Research On Visual Attention Models And Application Imagery Processing

With the development of bionics, many researchers in the computer vision have developed many novel machine algorithems in terms of outcomes from the research of neuroanatomy and visual neurophysiology. By simulating the characteristcs of human vision, some novel computer vision models are proposed. Visual attention models, which are included in these computer vision models, are proposed by simulating the bottom-up phase of human vision. They can be used to detect important objects which attract human eye in scene.In visual attention models, one classic and representative model is Itti model [8]. This model can process a scene image to generate a saliency map in which some objects, which attract human eye and are named as saliency objects, in the scene image are popped out. As well known, the reason why the saliency map from Itti model can pop out saliency objects was just explained in terms of the viewpoint of biologically-plausible, which results in an obstacle that it is hard to understand the real nature of Itti model. In order to find the real nature of Itti model, we analyse the model in detail from the viewpoint of image processing and mathmatics. Based on the analysis, we find the reason why Itti model can pop out saliency objects and propose some new ways to generate saliency map. These new ways and theory analysis are described as follows.(1) In Itti model, Gaussian pyramid is used to generate intensity conspicuity map. In our research, an interesting phenomenon is discovered. The phenomenon is that all of low-pass pyramids, including Gaussian pyramid, average pyramid, and wavelet pyramid generated by using the low-pass part of wavelet transform, can be used to generate intensity conspicuity map. Furthermore, these intensity conspicuity maps from low-pass pyramids are very similar to each other. As well known, the reason why intensity conspicuity map from Itti model can pop out saliency objects was just explained in terms of the viewpoint of biologically-plausible, which results in an obstacle that it is hard to understand the real nature of the intensity conspicuity map. In this paper, intensity conspicuity map from average pyramid is analyzed in detail from the aspect of image processing. The reason why the regions that have high intensity contrast can be popped out in the intensity conspicuity maps is explained. Meanwhile, the reason, why the conclusion from analyzing the intensity conspicuity map from average pyramid can be seen as the conclusion of the intensity conspicuity maps from all of low-pass pyramids, will be explained briefly.(2) Orientation conspicuity map is an important element in forming saliency map. Here, we discover other four ways which can be used to generate orientation feature maps besides the way used in Itti model. The orientation feature maps from these ways are similar to each other. We analyze these ways of generating orientation feature maps from the viewpoint of image processing. Based on the analysis, we find that the regions having high intensity contrast can be popped out in orientation conspicuity map.(3) We abstract three requirements which are used to ensure that the orientation conspicuity map from Gabor filter can be used to saliency detection. In addition, besides the three requirements, we add a modified requirement. If a new function satisfies the modified requirement besides the three requirements, the new function would be superior to the Gabor fitler when they are used to generate orientation conspicuity maps. Based on the theoretical analysis for orientation conspcuity map from Gabor fitler and four requirements, we propose three new functions which can be used to generate orientation conspicuity maps. The orientation conspicuity maps from two of three new functions will be better than the orientation conspicuity maps from Gabor fitler when they are used to generate orientation conspicuity maps.(4) Based on the theory analysis for orientation conspicuity map from Gabor filter, we propose two new ways to generate orientation map and analyse an existing saliency model. A new saliency model is based on wavelet transform. The other is based on phase spectrum of color information.(5) Color conspicuity map is an important component in the process of forming saliency map. In this paper, we study the way of generating color feature map and find that it is similar to the way of generating intensity feature map. Therefore, all of the low-pass pyramids used in generating intensity feature map can be applied to color feature map. Because in intensity feature map and orientation conspicuity map all the salient regions describe the intensity contrast between object and background, the method of generating orientation conspicuity map can also be used to generate color conspicuity map. Itti model has the merit of robust to noise. We analyze the model and discover that the robustness comes from the operation that all of the feature maps under different scales are resized to a same scale (σ=4). Further, we verify the theoretical analysis of the two aspects of saliency map studied in this paper by experiments.(6) A novel technique based on visual attention and context-sensitive is proposed for noise reduction in unsuperivised change detection. The technique is composed of two steps. The first step is that the intensity conspicuity maps algorithm of Itti model is used to process the difference image produced by comparing images acquired on the same area at different times. And a comparison map is produced. The second step is as follows: Bayes rule is used to distinguish the changed pixel in the comparion map. A changed detection map is made. Then, Markov Rondom Fields model is used to process the changed detection map. And the false changed pixels are removed. Experimental results confirm that the model can still detect the changed areas exactly when the noise intensity value in the images acquired on same area at different time is very large.Furthermore, a novel edge-grouping model is proposed in this paper. Edge-grouping belongs to visual attention. Most of existing edge-grouping models only detect the boundaries with closure, good continuation, proximity, convex and symmetry. In the poposed model, the boundaries of parallelism structure can be detected. This model is applied to airport detection. The accuracy of this model for airport detection is attractive.