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Recently, very deep two-stream ConvNets have achieved great discriminative power for video classification, which is especially the case for the temporal ConvNets when trained on multi-frame optical flow. However, action recognition in videos often fall prey to the wild camera motion, which poses challenges on the extraction of reliable optical flow for human body. In light of this, we propose a novel...
Since Mean Shift algorithm can not track multiple objects, a full automatic multi-object tracking algorithm based on improved Mean Shift is proposed. The background subtraction image kernel density estimation algorithm is used to detect the foreground. The extracted moving objects are used as candidate template to eliminate the influence of background. By adopting object matching based on distance...
This paper proposes a promising palmprint image enhancement algorithm. Under the constraint that keeps original characteristic of palmprint, the method improves contrast of images, in order to extract features from palmprint by Scale Invariance Feature Transformation (SIFT) descriptor. However, the existing image enhancement algorithms can hardly yield SIFT feature from low resolution palmprint images,...
This paper investigates the possibility that uses Scale-Invariance Feature Transform (SIFT) feature for face identification. However, it is impossible to employ these SIFT keys,i.e. feature vectors, for identification directly, due to the space incompatible of such SIFT keys. To this end, the Bag-of-words (Bow) vector quantization introduced from scene or text classification is conducted for unifying...
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