Preface.
Chapter 1 Introduction; Jae-seok Kim, Hyun-chul Shin. 1.1 Introduction to the advanced driver assistance system. 1.2 Industrial Developments for ADAS. 1.3 System-on-chip platform architecture for automobile vision systems. References.
Chapter 2 Lens Correction and Gamma Correction; Sang-Bock Cho. 2.1 Lens Correction. 2.2 Gamma Correction. References.
Chapter 3 Super Resolution; Hyo-Moon Cho. 3.1 Introduction. 3.2 Observation model. 3.3 Survey of the Super Resolution algorithms. 3.4 Novel Super Resolution registration algorithm based on Frequency. 3.5 Conclusion. References.
Chapter 4 Image enhancement for improving object recognition; Jae-Seok Kim. 4.1 General Image Enhancement Techniques. 4.2 Image Enhancement Techniques for Automobile Application. References.
Chapter 5 Detection of Vehicles and Pedestrians; Hyunchul Shin, Irfan Riaz. 5.1 Introduction to Vehicle/Pedestrian Detection. 5.2 Vehicle Detection. 5.3 Pedestrian Detection. 5.4 Night-time Pedestrian Detection. References.
Chapter 6 Monitoring Driver's State and Predicting Unsafe Driving Behavior; Hang-Bong Kang. 6.1 Introduction. 6.2 Driver Drowsiness Measurement. 6.3. Driver Distraction Detection. 6.4 Predicting Unsafe Driving Behavior. 6.5 Discussion. 6.6 Conclusions. References
Chapter 7 SoC Architecture for Automobile Vision System; Kyounghoon Kim, Kiyoung Choi. 7.1 Automotive Applications. 7.2 Architectural Consideration for Vision. 7.3 Example - Pedestrian Detection. 7.4 Comparison of COTS Architectures. 7.5 More on GPU. 7.6 Comparison of VLIW and COTS Architecture. 7.7 Memory/Bus Requirement. 7.8 Vision Processors. 7.9 Yet another Approach. 7.10 Conclusions. References.
Chapter 8 Hardware accelerator for feature point detection and Matching; Jun-Seok Park, Lee-Sup Kim. 8.1. Introduction to interest point detection and matching. 8.2. Interest point detection hardware with joint algorithm-architecture optimization. 8.3. Unified Datapath. 8.4. Chip implementation. 8.5. Application. 8.6. Conclusions. References.
Chapter 9 Software Development Environment for Automotive SoC; Jeonghun Cho. 9.1. Introduction. 9.2. AUTOSAR architecture. 9.3. Demonstration of AUTOSAR ECUs. 9.4. Conclusions. References.
Chapter 10 Reliability issues for automobile SoCs; Sungju Park. 10.1 Introduction. 10.2 Conclusions. References.
About the Author: Prof. Jaeseok Kim received B.S degree from Yonsei University in Korea, M.S degree from KAIST in Korea and Ph. D degree from RPI, USA in 1988. From 1988 to 1993, he was a member of technical staff at the AT&T Bell Lab., Murray Hill, NJ, USA. He is currently a professor of the electrical and electronic engineering department at Yonsei University, Seoul, Korea.
He was the best research achievement professor of year 2006 at Yonsei University and received the best brain award in Korea semiconductor technology area in 2005.
He has published more than a hundred refereed papers with five text books in electrical engineering area. He holds 45 patents and transferred 5 patents to ten companies.
He is now serving as a distinguished lecturer of IEEE Circuit & System society. His current research interests include wireless communication SoC design, high performance video codec and image signal processing algorithm and SoC platform architecture.
Prof. Hyunchul Shin received B.S. degree and M.S. degree, from SNU and KAIST, respectively and Ph. D degree from U.C. Berkeley in 1987. He received Fulbright Scholarship. From 1987 to 1989, he was a member of technical staff at the AT&T Bell Lab., Murray Hill, NJ, USA. Since 1989, he has been a professor of the Department of Electronics Engineering at Hanyang University, Ansan, Korea. He has published more than a hundred technical papers and holds 15 patents. He worked as a Technical Adviser for several companies, including Samsung Electronics Co. and Atrenta. He served as General Chair of ISOCC 2007 and as Program Chair of ASP-DAC 2011. He is the president of IDEC Platform Center at Hanyang University. His research interests include SoC design, Computer Aided Design and Design and Synthesis of Integrated Systems for Vision and Image Processing applications.
