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Computational vision

Computational vision (CLPS 1520, Fall, Serre):

A detailed introduction to computational models of biological and machine vision summarizing traditional approaches and providing experience with state-of-the-art methods. Topics include fundamentals of image processing, visual perception (surfaces, color, depth, texture and motion) as well as object recognition and scene understanding. Graduate, Undergraduate

Introduction to Computer Vision (CSCI 1430, Fall, Hayes):

This course treats vision as inference from noisy and uncertain data and emphasizes probabilistic and statistical approaches. Topics may include perception of 3D scene structure from stereo, motion, and shading; segmentation and grouping; texture analysis; learning, object recognition; tracking and motion estimation. Strongly recommended: basic linear algebra, calculus, and probability. Graduate, Undergraduate

Image Understanding (ENGN 1610, Fall, Kimia):

Image processing is a technology experiencing explosive growth; it is central to medical image analysis and transmission, industrial inspection, image enhancement, indexing into pictorial and video databases, e.g., WWW, and to robotic vision, face recognition, and image compression. This senior-level undergraduate course covers theoretical underpinnings of this field and includes a series of practical MATLAB image processing projects. Graduate, Undergraduate

    Academic Resources at Brown
  1. Recommended coursework for undergraduate students
  2. Faculty with computational neuroscience research
    Recommended Courses on Computational Modeling
  1. Methods & programming
  2. Biostats & bioengineering
  3. Methods & data analysis
  4. Computational vision
  5. Computational cognitive science / neuroscience
  6. Neuroscience
  7. Mathematical & Computations