State-of-the-art objective image quality metrics are summarized and analyzed from a new perspective. Performance comparisons of existing metrics are firstly conducted on simulated turbulence-degraded images. Then expl...
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A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient *** iterative method is introduced to s...
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A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient *** iterative method is introduced to solve this equation *** unknown probability density functions as well as their first and second derivatives in the gradient equation are estimated by kernel density *** simulations on artificially generated signals and gray scale natural scene images confirm the efficiency and accuracy of the proposed algorithm.
By the guidance of attention, human visual system is able to locate objects of interest in complex scene. We propose a new visual saliency detection model for both image and video. Inspired by biological vision, salie...
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By the guidance of attention, human visual system is able to locate objects of interest in complex scene. We propose a new visual saliency detection model for both image and video. Inspired by biological vision, saliency is defined locally. Lossy compression is adopted, where the saliency of a location is measured by the Incremental Coding Length(ICL). The ICL is computed by presenting the center patch as the sparsest linear representation of its surroundings. The final saliency map is generated by accumulating the coding length. The model is tested on both images and videos. The results indicate a reliable and robust saliency of our method.
In this paper, we propose a novel fusion-based gender classification method that is able to compensate for facial expression even when training samples contain only neutral expression. We perform experimental investig...
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In this paper, we propose a novel fusion-based gender classification method that is able to compensate for facial expression even when training samples contain only neutral expression. We perform experimental investigation to evaluate the significance of different facial regions in the task of gender classification. Three most significant regions are used in our fusion-based method. The classification is performed by using support vector machines based on the features extracted using two-dimension principal component analysis. Experiments show that our fusion-based method is able to compensate for facial expressions and obtained the highest correct classification rate of 95.33%.
In this paper a framework for multichannel image restoration based on optimization of the structural similarity (SSIM) index is presented. The SSIM index describes the similarity of images more appropriately for the h...
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Accurately predicting non-peak traffic is crucial to daily traffic for all forecasting models. In the paper, least squares support vector machines (LS-SVMs) are investigated to solve such a practical problem. It is th...
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Accurate traffic parameters such as traffic flow, travel speeds and occupancies, are crucial to effective management of intelligent transportation systems (ITS). Some traffic data from loop detectors settled in arteri...
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Accurate traffic parameters such as traffic flow, travel speeds and occupancies, are crucial to effective management of intelligent transportation systems (ITS). Some traffic data from loop detectors settled in arterial streets are incomplete, and the importance of effectively imputing the missing values emerges. In this paper, a technique called least squares support vector machines (LS-SVMs) is introduced to predict missing traffic flow based on spatio-temporal analysis in urban arterial streets. To the best of our knowledge, it is the first time to apply the rising computational intelligence (CI) technique incorporating with state space approach in missing traffic data imputation. Having good generalization ability and guaranteeing global minima ensure its well performance in the area. A baseline imputation technique, expectation maximization/data augmentation (EM/DA), is selected for comparison because of its proved effectiveness in missing data recovery. Through persuasive comparisons of the techniques, the proposed model is proved to be more applicable and performs better in stability and adaptability, which reveals that it is a promising approach in missing data prediction.
We consider the problem of 3D modeling under the environments where colors of the foreground objects are similar to the background, which poses a difficult problem of foreground and background classification. A purely...
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We consider the problem of 3D modeling under the environments where colors of the foreground objects are similar to the background, which poses a difficult problem of foreground and background classification. A purely image-based algorithm is adopted in this paper, with no prior information about the foreground objects. We classify foreground and background by fusing the information at the pixel and region levels to obtain the similarity probability map, followed by a Bayesian sensor fusion framework to infer the space occupancy grid. The estimation of the occupancy allows incremental updating once a new observation is available, and the contribution of each observation can be adjusted according to its reliability. Finally, three parameters in the algorithm are analyzed in detail and experiments show the effectiveness of this method.
In this paper, we present a scheme of similarity measure learning based on kernel optimization. Employing a data-dependent kernel model, the proposed scheme optimizes the spatial distribution of the training data in t...
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In this paper, we present a scheme of similarity measure learning based on kernel optimization. Employing a data-dependent kernel model, the proposed scheme optimizes the spatial distribution of the training data in the feature space, aiming to maximize the class separability of the data in the feature space. The learned similarity measure, derived from the optimized kernel, exhibits a favorable feature to the task of pattern classification, that the spatial resolution of the embedding space is expanded around the boundary areas, and shrunk around the homogeneous areas. Experiments demonstrate that using the learned similarity measure can substantially improve the performances of the K-nearest-neighbor classifier.
In this paper, we present a new algorithm for camera calibration using concentric circles, which is a linear approach. Different from previous methods, we take the projective equations of three-dimensional circles, wh...
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In this paper, we present a new algorithm for camera calibration using concentric circles, which is a linear approach. Different from previous methods, we take the projective equations of three-dimensional circles, which include the intrinsic parameter matrix of the camera, as the basis of our calibration approach. According to the special structure of the projective equations in algebra, we can get a linear equation system about the intrinsic parameters. After enough equations constructed, the calibration can be easily finished. Our method needs three images of the two concentric circles at least, and all the five intrinsic parameters can be recovered. Experiments using computer simulated data demonstrate the robustness and accuracy of our method.
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