Lecture 20: Carmichael discusses three main ways of obtaining medical imaging data: CT (Computed Tomography) scans, MRIs (Magnetic Resonance Imaging) and PET (Positron Emission Tomography)

Medical Image Acquisition

Lecture 19: Carmichael discusses some problems that can arise in range data-based detection and how these problems can be fixed by creating  transformation-consistent clusters.

Object Detection from Range Data

Lecture 18: Carmichael discusses advantages of local shape representations and two methods for creating them: the segmentation method and the vertex-based method.

Local Shape Representations

Lecture 17: The instructor discusses ways of storing and searching 3D models.  Explains database querying, geons, object signatures and shape histograms.

Global Shape Representations

Lecture 16: Carmichael discusses two approaches (mesh-based and volumetric) for combining multiple meshes to form a single closed surface.

Combining Multiple Meshes

Lecture 15: In the second lecture on mesh alignment, Carmichale explains nonrigid alignment and how to accomplish this process using a technique called deformable registration.

Mesh Alignment II

Lecture 14: In the first of two lectures, Carmichale discusses rigid alignment of meshes as well as the metrics and transformation models involved.

Mesh Alignment I

Lecture 13: The instructor discusses using mesh smoothing to remove noise from three-dimensional data.  Also includes Gaussian smoothing and mesh shrinkage.

Mesh Smoothing

Lecture 12: Carmichael explains how to obtain an image with three dimensions.  The unit covers two principles for doing so: time of flight and triangulation.

3D Image Acquisition

Lecture 11: Carmichale explains the use of invariants for dense and sparse matching, as well as some various kinds of invariants.

Invariants

This course deals with the 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.

Lecture 10: The instructor explains using neighborhood operations to access different pixels.  Also covers implementation of a correlation filter.

Neighborhoods

Lecture 09: Carmichael explains why it is useful to study the textures of an image and methods for detecting them in images.

Texture Analysis

Lecture 08: The second of two methods is presented for grouping pixels in an image.  The lecture covers deformable contours, parameterizations and gradient descent.

Top-Down Image Segmentation

Lecture 07: One method for grouping pixels in an image is presented.  Carmichael discusses pairwise coherence, cluster modeling and modeling with metric spaces.

Bottom-Up Image Segmentation

Lecture 06: Carmichael explains how to go about detecting certain objects within an image if those objects cannot be outlined with just rectangles.

Object Detection Continued

Lecture 05: Carmichael discusses finding certain rectangular objects within an image, and explains various methods for doing so, such as PCA (principal component analysis) and dimensionality reduction.

Object Detection

Lecture 04: What it means to have a region of an image be 'salient' and the algorithms for finding such areas.

Saliency and Scale

Lecture 03: The lecture covers edge and corner detection using the Canny and Harris corner detector methods.

Edge and Corner Detection

Lecture 02: Introduction to Fourier analysis, as well as the subject of wavelets.

Fourier Analysis

Lecture 01: An introduction to image processing and analysis; covers image processing on a broad scope as well as course logistics.

Course Introduction

Image Processing and Analysis

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