Xie Hongmei

Regions of interest detection with edge and depth information

Traditional saliency map based regions of interest (ROI) detection often has the problems of not able to locate ROI accurately as human visual attention should be, and its ROI shifting usually exists similar problems. In this paper, two kinds of visual features which are edge and depth information are introduced during feature extraction to access more fitable saliency map to human vision perception, and the dilation operation is used to the binary saliency map to connect parts of one object together. Experimental results show that the modified method is more similar to human vision perception, and works better.

Mixed scheme for image edge detection based on Wavelet Transform

A mixed scheme based on Wavelet Transformation (WT) is proposed for image edge detection. The scheme combines the wavelet transform and traditional Sobel and LoG (Laplacian of Gaussian) operator edge-detection algorithms. The precise theory analysis is given to show that the wavelet transformation has an advantage for signal processing. Simulation results show that the new scheme is better than only using the Sobel or LoG methods. Complexity analysis is also given and the conclusion is acceptable, therefore the proposed scheme is effective for edge detection.A mixed scheme based on Wavelet Transformation (WT) is proposed for image edge detection. The scheme combines the wavelet transform and traditional Sobel and LoG (Laplacian of Gaussian) operator edge-detection algorithms. The precise theory analysis is given to show that the wavelet transformation has an advantage for signal processing. Simulation results show that the new scheme is better than only using the Sobel or LoG methods. Complexity analysis is also given and the conclusion is acceptable, therefore the proposed scheme is effective for edge detection.