Study Of Rehabilitative Robtics Object Recognition Method Based On Local Image Feature

In order to assist people with disabilities to complete a variety of behaviors in daily life, rehabilitation robot perceives the external environment through vision system and sends the visual information back to its control system to realize navigation and positioning or control its mechanical arms to recognize and manipulate the object. Object recognition mothod is the key issue with regard to rehabilitation robot perceives the external environment information and manipulate. Object features extraction and matching is a key technology of object recognition. Local features extracted from the image which are invariant with respect to the viewpoint change, scale change,rotation and illumination conditions has a decisive influence on the final results of object recognition in cluttered background. Meanwhile, the accuracy of local image features extraction directly affects camera calibration of the rehabilitate robot and its visual systems’ pose estimation for the objects. Based on the fact the user of the rehabilitate robot are the disabled people or old people, another major work for the rehabilitate robot visual system is to identify the facial information of its user. According to the research needs of rehabilitate robot FRIENDⅢ, this paper implements the research around key issues such as object recognition method based on local features of image, etc. The main research works are as follows:1. Identify the main tasks of rehabilitate robot object recognition. Give an overview of the key issues about robot vision object recognition methods and a comparison of object recognition methods between based on global information and local features. Analyze the typical object features extraction methods, feature descriptors building and image feature matching strategy successively.2. Propose an object recognition method based on fast SIFT algorithm. The main flaw of the SIFT algorithm is that high dimensionality of SIFT feature descriptors adds the computational complexity which leads to its poor real-time performance. In order to simply the calculation complexity without losing accurate matching features, a scale space of object image is established firstly. After the key-point detection, the SIFT features are split into two types based on its size. Then extend the SIFT angle property and four new angles are computed from the sub-region orientation histogram, which represent the orientation information of each SIFT feature. Finally, the progress of feature matching is limited in a range based on SIFT features’ angles and size, which leads to a significant simplification of the algorithm, thus a fast SIFT algorithm is obtained. The object recognition experimental results show that the fast SIFT algorithm effectively improve the efficiency of object recognition.3. In order to achieve robust object recognition under cluttered background, a SIFT features matching method based on image scale factor is proposed according to fast SIFT algorithm. One of the main tasks of rehabilitate robot FRIEND Ⅲvision system is to accomplish the mission of object recognition under cluttered conditions, such as object partial external occlusions, rotation and illumination changing. However, the number of SIFT features can be extracted is reduced which leads to lower rate of recognition accuracy. By calculating the scale factor between object image and target image, the progress of object recognition is operated under the constraint determined by the scale factor that can guarantee the number of correct matches. At the same time, the distance query between nearest-neighbor and next nearest neighbor feature point is limited to a particular range. By reducing their ratio, it can effectively restore the correct matches which are excluded by mistake to ensure target recognition accuracy under complex background.4. A fuzzy closed loop control strategy based on extended SIFT feature is proposed. Maximum and minimum feature points of the object image and target image are extracted respectively for the use of SIFT feature points matching. The difference between each one affine transformation and unit matrix are passed to the fuzzy controller for improving the matching results. With Mamdani fuzzy controller, fuzzification is achieved by implementing triangular and trapezoidal model. A reasonable fuzzy rule table is constructed through the experimental analysis of the rehabilitate robot object recognition. By taking advantage of centroid defuzzification method, a closed loop fuzzy control object recognition optimization strategies based on extension of SIFT feature points is achieved ultimately.5. The study of target pose estimation and camera calibration of the rehabilitate robot vision system are conducted. The closed loop fuzzy control strategy based on SIFT feature points are applied to estimate the pose of the robot target image. The geometric information of the three-dimensional spatial object is gained from the image information through the experimental data. Camera calibration of the rehabilitate robot vision system is achieved by calculating the geometric model of camera imaging. The study of rehabilitate robot image-based visual servo method is also conducted.6. The robot user face recognition method based on Adaboost algorithm combined with improved SIFT method is proposed. The visual servo system of rehabilitate robot FRIENDⅢ requires real-time information collected form user face. The face detection is processed through Adaboost machine learning algorithm based on Haar-like facial features. Face recognition are realized under the cluttered conditions where the illumination, posture or expression are changing by using the improved SIFT algorithm to extract the local features of human face. The SIFT algorithm for face recognition has no needs to normalize face image or train samples. Meanwhile the improved SIFT algorithm most focused on optimizing algorithm speed, which can meet the real-time face recognition requirements of the visual system.