1. Penalty for unethical activity is a FF and expulsion from the department
2. You are expected to attend all classes
3. There are no make ups for worksheets in any circumstances.
4. If you miss an examination due to a valid, documented reason, considerations for a makeup or prorated grading might be considered. Please get in touch with Dr. Sarkar as soon as possible regarding this.
5. Students who anticipate to be absent from class due to religious observance should inform Dr. Sarkar by email by second class meeting.
6. You do not have the right to sell notes or tapes of lectures generated from this class. Click here for USF’s policy on Course Notes and Recording
You will develop awareness of key journals and conferences in the area.
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)
Computer Vision and Image Understanding (CVIU)
IEEE Transactions on Image Processing
International Journal of Computer Vision (IJCV)
IEEE Transactions on Systems, Man, and Cybernetics – Part B (SMC)
Image and Vision Computing (IVC)
International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI)
Pattern Analysis and Applications (PAA)
Pattern Recognition (PR)
International Conference on Computer Vision (ICCV)
International Conference on Computer Vision and Pattern Recognition (CVPR)
International Conference on Pattern Recognition (ICPR)
International Conference on Image Processing (ICIP)
You will learn to read, understand, critique, and present ideas presentation in published papers.
You will learn to communicate and hone your presentation skills.
Assignments, in-class work: 20%
Paper Report: 10%
Book Report: Presentation: 10%, Report 10%
Project: Presentations (Proposal: 10%, Final: 20%), Report 20%
Computer Vision Fact and Fiction in Film (3D Vision)
Computer Vision World Page for Datasets, Code, etc…
Machine Vision Toolbox in Matlab (camera calibration, feature extraction, matching, segmentation)
Segmentation and Grouping
Graph based methods
Minimal Spanning Trees, Connected Components, Cliques etc
· C. T. Zahn, “Graph Theoretical Methods for Detecting Gestalt Clusters,” July 1970
¨ Figure for the above paper are available HERE
Graph Spectral Methods:
· Coherent clusters: S. Sarkar and K. Boyer, “Quantitative Measures of Change Based on Feature Organization: Eigenvalues and Eigenvectors,” CVIU, July 1998
Laplacian Cut: P.
· Normalized Laplacian Cut: J. Shi and J. Malik, “Normalized Cuts and Image Segmentation,” PAMI, Aug 2000
Histogram based methods