Yong-Ge Wu

Obstacle detection and environment modeling based on multisensor fusion for robot navigation

This paper presents a multisensor integrated vision system and sensor fusion algorithm for the navigation of an autonomous mobile robot equipped with a laser range finder radar (LRFR) and a color CCD camera to acquire information about the environment. The 2D model of the environment is constructed and the obstacles on the road are detected by fusing knowledge included in the range of images obtained by the LRFR and the color camera. The fusion algorithm is based on the generalized Dempster-Shafers theory of evidence (DSTE). The Dempsters rule of combination in the DSTE requires that the combined evidence should be independent of each other, but, as our research has proved, it is more reasonable to assume that the information obtained by the vision system is dependent, and under this assumption the data fusion results are more reliable in many cases. But to achieve this, generalizing the Dempsters rule of combination to the dependent conditions is necessary, so this forms the other major subject of this paper. The presented system and algorithm have been tested in real environments, and their effectiveness has been proved.

Aircraft identification based on the algebraic method

This paper addresses the automatic interpretation of digital image of three-dimensional scenes, especially automatic recognition of three-dimensional aircraft types from digital images. First, an efficient coordinate transform from a series of two-dimensional aircraft posture silhouette images to invariant matrices is developed. The invariant matrix is independent of its translation, scaling, and rotation. Next, on the basis of the invariant matrix, an effective algebraic feature extraction method is proposed. The method is based on singular value decomposition (SVD) of the matrix. To compress the dimensionality of the singular value vector, an optimal discriminant transform for a small number of samples is introduced to transform an original feature space of singular value vector into a new feature space in which its dimensionality is very low. Finally, our method is used to recognize three-dimensional aircraft types Experimental results show that our algebraic method as a high recognition rate, and it is insensitive to translation, scaling, rotation, and noise.

Image processing system for particle distributions auto-analysis of in-line holograms

This presentation describes an on-line image analysis system for the automatic distribution analysis of holographic particles in 3D space. To obtain the 3D distribution parameters of particles, sequences of 2D cross-sectional retrieved images of the particle hologram are obtained using the in-line retrieval method, and the processing of the 2D retrieved images is discussed in this presentation. To segment the candidate particles, an entropy based automatic threshold selection method is adopted. In the process of out-of-focus particle removal, the radial intensity profile of the candidates in the original image and the clearness of the candidate neighboring areas in the Sobeled image are analyzed. Experimental results are presented to show the efficiency of the approach described in this presentation.

Efficient segmentation of nuclei in different color spaces

This presentation discusses the problem of segmentation of nuclei in cytological color images in different color spaces, namely RGB and HSI color spaces, for the detection of lung cancer cells. For the segmentation in each color space, the background and foreground of the images are first defined, and the chromatic mean values of the background and foreground are then extracted. In the learning phase, based on the chromatic mean values of the background and foreground of training samples, an adaptive threshold function is constructed for each color space using the B-Spline technique. The nuclei are then segmented by thresholding using the adaptive threshold function obtained in the learning phase. Comparisons between the segmentation in RGB color space and in HSI color space are carried out.

Character recognition by algebraic invariant matrix

This paper introduces a robust method which can be used to recognize English characters. In our method, we first present an Invariant Matrix (IM) corresponding to a unique character image under the polar system. This kind of matrix has many properties and it is insensitive to image translation, scaling rotation and noise. On the basis of invariant matrix, a set of similar discriminant functions (SDF) of English characters are established. Then, a feature extraction and recognition method based on the SDF functions is proposed. Feature vectors extracted by our method are reliable and have the maximum similarity for the same class of English character samples. Finally, according to our recognition model, we design a hierarchical classifier to recognize English characters. Experimental results show that the SDF function based on IM is an efficient criterion for feature extraction of English characters and our recognition model can obtain the recognition accuracies of 100 percent for all English characters.

Generalized integration method of evidences with dependency information

Multisensor information fusion was defined as the integration of data and information from different sensors with the goal to produce a consistent description of the environment being sensored. Most methods make routine assumptions on the type of relation between these evidences, that is, the evidences are independent. The problem of dependent evidences has been receiving little attention in the literature. In this paper, we propose a generalized integration method of dependent evidences represented by an interval probability. A dependent parameter (DP) of uncertain evidences is first introduced. The dependent parameter DP can be represented as an interval, too. The following four types of dependency relation have been considered: minimum dependence, maximum dependence, independence, and unknown dependence. Based on the DP parameter, the algorithm to combine two evidences with dependency information is presented. The proposed method particularly well suits to computerization in the case of dependency information and obtains a satisfactory hypothesis value.

Novel stereo image matching technology directed by range information

High resolution 3D information is useful in computer vision. The most common methods of acquiring 3D data are stereo technique and laser range finder, but both of them have some problem in applications. In this paper, a novel stereo image matching algorithm directed by range images is proposed from the view of sensor fusion. At first, the transformation between the range images and camera images is built up, then information extracted from range images is used to constrain the search in stereo matching since the computation of 3D feature points is fast in it. As a result, the workload of point correspondence in stereo can drastically reduce. The experiments have proved the efficiency of our proposed method.

Terrain description and obstacle detection from range image

This paper studies range images of man-made objects in outdoor environments. Our objective is to give the description of terrain, separate man-made objects from background and localize it. With the aim of this, a slope surface fitting method is proposed and applied to homogeneous dense high images derived from the range images, then a terrain description is constructed and the object detection algorithm is proposed. The final results show that our method works effectively.

Global optimal discriminant vectors under the global Fisher discriminant criterion

The Fisher linear discriminant vector has been used as the optimal linear method in solving pattern classification problems. This paper proposes an iterative algorithm to calculate a global optimal set of discriminant vectors under the global Fisher discriminant criterion. The main advantage of our algorithm is that the scatter matrices in the subspace spanned by all discriminant vectors in the proposed optimal set have the global minimum within-class scatter and global maximum between-class scatter as compared to the Foley-Sammon local optimal set.

Pattern classification by learning from example and extension matrix

Sometimes the classifiers based on the features extracted from patterns may not be robust, in this case, to obtain better classification results, mans interruption is needed, then subjectivity and uncertainty due to mans action are followed as a result. In this paper, an algorithm able to automatically create a classifier is provided by the technique of learning from examples, with which pattern recognition, such as the facial images recognition, are completed.