Episodes
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)
Published 11/10/10
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.
Published 11/08/10
Lecture 18: Carmichael discusses advantages of local shape representations and two methods for creating them: the segmentation method and the vertex-based method.
Published 11/05/10
Lecture 17: The instructor discusses ways of storing and searching 3D models. Explains database querying, geons, object signatures and shape histograms.
Published 11/03/10
Lecture 16: Carmichael discusses two approaches (mesh-based and volumetric) for combining multiple meshes to form a single closed surface.
Published 10/29/10
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.
Published 10/27/10
Lecture 14: In the first of two lectures, Carmichale discusses rigid alignment of meshes as well as the metrics and transformation models involved.
Published 10/25/10
Lecture 13: The instructor discusses using mesh smoothing to remove noise from three-dimensional data. Also includes Gaussian smoothing and mesh shrinkage.
Published 10/22/10
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.
Published 10/20/10
Lecture 11: Carmichale explains the use of invariants for dense and sparse matching, as well as some various kinds of invariants.
Published 10/18/10
Lecture 09: Carmichael explains why it is useful to study the textures of an image and methods for detecting them in images.
Published 10/15/10
Lecture 10: The instructor explains using neighborhood operations to access different pixels. Also covers implementation of a correlation filter.
Published 10/15/10
Lecture 08: The second of two methods is presented for grouping pixels in an image. The lecture covers deformable contours, parameterizations and gradient descent.
Published 10/13/10
Lecture 07: One method for grouping pixels in an image is presented. Carmichael discusses pairwise coherence, cluster modeling and modeling with metric spaces.
Published 10/11/10
Lecture 06: Carmichael explains how to go about detecting certain objects within an image if those objects cannot be outlined with just rectangles.
Published 10/08/10
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.
Published 10/06/10
Lecture 04: What it means to have a region of an image be 'salient' and the algorithms for finding such areas.
Published 10/04/10
Lecture 03: The lecture covers edge and corner detection using the Canny and Harris corner detector methods.
Published 10/01/10
Lecture 02: Introduction to Fourier analysis, as well as the subject of wavelets.
Published 09/27/10
Lecture 01: An introduction to image processing and analysis; covers image processing on a broad scope as well as course logistics.
Published 09/24/10