人工智能(Nilson版 英文课件)_Chap06-1.ppt

(c) 2000, 2001 SNU CSE Biointelligence Lab Robot Vision Chapter 6. Introduction Computer vision Endowing machines with the means to “see” Create an image of a scene and extract features Very difficult problem for machines Several different scenes can produce identical images. Images can be noisy . Cannot directly ‘invert’ the image to reconstruct the scene. Human Vision (1) Human Vision (2) Human Vision (3) Steering an Automobile ALVINN system [Pomerleau 1991,1993] Uses Artificial Neural Network Used 30*32 TV image as input (960 input node) 5 Hidden node 30 output node Training regime: modified “on-the-fly” A human driver drives the car, and his actual steering angles are taken as correct labels for the corresponding inputs. Shifted and rotated images were also used for training. ALVINN has driven for 120 consecutive kilometers at speeds up to 100km/h. Steering an Automobile-ALVINN Two stages of Robot Vision (1) Finding out objects in the scene Looking for “edges” in the image Edge:a part of the image across which the image intensity or some other property of the image changes abruptly. Attempting to segment the image into regions. Region:a part of the image in which the image intensity or some other property of the image changes only gradually. Two stages of Robot Vision (2) Image processing stage Transform the original image into one that is more amendable to the scene analysis stage. Involves various filtering operations that help reduce noise, accentuate edges, and find regions. Scene analysis stage Attempt to create an iconic or a feature-based description of the original scene, providing the task-specific information. Two stages of Robot Vision (3) Scene analysis stage produces task-specific information. If only the disposition of the blocks is important, appropriate iconic model can be (C B A FLOOR) If it is important to determine whether there is another block on top of the block labeled C, adequate description will include the value of a feature, CLE