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The spatial geometrical features of the objects are widely used in the image matching and retrieval. The popular algorithms of the spatial feature detection are designed for gray images, which failed to make use of the color information in color images. However, the color information is absolutely nontrivial in discrimination of different objects, vacancy of which would lead to miss judgment. Based...
SIFT (Scale Invariant Feature Transform) has proved to be the most robust local invariant feature descriptor in object recognition and matching. Being designed mainly for the gray images, SIFT shows its vulnerability when deal with color images. To overcome this problem and increase the descriptor's distinctiveness, we introduce a new descriptor, a combination of the SIFT approach and the improved...
This paper presents a three-step framework to remove the highlight exists on objects in certain conditions. Unlike traditional HDR (High Dynamic Range) technology requires multiple registrated image sequence; our method needs only two arbitrary images. SURF (Speeded Up Robust Features) matching algorithm is first applied to find corresponding point pairs between images; homography is then found by...
Aiming at the cartoon industry which has tremendous applications, a cartoon scene matching system is designed using an improved matching algorithm which fuses color feature and CSIFT (Colored invariant feature transform) feature. The global color statistical feature is obtained by calculating the distribution of three parameters: H, S and I. The CSIFT global feature is calculated based on the color...
Matching images taken from widely different viewpoints is still an open problem being extremely challenging. In this paper is presented a new method that manipulates effectively the color in order to improve the matching performances of the well-known SIFT operator for the wide-baseline case. Without exploiting additional information, the algorithm employs a new model that preserves efficiently the...
In content-based image retrieval (CBIR), the apparent color of objects are strongly influenced by the illumination variation, and this may affect retrieval results adversely. In this work, we propose a framework which can find partial object matchings by using illumination invariant local features, and have achieved image retrieval robust to apparent color changes. Additionally, partial similarities...
This paper proposes a novel and effective method to match objects between multiple cameras, which is based on wavelet salient features consisting of color information and salient values at salient points. Learning templates are set up and updated based on salient features in one camera, and used to match objects in the other camera without any geometric constraints. Experiments show the effectiveness...
In this paper we propose a computationally efficient scale adaptive tracking method using a hybrid color histogram matching scheme. Firstly, we report an important property of the Chi-squared measure- It outperforms Bhattacharyya measure in the task of histogram matching from a few significantly similar multimodal histograms. Also, Bhattacharyya measure performs better while selecting matches from...
SIFT (scale invariant feature transform) is an important local invariant feature descriptor. Since its expensive computation, SURF (speeded-up robust features) is proposed. Both of them are designed mainly for gray images. However, color provides valuable information in object description and matching tasks. To overcome the drawback and to increase the descriptor's distinctiveness, this paper presents...
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