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This paper proposes a novel vehicle color classification method which uses the concept of probabilistic latent semantic analysis (pLSA) to overcome the problem of sparse representation in data classification. Sparse representation is widely used and quite successful in many vision-based applications. However, it needs to calculate the sparse reconstruction cost (SRC) of each sample to find the best...
This paper presents a novel color correction technique for classifying vehicles under different lighting conditions using their colors. To reduce the lighting effects, a reference image is first selected for building the mapping function between the current frame and the reference image. With this mapping function, the color distortions between frames can be reduced to minimum. In addition to lighting...
This paper proposes a vision-based vehicle surveillance system for parking lot management in outdoor environments. Due to the limited field of view of camera, this system uses multiple cameras for monitoring a wide parking area. Then, an affine transformation is used for merging the scenes obtained from these multiple cameras. Two major components are included, i.e., vehicle counting and parking lot...
This paper presents a novel color correction technique for object identification across different cameras. First of all, we project the analyzed object onto the LAB color space and then find its principal color axis through the principal component analysis. Since the L axis corresponds to the intensity, we then rotate the found principal color axis for making it parallel to the L axis. After this...
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