Journal ArticleDOI
Semi-autonomous evolution of object models for adaptive object recognition
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TLDR
The paper presents both an outline of the iterative evolution methodology and the investigation of an incremental model generalization approach using the example of a texture recognition problem.Abstract:
The paper presents a semi-autonomous model evolution approach to object recognition under variable perceptual conditions. The approach assumes that (i) the system has to recognize objects on separate images of a sequence, and (ii) the images demonstrate the variability of conditions under which objects are perceived (gradual change in resolution, lighting, positioning). The adaptation of object models is executed due to perceived, over a sequence of images, variabilities of object characteristics. This adaptation involves (i) the application of learned models to the next image, (ii) the monitoring of recognition effectiveness of the models, and (iii) an activation of learning processes if needed (i.e., when the recognition effectiveness of the models decreases). Model adaptation (evolution) integrates recognition processes of computer vision with incremental knowledge acquisition processes of machine learning in a closed loop. The paper presents both an outline of the iterative evolution methodology and the investigation of an incremental model generalization approach using the example of a texture recognition problem. Experiments were run in a semi-autonomous mode where a teacher secured soundness behavior of the evolution system. The experiments are compared for three system configurations: (i) a one-level control structure, (ii) a two-level control structure, and (iii) a two-level control structure with data filtering. The obtained results are evaluated for system recognition effectiveness, recognition stability, and predictability of evolved models. >read more
Citations
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Book ChapterDOI
Minimal RBF networks by gaussian mixture model
Sung Mahn Ahn,Sung Wook Baik +1 more
TL;DR: A basis function optimization method using a mixture density model that generalizes the Gaussian radial basis functions to arbitrary covariance matrices, in order to fully utilize theGaussian probability density function.
Book ChapterDOI
Adaptive Texture Recognition in Image Sequences with Prediction through Features Interpolation
Sung Wook Baik,Ran Baik +1 more
TL;DR: The results of the investigation justify the need for an on-line model modification over the entire sequence of images in order to preserve the system recognition capability, and present the possibility of prediction by finding partial patterns of texture characteristics change.
Proceedings Article
On-line model modification for adaptive object recognition
TL;DR: This paper presents and validates a method for adaptive object recognition in image sequences under dynamic perceptual conditions, and consequently, under changing object characteristics, and builds a close-loop interaction between object recognition and model modification systems.
References
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Journal ArticleDOI
Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Journal ArticleDOI
Pattern Classification and Scene Analysis.
Book
Pattern classification and scene analysis
Richard O. Duda,Peter E. Hart +1 more
TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
Journal ArticleDOI
Statistical and structural approaches to texture
TL;DR: This survey reviews the image processing literature on the various approaches and models investigators have used for texture, including statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models.
Journal ArticleDOI
Knowledge acquisition via incremental conceptual clustering
TL;DR: COBWEB is a conceptual clustering system that organizes data so as to maximize inference ability, and is incremental and computationally economical, and thus can be flexibly applied in a variety of domains.