In the image recognition problem, it is very important how we represent the image. Considering this, we propose a new representational method of images based on the stability in scale-space. In our method, the image is segmented and represented as a hierarchical region graph in scale-space. The object is represented as feature graph, which is subgraph of region graph. In detail, the region graph is defined on the image with the relation of each segment hierarchically. And the feature graph is determined based on the "life-time" of the graph of the object in scale-space. This "life-time" means how long feature graph lives when the scale parameter is increased. We apply our method to the face detection problem, which is foundmental and difficult problem in face recognition. We determine the feature graph of the frontal human face statistical point of view. We also build the face detection system using this feature graph to show how our method works efficiently.

In this paper, we propose a fixed monocular camera, which changes the focus cyclically to recognize completely the three-dimensional translational motion of a rigid object. The images captured in a half cycle of the focus change form a multi-focus image sequence. The motion in depth or the focus change of the camera causes defocused blur. We develop an in-focus frame tracking operator in order to automatically detect the in-focus frame in a multi-focus image sequence of a moving object. The in-focus frame gives a 3D position in the motion of the object at the time that the frame was captured. The reconstruction of the motion of an object is performed by utilizing non-uniform sampling theory for the 3D position samples, of which information were inferred from the in-focus frames in the multi-focus image sequences.

This paper presents a novel control method based on predictions of a neural network in coordination with a conventional PID control to improve transient characteristics of digitally controlled switching dc-dc converters. Power supplies in communication systems require to achieve a superior operation for electronic equipment installed to them. Especially, it is important to improve transient characteristics in any required conditions since they affect to the operation of power supplies. Therefore, dc-dc converters in power supplies need a superior control method which can suppress transient undershoot and overshoot of output voltage. In the presented method, the neural network is trained to predict the output voltage and is adopted to modify the reference value in the PID control to reduce the difference between the output voltage and its desired one in the transient state. The transient characteristics are gradually improved as the training procedure of the neural network is proceeded repetitively. Furthermore, the timing and duration of neural network control are also investigated and devised since the time delay, which is one of the main issue in digital control methods, affects to the improvement of transient characteristics. The repetitive training and duration adjustment of the neural network are performed simultaneously to obtain more improvement of the transient characteristics. From simulated and experimental results, it is confirmed that the presented method realizes superior transient characteristics compared to the conventional PID control.

The purpose of this paper is to propose a flexible load-dependent digital soft-start control method for dc-dc converters in a 380Vdc system. The soft-start operation is needed to prevent negative effects such as large inrush current and output overshoot to a power supply in the start-up process of dc-dc converters. In the conventional soft-start operation, a dc-dc converter has a very slow start-up to deal with the light load condition. Therefore, it always takes a long time in any load condition to start up a power supply and obtain the desired output. In the proposed soft-start control method, the speed of the start-up process is flexibly controlled depending on the load condition. To obtain the optimal speed for any load condition, the speed of the soft-start is determined from a approximated function of load current, which is estimated from experiment results in advance. The proposed soft-start control method is evaluated both in simulations and experiments. From results, it is confirmed that the proposed method has superior soft-start characteristics compared to the conventional one.