Boyer - 4 
Additional Reading 
The reading list below provides more detail on our ideas regarding the role and 
implementation of perceptual organization in computer vision systems, and covers 
several applications. The first item in the list offers an extensive review of the 
literature from both the machine and biological vision research communities, as well 
as proposing a framework for discussion, and would be a good starting point for 
readers wanting to get a more global perspective on the issue. 
• S. Sarkar and K. L. Boyer, “Perceptual Organization in Computer Vision: A 
Review and a Proposal for a Classificatory Structure,” IEEE Transactions on 
Systems, Man, and Cybernetics , Vol. 23, No. 2, pp. 382-399, March 1993. 
• S. Sarkar and K. L. Boyer, “Computing Perceptual Organization in Computer 
Vision,” World Scientific Series on Machine Perception and Artificial Intelli 
gence, Singapore: World Scientific, 1994. 
• S. V. Raman, S. Sarkar, and K. L. Boyer, “Generating Structure Hypothe 
ses in Cerebral Magnetic Resonance Images Using Segment-Based Focusing 
and Graph-Theoretic Cycle Enumeration,” in Advances in Image Analysis, 
Y. Mahdavieh and R. C. Gonzalez (eds.), SPIE Optical Engineering Press, 
1992, pp. 429-453. 
• S. Sarkar and K. L. Boyer, “Using Perceptual Inference Networks to Manage 
Vision Processes,” CVGIP: Image Understanding , Vol. 62, No. 1, pp. 27-46, 
July 1995. 
• S. Sarkar and K. L. Boyer, “A Computational Structure for Preattentive Per 
ceptual Organization: Graphical Enumeration and Voting Methods,” IEEE 
Transactions on Systems, Man, and Cybernetics , Vol. 24, No. 2, pp. 246-267, 
February 1994. 
• S. Sarkar and K. L. Boyer, “Integration, Inference, and Management of Spa 
tial Information Using Bayesian Networks: Perceptual Organization,” IEEE 
Transactions on Pattern Analysis and Machine Intelligence , Special Issue on 
Probabilistic Reasoning, Vol. 15, No. 3, pp. 256-274, March 1993. 
• S. V. Raman, S. Sarkar, and K. L. Boyer, “Hypothesizing Structures in Edge 
Focused Cerebral Magnetic Resonance Images using Graph-Theoretic Cycle 
Enumeration,” CVGIP: Image Understanding , Vol. 57, No. 1, pp. 81-98, Jan 
uary 1993. 
• S. Sarkar and K. L. Boyer, “Automated Design of Bayesian Perceptual In 
ference Networks,” 1994 IEEE Computer Society Conference on Computer 
Vision & Pattern Recognition , Seattle, WA, June 1994, pp. 98-103.