000			04938cam a2200457Ii 4500
001			94813
003			ES-MaUEC
005			20230102112633.0
006			m o d
007			cr cnu|||unuuu
008			161102t20162017sz a o 101 0 eng d
020			_a3319472747 _q(electronic bk.)
020			_a9783319472744 _q(electronic bk.)
020			_z9783319472737 _q(print)
035			_a(OCoLC)961910276 _z(OCoLC)964931330
040			_aN$T _cN$T _dIDEBK _dGW5XE _dOCLCO _dEBLCP _dN$T _dOCLCO _dYDX _dOCLCF _dUAB _dIOG _dESU _dZ5A _dJBG _dIAD _dICW _dICN _dOTZ _dOCLCQ _dU3W _dES-MaUEC _bspa
050		4	_aTA1637 _b.I434 2016 EB
111	2		_aInternational Conference on Image Processing and Communications _n(8th : _d2016 : _cBydgoszcz, Poland)
245	1	0	_aImage processing and communications challenges 8 : _b8th International Conference, IP&C 2016 Bydgoszcz, Poland, September 2016 Proceedings _cRyszard S. Choraś, editor.
246	3		_aIP&C 2016
264		1	_aCham, Switzerland _bSpringer _c[2016].
264		4	_c2017
300			_a1 recurso en línea (xi, 280 páginas) _bilustraciones
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aAdvances in intelligent systems and computing _x2194-5357 _vvolume 525
500			_aIncludes author index.
500			_aInternational conference proceedings.
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
505	0		_aPreface; 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
505	8		_a1 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
505	8		_a2.2 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
505	8		_a3 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
505	8		_a6 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)
650		7	_aRedes informáticas _2embne _0(OCoLC)fst00872297 _0 _9141354
700	1		_aChoraś, Ryszard S., _eeditor literario _997585
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-47274-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017A
998			_b02/2018 _dz _e- _zSI
999			_c94813 _d94813 _x1