Object and Face Recognition
MIT
Spring, 2001
Instructor: Prof. Pawan Sinha
sinha@ai.mit.edu
Lecture plan:
Feb 6: Introduction; Course overview
Feb 8: Pattern recognition in insects, octopii and insect-inspired robots
Feb 13: Pattern recognition in birds
Feb 15: Characteristics of human object recognition psychophysics based on psychophysical experiments
Feb 20: No class (Monday schedule)
Feb 22: Brain mechanisms of object recognition – neurophysiological studies
Feb 27: Effects of brain damage on primate recognition performance
Mar 1: Scene perception and contextual influences on individual object recognition
Mar 6: Development of object perception in childhood and in adults
Mar 8: Classical pattern classification theory
Mar 13: Theories of recognition: 1. Theories based on 3D models of recognition; 2. Theories based on 2D image models
Mar 15: In Arlington for DARPA; Linear object models
Mar 20: Image and model correspondence; Image and object segmentation
Mar 22: Feedforward and feedback network models of recognition
Mar 27: Spring break
Mar 29: Spring break
Apr 3: Notable case-studies of machine-based recognition systems
Apr 5: In-class Midterm exam
Apr 10: Are faces special? Evidence from physiology, neuropsychology, psychophysics, imaging; developmental studies
Apr 12: Is face recognition feature-based or holistic? Studies of cue saliency in faces; caricaturing effects
Apr 17: Patriot’s day; no class
Apr 19: Perception of facial affect, gaze, and aesthetics
Apr 24: Psycho-forensic aspects of face recognition
Apr 26: Case-studies of implemented face recognition systems
May 1: Top-down influences of recognition on perception
May 3: Recognition in other sensory modalities; Synthesis and open questions
May 8: Away at Vision Sciences Meeting; No class
May 10: An overview of object recognition research at MIT
May 15: Project presentations
May 17: Project presentations
Requirements:
10%: Class participation
15%: Lead a class discussion and scribe notes for one lecture; Roving microphone
10%: Send three questions to scribe after each lecture:
1. Open research question/project idea
2. A short answer question
3. A multiple choice question
25%: Mid-term exam
40%: Term project