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The classical mean-shift tracking algorithm is based on histogram of colors, which is vulnerable to light change. In order to overcome the drawback, we presents a new metric used for tracking. Firstly, we compute the curvature property of an image and choose scale through maximizing the second derivative in horizontal direction of points in the inner elliptical region on the target. Then we compute...
Visual tracking is a hot issue in computer vision, and visual target modeling plays an important role in the robustness and effectiveness of tracking system. In order to adapt target's observation to the complex tracking environment, this paper proposed a multi-cue integration method, which extracts color, edge, and texture features using histogram method, and integrates them adaptively. By making...
In this paper, we present the fusional feature composed of Affine-SIFT, MSER and color moment invariants. The fusional feature is more robust and distinctive than a single local feature. Instead of adding three local features together simply, an efficient two-level matching strategy is devised with the fusional feature, which speeds up the establishment of the local correspondences. To remove partial...
Illumination-robust optical flow algorithms are needed in numerous machine vision applications such as vision-based intelligent vehicles, surveillance and traffic monitoring. Recently, we have proposed an implicit nonlinear scheme for variational optical flow that assumes no particular analytical form of energy functional and can accommodate various image components and data metrics. Using test data...
Finding reliable correspondence in two or more images remains a difficult and critical step in many computer vision tasks. The performance of descriptors determines the matching results directly. Compared with other descriptors, the Scale Invariant Feature Transform (SIFT) has been used widely for its superiority in invariant attributes, while it will fail in the case of locally visual aliasing. To...
In this paper, a novel scene retrieval method has been proposed. Under this framework, homogeneous color regions are detected and then described by color moment invariants. Different from most existing methods, color and spatial information are combined as the uniform descriptor to capture the image property. Moreover, the image descriptor in our method has alterable data structure, whose size is...
Identifying moving objects in video sequence is fundamental and important task in visual tracking systems and computer vision applications. Background removal algorithms are usually used to separate the foreground from the background. Despite the existence of many background removal algorithms, they didn't solve some problems such as cast shadows, highlighting and ghost effect. In this paper we propose,...
Analysis of activities in low-resolution videos or far fields is a research challenge which has not received much attention. In this application scenario, it is often the case that the motion of the objects in the scene is the only low-level information available, other features like shape or color being unreliable. Also, typical videos consist of interactions of multiple objects which pose a major...
Kernel tracking of density-based appearance models is implemented in this paper for real-time object tracking applications. First a ROI, i.e., the region of interest is selected in real-time to create a model. Then the matching and locating of the search object is achieved by using mean-shift algorithm. Experimental results show that this method can find perform object tracking with adaptation to...
Reconstructing 3D shapes from 2D images based on structured light is becoming an increasingly important topic in computer vision. However, low resolution and sensitive to environment illumination are the main restriction of this technology for practical application. This paper proposes a new color coded structured light technique for reconstructing object shape from a single image. This technique...
Target recognition and tracking is a tough task in computer vision. How to recognize a target accurately and tracking robustly is still a core problem studied by researchers. And the task is even more challenging in outdoor environment. In this paper, a recognition and tracking algorithm suitable for a natural target in outdoor environment is introduced. The natural target we choose is the overhead...
Natural image matting is an interesting and difficult problem of computer vision because of its under-constrained nature. It often requires a user interaction, a trimap, to aid the algorithm in identifying the initial definite foreground and background regions. Current techniques use local or global image statistics of these definite regions to estimate the alpha matte for the undefined region. In...
Adding high-density information to printed materials enables and improves interesting hardcopy document applications involving security, authentication, physical-electronic round tripping, item-level tagging, and consumer/product interaction. This investigation of robust and high capacity print codes aims to maximize information payload in a given printed page area, subject to robustness to channel...
Partial occlusion is a difficult problem in computer vision since whether the object is changed or occluded is ambiguous, especially when distinguishing it only from the object boundary. In this paper, we proposed a novel idea to solve this problem by taking shape matching as a morphing processing. A mass-spring model is constructed from the point set which is sampled from a template (or reference)...
We propose a vision-based method that robustly extracts hands from backgrounds irrespective of illumination conditions. Many hand tracking systems build a skin color model before the system runs and then they track hands by using the color model. However, the system is unstable because the pre-defined colors cannot be adapted to various illuminations and human skin colors. To circumvent the problem,...
Aiming at the problem of real time and robustness of visual object tracking in a clutter background, an adaptive fusion of color rectangle feature and edge strength local mean tracking algorithm based on particle filter is put forward. To improve the tracking speed and precision, integral image is used to quickly compute the color rectangle feature and edge strength local mean, besides fuzzy logic...
Motion detection is a fundamental and important part of many visual tracking systems and of other computer vision applications. In this paper, we present a robust and effective motion detection method for detecting foreground moving objects even with quick lighting changes and shadows in scenes. The proposed approach integrates YCbCr color features with texture by a fuzzy method based on Choquet integral...
Correspondence point matching is one of the necessary jobs in computer vision and it is not easy to find correspondence point in various environments, like environments with changing scale, rotation, view point and illumination. SURF algorithm which based on local geometric feature works faster than SIFT while maintaining matching performance. However, SURF algorithm can only use gray level information...
The traditional color-based mean shift tracking algorithm is unable to accurately track the object. To address the problems, this paper presents an improved tracking algorithm. The improved tracker integrates the color and motion cues which characterize the appearance and motion information of object respectively. During the tracking process, this integration strategy can dynamically adjust the weight...
This paper introduces a real-time method for tracking hands through image sequences. Our method combines efficiently calculated color likelihood maps with a modified version of the maximally stable extremal region (MSER)-tracker. The proposed algorithm allows to robustly track hands through image sequences and additionally provides accurate hand segmentations per frame. Experimental evaluation proves...
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