In order to automatically recognize different kinds of objects from their backgrounds, a self-adaptive segmentation algorithm that can effectively extract the targets from various surroundings is of great importance. Image thresholding is widely adopted in this field because of its simplicity and high efficiency. The entropy-based and variance-based algorithms are two main kinds of image thresholding methods, and have been independently developed for different kinds of images over the years. In this paper, their advantages are combined and a new algorithm is proposed to deal with a more general scope of images, including the long-range correlations among the pixels that can be determined by a nonextensive parameter. In comparison with the other famous entropy-based and variance-based image thresholding algorithms, the new algorithm performs better in terms of correctness and robustness, as quantitatively demonstrated by four quality indices, ME, RAE, MHD, and PSNR. Furthermore, the whole process of the new algorithm has potential application in self-adaptive object recognition.

Focusing on the autonomous location method of lower extremity exoskeleton robot in complex environment, this paper presents a gait type recognition method based on support vector machine (SVM)C and an autonomous location method based on inertial information mapping model and system reconstruction. In this paper, support vector machine is used to effectively recognize various kinds of conventional gait types of exoskeleton robots. The inertial information mapping models among different parts of the lower limbs of the robots under different gaits are established respectively. Aiming at the problem of the failure of the inertial measurement unit at the end of the limb in the impact or high overload motion, a robust autonomous location method based on system reconstruction is studied. The experiment results show that, using different neural network model parameters under different gaits can be used to reduce the complexity of the network model. While the inertial measurement unit at the end of the limb fails, the location performance of the exoskeleton robot navigation system based on this method, is equal to that of the inertial navigation system at the end of the limb with same sensor precision.