Advances in Pattern Recognition — ICAPR 2001: Second by H. Bunke, S. Günter, X. Jiang (auth.), Sameer Singh, Nabeel

By H. Bunke, S. Günter, X. Jiang (auth.), Sameer Singh, Nabeel Murshed, Walter Kropatsch (eds.)

The paper is geared up as follows: In part 2, we describe the no- orientation-discontinuity interfering version according to a Gaussian stochastic version in examining the homes of the interfering strokes. In part three, we describe the enhanced canny area detector with an ed- orientation constraint to realize the perimeters and get well the susceptible ones of the foreground phrases and characters; In part four, we illustrate, speak about and overview the experimental result of the proposed process, demonstrating that our set of rules considerably improves the segmentation caliber; part five concludes this paper. 2. The norm-orientation-discontinuity interfering stroke version determine 2 exhibits 3 usual samples of unique photo segments from the unique records and their value of the detected edges respectively. The significance of the gradient is switched over into the grey point price. The darker the sting is, the bigger is the gradient significance. it's noticeable that the topmost robust edges correspond to foreground edges. it may be famous that, whereas often, the foreground writing looks darker than the heritage snapshot, as proven in pattern photo determine 2(a), there are situations the place the foreground and history have related intensities as proven in determine 2(b), or worst nonetheless, the history is extra renowned than the foreground as in determine 2(c). So utilizing in basic terms the depth worth isn't really adequate to tell apart the foreground from the heritage. (a) (b) (c) (d) (e) (f)

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Extra info for Advances in Pattern Recognition — ICAPR 2001: Second International Conference Rio de Janeiro, Brazil, March 11–14, 2001 Proceedings

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In order to achieve the continuous locomotion, the end posture of first swing should be useful for the second swing. We therefore applied the adaptation algorithm to tune the secondary swing motion controller with two parameters. Fig. 3. Brachiation motion of a long-armed ape Fig. 4. B13-link brachiation robot Learning and Adaptation in Robotics 19 Task Robot Evaluator Brachiation Activation Value rb1 Behavior Coordinator Preliminary Swing rp1 Behavior Controller rs1 Leg Swing Feedback Controller rb3 rb2 Secondary Swing Activation Value rp2 rs2 Body Rotation 1 Locomotion rl1 Leg Stretch rl2 rl3 Body Rotation 2 Activation Value rl4 Body Lift Arm Reaching Desired Trajectory Right Knee Left Knee Right Thigh Left Thigh DCMotor DCMotor DCMotor DCMotor Wrist of Shoulder Shoulder Elbow of Catching of Catching of Catching Catching A rm A rm Arm Arm Wrist of Shoulder Shoulder Elbow of Free Arm of FreeArm of Free Arm Free Arm DCMotor DCMotor DCMotor DCMotor DCMotor DCMotor DCMotor DCMotor Video Tracking System Encorder Environment Fig.

Learning and Adaptation in Robotics 17 Evolving neural networks Evolutionary Computation Neural Networks Learning capability Fuzzy-neural networks Intelligent System Optimization Diversity Self-tuning fuzzy model Fuzzy Logic Linguistic if-then rule Fig. 2. 3). A lot of research about a brachiation type locomotion robot had been carried out. Saito et al [25]-[23] proposed the heuristic learning method for generating feasible trajectory for two-link brachiation robot. Fukuda et al [28] propose the self-scaling reinforcement learning algorithm to generate feasible trajectory with robust property against some disturbances.

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