Computer Vision I Introduction Raul Queiroz Feitosa

6/11/2015 Introduction 2 Content What is CV? CV Applications Fundamental Steps From DIP to CV Course Content

6/11/2015 Introduction 3 What is Computer Vision “Computer Vision is the science that develops the theoretical and algorithmic basis by which useful information about the world can be automatically extracted and analyzed from an observed image, image set, or image sequence from computations made by a... computer.” R. B. Haralick, L.G. Shapiro

6/11/2015 Introduction 4 Applications Medical Image Analysis Analysis of Remote Sensing Data Biometrics Security Microscopy Industrial Inspection …

6/11/2015 Introduction 5 Applications Medical ImagesMicroscopy Industry SecurityRobot Vision Biometrics Remote Sensing much more

6/11/2015 Introduction 6 LVC Topics: Face Recognition

6/11/2015 Introduction 7 Controle de Passaportes Registro Único de Identidade Civil RIC Controle de Acesso Aplicações Criminais LVC Topics: Face Recognition

6/11/2015 Introduction 8 Suspect Behavior Tracking Recognition Frontal View LVC Topics: Face Recognition from Video

6/11/2015 Introduction 9 LVC Topics: Medical Image Analysis

LVC Topics: Remote Sensing 6/11/2015 Introduction 10

6/11/2015 Introduction 11 LVC Applications: Remote Sensing Geometric features are used to distinguish landing lanes from other targets in the forest. Illegal runways SAR R99B (SIPAM) Alves et al., 2009

6/11/2015 Introduction 12 Fundamental Steps Image Acquisition: digitizes the electromagnetic energy (quem / o que) Physical image digital image gray level physical image digital image (pixels) Acquisition EnhancementSegmentation Feature extraction Recognition Post- processing

6/11/2015 Introduction 13 Fundamental Steps Image Enhancement: improves image quality digital image Acquisition EnhancementSegmentation Feature extraction Recognition Post- processing

6/11/2015 Introduction 14 Fundamental Steps Segmentation: partitions the image into meaningfull objects segments digital image Acquisition EnhancementSegmentation Feature extraction Recognition Post- processing

6/11/2015 Introduction 15 Fundamental Steps Post-Processing: support segmentation/description segments Acquisition EnhancementSegmentation Feature extraction Recognition Post- processing

6/11/2015 Introduction 16 Fundamental Steps Description: converts the data into a form suitable for processing segmentsdescription Acquisition EnhancementSegmentation Feature extraction Recognition Post- processing x 1 =(x 11 … x 1n ) T x i =(x i1 … x in ) T x p =(x p1 … x pn ) T · · ·

6/11/2015 Introduction 17 Fundamental Steps Recognition: assigns a label to the image objects descriptionlabel Acquisition EnhancementSegmentation Feature extraction Recognition Post- processing x 1 =(x 11 … x 1n ) T x i =(x i1 … x in ) T x p =(x p1 … x pn ) T · · · paprika pepper cabbage · · ·

6/11/2015 Introduction 18 From DIP to CV Digital Image Processing  Input and output are images!  From image up to recognition! Acquisition EnhancementSegmentation Feature extraction Recognition Post- processing DIP

6/11/2015 Introduction 19 From DIP to CV Image Analysis/Understanding  From segmentation up to recognition. Acquisition EnhancementSegmentation Feature extraction Recognition Post- processing Image Analysis

6/11/2015 Introduction 20 From DIP to CV Computer Vision  Tries to emulate human intelligence.  Emphasis on 3D analysis. Acquisition EnhancementSegmentation Feature extraction Recognition Post- processing Computer Vision

6/11/2015 Introduction 21 From DIP to CV Process Levels  Low-level: input and outputs are images  Mid-level: image as input and attributes as output.  High-level: “making sense” of an ensemble of objects Acquisition EnhancementSegmentation Feature extraction Recognition Post- processing LowMid High

6/11/2015 Introduction 22 Image Analysis develops methods and algorithms able to extract automatically useful information about the world. Image Analysis

6/11/2015 Introduction 23 Computer Graphics develps techniques for visualization and manipulation of ideas that exist only conceptually or in form of mathematical description, but not as concrete object. Computer Graphics

6/11/2015 Introduction 24 Course Content Main:  Introduction  Digital Image Fundamentals  Image Enhancement in Spatial Domain  Image Enhancement in Frequency Domain  Morphological Image Processing  Segmentation  Representation and Description  Object Recognition Appendices:  Mathematical Foundation  Dimensionality Reduction (top)top

6/11/2015 Introduction 25 Bibliography 1.R. G. Gonzalez, R. E. Woods, Digital Image Processing; Prentice Hall, 3rd Ed., R. G. Gonzalez, R. E. Woods, Digital Image Processing; Prentice Hall, 2nd Ed., R. G. Gonzalez, R. E. Woods, S.L. Eddings, Digital Image Processing using MATLAB; Prentice Hall, M. Nixon, A. Aguado, Feature Extraction & Image Processing, Newnes, R. O. Duda, Peter E. Hart, D. G. Stork, Pattern Classification, Wiley- Interscience; 2nd edition, 2000.

6/11/2015 Introduction 26 Next Topic Digital Image Fundamentals