Multi-view cosegmentation for the same object is the basis of true three-dimensional imaging. Due to changes in the foreground and interferences from the background of the images, traditional cosegmentation algorithms often cannot fully and effectively extract common areas. To solve this problem, in this paper,we propose a new image cosegmentation algorithm which incorporates the minimum fuzzy divergence and active contours model.Considering the foreground similarity and background consistency between multiple images,the energy functions of images are generated. We lead color information covered by an image into the energy function of another image to enhance the robustness of curve evolution.Then we minimize the energy function value via the minimum fuzzy divergence. The experimental demonstrate that the proposed method can effectively segment the common objects from multi-view image pairs with generating lower error rates than that of traditional cosegmentation methods.
Compared to traditional integrated imaging, the one-dimensional integrated imaging system abandons the stereoscopic effect in the vertical direction, leaving only the stereoscopic effect in the horizontal direction. While satisfying the stereoscopic perception, it greatly reduces the storage space of data and alleviates the large attenuation of resolution caused by integrated imaging. However, at present, there is no 3D film source suitable for a one-dimensional integrated imaging system. In order to address this problem, we propose an array image generation and padding algorithm based on a one-dimensional integrated imaging system. First, based on the theory of geometric optics and DIBR(Depth-image Based Rendering), we use depth maps to simulate viewpoint images at arbitrary locations. In the process, we classify the points mapped to the viewpoint image to make the generated viewpoint image more accurate. Secondly, when conducting hole filling, we first use the optical flow method to fill the large holes inside the image. We extract the edge of the hole, compare the depth values on both sides of the edge, and estimate the depth value of the hole by taking the optical flow value on the larger side, so as to calculate the mapping block of the hole on the reference frame. Finally, the other holes are filled using the Criminisi image restoration algorithm.
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