A state-of-the-art approach to measure the similarity of two images is to model each image by a continuous distribution, generally a Gaussian mixture model (GMM), and to compute a probabilistic similarity between the ...
详细信息
ISBN:
(纸本)9781424439928
A state-of-the-art approach to measure the similarity of two images is to model each image by a continuous distribution, generally a Gaussian mixture model (GMM), and to compute a probabilistic similarity between the GMMs. One limitation of traditional measures such as the Kullback-Leibler (KL) divergence and the Probability Product Kernel (PPK) is that they measure a global match of distributions. This paper introduces a novel image representation. We propose to approximate an image, modeled by a GMM, as a convex combination of K reference image GMMs, and then to describe the image as the K-dimensional vector of mixture weights. The computed weights encode a similarity that favors local matches (i.e. matches of individual Gaussians) and is therefore fundamentally different from the KL or PPK. Although the computation of the mixture weights is a convex optimization problem, its direct optimization is difficult. We propose two approximate optimization algorithms: the first one based on traditional sampling methods, the second one based on a variational bound approximation of the true objective function. We apply this novel representation to the image categorization problem and compare its performance to traditional kernel-based methods. We demonstrate on the PASCAL VOC 2007 dataset a consistent increase in classification accuracy
Image mosaic technology is an important research field of image processing and a research focus on the computervision and computer graphics. The traditional method is to select the feature points by manual selection ...
详细信息
Background subtraction technique has been widely used in the single camera based multi-touch system. The solution based on fixed-rate background accumulation can only be used to the applications with gradual change of...
详细信息
We propose an automatic approximation of the intrinsic manifold for general semi-supervised learning problems. Unfortunately it is not trivial to define an optimization function to obtain optimal hyperparameters. Usua...
详细信息
ISBN:
(纸本)9781424439928
We propose an automatic approximation of the intrinsic manifold for general semi-supervised learning problems. Unfortunately it is not trivial to define an optimization function to obtain optimal hyperparameters. Usually, pure cross-validation is considered but it does not necessarily scale up. A second problem derives from the suboptimality incurred by discrete grid search and overfitting problems. As a consequence, we developed an ensemble manifold regularization (EMR) framework to approximate the intrinsic manifold by combining several initial guesses. Algorithmically, we designed EMR very carefully so that it (a) learns both the composite manifold and the semi-supervised classifier jointly;(b) is fully automatic for learning the intrinsic manifold hyperparameters implicitly;(c) is conditionally optimal for intrinsic manifold approximation under a mild and reasonable assumption;and (d) is scalable for a large number of candidate manifold hyperparameters, from both time and space perspectives. Extensive experiments over both synthetic and real datasets show the effectiveness of the proposed framework.
The proceedings contain 186 papers. The topics discussed include: removing shift-variant motion blur from an image using Poisson interpolation;online real AdaBoost with co-training for object tracking;object detection...
ISBN:
(纸本)9780819478061
The proceedings contain 186 papers. The topics discussed include: removing shift-variant motion blur from an image using Poisson interpolation;online real AdaBoost with co-training for object tracking;object detection based on multiscale discrete points sampling and grouping;real-time detection of removed objects based on multi-cue fusion;tabletop interaction system using plate position recognition by computervision;transfer network learning based remote sensing target recognition;optimum selection of common master image for multitemporal InSAR;real-time object detection based on the improved boosted features;an automated detection method of permanent scatterers in radar interferometry based on ICA;the small infrared target detection in complicated background based on adaptive morphological filter;a fuzzy-logic-based context model for multiscale segmentation fusion;and recognition of sand dredges in the Changjiang River based on ASAR remote sensing data.
Applications of machine vision for automated inspection and sorting of fruits have been widely studied by scientists and engineers. In these applications, edge detection, segmentation, and shape recovery are difficult...
详细信息
computervision techniques have been widely used in various applications. In recent years, as energy efficiency have gradually become a important issues, computervision techniques can be integrated into a smart contr...
详细信息
ISBN:
(纸本)9781424453306
computervision techniques have been widely used in various applications. In recent years, as energy efficiency have gradually become a important issues, computervision techniques can be integrated into a smart control system that helps increase the energy efficiency by controlling the turn on of the light based on human detection. However, implement such system that detect walking human in a semi-dark environment remains a challenge. This paper proposes a novel detection technique combining movement analysis and SVM classifier to tackle this problem. This technique consist of a few steps: a statistical background model to segment moving objects as foreground, followed by an analysis model to generate pedestrian candidates based on the movement of foreground objects and lastly a SVM classifier that verify the pedestrian candidates based on the shape features.
In this paper, we address the problem of human identification using gait. Considering the recent work of Lee et al. (Lee et al., 2007) proposed for gait recognition. First we will introduce the algorithm proposed by L...
详细信息
ISBN:
(纸本)9789898111692
In this paper, we address the problem of human identification using gait. Considering the recent work of Lee et al. (Lee et al., 2007) proposed for gait recognition. First we will introduce the algorithm proposed by Lee et al.. This method has two main steps: (1) extract key frames to define the gait cycle pattern, and (2) compute Shape Variation-based frieze patterns. These patterns are then used to classify and perform the gait identification. We modify the utilized features in this approach. We try to omit redundant features based on the effect of each feature on recognition rate and in next step, we improve performance of this approach by making some changes in way of feature extraction. Finally, we use the statistical characteristics of employed features instead of direct applying of remaining features. We test the proposed method on CASIA database. The experimental results are used to compare the proposed method with Lee et al. method.
High-definition 3D video is one of the features that the next generation of telecommunication systems is exploring. Real-time requirements limit the execution time of stereo-vision techniques to 40-60 milliseconds. Cl...
详细信息
ISBN:
(纸本)9783642102677
High-definition 3D video is one of the features that the next generation of telecommunication systems is exploring. Real-time requirements limit the execution time of stereo-vision techniques to 40-60 milliseconds. Classical belief propagation algorithms (BP) generate high quality depth maps. However, the huge number of required memory accesses limits their applicability in real systems. This paper proposes a real-time (latency inferior to 40 millisenconds) high-definition (1280x720) stereo matching algorithm using belief propagation. There are two main contributions. The first one is an improved BP algorithm with occlusion, potential errors and texture-less handling that outperforms classical multi-grid bipartite-graph BP while reducing the number of memory accesses. The second one is an adaptive message compression technique with low performance penalty that greatly reduces the memory traffic. The combination of these techniques outperforms classical BP by about 6.0% while reducing the memory traffic by more than 90%.
Under the support vector machine framework, the support value analysis-based image fusion has been studied, where the salient features of the original images are represented by their support values. The support value ...
详细信息
暂无评论