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Table of Contents
Preface; Organization; Organization Committee; Conference Chair; International Program Committee; Organizing Committee; Contents; Image Processing; 20 Years of Progress in Video Compression
from MPEG-1 to MPEG-H HEVC. General View on the Path of Video Coding Development; 1 Video Compression
What Is It About?; 2 What Algorithms and Compression Technologies Have Been Developed?; 2.1 Algorithms of Data Encoding; 2.2 Video Compression Technologies; 3 Milestones in History of Hybrid Video Compression Development; 4 Is There Any Pattern in the Chaos?; 5 New Replacing the Old
6 What Was the Driving Force for Video Compression Enhancements?7 Evolution of Functionalities; 8 Developing New Encoders
Change of Paradigms; 9 What Will Come in Upcoming Years?; References; Automatic Tongue Recognition Based on Color and Textural Features; 1 Introduction; 2 Preprocessing; 3 Feature Extraction; 3.1 Feature Extraction Based on Color Moments; 3.2 Gabor Filters for Feature Extraction; 4 Conclusion; References; A First Attempt to Construct Effective Concept Drift Detector Ensembles; 1 Introduction; 2 Combined Concept Drift Detectors; 2.1 ALO (At Least One detects drift)
2.2 ALHWD (At Least Half of the Detectors returns Warnings or detect Drift)2.3 ALHD (At Least Half of the detectors detect Drift); 2.4 AWD (All detectors return Warnings or detect Drift); 2.5 AD (All detectors detect Drift); 3 Experimental Research; 3.1 Goals; 3.2 Set-Up; 3.3 Results; 3.4 Discussion; 4 Final Remarks; References; Quality Prediction of Compressed Images via Classification; 1 Introduction; 2 Related Works; 3 Proposed Classification-Based Compression Approach; 4 Experimental Investigation; 5 Conclusions; References; Image Despeckling Using Non-local Means with Diffusion Tensor
1 Introduction2 Theoretical Considerations of NLM; 2.1 Classical NLM (NLMC); 2.2 NLM with Structure Tensor (NLMST); 2.3 NLM with Diffusion Tensor (NLMDT); 3 Experimental Results; 4 Conclusions; References; Face Recognition with 3D Face Asymmetry; 1 Introduction; 2 Proposed Method; 2.1 Preprocessing; 2.2 Measurement of the Asymmetry; 2.3 Recognition System; 3 Experiments; 4 Conclusion; References; Best-Fit Segmentation Created Using Flood-Based Iterative Thinning; 1 Introduction; 2 The Problem of Imprecise Segmentation; 2.1 Thinning; 2.2 Adjusting the Valley Segmentation Courses
3 Flood-Based Iterative Thinning Algorithm (FIT)3.1 Improvement of Thinning; 3.2 Testing of Flood-Based Iterative Thinning; 4 Conclusions; References; A Comparative Study of Image Enhancement Methods in Tree-Ring Analysis; 1 Introduction; 2 Attempts to Enhancement of Wood Core Images; 2.1 Thresholding; 2.2 Contrast Enhancement Methods; 2.3 Textural Features; 2.4 Using Convolution Filters; 3 The Assessment of the Results; 4 Conclusions; References; Key Frames Detection in Motion Capture Recordings Using Machine Learning Approaches; 1 Introduction; 2 Materials and Methods; 2.1 Features Set
from MPEG-1 to MPEG-H HEVC. General View on the Path of Video Coding Development; 1 Video Compression
What Is It About?; 2 What Algorithms and Compression Technologies Have Been Developed?; 2.1 Algorithms of Data Encoding; 2.2 Video Compression Technologies; 3 Milestones in History of Hybrid Video Compression Development; 4 Is There Any Pattern in the Chaos?; 5 New Replacing the Old
6 What Was the Driving Force for Video Compression Enhancements?7 Evolution of Functionalities; 8 Developing New Encoders
Change of Paradigms; 9 What Will Come in Upcoming Years?; References; Automatic Tongue Recognition Based on Color and Textural Features; 1 Introduction; 2 Preprocessing; 3 Feature Extraction; 3.1 Feature Extraction Based on Color Moments; 3.2 Gabor Filters for Feature Extraction; 4 Conclusion; References; A First Attempt to Construct Effective Concept Drift Detector Ensembles; 1 Introduction; 2 Combined Concept Drift Detectors; 2.1 ALO (At Least One detects drift)
2.2 ALHWD (At Least Half of the Detectors returns Warnings or detect Drift)2.3 ALHD (At Least Half of the detectors detect Drift); 2.4 AWD (All detectors return Warnings or detect Drift); 2.5 AD (All detectors detect Drift); 3 Experimental Research; 3.1 Goals; 3.2 Set-Up; 3.3 Results; 3.4 Discussion; 4 Final Remarks; References; Quality Prediction of Compressed Images via Classification; 1 Introduction; 2 Related Works; 3 Proposed Classification-Based Compression Approach; 4 Experimental Investigation; 5 Conclusions; References; Image Despeckling Using Non-local Means with Diffusion Tensor
1 Introduction2 Theoretical Considerations of NLM; 2.1 Classical NLM (NLMC); 2.2 NLM with Structure Tensor (NLMST); 2.3 NLM with Diffusion Tensor (NLMDT); 3 Experimental Results; 4 Conclusions; References; Face Recognition with 3D Face Asymmetry; 1 Introduction; 2 Proposed Method; 2.1 Preprocessing; 2.2 Measurement of the Asymmetry; 2.3 Recognition System; 3 Experiments; 4 Conclusion; References; Best-Fit Segmentation Created Using Flood-Based Iterative Thinning; 1 Introduction; 2 The Problem of Imprecise Segmentation; 2.1 Thinning; 2.2 Adjusting the Valley Segmentation Courses
3 Flood-Based Iterative Thinning Algorithm (FIT)3.1 Improvement of Thinning; 3.2 Testing of Flood-Based Iterative Thinning; 4 Conclusions; References; A Comparative Study of Image Enhancement Methods in Tree-Ring Analysis; 1 Introduction; 2 Attempts to Enhancement of Wood Core Images; 2.1 Thresholding; 2.2 Contrast Enhancement Methods; 2.3 Textural Features; 2.4 Using Convolution Filters; 3 The Assessment of the Results; 4 Conclusions; References; Key Frames Detection in Motion Capture Recordings Using Machine Learning Approaches; 1 Introduction; 2 Materials and Methods; 2.1 Features Set