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Although light field data provides abundant cues for depth estimation, light field depth estimation suffers from occlusion and uncertain edges. In this paper, we propose occlusion robust light field depth estimation using segmentation guided bilateral filtering. First, we calculate refocused images from light field data using digital refocusing. Second, we perform support vector machines (SVM) classification...
Gait recognition plays a very vital role in many practical applications of computer and robot vision in smart environments such as health care for elderly using smart home technology. Hence, it has been attracting considerable attentions from many machine vision researchers in last decades. In this paper, we propose a novel method for depth video-based gait recognition using robust features and deep...
Detecting fraudulent users in online social networks is a fundamental and urgent research problem as adversaries can use them to perform various malicious activities. Global social structure based methods, which are known as guilt-by-association, have been shown to be promising at detecting fraudulent users. However, existing guilt-by-association methods either assume symmetric (i.e., undirected)...
The challenge in blind image deblurring is to remove the effects of blur with limited prior information about the nature of the blur process. Existing methods often assume that the blur image is produced by linear convolution with additive Gaussian noise. However, including even a small number of outliers to this model in the kernel estimation process can significantly reduce the resulting image quality...
A method is proposed for estimation of occluded space and generation of auxiliary points for 3D position estimation of strongly occluded objects. First, occlusion space detection calculates 3D keypoints at the rear side of a target object, thus obtaining a silhouette around the object on the near side, as found from a camera image by an object detector. The method calculates the space containing the...
In this paper, a robust level set method is proposed for image segmentation. Traditional level set methods are sensitive to noise in images which greatly limits its application in real project. To overcome this shortcoming, the fractional order regularization and Markov random fields term are incorporated into the traditional level methods in this paper. The fractional order regularization can reveal...
With the growing number of automated welding systems present throughout manufacturing, achieving high precision is naturally a key objective. The alignment of weld tip to weld seam, particularly in very long welds (such as in pipes), is a technical challenge in which computer vision has much to offer. This paper introduces a real-time methodology for weld-seam tracking. The key challenge associated...
To overcome visual object tracking challenges, various feature-based object trackers use feature combination. Each feature component is developed to overcome certain tracking challenges, but the interaction between the components may cause tracking errors. We propose a tracking solution based on human vision principles to reduce combination errors by adaptively fusing each feature using its previous...
In order to remove the false edge points extracted by the Canny operator in etched character recognition, a method based on the simplified neighborhood feature and AdaBoost algorithm was proposed. In conventional neighborhood feature, canny edge points are taken as the center and neighborhood pixels are extracted as the feature. However, the dimension of the neighborhood feature rises with the square...
Deblurring images with outliers has attracted considerable attention recently. However, existing algorithms usually involve complex operations which increase the difficulty of blur kernel estimation. In this paper, we propose a simple yet effective blind image deblurring algorithm to handle blurred images with outliers. The proposed method is motivated by the observation that outliers in the blurred...
Handwritten document images are a special class of document images having a lot of variations in font-style, font size, thickness and interspacing of words and lines. Designing an effective fragile watermarking scheme to protect such images needs capturing both structure and information content which is an open research challenge. A novel contourlet based fragile watermarking technique for optimal...
The main issue in the digital content marketplace is copyright protection of digital content. Sometimes the digital information may copy or alter during transmission. Thus, there is a need to develop an authentication method, which is very effective to protect the digital data. There are different watermarking techniques, which use different transforms. In this paper, a Discrete Wavelet Transform...
This paper introduced a watermarking method using the CRT and Canny Algorithm that able to improve the imperceptibility of watermarked image and preserving the robustness of watermark image as well. The classical CRT algorithm is spread the watermark bits evenly on the image area. It causes significant degradation when the embedding location lies on the least significant region or in the homogeny...
The goal of this article is to analyze the assurance of permissible quality indices in an interval system through the construction of the edge route and the use of D-partition method. There were obtained conditions for construction of D-partition domains on one and two edges of one face. On the basis of these conditions the technique for assurance of the permissible degree of robust stability and...
Rain deteriorates outdoor vision and causes challenge for most vision based intelligent systems. In this paper we propose a method to efficiently remove the rain present in light field data. Firstly, the sub-view image sequence is globally aligned to the central view. Robust Principle Component Analysis (RPCA) are then applied to decompose the sequence into two parts, i.e., the low-rank data, and...
This paper presents BIND (Binary Integrated Net Descriptor), a texture-less object detector that encodes multi-layered binary-represented nets for high precision edge-based description. Our proposed concept aligns layers of object-sized patches (nets) onto highly fragmented occlusion resistant line-segment midpoints (linelets) to encode regional information into efficient binary strings. These lightweight...
In this paper, we introduce robust and synergetic hand-crafted features and a simple but efficient deep feature from a convolutional neural network (CNN) architecture for defocus estimation. This paper systematically analyzes the effectiveness of different features, and shows how each feature can compensate for the weaknesses of other features when they are concatenated. For a full defocus map estimation,...
Intensity inhomogeneity often occurs in real images. Local information based level set methods are comparatively effective in segmenting image with inhomogeneous intensity. However, in practice, these models suffer from local minima and high computational cost. In this paper, a novel region-based level set method based on Bregman divergence and local binary fitting, hereafter referred to as Bregman-LBF,...
Structure-from-Motion (SfM) methods can be broadly categorized as incremental or global according to their ways to estimate initial camera poses. While incremental system has advanced in robustness and accuracy, the efficiency remains its key challenge. To solve this problem, global reconstruction system simultaneously estimates all camera poses from the epipolar geometry graph, but it is usually...
The detection of spatially-varying blur without having any information about the blur type is a challenging task. In this paper, we propose a novel effective approach to address this blur detection problem from a single image without requiring any knowledge about the blur type, level, or camera settings. Our approach computes blur detection maps based on a novel High-frequency multiscale Fusion and...
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