Yu-Kumg Chen

The fastest carry lookahead adder

Adder is a very basic component in a central processing unit. The speed of compute becomes the most considerable condition for a designer. The carry lookahead adder is the highest speed adder nowadays. In this paper, a new method for modifying the carry lookahead adder is proposed. Based on the analysis of gate delay and simulation, the proposed modified carry lookahead adder is faster than the carry lookahead adder.

A Weighting Mean-Separated Sub-Histogram Equalization for Contrast Enhancement

In the last decade, improvement of the visual image quality has been actively developed using the contrast enhancement techniques due to the need to better show the visual information contained within the image for all vision-based systems. Histogram equalization is the most commonly used contrast enhancement method. However, the conventional histogram equalization methods usually result in excessive contrast enhancement, which causes the unnatural look and visual artifacts of the processed image. In this paper, we propose a novel histogram equalization method using the precise histogram separation along with the piecewise transformed function. The contrast enhancement results of the proposed method were not only analyzed through qualitative visual inspection and for quantitative accuracy, but are also compared to the results of other state-of-the-art methods.

GPS correction with the sky-subimage recognition

In the present time almost every new car has been introduced by the manufacturing companies with the facilities of GPS and vehicle event data recorder (VEDR). The function of GPS satellite navigation is often used as a vehicle navigation purposes, and VEDR is used for recording videos during driving. When a car drives through tunnel, underpass, or under the viaduct, etc., GPS will not be able to receive the satellite signals with shading, and it is resulted in the error GPS satellite navigation. In order to resolve this problem, a sky-image recognition technique for the video of VEDR is proposed in this paper to complete this task. Since the gray-level range of color sky image is too larger to be separated from original image, the color input image is converted to gray scale image in the proposed algorithm first. Then, based on the entropy of image histogram within sky subimage, the road conditions of tunnel, underpass, viaduct, and T-junction are distinguished in the proposed algorithm. Some different road conditional images are collected and experimented with our proposed algorithm. Our experimental results show our proposed algorithm succeed in recognition of the road condition in the daytime and computational inexpensiveness.

A base function for generating contour traversal paths in stereolithography apparatus applications

The technique of layered manufacturing in rapid prototyping is to fabricate product prototype by scanning the cross-sectional contours of the product using a laser beam layer by layer. The outlines of geometrical objects on each layer are different, and each layer may contain several geometrical objects. In order to simplify the problem, each geometrical object on the same plane is approximating by its own minimum circumscribed circle. Therefore, the minimum traversal path of circles can be the based model of the minimum traversal path of scanning geometrical objects. Furthermore, the minimum traversal path of three circles is the degenerated case of the minimum traversal path of plural circles. And the problem of the minimum traversal path of three circles can be transferred to the problem of the minimum traversal path of one circle and two points in this paper. By using the concepts of reflection of light in physics and geometrical mathematics, the equation of the minimum traversal path of three circles is derived in this paper. This equation can be easily implemented in many areas of application, including robotic motion planning and path planning for submarine, ship, and airplane.

Sky recognitions for driving-view images

Most of the cars today have facilities of GPS and vehicle event data recorder (VEDR) while driving on roads. When a car drives through underpass, tunnel, or under the viaduct, etc., GPS will not be able to receive the satellite signals and makes the error navigation. In order to resolve this problem, a new sky recognition technique for driving-view image from the video of VEDR is proposed in this paper. Based on the theory of entropy, the road conditions of T-junction, skewed road, and shaded trees are determined in the sky subimage of driving-view image. Then, with analysis and statistics of gray scale image from the sky subimage of driving-view image, the road conditions of tunnel, underpass, and viaduct are separated from the road conditions of sky. Experimental results are carried out with some varied conditions of driving-view images, and show our proposed algorithm succeed in recognition of these road conditions.

Motion Detection with Entropy in Dynamic Background

The traditional automatic smart image surveillance system can usually be used in the environment with still background. That is, the background image must not contain the moving objects. If there is waving ocean, waving tree, floating cloud, or raining in the background image, the traditional methods do not work well. In order to improve this problem, a new motion detection method based on the theory of entropy and combined a multi-periods Sigma-Delta background estimation algorithm is developed in this paper. Based on the theory of moving average, a moving thresholding method is designed in this paper to obtain a sequence of alarm announcements. Experiments are carried out for some samples with dynamic backgrounds to demonstrate the computational advantage of the proposed method.

Effective S-? Background Estimation for Video Background Generation

Motion detection is a very important function in an intelligent video surveillance system. In order to detect the moving objects, it is necessary to generate the correct background image for each frame of the surveillance video. By subtracting the correct background image from the original image, the motion objects are derived in each frame of the surveillance video. Based on removing the motion objects in the Sigma-Delta filter approach, a new background generation algorithm is proposed in this paper. Experiments are carried out for some surveillance videos to demonstrate the computational advantage of the proposed method.

Global automatic thresholding with edge information and moving average on histogram

Optical character recognition occupies a very important field in digital image processing. It is used extensively in daily life. If the given image does not have a bimodal intensity histogram, it would cause segmenting mistake easily for the previous bi-level algorithms. In order to solve this problem, a new algorithm is proposed in this paper. The proposed algorithm uses the theory of moving average on the histogram of the fuzzy image, and then derives the better histogram. Since use only one thresholding value cannot solve this problem completely, the edge information and the window processing are introduced in this paper for advanced thresholding. Thus, a more refine bi-level image is derived and it will result in the improvement of optical character recognition. Experiments are carried out for some samples with shading to demonstrate the computational advantage of the proposed method

Computing accumulation density of particles on a line with parabolic trajectories

In this paper, an ideal particle accumulation problem is studied, in which particles are emitted from a fixed point at the same emission velocity onto a plane with a negative y-intercept. Since particles acted on by gravity travel in space via parabolic trajectories, by studying such families of parabolas, the accumulation of particles on a plane can be deduced. An analytical equation, namely the accumulation density function, is subsequently established for representing particle accumulation on such a plane. This accumulation function provides a basis for simulating accumulation of materials on the surfaces in applications such as chemical vapor deposition and spray painting.

Motion detection with using theory of entropy

The traditional automatic smart image surveillance system can usually be used in the environment with still background. That is, the background image must not contain the moving objects. If there is waving ocean, waving tree, floating cloud, or raining in the background image, the traditional methods do not work well. In order to improve this problem, a new motion detection method based on the theory of entropy and combined a multi-periods Sigma-Delta background estimation algorithm is developed in this paper. Based on the theory of moving average, a moving thresholding method is designed in this paper to obtain a sequence of alarm announcements. Experiments are carried out for some samples with dynamic backgrounds to demonstrate the computational advantage of the proposed method.