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CS280: Computer Vision
Computer Science Division
University of California Berkeley

INSTRUCTOR: Jitendra Malik
GSI: Pulkit Agrawal
GSI: Yuansi Chen 
SEMESTER: Spring 2015


Computer vision seeks to develop algorithms that replicate one of the most amazing capabilities ofthe human brain – inferring properties of the external world purely by means of the light reflectedfrom various objects to the eyes. We can determine how far away these objects are, how they areoriented with respect to us, and in relationship to various other objects. We reliably guess theircolors and textures, and we can recognize them - this is a chair, this is my dog Fido, this is a pictureof Bill Clinton smiling. We can segment out regions of space corresponding to particular objectsand track them over time, such as a basketball player weaving through the court.

In this course, we will study the concepts and algorithms behind some of the remarkable suc-cesses of computer vision – capabilities such as face detection, handwritten digit recognition, re-constructing three-dimensional models of cities, automated monitoring of activities, segmentingout organs or tissues in biological images, and sensing for control of robots. We will build thisup from fundamentals – an understanding of the geometry and radiometry of image formation,core image processing operations, as well as tools from statistical machine learning. On completingthis course a student would understand the key ideas behind the leading techniques for the mainproblems of computer vision - reconstruction, recognition and segmentation – and have a sense ofwhat computers today can or can not do.


  • Introduction - The Three R's - Recognition, Reconstruction, Reorganization
  • Static Perspective - the pinhole camera model
  • Transformations - rotation, translation, affine and projective
  • Dynamic perspective and optical flow
  • Radiometry of image formation
  • Basic image processing operations - filters, features and flow
  • Biological visual processing - retina, V1 and beyond
  • The feedforward model of visual processing - convolutional networks
  • Object recognition case study - Identifying digits with multiple approaches
  • Recognizing objects in scene - sliding windows and object proposals.
  • Feature Histograms
  • Convolutional Neural Network (ConvNet) based approaches to visual recognition of objects and scenes
  • Attributes, pose and actions
  • Controur detection and bottom-up segmentation, Gestalt grouping heuristics
  • Semantic Segmentations - instance segmentation and pixel classification
  • 3D reconstruction from multiple views
  • 3D reconstruction from pictorial cues
  • Scene understanding from RGBD images
  • Face Recognition
  • Video Analysis





Lecture 1: Introduction


Lecture 2: Fundamentals of Image Formation (Static Perspective)


Lecture 3: Transformations

Pinhole Camera




Lecture 4: Dynamic Perspective



Lecture 5: Radiometry of Image Formation



Lecture 6: Basic Image Processing


Lecture 7: Biological Visual Processing



Lecture 8: Handwritten Digit Recognition


Lecture 9: VIsual Grouping



Lecture 10: Object Detection Using ConvNets



Lecture 11: Deformable Parts Model (DPM)

Lecture 12: Binocular Stereopsis


Lecture 13: Binocular Stereopsis II



Lecture 14: Markov Random Fields in Computer Vision



Lecture 15: Solving for Stereo Correspondence



Lecture 16: Optical Flow


Lecture 17: Action Recognition



Lecture 18: Simultaneous Detection and Segmentation




Lecture 19: Pose and Keypoint Estimation




Lecture 20: Review of Differential Geometry




Lecture 21: Scene Understanding from RGBD Images




Lecture 22: 3D Perception from a Single image





Lecture 23: Face Recognition



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