Research And Application Of Image Correspondence Constraint

Correspondence constraint is the hot issue in computer vision. Most existing corre-spondence constraint methods focus on the similar objects in multiple images, and theyare often computationally demanding. In this paper, we study the theory of correspon-dence constraint, demonstrate potential usages of the correspondence constraint. On onehand, we discover the correspondence constraint between the multiple images, and pro-mote its performance. On other hand, we extend the idea of correspondence constraintinto the single image processing, and generate the relation corresponding between themultiple regions and processing sources of the single image. Our paper includes:1) Correspondence constraint between the multiple images: we employ the cluster-ing to generate the correspondence constraint between the multiple images. We introducea new cluster-based algorithm for co-saliency detection, which is based on laws of thevisually salient stimuli and correspondence constraint. Global correspondence betweenthe multiple images is implicitly learned during the clustering process. Three visual at-tention cues are devised to efectively measure the cluster saliency. The advantage ofour method is mostly bottom-up without heavy learning, and has the property of beingsimple, general, efcient, and efective. Experimental results demonstrate the advantagesof the proposed method over the competing co-saliency methods. Our method on sin-gle image also outperforms most the state-of-the-art methods. Furthermore, we applythe co-saliency method on four applications to demonstrate the potential usages of theco-saliency map.2) Correspondence constraint between the multiple regions in the single image: weemploy the geometry structure to build correspondence constraint between the multipleregions in the single image. We take the geometry structure constraint into the foregroundextraction, and propose a novel geometry constraint segmentation method for extractingthe foreground. Firstly, the geometry foreground map is used to represent the geometrystructure of the image, which includes the geometry matching magnitude and the fore-ground location prior. Then, the geometry constraint model is built by introducing thisgeometry structure into the graph-based segmentation function. Finally, the segmenta-tion result is obtained via graph cut. Moreover, our geometry constraint segmentation isalso extended to the weak geometry object under a part-based framework. Experimentsdemonstrate that the high-level property of geometry constraint significantly improves thelow-level segmentation results. 3) Correspondence constraint between the multiple processing sources of the singleimage: we build the correspondence constraint between the single image and lens dis-tortion/multiple processing layers, and propose a novel forensic method for detecting theforgery object. We employ the radial distortion as the intrinsic property of the lens, whichcould ofer a global constraint. A modified spherical projection model is adopted, whichis equivalent to the other captured rays-based models of the fisheye lens with only onefree parameter. In this model, the straight world line is projected into a great circle onthe viewing sphere, which provides a unique geometric constraint. Two saliency measurecues are provided to compute the untrustworthy likelihoods of the candidate lines. Finally,a fake saliency map is obtained according to the untrustworthy likelihood to segment thefake region.Above all, we find out that the correspondence constraint is not only valid on themultiple images, but appears in the single image processing, which could be providedby multiple regions and processing sources. This correspondence constraint breaks thelimited of single image, which improves the performance of image processing, providesthe new semantics structure, and ofers the efective reference.