Improved Stereo Vision Algorithms For Robot Navigation

The main motivation of this research is to find the best depth and direction for navigating a robot using stereo vision by solving the difficulties in finding disparity value for low information, noisy and tilted images as problem statement. An adaptive window method is implemented in three approaches. Approach 1 and 2 use common cost functions such as SSD, SAD and GB (gradient). Approach 3 uses a Linear-based function. By using adaptive method, the error in computing the disparity value for SSD is 12%, for Gradient-based is 10% and for Linear-based is 7%. SSD is 8 and 2 times faster than Gradient-based and Linear-based functions respectively. The linear-based technique with 50% more accurate than SSD is a suitable tool for stereo vision applications. The proposed denoising method for Lena and Barbara images are compared with ROF model by computing Total Variation (TV). When TV is 0.88 for noisy Lena image, ROF reduces TV to 0.21, Linear-based and Gradient-based decreases TV to 0.24 and 0.26 respectively. When TV is 0.88 for noisy Barbara image, TV is decreased to 0.68 by ROF, 0.62 by Linear-based and 0.65 by Gradient-based techniques. A stable platform system is designed and developed for stabilizing vision line horizontally for a moving robot. The system is equipped by a closed loop tilt and pan stabilizer using a dual-axis accelerometer sensor. It stabilizes the tilt and pan angles with 0.5 second time constant and steady state error lower that 0.025 radian. By using adaptive stereo matching that uses linear cost function, the quality of the best direction for navigation is improved to 88% of success rate

A Non-Local Structure Tensor Based Approach for Multicomponent Image Recovery Problems

Non-Local Total Variation (NLTV) has emerged as a useful tool in variational methods for image recovery problems. In this paper, we extend the NLTV-based regularization to multicomponent images by taking advantage of the Structure Tensor (ST) resulting from the gradient of a multicomponent image. The proposed approach allows us to penalize the non-local variations, jointly for the different components, through various $\ell_{1,p}$ matrix norms with $p \ge 1$. To facilitate the choice of the hyper-parameters, we adopt a constrained convex optimization approach in which we minimize the data fidelity term subject to a constraint involving the ST-NLTV regularization. The resulting convex optimization problem is solved with a novel epigraphical projection method. This formulation can be efficiently implemented thanks to the flexibility offered by recent primal-dual proximal algorithms. Experiments are carried out for multispectral and hyperspectral images. The results demonstrate the interest of introducing a non-local structure tensor regularization and show that the proposed approach leads to significant improvements in terms of convergence speed over current state-of-the-art methods

Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation