From image edges to geons to viewpoint-invariant object models: a neural net implementation

Irving Biederman; John E. Hummel; Peter C. Gerhardstein; Eric E. Cooper

doi:10.1117/12.58602

1 March 1992 From image edges to geons to viewpoint-invariant object models: a neural net implementation

Irving Biederman, John E. Hummel, Peter C. Gerhardstein, Eric E. Cooper

Proceedings Volume 1708, Applications of Artificial Intelligence X: Machine Vision and Robotics; (1992) https://doi.org/10.1117/12.58602
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

Three striking and fundamental characteristics of human shape recognition are its invariance with viewpoint in depth (including scale), its tolerance of unfamiliarity, and its robustness with the actual contours present in an image (as long as the same convex parts [geons] can be activated). These characteristics are expressed in an implemented neural network model (Hummel & Biederman, 1992) that takes a line drawing of an object as input and generates a structural description of geons and their relations which is then used for object classification. The model's capacity for structural description derives from its solution to the dynamic binding problem of neural networks: independent units representing an object's parts (in terms of their shape attributes and interrelations) are bound temporarily when those attributes occur in conjunction in the system's input. Temporary conjunctions of attributes are represented by synchronized activity among the units representing those attributes. Specifically, the model induces temporal correlation in the firing of activated units to: (1) parse images into their constituent parts; (2) bind together the attributes of a part; and (3) determine the relations among the parts and bind them to the parts to which they apply. Because it conjoins independent units temporarily, dynamic binding allows tremendous economy of representation, and permits the representation to reflect an object's attribute structure. The model's recognition performance conforms well to recent results from shape priming experiments. Moreover, the manner in which the model's performance degrades due to accidental synchrony produced by an excess of phase sets suggests a basis for a theory of visual attention.

Citation Download Citation

Irving Biederman, John E. Hummel, Peter C. Gerhardstein, and Eric E. Cooper "From image edges to geons to viewpoint-invariant object models: a neural net implementation", Proc. SPIE 1708, Applications of Artificial Intelligence X: Machine Vision and Robotics, (1 March 1992); https://doi.org/10.1117/12.58602

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available