In this paper, we propose a region based saliency detection algorithm using a total variation based regularizer. We aim to obtain salient objects that are uniformly highlighted. the use of the regularizer facilitates ...
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ISBN:
(纸本)1595930361
In this paper, we propose a region based saliency detection algorithm using a total variation based regularizer. We aim to obtain salient objects that are uniformly highlighted. the use of the regularizer facilitates the removal of textures from the image. this leads to an imagethat contains piecewise constant gray-valued segments. this texture-free image is sparsely segmented into a small number of regions using the expectation maximization algorithm assuming a Gaussian mixture model. We compute saliency of regions using their intensity and spatial features. the saliency map is then thresholded to obtain the salient regions of the image. Next we employ an image matting technique to extract the exact boundaries of the salient objects from the image. this approach leads to noise-free saliency maps containing uniformly highlighted salient regions. the experimental comparison with existing saliency detection algorithms demon-strates the superiority of the proposed technique. Copyright 2014 ACM.
Object tracking is a critical task in surveillance and activity analysis. One of the major issues in visual target tracking is variation in illumination. In this paper, we propose an improved weighted histogram approa...
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ISBN:
(纸本)1595930361
Object tracking is a critical task in surveillance and activity analysis. One of the major issues in visual target tracking is variation in illumination. In this paper, we propose an improved weighted histogram approach for mean shift tracking to achieve illumination invariance. the proposed scheme can effectively reduce changes in the illumination of targets with respect to background. Depending on changes in the target appearance, the target model is updated. the experimental results show that the proposed scheme can lead to faster convergence, more accurate localization and robust tracking in several illumination scenarios, when compared to existing methods. Copyright 2014 ACM.
this paper presents an efficient system for palm-dorsa vein pattern based recognition system. It can handle efficiently the problem of false palm-dorsa veins which can be created by many ways such as ink, tattoos, art...
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ISBN:
(纸本)1595930361
this paper presents an efficient system for palm-dorsa vein pattern based recognition system. It can handle efficiently the problem of false palm-dorsa veins which can be created by many ways such as ink, tattoos, artificial vein pattern paper fixed on the palm-dorsa. Hand-dorsa images acquired under visible and infrared lights are used. Since vein pattern from infrared light has spurious and genuine vein pattern, spurious vein pattern is removed from it by using vein pattern from visible light. It has been tested on 600 visible and 600 infrared hand-dorsa images. Experimental results indicate that the proposed system performs efficiently. Copyright 2014 ACM.
In this paper, we present a novel method for 3D reconstruction from epipolar plane (EP) representation of images and surface fitting for multiview or lightfield images. the proposed method detects parallelograms in EP...
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ISBN:
(纸本)1595930361
In this paper, we present a novel method for 3D reconstruction from epipolar plane (EP) representation of images and surface fitting for multiview or lightfield images. the proposed method detects parallelograms in EP images using mean shift segmentation. the slopes of the parallel lines along the left and right boundaries of the parallelograms are computed with Hough transform to estimate depth. In addition we also introduce a new concept of estimating depth from multiple EP images(diagonal EP images), making the resultant depth map denser. Multi-view depth maps are then combined and surface fitting is done to obtain the final depth map. Our method uses simple techniques to construct a quality depth map without being computationally demanding. Copyright 2014 ACM.
Shape-from-focus (SFF) is extensively used in imageprocessing for obtaining shape-maps using a sequence of images of a scene captured from same view point with different camera focus settings. Many focus measure oper...
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ISBN:
(纸本)1595930361
Shape-from-focus (SFF) is extensively used in imageprocessing for obtaining shape-maps using a sequence of images of a scene captured from same view point with different camera focus settings. Many focus measure operators have been proposed in the literature for SFF applications and their relative performance depends on the camera capabilities and scene properties. In this work, we propose a novel shape estimation method which fuses individual shape estimates obtained by multiple focus measures, and reconstructs an improved shape map in a patch-wise manner, within a sparse representation (SR) framework. the SR framework involves an over-complete dictionary containing shape patches, the linear combination of which is used to reconstruct each out-put shape patch. the experimental results demonstrate that the proposed approach estimates superior shape maps in both synthetic and real scenes as compared to the estimates obtained by individual focus measures. Copyright 2014 ACM.
Motion blur is a common phenomenon in the presence of camera shake or object motion. In this paper, we deal withthe challenging situation of underwater imaging. Specifically, we assume a static camera looking vertica...
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ISBN:
(纸本)1595930361
Motion blur is a common phenomenon in the presence of camera shake or object motion. In this paper, we deal withthe challenging situation of underwater imaging. Specifically, we assume a static camera looking vertically down-wards at a scene but through a flowing water surface. the source of motion blur is due to the dynamic medium between the scene and the camera. Under reasonable assumptions, we establish that the motion blur induced by commonly observed fluid flows can serve as a valuable cue for inferring the underlying depth layers of the scene. We validate our approach with synthetic and real examples. Copyright 2014 ACM.
Long range surveillance videos are often distorted by random perturbations of the optical pathways caused by atmospheric turbulence. While humans can easily perceive and separate the moving objects from the turbulent ...
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ISBN:
(纸本)1595930361
Long range surveillance videos are often distorted by random perturbations of the optical pathways caused by atmospheric turbulence. While humans can easily perceive and separate the moving objects from the turbulent motion, it is still a challenge for vision systems. In this paper, we present a near real-time approach to detecting moving objects in the presence of turbulence. As a first step, our method learns the dynamics of the latent turbulence and extracts an approximate foreground. Statistical analysis of the foreground object properties is used to eliminate noise due to turbulence preserving only the true moving objects. Our approach also results in a stable background with minimal turbulence. We tested our method on a wide range of scenarios corrupted by various levels of turbulence. Compared with state of the art, our results are quite promising. Copyright 2014 ACM.
2D Face recognition systems bound to fail on images with varying pose angles and occlusions. Many pose invariant methods are proposed in recent years but they are still not able to achieve very good accuracies. So in ...
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ISBN:
(纸本)1595930361
2D Face recognition systems bound to fail on images with varying pose angles and occlusions. Many pose invariant methods are proposed in recent years but they are still not able to achieve very good accuracies. So in order to achieve a better accuracy we need to extend algorithms over 3D faces. Due to the high cost involved in acquisition of 3D faces we developed our approach for low-cost and low-quality Microsoft Kinect Sensor and propose an algorithm to produce better results than existing 2D Face recognition techniques even after compromising on the quality of the images from the sensor. Our proposed algorithm is based on modified SURF descriptors on RGB images combined with various enhancements on automatically generated training images using Depth and Color images. We compare our results obtained with State Of the Art Techniques obtained on publicly available RGB-D Face databases. Our System obtained recognition rate of 98.07% on 30° CurtinFace Database, 89.28% on EURECOM Database, 98.00% on 15° Internal Database and 81.00% on 30° Internal Database.
A limitation of using the discriminant analysis techniques for dimension reduction in image classification tasks is that the number of classes is significantly smaller than the dimension of global feature vectors used...
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ISBN:
(纸本)1595930361
A limitation of using the discriminant analysis techniques for dimension reduction in image classification tasks is that the number of classes is significantly smaller than the dimension of global feature vectors used to represent the images. In such cases, the reduced dimension is restricted by the number of classes and that reduced dimension may not adequately capture the necessary discriminatory information for classification. We propose a method to perform the discriminant analysis using the concept level labels for local feature vectors extracted from the blocks of an image. the dimension reduction is carried out on the local feature vectors. We consider the indices of the clusters of local feature vectors of all the images as the unnamed concept level labels. As the number of concept level labels is larger than the number of class level labels, the reduced dimension for local feature vectors can be higher than the number of class level labels. We consider the Gaussian mixture model based classifiers and support vector machine based classifiers for image classification using the set of local feature vectors representation of images. Results of experimental studies on image classification for MIT-8 and Vogel-6 image datasets demonstrate the effectiveness of proposed concept level discriminant analysis techniques for dimension reduction. Copyright 2014 ACM.
Video interest points, in combination with local appearance descriptors, are used for human action recognition. Most of the previously proposed video interest point detectors are straightforward extensions of some ima...
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ISBN:
(纸本)1595930361
Video interest points, in combination with local appearance descriptors, are used for human action recognition. Most of the previously proposed video interest point detectors are straightforward extensions of some image interest point detector or the other. these methods treat the temporal dimension (inter-frame) similar to the spatial dimensions (intra-frame). We argue that certain unique properties of the temporal dimension beg a different treatment. We propose an interest point detector based on vector calculus of optical flow to take advantage of the unique properties of the temporal dimension. Compared to previously proposed methods, the proposed method exhibits higher repeatability (robustness) and lower displacement (stability) of interest points under two common video transformations tested-video compression and spatial scaling. It also shows competitive action recognition performance when paired with appropriate feature descriptors in a bag of features model. Copyright is held by the authors.
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