ECS 173 IMAGE PROCESSING AND ANALYSIS (4) II
Lecture: 3 hours
Discussion: 1 hour
Prerequisite: Course 60; Mathematics 67 or C- or better in Mathematics 22A
Grading: Letter; programming assignment (60%), midterm (15%), final (25%)
Catalog Description:
Techniques for automated extraction of high-level information from images generated by cameras, three-dimensional surface sensors, and medical devices. Typical applications include automated construction of 3D models from video footage and detection of objects in various types of images.
Expanded Course Description:
The course will be taught in three modules covering the fundamentals of automated analysis of photographic, three-dimensional surface, and volumetric imaging data.
- Analysis of 2D images (photographs)
- Low-level information extraction: Edge and corner detection, contour completion, and texture analysis will be discussed in detail. Specific algorithms to be discussed include Canny edge detection, Harris corner detection, random-field-based methods for contour continuation, and texton estimation
- Multiple-view geometry: Students will learn the mathematical models that describe the geometry of single cameras and geometric relationships between 2 or more images. Orthographic, perspective, and affine models for cameras will be presented. The fundamental matrix, trifocal tensor, and quadrifocal tensor will be presented as basic mathematical constructs for describing geometric relationships between pairs, triples, and quadruples of images
- Object recognition: Students will receive an overview of methods for describing and detecting objects in 2D images. Appearance-based methods based on principal components analysis, convolutional image filters, and raw image classification will be described. Shape-based object detection based on constellations of object parts, local edge features, and alignment to prototype shapes will be presented
- Analysis of 3D surface imagery
- 3D surface parameterization and representation: Representation of 3D surfaces based on points, parametric surface models, patches, and geons will be discussed. The effects of noise, partial occlusion, and sensor artifacts on these surface descriptions will also be described
- Automated model building from surface data: Semi-automated and fully-automated procedures for building complete 3D models from collections of partial 3D sensor data sets will be presented. Semi-automated techniques based on landmark placement will be discussed. Fully-automated techniques based on local surface descriptors, constrained data collection, and global surface descriptors will be shown as well
- Object recognition in 3D data: Detection of objects in 3D surface data sets will be discussed. The effects of partial occlusion and clutter will be discussed. Techniques based on alignment, local surface descriptors, and machine learning approaches will be described
- Analysis of volumetric images
- Low-level processing: Students will learn approaches to correct for image artifacts found in computed tomography (CT), magnetric resonance (MR), and functional MR images. Bias field correction, blowout, and scattering artifacts will be discussed
- Description and modeling of biological shapes: Methods for mathematically describing biological objects found in volumetric images will be presented. Representation of 3D solids will be discussed, and computational anatomy approaches to representation of populations of shapes will be presented. Localization and detection of objects: Principles and algorithms for localizing biological shapes in volumetric images will be presented. Shape-model-based techniques for estimating the location of constrained, expected objects such as the brain will be discussed. Low-level detection of amorphous, unexpected objects such as tumors and calcifications will also be presented
Textbook:
Terry S. Yoo (Editor), Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis, AK Peters Publishers, July 29, 2004
Computer Usage:
Three programming projects will be assigned to allow students to implement algorithms for processing each of the 3 image modalities.
- 2D images: Students will study and implement a selected algorithm for low-level image processing or multiple-view geometry estimation
- 3D surface data: Students will implement a selected algorithm for detection of 3D objects in radar image data
- Volumetric images: Students will implement an algorithm for modeling or detection of shapes in medical images
Program Projects:
The programming projects for this class are chosen to enhance the lecture material in the course.
Engineering Design Statement:
The individual student taking this class will design and document a set of software modules for automated analysis of a variety of image data that includes the well-established algorithms described in the course outline. Examinations will include questions based on design components of the course.
ABET Category Content: Engineering Science: 0 unit
Engineering Design: 0 unit
Student Outcomes
- An ability to apply knowledge of mathematics, science, and engineering
- An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability
- An ability to communicate effectively
- The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context
- A recognition of the need for, and an ability to engage in life-long learning
- An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice
Instructor: N. Amenta
Prepared by: N. Amenta (March 2008)
Overlap Statement:
Volumetric images are also treated in ECS 177, but the emphasis there is on the visualization rather than automated extraction of high-level data. Two-dimensional image processing is treated in greater mathematical depth in EEC 206 and EEC 208, graduate courses which are currently taught infrequently.
6/08
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