Yue Wang

Bio: Yue Wang is an academic researcher from Agency for Science, Technology and Research. The author has contributed to research in topics: Affine transformation & Segmentation. The author has an hindex of 16, co-authored 36 publications receiving 1652 citations. Previous affiliations of Yue Wang include Nanyang Technological University & Institute for Infocomm Research Singapore.

A weighted support vector machine for data classification

Abstract: This paper presents a weighted support vector machine (WSVM) to improve the outlier sensitivity problem of standard support vector machine (SVM) for two-class data classification. The basic idea is to assign different weights to different data points such that the WSVM training algorithm learns the decision surface according to the relative importance of data points in the training data set. The weights used in WSVM are generated by a robust fuzzy clustering algorithm, kernel-based possibilistic c-means (KPCM) algorithm, whose partition generates relative high values for important data points but low values for outliers. Experimental results indicate that the proposed method reduces the effect of outliers and yields higher classification rate than standard SVM does when outliers exist in the training data set.

Lane detection using catmull-rom spline

Abstract: In this paper, a Catmull-Rom spline based lane model which described the perspective effect of parallel lines was proposed for generic lane boundary. As Catmull-Rom spline can form arbitrary shapes by control points, it can describe a wider range of lane structures than other lane models such as straight and parabolic model. It formulates the lane detection problem in the form of determining the set of lane model control points. The proposed algorithm uses a Maximum likelihood approach to the lane detection problem. We have employed a multiresolution strategy to rapidly find an accurate solution. This coarse-to-fine matching offers an acceptable solution at an affordable computational cost and speeds up the detection. The proposed method is robust to the noise present in the road image with shadows, variations in illumination, marked and unmarked road.

Lane detection using B-snake

Abstract: We propose a B-snake based lane detection algorithm. Compared with other lane models, the B-snake based lane model is able to describe a wider range of lane structures, since B-spline can form any arbitrary shape by a set of control points. The problems of detecting both sides of lane markings (or boundaries) have been formulated here as the problem of detecting the mid-line of the lane, by using the knowledge of the perspective parallel lines. A robust algorithm called CHEVP is presented for providing a good initial position for the B-snake. Furthermore, a minimum energy method by MMSE (minimum mean square energy) is suggested to determine the control points of the B-snake model by the overall image forces on two sides of lane. Experimental results show that the proposed method is robust against noise, shadows, and illumination variations in the captured road images, and also applicable to both the marked and the unmarked roads, and the dash and the solid paint line roads.

Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation

Abstract: Driver-assistance systems that monitor driver intent, warn drivers of lane departures, or assist in vehicle guidance are all being actively considered. It is therefore important to take a critical look at key aspects of these systems, one of which is lane-position tracking. It is for these driver-assistance objectives that motivate the development of the novel "video-based lane estimation and tracking" (VioLET) system. The system is designed using steerable filters for robust and accurate lane-marking detection. Steerable filters provide an efficient method for detecting circular-reflector markings, solid-line markings, and segmented-line markings under varying lighting and road conditions. They help in providing robustness to complex shadowing, lighting changes from overpasses and tunnels, and road-surface variations. They are efficient for lane-marking extraction because by computing only three separable convolutions, we can extract a wide variety of lane markings. Curvature detection is made more robust by incorporating both visual cues (lane markings and lane texture) and vehicle-state information. The experiment design and evaluation of the VioLET system is shown using multiple quantitative metrics over a wide variety of test conditions on a large test path using a unique instrumented vehicle. A justification for the choice of metrics based on a previous study with human-factors applications as well as extensive ground-truth testing from different times of day, road conditions, weather, and driving scenarios is also presented. In order to design the VioLET system, an up-to-date and comprehensive analysis of the current state of the art in lane-detection research was first performed. In doing so, a comparison of a wide variety of methods, pointing out the similarities and differences between methods as well as when and where various methods are most useful, is presented

Recent progress in road and lane detection: a survey

Abstract: The problem of road or lane perception is a crucial enabler for advanced driver assistance systems. As such, it has been an active field of research for the past two decades with considerable progress made in the past few years. The problem was confronted under various scenarios, with different task definitions, leading to usage of diverse sensing modalities and approaches. In this paper we survey the approaches and the algorithmic techniques devised for the various modalities over the last 5 years. We present a generic break down of the problem into its functional building blocks and elaborate the wide range of proposed methods within this scheme. For each functional block, we describe the possible implementations suggested and analyze their underlying assumptions. While impressive advancements were demonstrated at limited scenarios, inspection into the needs of next generation systems reveals significant gaps. We identify these gaps and suggest research directions that may bridge them.

Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs)

Abstract: Remotely Piloted Aircraft (RPA) is presently in continuous development at a rapid pace. Unmanned Aerial Vehicles (UAVs) or more extensively Unmanned Aerial Systems (UAS) are platforms considered under the RPAs paradigm. Simultaneously, the development of sensors and instruments to be installed onboard such platforms is growing exponentially. These two factors together have led to the increasing use of these platforms and sensors for remote sensing applications with new potential. Thus, the overall goal of this paper is to provide a panoramic overview about the current status of remote sensing applications based on unmanned aerial platforms equipped with a set of specific sensors and instruments. First, some examples of typical platforms used in remote sensing are provided. Second, a description of sensors and technologies is explored which are onboard instruments specifically intended to capture data for remote sensing applications. Third, multi-UAVs in collaboration, coordination, and cooperation in remote sensing are considered. Finally, a collection of applications in several areas are proposed, where the combination of unmanned platforms and sensors, together with methods, algorithms, and procedures provide the overview in very different remote sensing applications. This paper presents an overview of different areas, each independent from the others, so that the reader does not need to read the full paper when a specific application is of interest