Kuo-Chin Fan

Lane Detection With Moving Vehicles in the Traffic Scenes

A lane-detection method aimed at handling moving vehicles in the traffic scenes is proposed in this brief. First, lane marks are extracted based on color information. The extraction of lane-mark colors is designed in a way that is not affected by illumination changes and the proportion of space that vehicles on the road occupy. Next, for vehicles that have the same colors as the lane marks, we utilize size, shape, and motion information to distinguish them from the real lane marks. The mechanism effectively eliminates the influence of passing vehicles when performing lane detection. Finally, pixels in the extracted lane-mark mask are accumulated to find the boundary lines of the lane. The proposed algorithm is able to robustly find the left and right boundary lines of the lane and is not affected by the passing traffic. Experimental results show that the proposed method works well on marked roads in various lighting conditions

The Application of a Convolution Neural Network on Face and License Plate Detection

In this paper, two detectors, one for face and the other for license plates, are proposed, both based on a modified convolutional neural network (CNN) verifier. In our proposed verifier, a single feature map and a fully connected MLP were trained by examples to classify the possible candidates. Pyramid-based localization techniques were applied to fuse the candidates and to identify the regions of faces or license plates. In addition, geometrical rules filtered out false alarms in license plate detection. Some experimental results are given to show the effectiveness of the approach

Form document identification using line structure based features

We present a novel form recognition method by analyzing the line structure embedded in an input form document. First, all vertical and horizontal lines embedded in the form image are extracted. Experimental results demonstrate the feasibility and efficiency of our proposed method in recognizing form documents.

Efficient human action and gait analysis using multiresolution motion energy histogram

Average Motion Energy (AME) image is a good way to describe human motions. However, it has to face the computation efficiency problem with the increasing number of database templates. In this paper, we propose a histogram-based approach to improve the computation efficiency. We convert the human action/gait recognition problem to a histogram matching problem. In order to speed up the recognition process, we adopt a multiresolution structure on the Motion Energy Histogram (MEH). To utilize the multiresolution structure more efficiently, we propose an automated uneven partitioning method which is achieved by utilizing the quadtree decomposition results of MEH. In that case, the computation time is only relevant to the number of partitioned histogram bins, which is much less than the AME method. Two applications, action recognition and gait classification, are conducted in the experiments to demonstrate the feasibility and validity of the proposed approach.

Real-time vehicle tracking on highway

We propose a macroscopic method, which can perform real-time tracking of moving vehicles on highway. In addition to dealing with the problem caused by occlusion and noise, the proposed method can also track a vehicle doing lane change. The proposed method consists of two phases: the detection phase and the tracking phase. In the detection phase, we use entropy-based features to check the existence of vehicles. Then, we use the flux theory, which is commonly used in fluid mechanics to perform the tracking task. By conducting a great number of experiments, we have demonstrated the efficiency as well as the effectiveness of the proposed system.

The use of thermal images of palm-dorsa vein-patterns for biometric verification

A novel personal verification method using the thermal images of palm-dorsa vein-patterns is presented in this paper. The characteristics of the proposed method are that no prior knowledge about the object is necessary and the parameters can be set automatically. In our work, an infrared (IR) camera is adopted as the input device to capture the thermal images of palm-dorsa. According to the heat conduction law (the Fourier law) multiple features can be extracted from each feature points of the vein-patterns (FPVPs). Multiresolution representations of images with FPVPs are obtained using multiple multiresolution filters (MRFs) that extract the dominant points by filtering miscellaneous features for each FPVP. A hierarchical integrating function is then applied to integrate multiple features and multiresolution representations. We also introduce a logical and reasonable method to select a trained threshold for verification. The experimental results demonstrate that our proposed approach is valid and effective for vein-pattern verification.

Motion detection based on two-piece linear approximation for cumulative histograms of ratio images in intelligent transportation systems

Motion detection is widely used as the key module for extracting moving objects from image sequences in intelligent transportation systems (ITS). In most of the motion detection methods, backgrounds are subtracted from the captured images. This category of methods is called background subtraction. Since standard intensity can be expressed as the multiplication of illumination and reflectance, illumination changes will produce a poor difference image from background subtraction and affect the accuracy of motion detection. In this paper, we use ratio images as the basis of motion detection. To suitably threshold the target images, two-piece linear approximation is proposed for cumulative histograms to prevent the problems in the searching of peaks and valleys in histograms. Experimental results demonstrate that two-piece linear approximation for cumulative histograms performs very well in thresholding the target images. Moreover, the superiority of motion detection based on ratio images over motion detection based on difference images is also depicted in the experiments.

Hierarchical lane detection for different types of roads

This paper presents a hierarchical lane detection system with the ability to deal with both structured and unstructured roads. The proposed system classifies the environment first before applying suitable algorithms for different types of roads. For environment classification, pixels with lane- marking colors are extracted as feature points. Eigenvalue decomposition regularized discriminant analysis is utilized in model selection and maximum likelihood estimation of Gaussian parameters in high dimensional feature space. For structured roads, the extracted feature points are reused for lane detection. For unstructured roads, mean-shift segmentation is applied to divide the scene into regions. Possible road boundary candidates are selected, and Bayes rule is used to choose the most probable boundary pairs. The experimental results show that the system is able to robustly find the boundaries of the lanes on different types of roads and various weather conditions.

Color image retrieval using shape and spatial properties

In this paper, we present a novel region-based color image retrieval system using geometric properties. A region-growing technique is firstly employed to cluster the connected color pixels with the same color in an image to form color regions. Then, two most important descriptive geometry features are extracted. One is the spatial relational graph (SRG), another is the Fourier description coefficients (FDC) of each color region. In the matching stage, the modified relational distance graph matching between two SRG is performed firstly to find the best matches with the minimum relational distance. Then, the shape matching is applied to obtain the best vertex match with the minimum geometric distance. Experimental results reveal the feasibility of our proposed approach in solving color image retrieval problem.

A recursive hierarchical scheme for radical extraction of handwritten Chinese characters

In this paper, we propose a recursive hierarchical scheme for radical extraction of Chinese characters. The proposed scheme, which combines structural method and statistical method, includes three modules. They are character pattern detection module, straight cut line detection module, and stroke clustering module. Experiments are conducted on 1056 constrained handwritten characters. The successful rate of radical extraction is 97.1%. The experimental results reveal the feasibility and validity of our proposed approach.