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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...
We are focused on how to describe a common image point distinctively, make its descriptor concise and invariant to general image transformations. We use neighborhood pixel characteristics, including HSV color space, Gaussian-weighted gradient magnitudes and orientations, sampled in specific window around interest point to enhance the description. The enhanced point descriptor (EPD) is a covariance...
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...
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