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Extracting the structure of space around a robot, is an important issue for robot navigation. In order to identify the structure of a corridor, it is necessary to analyze its composition. We use a camera to take some photos from the corridor in front of the robot, we apply the Sobel algorithm to detect edge lines in two directions, Sobel algorithm finds horizontal and vertical edge lines, the vertical and horizontal edges are respectively related to the wall and floor. then we use a threshold to reduce unnecessary edge linesc and refuse the useless edge lines and eliminate them by introduced threshold methods, so it reduces the computation time and prevent the impact of unnecessary data. We also use the intersection points of the edge lines to obtain the boundary when vertical and horizontal lines cannot give us the wall-floor boundary lines. for more robustness we use a segmentation algorithm based on clustering. These three together help to identify structural cues of corridor such as wall, floor, wall-floor borderline. These cues are necessary for robot navigation in indoor environment.

Since vectors include direction and magnitude, they have more information than scalars. So, converting the scalar images into the vector field leads achieving much information about the images that have been hidden in the spatial domain. In this paper, the proposed method fuses images after transforming the scalar field of images to a vector one. To transform the field, it uses Nabla operator. After that, the inverse transform is implemented to reconstruct the fused medical image. To show the performance of the proposed method and to evaluate it, different experiments and statistical comparisons were accomplished. Comparing the experimental results with the previous works, shows the effectiveness of the proposed method.

In this paper, a numerical method based on the finite element method and the least square scheme with the Tikhonov regularization method for nonlinear inverse diffusion problem is presented. For this propose, first finite element method and basis functions will be used to discretize the variational form of the problem; then the least square scheme and Tikhonov regularization method are proposed to correct diffusion. It is assumed that no prior information is available on the functional form of the unknown diffusion coefficient in the present study, and so, it is classified as the function estimation in inverse calculation. Numerical result shows that a good estimation on the unknown functions of the inverse problem can be obtained../files/site1/files/72/9Abstract.pdf