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A Robust Line Detection Method Using Unit Gradient Vectors A ROBUST LINE DETECTION METHOD USING UNIT GRADIENT VECTORS BY VEYHONG CHHOR A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE (ENGINEERING AND TECHNOLOGY) SIRINDHORN INTERNATIONAL INSTITUTE OF TECHNOLOGY THAMMASAT UNIVERSITY ACADEMIC YEAR 2014 A ROBUST LINE DETECTION METHOD USING UNIT GRADIENT VECTORS BY VEYHONG CHHOR A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE (ENGINEERING AND TECHNOLOGY) SIRINDHORN INTERNATIONAL INSTITUTE OF TECHNOLOGY THAMMASAT UNIVERSITY ACADEMIC YEAR 2014 Abstract A ROBUST LINE DETECTION METHOD USING UNIT GRADIENT VECTORS by VEYHONG CHHOR Bachelor of Engineering in Computer Science, Institute of Technology of Cambodia, 2012 In this thesis, we present a robust line detection method by using unit gradient vectors (UGVs). The proposed method comprises two parts. Robust edge detection is performed using UGVs in the first part. Since UGVs are a feature that is essentially invariant to varying lighting condition, edge can be detected regardless of various image contrasts even within the same image. We then employ a gray-scale version of Hough transform (GHT) as the second part. The benefit of using the GHT is that we do not need to adjust a threshold value to detect the edges. In the case of the UGVs based edge detection, peaks in Hough space largely depend on the lengths of the lines. Thus, we can easily set up a proper threshold value in Hough space. Simulation results show that the proposed method can detect lines successfully even in an image captured under the non-uniform illumination. Keywords: Line detection, Illumination-invariant, Hough transform, Gray-scale Hough transform ii Acknowledgements Foremost, I would like to express my sincere gratitude to my supervisor Assoc. Prof. Dr. Toshiaki Kondo for his encouragement, useful critiques, and insightful guidance throughout my study and research, which motivated me to devote myself into this research. Without his guidance this thesis work would not have been a success. I truely express my gratitude to the committee members, Assoc. Prof. Dr. Waree Kongprawechnon, Asst. Prof. Dr. Itthisek Nilkhamhang, and Asst. Prof. Dr Supatana Auethavekiat for their inspiration and enlightened suggestions. I am also grateful to Assoc. Prof. Dr. Kazunori Kotani, for offering me sponsor for the internship opportunity in his laboratory at Japan Advanced Institute of Science and Technology (JAIST). My heartily thanks to all the Sirindhorn International Institute of Technology (SIIT), faculty members and staffs for their benevolent and potential helps during my study and research in SIIT. I also thank my friends in SIIT for their love and company. My wholehearted thanks to all of my family members, especially my beloved mother, for her love, affection, and support both financial and motivation for my studies in Sirindhorn International Institute of Technology, Thammasat University, Thailand. iii Table of Contents Chapter Title Page Signature Page i Abstract ii Acknowledgments iii Table of Contents iv List of Tables v List of Figures vi List of Acronyms vii 1 Introduction 1 1.1 General Information 1 1.2 Statement of Problem 1 1.3 Purpose of Study 2 2 Review of Literatures 3 2.1 Development of the Hough Transform 3 2.1.1 Robust Hough Transform 4 2.1.2 Development of Application using HT. 4 2.1.3 Development of HT in Hough Space or HT Accumulator Space. 5 2.2 Gradient Orientation Information 6 2.3 Gray scale Hough Transform 7 3 Design Method and Procedures 9 3.1 Traditional Hough transform with Gradient Orientation Information 9 3.2 Gray-scale Hough Transform with Gradient Orientation Information 10 4 Experimental Result and Discussion 12 4.1 Comparison between traditional and unit-gradient vectors based edge detection methods 13 4.1.1 Experiment on artificial images 14 4.1.2 Experiment on real images 16 4.2 Comparison between the standard HT and the gray-scale HT 17 4.2.1 Experiment on artificial images 20 4.2.2 Experiment on real images 22 5 Conclusion 24 References 25 iv List of Tables Table Page 4.1 Comparison between edge detection result of HT and GT 22 4.2 Comparison table of proposed method and GT 22 4.3 Comaprison result of ht and proposed method 23 v List of Figures Figure Page 2.1 Hough Transform 7 3.1 First proposed method. 10 3.2 Second approached 11 4.1 Experimental result of High and low contrasted image 13 4.2 Line detected in low contrasted image by varying threshold value 14 4.3 Experimental result of shaded and non-shaded images by using HT 15 4.4 Experimental result of shaded and non-shaded images by using UGVs 16 4.5 Lane image with virtual shaded 17 4.6 An edge image obtained by the Sobel operator 17 4.7 An edge image obtained my proposed method 18 4.8 Experiment result of the traditional HT and proposed method. 18 4.9 Line detected by optimizing the threshold value 19 4.10 Original artificial image without shaded area 21 4.11 The edge extracted from all of the artificial image 22 4.12 A high-contrast shaded image with HT and proposed method 23 vi List of Acronyms ADAS Advance Driver Assistant System BG Background BW Black and White FN False Negative FP False Positive GSHT Gray Scale Hough Transform GT Ground Truth GOSTs Gradient Orientation Structure Tensors GOI Gradient Orientation Information HP Hough Peak HS Hough Space HT Hough Transform LPF Low Pass Filter ROI Region of Interest TN True Negative TP True Positive UGVs Unit Gradient Vectors vii Chapter 1 Introduction 1.1 General Information Line detection is a very important problem and an essential task in digital image pro- cessing, because most of the objects are constructed by line segments, and most of the re- gion of interest in image are also constructed by lines. For example, if we want to develop an application to detect the lane mark for advance driver assistant system (ADAS), streets, building and other objects of interest from satellite or auto-pilot airplane, and etc. In fact, at first phase of all, we must extract the line segments, and then find way to define that object later. In order to detect the line, there are two main tasks. Firstly, we extract edge from a gray scale image, and then use the Hough transform to extract the line. Edge extraction is a process of extracting information from the gray into black and white (BW) image or we call it a binary image. Since after edges are extracted, we get a binary image, yet some information in the original image might be lost. Therefore edge detection is a critical task in digital image processing. In order to be successfully extract the line from gray scale image by using Hough transform. Instead of using pixels information, we use gradient orientation information or unit gradient vectors (UGVs) because it is known to be robust to the illumination-variant within image. In computer vision and digital image processing, the Hough transform is one of the most widely used algorithms and a robust method for the detection of unconnected straight lines in nosy image [2,7]. Edges are first detected in an image before performing the Hough transform. The detected edges are then transformed into sinusoidal curves in the parameter space. The sinusoidal curves intersect at the same spot repeatedly, producing a prominent peak in the parameter space. The lines can be determined by reading the coordinate of the peak in the parameter space. 1.2 Statement of Problem In practice, images are often noisy when they are captured at night or with a low qual- ity camera, even some images are not taken properly (blur image caused by hand shakes). All of the problems mentioned are challenging problems that we are facing every day, and most of the time, it has been created the shaded image. Hence, shaded image is a practical problem to overcome in extracting the edges from image. For example, as the image that had been taken in Fig. 5, there is a shadow laid in the middle of the lane side. After performing the edge detection, image is completely binary (black and white), and it is ready to perform the Hough transform, But unfortunately if there are lots of information from the original image is lost, and that information is the interest region, and unfortunately after performing the edge detection we cannot recover the lost information. Therefore the line detection result is not correct. Thus, in the Hough transform, we cannot get any lines at all, so even we try to manually optimized the best performance of Hough transform, but we still cannot detect all lines. In short, we can conclude that all procedures start with edge detection which is equiva- lent to detecting large magnitudes of gradients in an image. This indicates that line detection 1 becomes more difficult in a low-contrasted image unless a threshold value for detecting edges is adaptively adjusted. The selection of a proper threshold value is not always an easy task to perform. It should be noted that the task will be far more difficult when contrast varies within the image. In this thesis, we propose a preprocessing for the Hough transform to overcome the illumination-variant within image. Since gradient orientation information is known to be robust to vary illumination within image [16], we aim to add one more essential feature to the Hough transform, an illumination-invariant Hough transform. 1.3 Purpose of Study Our purpose of study is to develop a new method which: can detect the edge robustly to low-high contrast intensity within an image, and illumination- • variant, extend the proposed method to detect line by combination with the line detection • method the Hough transform, use these methods to apply in real applications in robust lane mark detection, advanced • driver assistant system (ADAS).
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