Research On The Key Technology Of Image Processing In Visual Aid

Visual navigation aid technology substitutes computer vision for human eyes toperceive the surrounding environment, then extracts and understands interestedtargets, and accomplishes the corresponding visual task. At present, visual navigationaid has already been widely used in visual navigation aid for the blind, visualnavigation for vehicles, intelligent monitoring and other fields. Moreover, it has alsobeen applied in military, aerospace and other fields.The key technology of visualnavigation aid in image treatment includes image enhancement, target detection,target tracking, target identification and so on. There are some outstandingalgorithms for each of these treatments, however, problems still exist. For example,when large visual information occurs in complicated environment, how to extractclient-interested targets accurately and quickly, establish visual treatment channel,and compute the interested area in the information set; when the targets are shaded,blocked, rotated and there are similar targets, the present methods are hard tocomplete the tracking and identification tasks; under terrible weather condition,image quality reduction will also provide difficulty for the subsequent visual tasks.This paper made deep study on the key points of visual navigation aid, such asimage enhancement, target detection, target tracking, and target identification. Itcombined human visual properties to extract the interested targets in the complicatedenvironment, and fast enhance the halo and non-uniformed illumination imagesbased on physical model. Then, overall and local characteristics were used toeffectively identify the targets even when they were rotated or deformed. Andparticle filter technology was adopted to complete the tracking task even when therewere similar targets or the targets were blocked. Finally, DSP design of DaVinciseries was used to form a set of spacial non-cooperative target measuring system,which could be applied in fields such as missile defence, and ocean monitoring dueto its small size and high reliability.The main achievements of this paper are as follows:1. It studied human visual attention mechanism. Human visual system usesvisual attention mechanism to handle huge visual information and make timelyresponse. This paper raised a method to establish target detection and visualinformation handling channel based on human visual attention mechanism. It firstlyanalyzed human visual characteristics, which laid theoretical basis for visual attention mechanism to introduce target detection; Secondly, it extracted color,luminance and direction information from the images to build feature vectors,combined them with binocular stereo visual motion vectors of depth information andoptical flow field, and established saliency map by fusing saliency maps of eachchannel through information theory. Then, salient targets could be more effectivelyextracted from the video through saliency map to build visual information treatmentchannel. Interested targets stayed inside the channel, while outside were backgroundinformation. This laid basis for the subsequent target detection and tracking.2. When comes to the quality-deducted images due to terrible whethercondition, the current methods are slow in computation and the results are withobvious halo. To solve the problems, this paper raised an image enhancementalgorithm based on bilateral filter. As for foggy images, and according toatmospheric physics scattering model, this paper eliminated the influence of whitetargets in the ambient light to the scene restoration through grayscale-erosion-dilationoperation; then used fast joint bilateral filter algorithm to quickly and accuratelywork out atmospheric veil V, gained transmissivity t through V, which avoided thehalo artifacts possibly resulted by ordinary bilateral filtering. As for non-uniformedillumination images, this paper combined light reflection imaging model with objectRGB color channel reflection characteristics to raise a bright channel concept, andgave the computation method of illumination components from an analytical point ofview. Accurate illumination components were gained through fast joint bilateral filteralgorithm. Finally, target RGB channel reflection coefficient was solved by lightreflection imaging model. Experiments proved that the images restored by thisalgorithm were full of details, natural, vivid, and clearly visible. Compared withother enhancement methods, the speed of this algorithm was accelerated, which wasbeneficial for the hardware implementation.3. According to visual physiological and psychological studies, human visualsystem relies on attention selection mechanism, which combines with visualmemories to fast comprehend scene contents. According to visual perceptionmechanism, this paper combined overall characteristics with local ones to identifythe targets and comprehend scene. Gist vector can quickly gain overall scene keypoint semantics from a scene image, but it cannot extract feature characteristics ofspecific targets in the scene image. Visual attention mechanism extracted target3D-SIFT feature points and combined them with Gist overall information aftersegmenting target, and trained feature vectors by supporting vector machine (SVM). Experiments proved that this method had gained excellent results in targetidentification and scene comprehension.4. When there are multi-similar-targets, tracking results of particle filter willdiverge and tracking kernel function window width is fixed. To solve the problems,this paper raised a particle filtering method based on vague C mean value clustering.It made non-linear, non-Gaussian system target state estimation as mainline, usedvariable ellipse as particle region, gained clustering center of each target throughmean-shift algorithm after particle importance resampling. Then, FCM clusteringalgorithm was used to complete particle clustering and get corresponding targetparticle subblock. At last, the finally state of each target was estimated throughparticle subblock and kernel window width was corrected. Experiments proved that,compared with traditional particle filter algorithm, this algorithm solved thedivergent problem existed in traditional particle filter, and reduced particle quantity.It can real-timely and accurately track multi-targets even when the targets are rotated,blocked or changed in scale.5. This paper synthesized technologies such as video coding and decoding,network communication, target detection, and stereo visual measuring, designed andrealized a set of non-cooperative target position measurement system. This systemextracted, tracked, and accurately positioned the interested region based on Houghtransform linear characteristics; then made fast stereo match and3D coordinatecalculation according to feature point coordinates, established target coordinatesystem, and enhanced the accuracy of relative pose calculation by using RANSACalgorithm. This system used DM6467T core chip of DaVinci series as hardware, usedmodular design in the peripheral circuit. Simulative semi-physical simulationexperiment of non-cooperative spacecraft autonomous rendezvous proved that therelative position measuring accuracy of this algorithm was within±20mm, relativeattitude measuring accuracy was within±2°, and the measuring speed reached10fps. The advantages of this system were: embedded design, hardware real-timedetection and tracking, small in size, low power consumption, it can be applied infields such as spacecraft docking, ocean and battle field monitoring, and visualnavigation.