CP7012 COMPUTER VISION Syllabus - Anna University ME CSE 2nd Semester Regulation 2013 CP7012 Syllabus - www.annauniv.edu
OBJECTIVES:
To review image processing techniques for computer vision
To understand shape and region analysis
To understand Hough Transform and its applications to detect lines, circles, ellipses
To understand three-dimensional image analysis techniques
To understand motion analysis
To study some applications of computer vision algorithms
UNIT I IMAGE PROCESSING FOUNDATIONS
Review of image processing techniques – classical filtering operations – thresholding techniques – edge detection techniques – corner and interest point detection – mathematical morphology – texture
UNIT II SHAPES AND REGIONS
Binary shape analysis – connectedness – object labeling and counting – size filtering – distance functions – skeletons and thinning – deformable shape analysis – boundary tracking procedures – active contours – shape models and shape recognition – centroidal profiles – handling occlusion – boundary length measures – boundary descriptors – chain codes – Fourier descriptors – region descriptors – moments
UNIT III HOUGH TRANSFORM
Line detection – Hough Transform (HT) for line detection – foot-of-normal method – line localization – line fitting – RANSAC for straight line detection – HT based circular object detection – accurate center location – speed problem – ellipse detection – Case study: Human Iris location – hole detection – generalized Hough Transform (GHT) – spatial matched filtering – GHT for ellipse detection – object location – GHT for feature collation
UNIT IV 3D VISION AND MOTION
Methods for 3D vision – projection schemes – shape from shading – photometric stereo – shape from texture – shape from focus – active range finding – surface representations – point-based representation – volumetric representations – 3D object recognition – 3D reconstruction – introduction to motion – triangulation – bundle adjustment – translational alignment – parametric motion – spline-based motion – optical flow – layered motion
UNIT V APPLICATIONS
Application: Photo album – Face detection – Face recognition – Eigen faces – Active appearance and 3D shape models of faces Application: Surveillance – foreground-background separation – particle filters – Chamfer matching, tracking, and occlusion – combining views from multiple cameras – human gait
analysis Application: In-vehicle vision system: locating roadway – road markings – identifying road
signs – locating pedestrians
OUTCOMES:
Upon completion of the course, the students will be able to
Implement fundamental image processing techniques required for computer vision
Perform shape analysis
Implement boundary tracking techniques
Apply chain codes and other region descriptors
Apply Hough Transform for line, circle, and ellipse detections
Apply 3D vision techniques
Implement motion related techniques
Develop applications using computer vision techniques
REFERENCES:
1. E. R. Davies, “Computer & Machine Vision”, Fourth Edition, Academic Press, 2012.
2. R. Szeliski, “Computer Vision: Algorithms and Applications”, Springer 2011.
3. Simon J. D. Prince, “Computer Vision: Models, Learning, and Inference”, Cambridge University Press, 2012.
4. Mark Nixon and Alberto S. Aquado, “Feature Extraction & Image Processing for Computer Vision”, Third Edition, Academic Press, 2012.
5. D. L. Baggio et al., “Mastering OpenCV with Practical Computer Vision Projects”, Packt Publishing, 2012.
6. Jan Erik Solem, “Programming Computer Vision with Python: Tools and algorithms for analyzing images”, O'Reilly Media, 2012.
OBJECTIVES:
To review image processing techniques for computer vision
To understand shape and region analysis
To understand Hough Transform and its applications to detect lines, circles, ellipses
To understand three-dimensional image analysis techniques
To understand motion analysis
To study some applications of computer vision algorithms
UNIT I IMAGE PROCESSING FOUNDATIONS
Review of image processing techniques – classical filtering operations – thresholding techniques – edge detection techniques – corner and interest point detection – mathematical morphology – texture
UNIT II SHAPES AND REGIONS
Binary shape analysis – connectedness – object labeling and counting – size filtering – distance functions – skeletons and thinning – deformable shape analysis – boundary tracking procedures – active contours – shape models and shape recognition – centroidal profiles – handling occlusion – boundary length measures – boundary descriptors – chain codes – Fourier descriptors – region descriptors – moments
UNIT III HOUGH TRANSFORM
Line detection – Hough Transform (HT) for line detection – foot-of-normal method – line localization – line fitting – RANSAC for straight line detection – HT based circular object detection – accurate center location – speed problem – ellipse detection – Case study: Human Iris location – hole detection – generalized Hough Transform (GHT) – spatial matched filtering – GHT for ellipse detection – object location – GHT for feature collation
UNIT IV 3D VISION AND MOTION
Methods for 3D vision – projection schemes – shape from shading – photometric stereo – shape from texture – shape from focus – active range finding – surface representations – point-based representation – volumetric representations – 3D object recognition – 3D reconstruction – introduction to motion – triangulation – bundle adjustment – translational alignment – parametric motion – spline-based motion – optical flow – layered motion
UNIT V APPLICATIONS
Application: Photo album – Face detection – Face recognition – Eigen faces – Active appearance and 3D shape models of faces Application: Surveillance – foreground-background separation – particle filters – Chamfer matching, tracking, and occlusion – combining views from multiple cameras – human gait
analysis Application: In-vehicle vision system: locating roadway – road markings – identifying road
signs – locating pedestrians
OUTCOMES:
Upon completion of the course, the students will be able to
Implement fundamental image processing techniques required for computer vision
Perform shape analysis
Implement boundary tracking techniques
Apply chain codes and other region descriptors
Apply Hough Transform for line, circle, and ellipse detections
Apply 3D vision techniques
Implement motion related techniques
Develop applications using computer vision techniques
REFERENCES:
1. E. R. Davies, “Computer & Machine Vision”, Fourth Edition, Academic Press, 2012.
2. R. Szeliski, “Computer Vision: Algorithms and Applications”, Springer 2011.
3. Simon J. D. Prince, “Computer Vision: Models, Learning, and Inference”, Cambridge University Press, 2012.
4. Mark Nixon and Alberto S. Aquado, “Feature Extraction & Image Processing for Computer Vision”, Third Edition, Academic Press, 2012.
5. D. L. Baggio et al., “Mastering OpenCV with Practical Computer Vision Projects”, Packt Publishing, 2012.
6. Jan Erik Solem, “Programming Computer Vision with Python: Tools and algorithms for analyzing images”, O'Reilly Media, 2012.