34810 - Bioimaging and Vision 2nd cycle

Scheda insegnamento

Academic Year 2010/2011

Course contents

Introduction

Definition of computer vision. Definition of image processing and analysis system. Some applications: smart video surveillance (video analytics); people tracking for behaviour analysis; automatic event detection and analysis in traffic monitoring; automatic vehicle guidance, Unmanned Aerial Vehicles (UAV) and satellite attitude control; medical (TAC, RM, …) and biomedical (microscope cell) image analysis; automatic measurements and quality control in machine vision; robotics and 3D scene reconstruction.

Image formation

Image types: photographic, thermal, radiographic images. Image formation system. Image formation process and acquisition devices. Radiometry: radiance and irradiance. Dynamic range. Camera response function. Optical device: lens, aberration, focus, iris. Image acquisition and sampling process. Quantization and digitalization. Gray levels and colour channels. Spatial and photometric resolution. Acquisition technologies. Shot geometry: sensor position and perspectives. Images from non optical devices.

Image processing and enhancement

Image arithmetic and basic operations. Photometric transforms. Histogram definition. Histogram property and shape. Uses of histogram and point operations. Object segmentation: fixed and adaptive thresholding. Equalization. Contrast enhancement. Dynamic range enhancement. Cumulative histogram. Colour transformations. Gamma correction. Geometric operations and transforms: interpolation, scaling, rotation, translation, affine deformation. Local operations and convolutions. Template matching. Morphological operators and denoising. Digital filters. Object segmentation: labelling and watershed. Applications: computer graphics, automatic object detection, automatic light condition stabilization.

Pattern recognition and image analysis

Recall of probability and statistics for data analysis. Probability density and distribution function. Gaussian function. Decision theory and data classification. Bayesian classification, MAP and ML. Supervised classification and clustering. Proximity measures. Automatic image feature extraction. Photometrical, geometrical, statistical features. Texture analysis and moments. Multidimensional features. Dimensionality reduction techniques. Automatic object recognition. Applications: automatic object detection and recognition, machine vision.

Video and image sequence analysis

Image registration techniques and pattern matching. Automatic motion detection and analysis. Automatic features and object tracking. Applications: security, automatic event detection, quality control, machine vision.

Multiple view geometry and image reconstruction

3D stereopsis: principles and techniques. 3D image fomration from multiple 2D images. Attitude and pose recovery of moving cameras. Applications: computer graphics, image metrology and automatic static and dynamic object measurements, automatic vehicle guidance and control.

Readings/Bibliography

  • R. Gonzales, R. Woods: “Digital Image Processing”, Second Edition, Prentice-Hall, New-Jersey, USA, 2002
  • Richard O. Duda, Peter E. Hart, David G. Stork: “Pattern Classification”, Second Edition, Wiley Interscience, New York, 2001
  • R. I. Hartley, A. Zisserman: “Multiple View Geometry in Computer Vision”, Second Edition, Cambridge University Press, 2004
  • CVonline: Vision Related Books including Online Books and Book Support Sites (http://homepages.inf.ed.ac.uk/rbf/CVonline/books.htm)

Teaching methods

Classroom lessons and practice in lab. Each topic will be treated jointly with significant case studies developed in lab to highlight its meaningful applications. In order to make the students aware of the different topics, many homework exercises will be proposed and publicly corrected aftewards in labs.

Assessment methods

The students will be evaluated through the realization of a group project (max 3 students) and its individual discussion.

Teaching tools

In the teaching material section all the slides shown in class are available for download as well as the software tools for practice in lab

Links to further information

http://cvg.deis.unibo.it

Office hours

See the website of Alessandro Bevilacqua

See the website of Alessandro Gherardi