A Novel Implementation of Rotation Detection Algorithm using a Polar Representation of Extreme Contour Point based on Sobel Edge|RISS 상세보기

다국어 초록 (Multilingual Abstract)

We propose a fast algorithm using Extreme Contour Point (ECP) to detect the angle of rotated images, is implemented by rotation feature of one covered frame image that can be applied to correct the rotated images like in image processing for real time applications, while CORDIC is inefficient to calculate various points like high definition image since it is only possible to detect rotated angle between one point and the other point. The two advantages of this algorithm, namely compatibility to images in pre-processing by using Sobel edge process for pattern recognition. While the other one is its simplicity for rotated angle detection with cyclic shift of two 1×n matrix set without complexity in calculation compared with CORDIC algorithm. In ECP, the edge features of the sample image of gray scale were determined using the Sobel Edge Process. Then, it was subjected to binary code conversion of 0 or 1 with circular boundary to constitute the rotation in invariant conditions. The results were extracted to extreme points of the binary image. Its components expressed not just only the features of angle θ but also the square of radius r<SUP>2</SUP> from the origin of the image. The detected angle of this algorithm is limited only to an angle below 10 degrees but it is appropriate for real time application because it can process a 200 degree with an assumption 20 frames per second. ECP algorithm has an O (n<SUP>2</SUP>) in Big O notation that improves the execution time about 7 times the performance if CORDIC algorithm is used.

번역하기

목차 (Table of Contents)

Abstract
Ⅰ. INTRODUCTION
Ⅱ. MODIFYING IMAGE PROCESS
Ⅲ. EXPERIMENTAL RESULTS
Ⅳ. CONCLUSION

Abstract
Ⅰ. INTRODUCTION
Ⅱ. MODIFYING IMAGE PROCESS
Ⅲ. EXPERIMENTAL RESULTS
Ⅳ. CONCLUSION
REFERENCES

참고문헌 (Reference)

1 J. C. McCall, "Video-Based Lane Estimation and Tracking for Driver Assistance : Survey, System, and Evaluation" 7 (7): 20-37, 2006

2 J. E Volder, "The CORDIC Trigonometric Computing Technique" EC-8 (EC-8): 330-334, 1959

3 Z. Li, "Scale-and Rotation-Invariant Local Binary Pattern Using Scale-Adaptive Texton and Subuniform-Based Circular Shift" 21 (21): 2012

4 G. Zhao, "Rotation-Invariant Image and Video Description With Local Binary Pattern Features" 21 (21): 2012

5 M. Kuhlmann, "P-CORDIC: A Precomputation Based Rotation CORDIC Algorithm" 9 : 936-943, 2002

6 T. Ojala, "Multiresolution Gray scale and Rotation Invariant Texture Classification with Local Binary Patterns" 24 : 971-987, 2002

7 K. Shimura, "Mobility analysis of the aged pedestrians by experiment and simulation" 44 : 58-63, 2014

8 D. Kim, "Design of an Adaptive Cruise Control / Collision Avoidance with Lane Change Support for Vehicle Autonomous Driving" 2938-2943, 2009

9 L. Oliverira, "Context-aware Pedestrian Detection Using LIDAR" 773-778, 2010

10 S. Y. Park, "Analysis and Parameter Selections of MSR-CORDIC With Applications to FFT Designs" 60 (60): 2012