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The exponential growth of web videos brings content based copy detection into a crucial issue. Besides the image information, audio also plays an important role in copy detection. In this paper, the audio-based copy detection framework is introduced. Three contributions are presented: (1) the band energy difference based feature is improved by adding multi-scale information, which extends the candidate...
This paper presents a feature recognition method based on randomized trees. We aim to improve the performance of Lepetit's work, whose actual results are very sensitive to large changes of viewpoint due to its limited ability of samples synthesizing and learning. We propose an approach to alleviate its limitation, which simulates the image appearance changes under actual viewpoint changes by applying...
This paper presents an evaluation of the SIFT (scale invariant feature transform), Colour SIFT, and SURF (speeded up robust feature) descriptors on very low resolution images. The performance of the three descriptors are compared against each other on the precision and recall measures using ground truth correct matching data. Our experimental results show that both SIFT and colour SIFT are more robust...
In this paper, we present a method that detects intracranial space-occupying lesions in two-dimensional (2D) brain high-resolution CT images. Use of statistical texture atlas technique localizes anatomy variation in the gray level distribution of brain images, and in turn, identifies the regions with lesions. The statistical texture atlas involves 147 HRCT slices of normal individuals and its construction...
A real-time monocular vision based rear vehicle and motorcycle detection and tracking approach is presented for lane change assistant (LCA). To achieve robustness and accuracy this work detects and tracks multiple vehicles and motorcycles on road by combining multiple cues. To achieve real-time multi-resolution technology is used to reduce computing complexity, and all algorithms have been implemented...
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