Model validation for change detection [machine vision]

An important application of machine vision is to provide a means to monitor a scene over a period of time and report changes in the content of the scene. We have developed a validation mechanism that implements the first step towards a system for detecting changes in images of aerial scenes. By validation we mean the confirmation of the presence of model objects in the image. Our system uses a 3-D site model of the scene as a basis for model validation, and eventually for detecting changes and to update the site model. The scenario for our present validation system consists of adding a new image to a database associated with the site. The validation process is implemented in three steps: registration of the image to the model, or equivalently, determination of the position and orientation of the camera; matching of model features to image features; and validation of the objects in the model. Our system processes the new image monocularly and uses shadows as 3-D clues to help validate the model. The system has been tested using a hand-generated site model and several images of a 500:1 scale model of the site, acquired form several viewpoints.<<ETX>>

[1]  Robert L. Lillestrand,et al.  Techniques ror Change Detection , 1972, IEEE Transactions on Computers.

[2]  Berthold K. P. Horn.  Relative orientation revisited , 1991 .

[3]  Rajiv Gupta,et al.  Stereo from uncalibrated cameras , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  H. C. Longuet-Higgins,et al.  A computer algorithm for reconstructing a scene from two projections , 1981, Nature.

[5]  Ramakant Nevatia,et al.  Detecting buildings in aerial images , 1988, Comput. Vis. Graph. Image Process..

[6]  Ramakant Nevatia,et al.  Detection of buildings using perceptual grouping and shadows , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Berthold K. P. Horn.  Robot vision , 1986, MIT electrical engineering and computer science series.

[8]  K. Ramesh Babu,et al.  Linear Feature Extraction and Description , 1979, IJCAI.

[9]  O. D. Faugeras,et al.  Camera Self-Calibration: Theory and Experiments , 1992, ECCV.

[10]  M. S. Ulstad,et al.  An algorithm for estimating small scale differences between two digital images , 1973, Pattern Recognit..