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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 toCS 675 – Computer VisionFall 2014 Instructor: Marc Pomplun Computer Vision Lecture 1: Human Vision
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
The Visual Attention Lab Cognitive Science, esp. eye movements Computer Vision Lecture 1: Human Vision
A poor guinea pig: Computer Vision Lecture 1: Human Vision
Computer Vision: Computer Vision Lecture 1: Human Vision
Modeling of Brain Functions Computer Vision Lecture 1: Human Vision
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
Example: Distribution of Visual Attention Computer Vision Lecture 1: Human Vision
Selectivity in Complex Scenes Computer Vision Lecture 1: Human Vision
Selectivity in Complex Scenes Computer Vision Lecture 1: Human Vision
Selectivity in Complex Scenes Computer Vision Lecture 1: Human Vision
Selectivity in Complex Scenes Computer Vision Lecture 1: Human Vision
Selectivity in Complex Scenes Computer Vision Lecture 1: Human Vision
Selectivity in Complex Scenes Computer Vision Lecture 1: Human Vision
Human-Computer Interfaces: Computer Vision Lecture 1: Human Vision
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
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
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
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
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
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
Visible light is just a part of the electromagnetic spectrum Computer Vision Lecture 1: Human Vision 22
Cross Section of the Human Eye Computer Vision Lecture 1: Human Vision 23
Computer Vision Lecture 1: Human Vision
Photoreceptor Bipolar Ganglion Computer Vision Lecture 1: Human Vision
Major Cell Types of the Retina Computer Vision Lecture 1: Human Vision
Receptive Fields Computer Vision Lecture 1: Human Vision
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
Computer Vision Lecture 1: Human Vision
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
Computer Vision Lecture 1: Human Vision
Computer Vision Lecture 1: Human Vision