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)

Object Detection from Range Data

Nov 8th, 2010 51m 28s

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.

Local Shape Representations

Nov 5th, 2010 51m 42s

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

Global Shape Representations

Nov 3rd, 2010 55m 5s

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

Combining Multiple Meshes

Oct 29th, 2010 50m 54s

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

Mesh Alignment II

Oct 27th, 2010 51m 27s

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 I

Oct 25th, 2010 51m 27s

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

Mesh Smoothing

Oct 22nd, 2010 50m 29s

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

3D Image Acquisition

Oct 20th, 2010 53m 5s

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.

Invariants

Oct 18th, 2010 50m 8s

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

Neighborhoods

Oct 15th, 2010 33m 23s

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

Texture Analysis

Oct 15th, 2010 56m 58s

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

Top-Down Image Segmentation

Oct 13th, 2010 51m 21s

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

Bottom-Up Image Segmentation

Oct 11th, 2010 41m 23s

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

Object Detection Continued

Oct 8th, 2010 51m 4s

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

Oct 6th, 2010 52m 31s

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.