In this paper, we introduce a biologically-motivated model to detect image saliency. The model employs an isophote based operator to detect potential structure and global saliency information related to each pixel, which are then combined with integral image to build up final saliency maps. We show that the proposed model outperforms seven state-of-the-art saliency detectors in experimental studies.

In this paper, a spatio-temporal error concealment method of transmission errors for improving visual quality over the wireless channel is proposed, which makes use of geometric information extracted from the surrounding blocks. The geometric information is an isophote that is curves of constant intensity of image. To improve visual quality during video communication, the proposed method smoothly connects the isophotes disconnected due to transmission error using a genetic algorithm (GA) with an isophote constraint. In the proposed method, the error concealment problem is modeled as an optimization problem, which in our case, is solved by a cost function with isophotes constraint that is minimized using a GA. Experimental results shows more visually realistic than other error concealment methods.