tailieunhanh - Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 2

Tham khảo tài liệu 'machine learning and robot perception - bruno apolloni et al (eds) part 2', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 1 Learning Visual Landmarks for Mobile Robot Topological Navigation 17 Furthermore certain objects are always seen with the same orientation objects attached to walls or beams lying on the floor on or a table and so on. With these restrictions in mind it is only necessary to consider five of the eight . previously proposed X Y AX AY SkY. This reduction of the deformable model parameter search space increases significantly computation time. This simplification reduces the applicability of the system to planar objects or faces of 3D objects but this is not a loose of generality only a time-reduction operation issues for implementing the full 3D system will be given along this text. However many interesting objects for various applications can be managed in despite of the simplification especially all kind of informative panels. The 2D reduced deformable model is shown in Fig. . Its five parameters are binary coded into any GA individual s genome the individual s Cartesian coordinates X Y in the image its horizontal and vertical size in pixels AX AY and a measure of its vertical perspective distortion SkY as shown in equation 4 for the ith individual with G 5 . and q 10 bits per variable for covering 640 pixels . The variations of these parameters make the deformable model to rover by the image searching for the selected object. A Í c 1- h h _b hihihi V 1 h11 h12 h1q v21 h22 h2q hG1 hG 2 hGq X Y SkYi 7 6 For these . a point x0 y0 in model reference frame no skew sized AX0 AY0 will have x y coordinates in image coordinate system for a deformed model 18 M. Mata et al. AX AX 0 AX. SkY V AX0 A 0 AY AYo I yo 7 A fitness function is needed that compares the object-specific detail over the deformed model with the image background. Again nearly any method can be used to do that. Fig. . Selected object-specific detail set. a object to be learned b possible locations for the patter-windows c memorized pattern-windows