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This paper presents a robust motion detection technique using unit gradient vectors (UGVs). UGVs carry only gradient orientation information that is remarkably invariant to changes of image intensities. Hence, UGVs can be used as a stable textural feature in place of conventional image features intensities and gradients. We propose to detect motions in image sequences using the pixel-wise variations...
This paper shows that two conventional gradient-based motion estimation techniques can be significantly improved by using unit gradient vectors instead of image intensities. Since unit gradient vectors are insensitive to changing image intensities, the proposed motion estimation techniques are far more robust to irregular lighting conditions than the conventional techniques. In addition, the proposed...
This paper presents a technique for segmenting image sequences using the gradient structure tensor method (GSTM) and the self-organizing feature map neural network technique (SOM). GSTM accurately and robustly estimates motion vectors in an image sequence, while SOM classifies the estimated motion vectors in an unsupervised manner. Consequently, the segmentation of an image sequence is achieved. Simulation...
This paper describes a novel technique for motion estimation in image sequences using gradient orientation structure tensors (GOSTs). The technique differs from conventional motion estimation techniques using gradient structure tensors (GSTs) in that GOSTs are based on only gradient orientation information and independent of the gradient magnitude. Since gradient orientation is invariant to global...
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