Jen-Shiun Chiang

Saturation adjustment method based on human vision with YCbCr color model characteristics and luminance changes

This paper proposes a saturation adjustment method based on human vision with YCbCr color model characteristics and luminance changes. In the traditional color adjustment approach, people tried to separately adjust the luminance and saturation. However, this approach makes the color over-saturate very easily and makes the image look unnatural. In this work, we try to use the concept of exposure compensation to simulate the brightness changes and to find the relationship among luminance, saturation, and hue. The simulation indicates that saturation changes with the change of luminance and the simulation also shows there are certain relationships between color variation model and YCbCr color model. Together with all these symptoms, we also include the human vision characteristics to propose a new saturation method to enhance the vision effect of an image. The experimental results show that the proposed approach can make the image have better vivid and contrast. Most important of all, unlike the over-saturation caused by the conventional approach, our approach prevents over-saturation and further makes the adjusted image look natural.

Low resolution method using adaptive LMS scheme for moving objects detection and tracking

This paper presents a new model for adaptive filter with the least-mean-square (LMS) scheme to train the mask operation on low resolution images. The adaptive filter theory with adaptive least-mean-square scheme (ALMSS) uses the training mask for moving object detection and tracking. However, the successful moving objects detection in a real surrounding environment is a difficult task due to noise issues such as fake motion or Gaussian noise. Many approaches have been developed in constrained environments to detect and track moving objects. On the other hand, the ALMSS approach can effectively reduce the noise with low computing cost in both fake motion and Gaussian noise environments. The experiments on real scenes indicate that the proposed ALMSS method is effective for moving object detection and tracking in real-time.

VLSI architecture design of moving objects detection using adaptive least-mean-square scheme

This work presents a new model based on least-mean-square (LMS) scheme to train the mask operation on low resolution images. This efficient and real-time method with adaptive least-mean-square scheme (ALMSS) uses the training mask for moving objects detection and tracking on resource-limited systems. However, the scheme of moving objects detection and tracking in a real surrounding environment is a difficult task due to noise issues such as fake motion or noise. The ALMSS approach can effectively reduce the noise with low computing cost in both fake motion and noise environments. In the experiments on real scenes indicate that the proposed ALMSS method is effective for moving objects detection and tracking in real-time. Our approach can be implemented in hardware for the high resolution applications, such as Full-HD images. A prototype VLSI circuit was designed and simulated by TSMC 0.18µm 1P6M process.

A new temperature independent current controlled oscillator

This paper describes a circuit, which generates a low temperature-dependent bias currents. In this paper, low temperature coefficient reference is presented. The circuit is firstly employed to generate a current reference with temperature compensation, then, supply current to the current controlled ring oscillator (CCO). Because of the oscillation frequency of CCO is proportional to temperature, the current is designed to be inversely proportional to temperature to compensate the temperature variation of CCO. There are four different oscillation frequency with temperature compensation has been completed, they are 10MHz, 20MHz, 30MHz and 40MHz respectively. The proposed circuit has been design by a 0.18um CMOS technology process and using computer simulation to evaluate the thermal drift of the reference current. The temperature coefficient of the proposed CCO is 24 ppm/°C in the temperature range between −25 and 75°C at 10MHz. In the 20MHz, 30MHz and 40MHz oscillation frequency, the temperature coefficients are 32 ppm/°C, 38 ppm/°C and 34 ppm/°C respectively.