Jung Ming Wang

A non-parametric blur measure based on edge analysis for image processing applications

A nonparametric image blur measure is presented. The measure is based on edge analysis and is suitable for various image processing applications. The proposed measure for any edge point is obtained by combining the standard deviation of the edge gradient magnitude profile and the value of the edge gradient magnitude using a weighted average. The standard deviation describes the width of the edge, and its edge gradient magnitude is also included to make the blur measure more reliable. Moreover, the value of the weight is related to image contrast and can be calculated directly from the image. Experiments on natural scenes indicate that the proposed technique can effectively describe the blurriness of images in image processing applications.

Automated lecture recording system

Lecture recording plays an important role in online learning and distance education. Most of they are recorded by a cameraman or a static camera. In this paper, we propose an automatic lecture recording system. A Pan-Tilt-Zoom (PTZ) camera is shooting as it operated by a cameraman. Three parts are developed in this system. The first one is preprocessing for detecting the position of the lecturer and the screen. The second part is designed to track their motion to define the lecture information. According to the tracking result, we can control the PTZ camera in the third part based on the camera action table designed beforehand.

Video stabilization for a hand-held camera based on 3D motion model

In this paper, a video stabilization technique is presented. There are four steps in the proposed approach. We begin with extracting feature points from the input image using the Lowe SIFT (Scale Invariant Feature Transform) point detection technique. This set of feature points is then matched against the set of feature points detected in the previous image using the Wyk et al. RKHS (Reproducing Kernel Hilbert Space) graph matching technique. We can calculate the camera motion between the two images with the aid of a 3D motion model. Expected and unexpected components are separated using a motion taxonomy method. Finally, a full-frame technique to fill up blank image areas is applied to the transformed image.

Physics-based extraction of intrinsic images from a single image

A technique for extracting intrinsic images, including the reflectance and illumination images, from a single color image is presented. The technique first convolves the input image with a prescribed set of derivative filters. The pixels of filtered images are then classified into reflectance-related or illumination-related based on a set of chromatic characteristics of pixels calculated from the input image. Chromatic characteristics of pixels are defined by a photometric reflectance model based on the Kubelka-Munk color theory. From the classification results of the filtered images, the intrinsic images of the input image can be computed. Real images have been utilized in our experiments. The results have indicated that the proposed technique can effectively extract the intrinsic images from a single image.

Real-Time Vision-Based Driver Drowsiness/Fatigue Detection System

In this paper, a vision system for monitoring drivers vigilance is presented. The level of vigilance is determined by integrating a number of facial parameters. In order to estimate these parameters, the facial features of eyes, mouth and head are first located in the input video sequence. The located facial features are then tracked over the subsequent images. Facial parameters are estimated during facial feature tracking. The estimated parametric values are collected and analyzed every fixed time interval to provide a real-time vigilance level of the driver. A series of experiments on real sequences were demonstrated to reveal the feasibility of the proposed system.

Vision-based traffic measurement system

We present a vision-based traffic measurement system. The system automatically counts the vehicles passing through a designated segment of roadway and measures their speeds. Based on the obtained number of vehicles and their speeds, a variety of traffic parameters are readily calculated. A number of experiments with the video sequences taken under different weather, illumination and traffic conditions have been conducted. The results have revealed that the proposed system could perform well under different conditions.

Foreground Object Detection Using Two Successive Images

Detecting foreground object often need to face the problems of illumination change and image noise. In this paper, we propose an object detection method using two successive image frames. Illumination change would be very small in such short time, and then we can handle the first problem more easily. Image noise will confuse the detection of an object boundary. To handle this problem, we apply level set method to enclose the foreground object regions. The experiments show that our method can be applied to extract foreground objects in various environments and different cameras.

Extracting driver's facial features during driving

In this paper, a vision system for monitoring drivers facial features is presented. To begin, the drivers face is first located in the input video sequence. It is then tracked over the subsequent images. The facial features of eyes, mouth and head are kept detecting in the course of face tracking. Feature detection and tracking are performed in parall so that the precise can be improved. A number of video sequences with the drivers of different ages and genders under various illumination and road conditions were employed to demonstrate the performance of the proposed system. Future work is on how to extend the system to determine the level of vigilance of the driver.

Interference reflection separation from a single image

The interference image is defined as the superpositioning of a reflection image and an object image. A technique for separating reflection and object components of a single interference image is presented. The proposed method classifies edges of the interference image into either reflection or object related. Our method utilizes total variation (TV) method, blur measure, and region segmentation as evidence with a fuzzy integral technique to classify the edge pixels. Based on the results of edge pixel classification, the reflection and object components of the input image are reconstructed. Compared to previous published research, the proposed method is fast and requires no manual operations. The experimental results have demonstrated that the proposed method can perform separation of a single interference image effectively with small misadjustments and rapid convergence.

Omni-Directional Camera Networks and Data Fusion for Vehicle Tracking in an Indoor Parking Lot

A fixed single camera is not sufficient for monitoring a wide area. More cameras can be used, but a problem with integrating all of them will arise. In this paper, a monitoring system to detect and track moving objects in an indoor environment using multiple omni-directional cameras is proposed. Objects captured from different cameras can be integrated automatically, and we can add more cameras to enlarge the monitoring range without changing the system architecture. Such a system is currently being applied to a model of a parking lot for detecting the paths of vehicles.