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Welcome to CS 675 – Computer Vision Fall 2014

Welcome to CS 675 – Computer Vision Fall 2014. Instructor: Marc Pomplun. Instructor – Marc Pomplun. Office: S-3-171 Lab: S-3-135 Office Hours: Tuesdays 3:30-4:00, 5:15–7:00 Thursdays 5:15– 6:00 Phone: 287-6443 (office) 287-6485 (lab) E-Mail: marc@cs.umb.edu

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Welcome to CS 675 – Computer Vision Fall 2014

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  1. Welcome toCS 675 – Computer VisionFall 2014 Instructor: Marc Pomplun Computer Vision Lecture 1: Human Vision

  2. Instructor – Marc Pomplun • Office: S-3-171 • Lab: S-3-135 • Office Hours: Tuesdays 3:30-4:00, 5:15–7:00 Thursdays 5:15– 6:00 • Phone: 287-6443 (office) 287-6485 (lab) • E-Mail: marc@cs.umb.edu • Website: http://www.cs.umb.edu/~marc/cs675/ Computer Vision Lecture 1: Human Vision

  3. The Visual Attention Lab Cognitive Science, esp. eye movements Computer Vision Lecture 1: Human Vision

  4. A poor guinea pig: Computer Vision Lecture 1: Human Vision

  5. Computer Vision: Computer Vision Lecture 1: Human Vision

  6. Modeling of Brain Functions Computer Vision Lecture 1: Human Vision

  7. Modeling of Brain Functions unit and connection l a y e r + 1 l in the interpretive network unit and connection in the gating network unit and connection in the top-down bias network l a y e r l l a y e r - 1 l Computer Vision Lecture 1: Human Vision

  8. Example: Distribution of Visual Attention Computer Vision Lecture 1: Human Vision

  9. Selectivity in Complex Scenes Computer Vision Lecture 1: Human Vision

  10. Selectivity in Complex Scenes Computer Vision Lecture 1: Human Vision

  11. Selectivity in Complex Scenes Computer Vision Lecture 1: Human Vision

  12. Selectivity in Complex Scenes Computer Vision Lecture 1: Human Vision

  13. Selectivity in Complex Scenes Computer Vision Lecture 1: Human Vision

  14. Selectivity in Complex Scenes Computer Vision Lecture 1: Human Vision

  15. Human-Computer Interfaces: Computer Vision Lecture 1: Human Vision

  16. Your Evaluation • 4 sets of exercises (individual work) • paper-and-pencil questions: 10% • programming tasks: 30% • midterm (75 minutes)25% • final exam (2.5 hours) 35% Computer Vision Lecture 1: Human Vision

  17. Grading For the assignments, exams and your course grade, the following scheme will be used to convert percentages into letter grades: •  95%: A •  90%: A-  86%: B+ 82%: B 78%: B-  74%: C+  70%: C 66%: C-  62%: D+ 56%: D 50%: D-  50%: F Computer Vision Lecture 1: Human Vision

  18. Complaints about Grading • If you think that the grading of your assignment or exam was unfair, • write down your complaint (handwriting is OK), • attach it to the assignment or exam, • and give it to me or put it in my mailbox. • I will re-grade the exam/assignment and return it to you in class. Computer Vision Lecture 1: Human Vision

  19. Computer Vision • Computer Vision is the science of building systems that can extract certain task-relevant information from a visual scene. • Such systems can be used for applications such as optical character recognition, analysis of satellite and microscopic images, magnetic resonance imaging, surveillance, identity verification, quality control in manufacturing etc. Computer Vision Lecture 1: Human Vision

  20. Computer Vision • In a way, Computer Vision can be considered the inversion of Computer Graphics. • A computer graphics systems receives as its input the formal description of a visual scene, and its output is a visualization of that scene. • A computer vision system receives as its input a visual scene, and its output is a formal description of that scene with regard to the system’s task. • Unfortunately, while a computer graphics task only allows one solution, computer vision tasks are often ambiguous, and it is unclear what the correct output should be. Computer Vision Lecture 1: Human Vision

  21. Computer Vision • Digital Images • Binary Image Processing • Color • Image Filtering • Basic Image Transformation • Edge Detection • Image Segmentation • Shape Representation • Texture • Depth • Motion • Object Recognition • Image Understanding Computer Vision Lecture 1: Human Vision

  22. Visible light is just a part of the electromagnetic spectrum Computer Vision Lecture 1: Human Vision 22

  23. Cross Section of the Human Eye Computer Vision Lecture 1: Human Vision 23

  24. Computer Vision Lecture 1: Human Vision

  25. Photoreceptor Bipolar Ganglion Computer Vision Lecture 1: Human Vision

  26. Major Cell Types of the Retina Computer Vision Lecture 1: Human Vision

  27. Receptive Fields Computer Vision Lecture 1: Human Vision

  28. Coding of Visual Information in the Retina • Photoreceptors: Trichromatic Coding • Peak wavelength sensitivities of the three cones:Blue cone: Short- Blue-violet (420 nm) Green cone: Medium- Green (530 nm)Red Cone: Long- Yellow-green (560nm) Computer Vision Lecture 1: Human Vision

  29. Computer Vision Lecture 1: Human Vision

  30. Coding of Visual Information in the Retina • Retinal Ganglion Cells: • Opponent-Process Coding • Negative afterimage: • The image seen after a portion of the retina is exposed to an intense visual stimulus; consists of colors complimentary to those of the physical stimulus. • Complimentary colors: • Colors that make white or gray when mixed together. Computer Vision Lecture 1: Human Vision

  31. Computer Vision Lecture 1: Human Vision

  32. Computer Vision Lecture 1: Human Vision

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