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Preface; Table of Contents; Part I Image Processing; Two-Dimensional Hidden Markov Models in Road Signs Recognition; 1 Introduction; 2 Classic 1D HMM; 2.1 Three Basic Problems; 2.2 Solution to Problem 1; 2.3 Solution to Problem 3; 3 2DHMM; 3.1 Solution to 2D Problem 1; 3.2 Solution to 2D Problem 3; 4 Experiments; 5 Conclusion; References; Evaluating the Mutual Position of Objectson the Visual Scene Using MorphologicalProcessing and Reasoning; 1 Introduction; 2 Previous Works; 3 Extracting the Scene Description; 3.1 Finding the Relative Position; 3.2 Distance Computation.
3.3 Individual Features3.4 Scene Description Matrix; 4 Reasoning-Based Processing; 4.1 Obtaining the Predicate-Based Description; 4.2 Reasoning about Image Content; 5 Conclusions; References; Vascular Biometry; 1 Introduction; 2 Processing
Extraction of Vein Structure; 3 Steerable Filters; 4 Radon Transform; 5 Conclusion; References; Clustering-Based Retrieval of Similar Outfits Based on Clothes Visual Characteristics; 1 Introduction; 1.1 Motivation; 1.2 Previous Work; 2 Method Description; 2.1 Feature Extraction; 2.2 Similarity Measure; 2.3 Clustering; 2.4 Similar Outfits Retrieval.
3 Experimental Results4 Conclusions; References; Improving Shape Retrieval and ClassificationRates through Low-Dimensional Features Fusion; 1 Introduction; 1.1 Shape as a Descriptor; 1.2 Elementary Shape Descriptors; 1.3 Fusion and Scale Normalization; 2 Experiments; 3 Summary; References; Accelerating the 3D Random Walker ImageSegmentation Algorithm by Image GraphReduction and GPU Computing; 1 Introduction; 2 The Idea of Random Walking for Image Segmentation; 3 Reduction of the Image Graph Size; 4 Image Graph Creation Based on Super-Pixels; 5 Experimental Results; 6 Conclusions; References.
Computed Tomography Images Denoisingwith Markov Random Field Model Parametrizedby Prewitt Mask1 Introduction; 2 Proposed Algorithm; 2.1 Prewitt Operator; 2.2 Gaussian Filter; 2.3 Markov Random Field Model; 3 Experimental Results; 4 Conclusion; References; Neural Video Compression Algorithm; 1 Introduction; 2 Video Compression Algorithm; 2.1 Neuronal Image Compression; 2.2 Scene Detection; 3 Experimental Result; 4 Conclusions; References; Discovering Important Regions of CytologicalSlides Using Classification Tree; 1 Introduction; 2 Material and Methods; 2.1 Image Database.
2.2 Multi-level Thresholding2.3 Discovering Important Regions of Image; 3 Numerical Experiments; 4 Conclusions; References; Gaussian Mixture Model Based Non-Local MeansTechnique for Mixed Noise Suppressionin Color Images; 1 Introduction; 2 Region Homogeneity; 3 Gaussian Mixture Modeling; 4 Non-Local Means Algorithm; 5 Experimental Setup; 6 Noise Suppression Results; 7 Conclusions; References; Robust Image Retrieval Based on MixtureModeling of Weighted Spatio-color Information; 1 Introduction; 2 Gaussian Mixture Modeling; 3 Experimental Setup; 4 Evaluation of the Method Efficiency.
3.3 Individual Features3.4 Scene Description Matrix; 4 Reasoning-Based Processing; 4.1 Obtaining the Predicate-Based Description; 4.2 Reasoning about Image Content; 5 Conclusions; References; Vascular Biometry; 1 Introduction; 2 Processing
Extraction of Vein Structure; 3 Steerable Filters; 4 Radon Transform; 5 Conclusion; References; Clustering-Based Retrieval of Similar Outfits Based on Clothes Visual Characteristics; 1 Introduction; 1.1 Motivation; 1.2 Previous Work; 2 Method Description; 2.1 Feature Extraction; 2.2 Similarity Measure; 2.3 Clustering; 2.4 Similar Outfits Retrieval.
3 Experimental Results4 Conclusions; References; Improving Shape Retrieval and ClassificationRates through Low-Dimensional Features Fusion; 1 Introduction; 1.1 Shape as a Descriptor; 1.2 Elementary Shape Descriptors; 1.3 Fusion and Scale Normalization; 2 Experiments; 3 Summary; References; Accelerating the 3D Random Walker ImageSegmentation Algorithm by Image GraphReduction and GPU Computing; 1 Introduction; 2 The Idea of Random Walking for Image Segmentation; 3 Reduction of the Image Graph Size; 4 Image Graph Creation Based on Super-Pixels; 5 Experimental Results; 6 Conclusions; References.
Computed Tomography Images Denoisingwith Markov Random Field Model Parametrizedby Prewitt Mask1 Introduction; 2 Proposed Algorithm; 2.1 Prewitt Operator; 2.2 Gaussian Filter; 2.3 Markov Random Field Model; 3 Experimental Results; 4 Conclusion; References; Neural Video Compression Algorithm; 1 Introduction; 2 Video Compression Algorithm; 2.1 Neuronal Image Compression; 2.2 Scene Detection; 3 Experimental Result; 4 Conclusions; References; Discovering Important Regions of CytologicalSlides Using Classification Tree; 1 Introduction; 2 Material and Methods; 2.1 Image Database.
2.2 Multi-level Thresholding2.3 Discovering Important Regions of Image; 3 Numerical Experiments; 4 Conclusions; References; Gaussian Mixture Model Based Non-Local MeansTechnique for Mixed Noise Suppressionin Color Images; 1 Introduction; 2 Region Homogeneity; 3 Gaussian Mixture Modeling; 4 Non-Local Means Algorithm; 5 Experimental Setup; 6 Noise Suppression Results; 7 Conclusions; References; Robust Image Retrieval Based on MixtureModeling of Weighted Spatio-color Information; 1 Introduction; 2 Gaussian Mixture Modeling; 3 Experimental Setup; 4 Evaluation of the Method Efficiency.