Image processing with neural networks–a review

Mathematical Handbook for Scientists and Engineers

State of the art survey on MRI brain tumor segmentation.

Character recognition (CR) has been extensively studied in the last half century and has progressed to a level that is sufficient to produce technology-driven applications. Now, rapidly growing computational power is enabling the implementation of the present CR methodologies and is creating an increasing demand in many emerging application domains which require more advanced methodologies. This paper serves as a guide and update for readers working in the CR area. First, the historical evolution of CR systems is presented. Then, the available CR techniques, with their superiorities and weaknesses, are reviewed. Finally, the current status of CR is discussed and directions for future research are suggested. Special attention is given to off-line handwriting recognition, since this area requires more research in order to reach the ultimate goal of machine simulation of human reading.

An overview of character recognition focused on off-line handwriting

Character recognition—a review

http://cobweb.cs.uga.edu/~suchi/pubs/suchi-neurocomputing-1997.pdf

Multiscale image segmentation using a hierarchical self-organizing map

http://cobweb.cs.uga.edu/~suchi/pubs/suchi-nn-1995.pdf

A multilayer self-organizing feature map for range image segmentation

1 Introduction.- 1.1 Computer Vision.- 1.2 Three-Dimensional Object Recognition.- 1.2.1 Representation.- 1.2.2 Indexing.- 1.2.3 Constraint Propagation and Constraint Satisfaction.- 1.3 Common Goals of Three-Dimensional Object Recognition Systems.- 1.4 Qualitative Features.- 1.4.1 Study of Qualitative Properties in Low-level Vision Processes.- 1.4.2 Qualitative Features in Object Recognition.- 1.5 The Scope and Outline of the Book.- I Fundamentals of Range Image Processing and Three-Dimensional Object Recognition.- 2 Range Image Sensors and Sensing Techniques.- 2.1 Range Image Forms.- 2.2 Classification of Range Sensors.- 2.2.1 Radar Sensors.- 2.2.2 Triangulation Sensors.- 2.2.3 Sensors based on Optical Interferometry.- 2.2.4 Sensors Based on Focusing Techniques.- 2.2.5 Sensors Based on Fresnel Diffraction.- 2.2.6 Tactile Range Sensors.- 3 Range Image Segmentation.- 3.1 Mathematical Formulation of Range Image Segmentation.- 3.2 Fundamentals of Surface Differential Geometry.- 3.3 Surface Curvatures.- 3.4 Range Image Segmentation Techniques.- 3.4.1 Edge-based Segmentation Techniques.- 3.4.2 Region-based Segmentation Techniques.- 3.4.3 Hybrid Segmentation Techniques.- 3.5 Summary.- 4 Representation.- 4.1 Formal Properties of Geometric Representations.- 4.2 Wire-Frame Representation.- 4.3 Constructive Solid Geometry (CSG) Representation.- 4.4 Qualitative Representation using Geons.- 4.5 Aspect Graph Representation.- 4.6 EGI Representation.- 4.7 Representation Using Generalized Cylinders.- 4.8 Superquadric Representation.- 4.9 Octree Representation.- 4.10 Summary.- 5 Recognition and Localization Techniques.- 5.1 Recognition and Localization Techniques-An Overview.- 5.2 Interpretation Tree Search.- 5.3 Hough Clustering.- 5.4 Matching of Relational Structures.- 5.5 Geometric Hashing.- 5.6 Iterative Model Fitting.- 5.7 Indexing and Qualitative Features.- 5.8 Vision Systems as Coupled Systems.- 5.8.1 Object-Oriented Representation for Coupled Systems.- 5.8.2 Object-Oriented Representation for 3-D Object Recognition.- 5.8.3 Embedding Parallelism in an Object-Oriented Coupled System.- 5.9 Summary.- II Three-Dimensional Object Recognition Using Qualitative Features.- 6 Polyhedral Object Recognition.- 6.1 Preprocessing and Segmentation.- 6.1.1 Plane Fitting to Pixel Data.- 6.1.2 Clustering in Parameter Space.- 6.1.3 Post Processing of Clustering Results.- 6.1.4 Contour Extraction and Classification.- 6.1.5 Computation of Edge Parameters.- 6.2 Feature Extraction.- 6.3 Interpretation Tree Search.- 6.3.1 Pose Determination.- 6.3.2 Scene Interpretation Hypothesis Verification.- 6.4 Generalized Hough Transform.- 6.4.1 Feature Matching.- 6.4.2 Computation of the Transform.- 6.4.3 Pose Clustering.- 6.4.4 Verification of the Pose Hypothesis.- 6.5 Experimental Results.- 6.6 Summary.- 7 Recognition of Curved Objects.- 7.1 Representation of Curved Surfaces.- 7.1.1 Extraction of Surface Curvature Features from Range Images.- 7.2 Recognition Using a Point-Wise Curvature Description.- 7.2.1 Object Recognition Using Point-Wise Surface Matching.- 7.3 Recognition Using Qualitative Features.- 7.3.1 Cylindrical and Conical Surfaces.- 7.3.2 The Recognition Process Using Qualitative Features.- 7.3.3 Localization of a Cylindrical Surface.- 7.3.4 Localization of a Conical Surface.- 7.3.5 Localization of a Spherical Surface.- 7.3.6 An Experimental Comparison.- 7.4 Recognition of Complex Curved Objects.- 7.5 Dihedral Feature Junctions.- 7.5.1 Types of Dihedral Feature Junctions.- 7.5.2 Matching of Dihedral Feature Junctions.- 7.5.3 Pose Determination.- 7.5.4 Pose Clustering.- 7.6 Experimental Results.- 7.7 Summary.- III Sensitivity Analysis and Parallel Implementation.- 8 Sensitivity Analysis.- 8.1 Junction Matching and Pose Determination.- 8.2 Sensitivity Analysis.- 8.3 Qualitative Features.- 8.4 The Generalized Hough Transform.- 8.4.1 The Generalized Hough Transform in the Absence of Occlusion and Sensor Error.- 8.4.2 The Generalized Hough Transform in Presence of Occlusion and Sensor Error.- 8.4.3 Probability of Spurious Peaks in the Generalized Hough Transform.- 8.5 The Use of Qualitative Features in the Generalized Hough Transform.- 8.5.1 Reduction in the Search Space of Scene Interpretations due to Qualitative Features.- 8.5.2 Reducing the Effect of Smearing in Parameter Space using Qualitative Features.- 8.5.3 The Probability of Random Peaks in the Weighted Generalized Hough Transform.- 8.5.4 Determination of ?k(x), pk(x) and P(k).- 8.6 Weighted Generalized Hough Transform.- 9 Parallel Implementations of Recognition Techniques.- 9.1 Parallel Processing in Computer Vision.- 9.1.1 Parallel Architectures.- 9.1.2 Parallel Algorithms.- 9.2 The Connection Machine.- 9.2.1 System Organization.- 9.2.2 Performance Specifications.- 9.3 Object Recognition on the Connection Machine.- 9.3.1 Feature Extraction.- 9.3.2 Localization of Curved Surfaces.- 9.3.3 Computation of Dihedral Feature Junctions.- 9.3.4 Matching and Pose Computation.- 9.3.5 Pose Clustering.- 9.4 Object Recognition on the Hypercube.- 9.4.1 Scene Description.- 9.4.2 Model Data.- 9.4.3 Scene Feature Data.- 9.4.4 Pruning Constraints.- 9.4.5 Localization.- 9.5 Mapping the Interpretation Tree on the Hypercube.- 9.5.1 Breadth-First Mapping of the Interpretation Tree.- 9.5.2 Depth-First Mapping of the Interpretation Tree.- 9.5.3 Depth-First Mapping of the Interpretation Tree with Load Sharing.- 9.5.4 Experimental Results.

Three-Dimensional Object Recognition from Range Images

On machine recognition of hand-painted Chinese characters by feature relaxation

A self-organizing neural network for range image segmentation is proposed and described. The multi-layer Kohonen's self-organizing feature map (MLKSFM), which is an extension of the traditional single-layer Kohonen's self-organizing feature map (KSFM), is seen to alleviate the shortcomings of the latter in the context of range image segmentation. The problem of range image segmentation is formulated as one of vector quantization and is mapped onto the MLKSFM. The MLKSFM is currently implemented on the Connection Machine CM-2, which is a fine-grained single instruction multiple data (SIMD) computer. Experimental results using both synthetic and real range images are presented. >

Minsoo Suk

Papers

Multiscale image segmentation using a hierarchical self-organizing map

A multilayer self-organizing feature map for range image segmentation

Three-Dimensional Object Recognition from Range Images

On machine recognition of hand-painted Chinese characters by feature relaxation

A multi-layer Kohonen's self-organizing feature map for range image segmentation