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Appearance based person re-identification in real-world video surveillance systems is a challenging problem for many reasons, including ineptness of existing low level features under significant viewpoint, illumination, or camera characteristic changes to robustly describe a person's appearance. One approach to handle appearance variability is to learn similarity metrics or ranking functions to implicitly...
This paper introduces an effective active contour model for texture segmentation. To improve the robustness against noise and illumination, a novel descriptor named local statistical variation degree (LSVD) is presented to express textural features, which uses corner point deletion and isolated region detection operations to eliminate image patches unrelated with object regions. And then the fused...
Global illumination methods are used to simulate lighting in 3D scenes. They provide a progressive convergence to high quality photo-realistic images as proved by Monte Carlo theory. One of the problem of such methods is to determine a stopping condition in order to decide if the computation reaches a satisfactory convergence which allows the process to terminate. In this paper, an inductive learning...
The methodology for finding the same individual in a network of cameras must deal with significant changes in appearance caused by variations in illumination, viewing angle and a person's pose. Re-identification requires solving two fundamental problems: (1) determining a distance measure between features extracted from different cameras that copes with illumination changes (metric learning); and...
Due to the lack of classification accuracy in pattern recognition, in this paper we propose a new algorithm for pattern analysis based on the symbolic pattern method. Proposed algorithm is constructed by using symbolic method and finite state automata model and used for classifying the textures based on the patterns. This algorithm performs symbolization of the data and portioning the texture images...
Local ordinal signal relations, such as local binary or ternary patterns (LBP/LTPs) are invariant to frequent in practice spatially variant contrast/offset deviations that preserve image appearance. Our prior work extended this conventional LBP/LTP-based classifiers towards learning, rather than pre-scribing characteristic shapes, sizes, and numbers of such patterns. The learned LTPs showed more accurate...
A new method for recognition of content-less characters in low quality images using the phase congruency and local energy model is proposed. New phase features invariant to nonuniform illumination and slight geometric distortions are obtained. With the help of computer simulation the performance of the proposed method for character recognition in degraded images is presented and compared with that...
Apart from wearable sensors and floor sensors, remote fall detection systems can be realized using camera sensors and computer visions methods and this visual based system is accurate, non-intrusive and capable to perform post fall event analysis with the recorded video. To implement visual based fall detection, the foreground segmentation process is crucial in order to provide the right foreground...
This paper presents an approach for reidentification based on appearance. The person re-identification is recently introduced and yet an unsolved problem in computer vision. Re-identification refers to identify an individual who has already been observed by different cameras. The appearance of an individual in different cameras looks unlike due to illumination variations and arbitrary pose alternations...
The classical mean shift algorithm is easy to pass into local maxima, which is caused by the lack of appropriate target model updating mechanism. In this paper, a SIFT-based mean shift algorithm is proposed, which can be used for continuous vehicle tracking in complex situations, such as the shape and the illumination of the vehicle object change. In our algorithm, the mean shift algorithm is utilized...
In this paper, we propose a new visual discomfort prediction method for stereoscopic 3D contents. Our features are computed from stereoscopic 3D video such as disparity, motion, contrast, spatial complexity of salient objects and brightness and binocular asymmetries degree between left and right image in a 3D scene. The salient object is detected by region based multimodal information such as color,...
In this study, we develop a novel vision-based Kodály musical hand signs recognition system to recognize the gestures of the musical notes. Vision-based gesture recognitions often face the following problems. First, the illumination change can influence the hand detections. Second, the hand tracking will become difficult under the complex background. To overcome the aforementioned problems, we propose...
State-of-the-art object detectors typically use shape information as a low level feature representation to capture the local structure of an object. This paper shows that early fusion of shape and color, as is popular in image classification, leads to a significant drop in performance for object detection. Moreover, such approaches also yields suboptimal results for object categories with varying...
SIFT (Scale-invariant feature detection) feature has been applied on image registration. However, how to achieve an ideal matching result and reduce the matching time are the most important steps that we study in our work. The original SIFT algorithm is famous for its abundant feature points, but the final keypoints are so excessive that the matching speed is very slow at the next step of searching...
Augmented reality has been a topic of intense research for several years for many applications. It consists of inserting a virtual object into a real scene. The virtual object must be accurately positioned in a desired place. Some measurements (calibration) are thus required and a set of correspondences between points on the calibration target and the camera images must be found. In this paper, we...
Accurate moving objects segmentation is an essential problem in intelligent video surveillance system. However, the existence of unexpected moving cast shadows frequently lead to errors in further scene analysis. This paper presents a novel method that combines color space and corner feature to detect and remove cast shadows of moving vehicles in traffic scenes. The two features cooperate well to...
A photograph that has visually dominant subjects in general induces stronger aesthetic interest. Inspired by this, we have developed a new approach to enhance image aesthetics through saliency retargeting. Our method alters low-level image features of the objects in the photograph such that their computed saliency measurements in the modified image become consistent with the intended order of their...
Lip contour extraction is crucial to the success of a lipreading system. This paper presents a lip contour extraction algorithm using localized active contour model with the automatic selection of proper parameters. The proposed approach utilizes a minimum-bounding ellipse as the initial evolving curve to split the local neighborhoods into the local interior region and the local exterior region, respectively,...
This paper presents a new approach of image decomposition for underwater target detection by inhomogeneous illumination based on G-Space and Partial Differential Equation (PDE). Underwater target images with high contrast visibility (less back-scattering) can be obtained within the inhomogeneous illumination field which power density is allocated inversely propotional to the rule of the light attenuation...
In this work we present a novel idea of evaluating natural feature-point based tracking targets. Our main objective is to evaluate the inherent characteristics of natural feature-point sets with respect to vision-based pose estimation algorithms. Our work attempts to break new ground by concentrating on evaluating complete tracking targets, rather than evaluating tracking methods or single features...
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