Vision Candidacy Exam Syllabus

pdf of exam syllabus

Exam Format:

Students will prepare and deliver a 20 minute presentation on a research project they have conducted. This will be followed by an open question and answer session on the students research as well as general topics in computer vision described below.

The candidacy committee will consist of five faculty members, the majority of whom must be members of the student’s program, to administer the exam according to UCI Senate Policy. Please see the ICS Graduate Office for policies regarding the advancement committee membership and timing.

Affiliated Faculty:

Fowlkes, Majumder, Jain, Ramanan, Welling

Recommended Coursework:


CS 216: Image Understanding
CS 217: Light and Geometry
CS273A: Machine Learning
CS274A: Probabilistic Learning
CS 260: Fundamentals of the Design and Analysis of Algorithms
CS 271: Introduction to Artificial Intelligence
+2 additional Core Classes (from departmental requirements)

Additional relevant courses:


CS 211A: Visual Computing
CS 261: Data Structures
CS 263: Analysis of Algorithms
CS 265: Graph Algorithms
CS 266: Computational Geometry
CS 273B: Kernel Methods
CS 274B: Learning in Graphical Models
CS 275: Network-Based Reasoning/Constraint Networks
CS 276: Network-Based Reasoning/Belief Networks
CS 282: Scientific Computing


Topics and Reading for Exam

Students are expected to demonstrate an in-depth knowledge of topics in computer vision research. In addition to material from CS 216 and 217, students are expected to have read the following research papers and be able to explain the main ideas contained therein. Students should choose a subset of 10 (that span all 4 areas) to focus on prior to their exam and indicate this choice to their exam committee.

archive of all papers below

  • Object Recognition and Detection:
    • ``Distinctive image features from scale-invariant keypoints'' D. Lowe. IJCV 2004. link
    • ``Robust real-time object detection'' P. Viola, M. Jones. IJCV 2002. link
    • ``Histograms of Oriented Gradients for Human Detection'' N. Dalal, B. Triggs. CVPR 2005. link
    • ``Object Detection with Discriminatively Trained Part-Based Models'' P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan. PAMI 2009. link
    • ``Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories'' S. Lazebnik, C. Schmid, J. Ponce. CVPR 2006. link
    • ``Shape matching and object recognition using shape contexts'' S. Belongie, J. Malik, J. Puzicha. PAMI 2002. link
    • ``Pictorial structures for object recognition'' P. Felzenszwalb, D. Huttenlocher. IJCV 05. link
    • ``Active appearance models'' T. Cootes, G. Edwards, C. Taylor. PAMI 2001. link
    • ``An Implicit Shape Model for Combined Object Categorization and Segmentation'' B. Liebe, A. Leonardis, B. Schiele. LNCS 2006. link
    • ``Generalizing the Hough transform to detect arbitrary shapes'' D. Ballard. Pattern Recogntion 1981. link
    • ``Names and Faces in the News'' T. Berg, et al. CVPR 04. link
    • ``Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope'' A. Oliva, A. Torralba. IJCV 01. link
  • Segmentation and Low-level vision:
    • ``The Laplacian pyramid as a compact image code'' P. Burt, T. Adelson, IEEE Trans. Com. 1983 link
    • ``Normalized cuts and Image Segmentation'' J. Shi, J. Malik. PAMI 2000. link
    • ``Learning to detect boundaries in natural images'' D. Martin, C. Fowlkes, J. Malik. PAMI 2003 link
    • ``Mean shift analysis and applications'' D. Comaniciu, P. Meer. PAMI 2002. link
    • ``Fast approximate energy minimization via graph cuts'' Y. Boykov, O. Veksler, R. Zabih. PAMI 2002. link
  • Geometry and Photometry:
    • ``Visual modeling with a hand-held camera'' M. Pollefeys et al. IJCV 2004. link
    • ``A taxonomy and evaluation of dense two-frame stereo correspondence algorithms'' D. Scharstein, R. Szeliski. IJCV 2002. link
    • ``In defense of the 8pt algorithim'' R. Hartley. PAMI 1997. link
    • ``What is the set of images of an object under all possible lighting conditions?'' P. Belhumuer, D. Kreigman. IJCV 1998. link
    • ``Lambertian reflectance and linear subspaces'' R.Basri, D. Jacobs. PAMI 2003. link
    • ``Putting Objects in Perspective'' D. Hoeim, A. Efros, M. Hebert. IJCV 2008. link
    • ``Modeling the world from Internet photo collections'', Snavely, Seitz, Szeliski, IJCV 2008. link
    • ``Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography'', M. Fischler, R. Boyles. Communications of the ACM 1981. link
  • Actions and Motion:
    • ``Recognizing action at a distance'' A. Efros, A. Berg, G. Mori J. Malik. ICCV 2003 link
    • ``Tracking people by learning their appearance'' D. Ramanan, D.A. Forsyth, A. Zisserman. PAMI 2007. link
    • ``Lucas-Kanade, 20 years on: A unifying framework'' S. Baker, I. Matthews. IJCV 2004. link
    • ``Twist based Acquisition and Tracking of Animal and Human Kinematics'' C. Bergler, J. Malik, K. Pullen. IJCV 2004. link
    • ``A unified mixture framework for motion segmentation: Incorporating spatial coherence and estimating the number of models'' Y. Weiss, T. Adelson. CVPR 1996 link