Research On Degradation Invariant In Robot Vision

Mobile robot has been more and more widely applied in life, defense and scientific exploring, and the vision system is an important implement technology of its intelligence. In outdoor unstructured environment, image degradation as a popular phenomenon is focused on in vision system in the dissertation. Degraded image quality measure is studied firstly, and then image restoration technoloty is systematically analyzed, based which image degradation invariant is introduced, analyzed and applied. The main contents and innovations are as follow:1. The research history of robot vision is reviewed and the importance of image degradation and the limitation of research on it is pointed out.2. The type of image degradation in unstructured environment is analyzed, and the definition of image degradation is extended to geometric degradation besides traditional photometric degradation, including image noise, image motion blur, illumination change and shadow, and camera distortion and space transformation, according to which the definition of image quality measure is extended to different image size. Single value decomposition and weighted region of interest based image quality measure method is proposed, whose consistency with human vision system is then proved and analyzed by experiments.3. The definition of image restoration is extended to restoration of geometric degradation besides traditional restoration of photometric degradation. Then restoration algorithm of different degradation is systematically analyzed, and both the advantages and disadvantages are analyzed and evaluated by experiments, concluding that the accuracy of each restoration algorithm is finite and prior knowledge of degradation type is needed, or some unexpected results will be produced. Consequently the concept of image degradation invariant is proposed, which is the feature invariant to all kinds of image degration. So the problems in image restoration are avoided, and the degraded images are direct processing objects in vision system.4. The construction of image degradation invariant is analyzed. The photometric degradation invariance of some classic geometric invariants is analyzed, and the experiments show that interest point of Harris-Affine and Hessian-Affine and SIFT invariant can be viewed as image degradation invariants. Since image motion blur occurs very often, image motion blur invariant is specially proposed, and some motion blur invariants obtained by experiments are compared, and the applying condition of each of them is proposed.5. The implement of SIFT invariant is analyzed and optimized, which decreased the computation time. SIFT based bidirectional matching algorithm is proposed, which improved the accuracy of image matching results.6. The effects of image degradation on stereo matching and object tracking are analyzed by expriments, and image degradation invariant theory is applied to solve the problems. The expriments demonstrate the effectivity and engineering value of image degradation invariant proposed in the dissertation. What image degradation invariant applied to engineering application should be improved is also proposed.The research is supported by project of National Natural Science Foundation"vision/ mechanics/ displacement combined detection and control technology for the intelligent micro mechatronic system"(Project No. 50275078).