. of Comp. and System Sc. Visva-Bharati University Santiniketan- 731235 India

ISBN: (纸本)9781450316606

This paper presents a finite mixture model that involves a pair-copula based construction of a multivariate distribution. The advantage of such a model is that the margins and the dependence structures are de-coupled from each other. Also, they could be modeled separately. In effect the mixture model (called DVMM) is capable of capturing a broader family of distributions including non-Gaussian models. As an application, we consider the task of color image segmentation in CIE-LUV color space. The process of segmentation could be viewed as an unsupervised clustering of the image pixels. The clusters usually represent the possible segments in the image. Here, the image pixels are assumed to be samples from a DVMM. The expectation maximization algorithm is used to estimate the model parameters. One cluster generally consists of several connected components inside an image. We further note the existence of redundant connected components that represent the boundary of two adjacent components. We here propose a methodology that merges such component pixels to the other two components. We conduct extensive experiments on Berkeley segmentation data set. We take a number of error measures to evaluate the quality of segmentation. On the basis of a comparison with two existing mixture model based segmentation approaches, we find our results encouraging. © 2012 ACM.

关键词： Pixels

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Detection and segmentation of approximate repetitive patterns in relief images 12

Detection and segmentation of approximate repetitive pattern...

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Agrawal, Harshit Namboodiri, Anoop M. Center for Visual Information Technology IIIT-Hyderabad India

ISBN: (纸本)9781450316606

Algorithms for detection of repeating patterns in images often assume that the repetition is regular and highly similar across the instances. Approximate repetitions are also of interest in many domains such as hand carved sculptures, wall decorations, groups of natural objects, etc. Detection of such repetitive structures can help in applications such as image retrieval, image inpainting, 3D reconstruction, etc. In this work, we look at a specific class of approximate repetitions: those in images of hand carved relief structures. We present a robust hierarchical method for detecting such repetitions. Given a single image with reliefs, our algorithm finds dense matches of local features across the image at various scales. The matching features are then grouped based on their geometric configuration to find repeating elements. We also propose a method to group the repeating elements to segment the repetitive patterns in an image. In relief images, foreground and background have nearly the same texture, and matching of a single feature would not provide reliable evidence of repetition. Our grouping algorithm integrates evidences of repetition to reliably find repeating patterns. Input image is processed on a scale-space pyramid to effectively detect all possible repetitions at different scales. Our method has been tested on images with large varieties of complex repetitive patterns and the qualitative results show the robustness of our approach. © 2012 ACM.

关键词： image retrieval

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A pipeline for building 3D models using depth cameras 12

A pipeline for building 3D models using depth cameras

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Chatterjee, Avishek Jain, Suraj Govindu, Venu Madhav Department of Electrical Engineering Indian Institute of Science Bengaluru 560012 India

ISBN: (纸本)9781450316606

In this paper we describe a system for building geometrically consistent 3D models using structured-light depth cameras. While the commercial availability of such devices, i.e. Kinect, has made obtaining depth images easy, the data tends to be corrupted with high levels of noise. In order to work with such noise levels, our approach decouples the problem of scan alignment from that of merging the aligned scans. The alignment problem is solved by using two methods tailored to handle the effects of depth image noise and erroneous alignment estimation. The noisy depth images are smoothed by means of an adaptive bilateral filter that explicitly accounts for the sensitivity of the depth estimation by the scanner. Our robust method overcomes failures due to individual pairwise ICP errors and gives alignments that are accurate and consistent. Finally, the aligned scans are merged using a standard procedure based on the signed distance function representation to build a full 3D model of the object of interest. We demonstrate the performance of our system by building complete 3D models of objects of different physical sizes, ranging from cast-metal busts to a complete model of a small room as well as that of a complex scale model of an aircraft. © 2012 ACM.

关键词： Alignment

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Enhanced eigenspace separation transform for classification 12

Enhanced eigenspace separation transform for classification

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Murthy, K. Ramachandra Ghosh, Ashish Machine Intelligence Unit Indian Statistical Institute 203 B. T. Road Kolkata-108 India

ISBN: (纸本)9781450316606

This paper presents Enhanced Eigenspace Separation Transform (EEST), a novel supervised dimensionality reduction technique for classification. EEST is motivated from the Eigenspace Separation Transform (EST). The criterion of EEST is to maximize the difference in the average lengths of vectors in the underlying two classes, and minimize the intra-class average distances, to improve the generalization capacity of a classifier. We propose upper bounds of the criterion, and a specific solution space to attain these bounds. Existence of such a solution is restricted, thereby we have considered the orthonormal space of the upper bounds in order to achieve better dimensionality reduction, and improve the generalization accuracy of the classifier. A simple Nearest Neighborhood (NN) classification approach is adopted for classification to highlight the novelty of the proposed scheme. Theoretical analysis of the proposed techniques is also carried out. Different synthetic data sets have been used to evaluate the advantages of EEST over EST. Extensive empirical studies are made;and the proposed method is compared with three closely related schemes using real-world data sets to verify the efficiency of the proposed method. © 2012 ACM.

关键词： EEST

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LBP-SURF descriptor with color invariant and texture based features for underwater images 12

LBP-SURF descriptor with color invariant and texture based f...

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Prabhakar, C.J. Praveen Kumar, P.U. Department of P.G. Studies and Research in Computer Science Kuvempu University Shankaraghatta - 577451 Karnataka India

ISBN: (纸本)9781450316606

In this paper, we introduce LBP-SURF, a local image descriptor for underwater environment, which is very efficient to extract color invariant and texture based features of underwater images. The current state-of-the-art feature descriptors viz. SIFT, SURF, DAISY, GLOH and variants are well known techniques for detecting and describing features of objects captured in the out-of-water environment. These standard descriptors have been proven to be the most robust to geometric variations. Nearly, all these geometrical invariant approaches avoid dealing with color images due to the color constancy problem. In underwater images, variation in color is very high compared to variations in geometrical properties due to propagation properties of light. The literature survey reveals that, the texture parameters that remain constant for the scene patch for the whole underwater image sequence. This motivated us to consider texture and color invariant features of underwater images, instead of using the gray-based geometrical invariant features. We normalize the color image using comprehensive color image normalization method to render the color values changed by the various radiometric factors of underwater environment. Our method uses Speeded Up Robust Features (SURF) to detect interest points from the normalized image. The texture features are extracted, and description is stored using Center-Symmetric Local Binary Patterns (CS-LBP) descriptor. The combination of SURF and CS-LBP, called LBP-SURF is evaluated extensively to verify its effectiveness with datasets acquired in underwater environment. © 2012 ACM.

关键词： Color

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