Semi-autonomous evolution of object models for adaptive object recognition
TL;DR: 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. >
Citations
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Proceedings Article•
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TL;DR: An axiomatic definition for the notion of "segmentation" in image processing is proposed, which is based on the idea of a maximal partition and a key theorem links segmentation with connection, on the one hand, and with connective criteria on the other one.
Abstract: Firstly, the paper proposes an axiomatic definition for the notion of "segmentation" in image processing, which is based on the idea of a maximal partition. Then a key theorem links segmentation with connection, on the one hand, and with connective criteria on the other one. A series of lattice properties are then developed. In a last part, two examples of segmentations are proposed.
386 citations
TL;DR: In spite of many remaining unsolved problems and need for further research and development, use of knowledge and semi-automation are the only viable alternatives towards development of useful object extraction systems, as some commercial systems on building extraction and 3D city modelling as well as advanced, practically oriented research have shown.
Abstract: The paper focuses mainly on extraction of important topographic objects, like buildings and roads, that have received much attention the last decade. As main input data, aerial imagery is considered, although other data, like from laser scanner, SAR and high-resolution satellite imagery, can be also used. After a short review of recent image analysis trends, and strategy and overall system aspects of knowledge-based image analysis, the paper focuses on aspects of knowledge that can be used for object extraction: types of knowledge, problems in using existing knowledge, knowledge representation and management, current and possible use of knowledge, upgrading and augmenting of knowledge. Finally, an overview on commercial systems regarding automated object extraction and use of a priori knowledge is given. In spite of many remaining unsolved problems and need for further research and development, use of knowledge and semi-automation are the only viable alternatives towards development of useful object extraction systems, as some commercial systems on building extraction and 3D city modelling as well as advanced, practically oriented research have shown.
277 citations
Cites background from "Semi-autonomous evolution of object..."
...Learning can refer to various aspects of an image analysis system, e.g. strategies, object models and relations (Pachowicz, 1994; Palhang and Sowmya, 1996; Englert, 1997; Sester, 2000), performance and selection of processing methods and of their parameter values (Shekhar et al., 1999), etc.…...
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TL;DR: Jamshidi provides a timely pedagogic discussion of this important field and, by examining past, present and potential future trends, gives scientists who are unfamiliar with these recent advances an opportunity to comprehend the subject through a balanced overview.
Abstract: (1983). Large-Scale Systems: Modeling and Control. Journal of the Operational Research Society: Vol. 34, No. 10, pp. 1016-1016.
239 citations
Book•
03 May 2010
TL;DR: A Selected List of Books on Image Processing and Computer Vision from Year 2000.
Abstract: PART I: FUNDAMENTALS. 1 INTRODUCTION. 1.1 The World of Signals. 1.2 Digital Image Processing. 1.3 Elements of an Image Processing System. Appendix 1.A Selected List of Books on Image Processing and Computer Vision from Year 2000. References. 2 MATHEMATICAL PRELIMINARIES. 2.1 Laplace Transform. 2.2 Fourier Transform. 2.3 Z-Transform. 2.4 Cosine Transform. 2.5 Wavelet Transform. 3 IMAGE ENHANCEMENT. 3.1 Grayscale Transformation. 3.2 Piecewise Linear Transformation. 3.3 Bit Plane Slicing. 3.4 Histogram Equalization. 3.5 Histogram Specification. 3.6 Enhancement by Arithmetic Operations. 3.7 Smoothing Filter. 3.8 Sharpening Filter. 3.9 Image Blur Types and Quality Measures. 4 MATHEMATICAL MORPHOLOGY. 4.1 Binary Morphology. 4.2 Opening and Closing. 4.3 Hit-or-Miss Transform. 4.4 Grayscale Morphology. 4.5 Basic Morphological Algorithms. 4.6 Morphological Filters. 5 IMAGE SEGMENTATION. 5.1 Thresholding. 5.2 Object (Component) Labeling. 5.3 Locating Object Contours by the Snake Model. 5.4 Edge Operators. 5.5 Edge Linking by Adaptive Mathematical Morphology. 5.6 Automatic Seeded Region Growing. 5.7 A Top-Down Region Dividing Approach. 6 DISTANCE TRANSFORMATION AND SHORTEST PATH PLANNING. 6.1 General Concept. 6.2 Distance Transformation by Mathematical Morphology. 6.3 Approximation of Euclidean Distance. 6.4 Decomposition of Distance Structuring Element. 6.5 The 3D Euclidean Distance. 6.6 The Acquiring Approaches. 6.7 The Deriving Approaches. 6.8 The Shortest Path Planning. 6.9 Forward and Backward Chain Codes for Motion Planning. 6.10 A Few Examples. 7 IMAGE REPRESENTATION AND DESCRIPTION. 7.1 Run-Length Coding. 7.2 Binary Tree and Quadtree. 7.3 Contour Representation. 7.4 Skeletonization by Thinning. 7.5 Medial Axis Transformation. 7.6 Object Representation and Tolerance. 8 FEATURE EXTRACTION. 8.1 Fourier Descriptor and Moment Invariants. 8.2 Shape Number and Hierarchical Features. 8.3 Corner Detection. 8.4 Hough Transform. 8.5 Principal Component Analysis. 8.6 Linear Discriminate Analysis. 8.7 Feature Reduction in Input and Feature Spaces. 9 PATTERN RECOGNITION. 9.1 The Unsupervised Clustering Algorithm. 9.2 Bayes Classifier. 9.3 Support Vector Machine. 9.4 Neural Networks. 9.5 The Adaptive Resonance Theory Network. 9.6 Fuzzy Sets in Image Analysis. PART II: APPLICATIONS. 10 FACE IMAGE PROCESSING AND ANALYSIS. 10.1 Face and Facial Feature Extraction. 10.2 Extraction of Head and Face Boundaries and Facial Features. 10.3 Recognizing Facial Action Units. 10.4 Facial Expression Recognition in JAFFE Database. 11 DOCUMENT IMAGE PROCESSING AND CLASSIFICATION. 11.1 Block Segmentation and Classification. 11.2 Rule-Based Character Recognition System. 11.3 Logo Identification. 11.4 Fuzzy Typographical Analysis for Character Preclassification. 11.5 Fuzzy Model for Character Classification. 12 IMAGE WATERMARKING. 12.1 Watermarking Classification. 12.2 Spatial Domain Watermarking. 12.3 Frequency-Domain Watermarking. 12.4 Fragile Watermark. 12.5 Robust Watermark. 12.6 Combinational Domain Digital Watermarking. 13 IMAGE STEGANOGRAPHY. 13.1 Types of Steganography. 13.2 Applications of Steganography. 13.3 Embedding Security and Imperceptibility. 13.4 Examples of Steganography Software. 13.5 Genetic Algorithm-Based Steganography. 14 SOLAR IMAGE PROCESSING AND ANALYSIS. 14.1 Automatic Extraction of Filaments. 14.2 Solar Flare Detection. 14.3 Solar Corona Mass Ejection Detection. INDEX.
237 citations
TL;DR: A novel top-down region dividing based approach is developed for image segmentation, which combines the advantages of both histogram-based and region-based approaches, and Experimental results show that the algorithm can efficiently perform image segmentsation without distorting the spatial structure of an image.
37 citations
Cites background from "Semi-autonomous evolution of object..."
...It is a critical preprocessing step to the success of image recognition [1], image compression [2], image visualization [3], and image retrieval [4]....
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References
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01 Nov 1973
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.
Abstract: Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.
20,442 citations
14,948 citations
Book•
01 Jan 1973
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.
Abstract: Provides a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition. The topics treated include 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.
13,647 citations
01 Jan 1979
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.
Abstract: In this survey we review the image processing literature on the various approaches and models investigators have used for texture. These include statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models. We discuss and generalize some structural approaches to texture based on more complex primitives than gray tone. We conclude with some structural-statistical generalizations which apply the statistical techniques to the structural primitives.
5,112 citations
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.
Abstract: Conceptual clustering is an important way of summarizing and explaining data. However, the recent formulation of this paradigm has allowed little exploration of conceptual clustering as a means of improving performance. Furthermore, previous work in conceptual clustering has not explicitly dealt with constraints imposed by real world environments. This article presents COBWEB, a conceptual clustering system that organizes data so as to maximize inference ability. Additionally, COBWEB is incremental and computationally economical, and thus can be flexibly applied in a variety of domains.
2,045 citations