Estimation of Salient Regions Based on Local Extrema of Images

Estimating the salient regions of an image plays a key role in scene analysis and image understanding. We can also apply saliency-based image processing techniques to image compression, evaluation, and effective searching methods. One of the most difficult problems is estimating the regions before recognizing what is in the image -- a problem that can be solved by accessing information of the low-level structures of objects in the image. This paper describes a method for estimating salient regions in images based on the distribution stability of local extrema of luminance during image blurring. Under blurring conditions, if an object\u27s region has a more stable structure compared to another area, it must be more salient, so the saliency of these regions must be defined based on their stability for blurring. In the developed method, the local extrema of images are used to describe the complexity of the image\u27s objects and background. Salient regions are estimated based on the stability of the local extrema for the blurring parameter. Experiments were conducted to compare the estimated result of salient regions and the psychophysical result obtained from the analysis of eye movement recordings. Results show that our method successfully extracts salient regions of natural images

A New Digital Control DC-DC Converter with Multi-layer Neural Network Predictor

The purpose of this paper is to present a new digital control method of the forward type multiple-output dc-dc converter with both a P-I-D feedback and a new feed forward control. In this converter, two novel control methods are proposed. The first new control method is a model method and the second new method is a neural network predictor. The dynamic characteristics of digital control dc-dc converter are improved as compared with the conventional one. Especially, the digital control dc-dc converter with method of the neural network can be realized excellent dynamic characteristics. As a result, the undershoot of the output voltage and the overshoot of reactor current are improved to 45% and 26% , respectively.2009 International Conference on Machine Learning and Applications (ICMLA) : Miami, FL, USA, 2009.12.13-2009.12.1

A New Digital Control DC-DC Converter with Multi-layer Neural Network Predictor

The purpose of this paper is to present a new digital control method of the forward type multiple-output dc-dc converter with both a P-I-D feedback and a new feed forward control. In this converter, two novel control methods are proposed. The first new control method is a model method and the second new method is a neural network predictor. The dynamic characteristics of digital control dc-dc converter are improved as compared with the conventional one. Especially, the digital control dc-dc converter with method of the neural network can be realized excellent dynamic characteristics. As a result, the undershoot of the output voltage and the overshoot of reactor current are improved to 45% and 26% , respectively.2009 International Conference on Machine Learning and Applications (ICMLA) : Miami, FL, USA, 2009.12.13-2009.12.1

Recognition of Motion in Depth by a Fixed Camera

Abstract. The research on perception of motion has important applications for surveillance and autonomous robot navigation in dynamic environments. The issue of estimation of motion in depth is the crucial point of the problem of recognition 3D motion. In this paper, we propose a fixed monocular camera with focus changed cyclically to recognize the absolute translational motion in depth of a rigid object.