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Image Processing


  • 1:42:18 An Introduction to Macro Photography

    An Introduction to Macro Photography

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    Tim Cooper Photography: http://timcooperphotography.com/Upcoming B&H Event Space Seminars: http://bit.ly/bheventspaceFrom backyards to city streets, to the National Parks, Macro Photography allows you to become delightfully lost in the intimate detail of

  • 51:38 Course Introduction

    Course Introduction

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

  • 50:23 Fourier Analysis

    Fourier Analysis

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

  • 53:32 Edge and Corner Detection

    Edge and Corner Detection

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

  • 51:18 Saliency and Scale

    Saliency and Scale

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

  • 52:32 Object Detection

    Object Detection

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    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.

  • 51:05 Object Detection Continued

    Object Detection Continued

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

  • 41:24 Bottom-Up Image Segmentation

    Bottom-Up Image Segmentation

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

  • 51:22 Top-Down Image Segmentation

    Top-Down Image Segmentation

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

  • 56:59 Texture Analysis

    Texture Analysis

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

  • 33:24 Neighborhoods

    Neighborhoods

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

  • 50:09 Invariants

    Invariants

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

  • 53:06 3D Image Acquisition

    3D Image Acquisition

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    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.

  • 50:30 Mesh Smoothing

    Mesh Smoothing

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

  • 51:28 Mesh Alignment I

    Mesh Alignment I

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

  • 51:28 Mesh Alignment II

    Mesh Alignment II

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    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.

  • 50:55 Combining Multiple Meshes

    Combining Multiple Meshes

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

  • 55:06 Global Shape Representations

    Global Shape Representations

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

  • 51:43 Local Shape Representations

    Local Shape Representations

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

  • 51:30 Object Detection from Range Data

    Object Detection from Range Data

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    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.

  • 44:35 Medical Image Acquisition

    Medical Image Acquisition

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    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)

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