'Image gradient' diaporamas de présentation

Detectors and Descriptors

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CS559: Computer Graphics

CS559: Computer Graphics. Lecture 6: Edge Detection, Image Compositing, and Warping Li Zhang Spring 2010. Last time: Image Resampling and Painterly Rendering. Last time: Image Resampling and Painterly Rendering. Properties of Kernels.

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Paper Overviews

Paper Overviews. 3 types of descriptors : SIFT / PCA-SIFT ( Ke , Sukthankar ) GLOH ( Mikolajczyk , Schmid ) DAISY ( Tola , et al, Winder, et al) Comparison of descriptors ( Mikolajczyk , Schmid ). Paper Overviews. PCA-SIFT: SIFT-based but with a smaller descriptor

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CS559: Computer Graphics

CS559: Computer Graphics. Lecture 3: Digital Image Representation Li Zhang Spring 2008. Today. Eyes Cameras. Represented by a matrix. Image as a discreet function. 10. 5. 3. 4. 5. 7. 1. 1. 1. 7. Some function. Local image data. Modified image data. What is image filtering?.

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Lecture 3 (2.5.07) Image Enhancement in Spatial Domain

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OpenCV Tutorial

OpenCV Tutorial. OpenCV 2.4.3 Windows 7 Microsoft Visual C++ Express 2010. OpenCV Tutorial. Installing Microsoft Visual C++ 2010 Express http://www.microsoft.com/visualstudio/eng/downloads#d-2010-express Visual C++ NOT Visual Basic/Studio. Microsoft Visual C++ 2010 Express.

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EE 4780

EE 4780. Edge Detection. Detection of Discontinuities. Matched Filter Example >> a=[0 0 0 0 1 2 3 0 0 0 0 2 2 2 0 0 0 0 1 2 -2 -1 0 0 0 0]; >> figure; plot(a); >> h1 = [-1 -2 2 1]/10; >> b1 = conv(a,h1); figure; plot(b1);. Detection of Discontinuities. Point Detection Example:

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Announcements

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Detecting and Tracking Moving Objects for Video Surveillance

Detecting and Tracking Moving Objects for Video Surveillance. Isaac Cohen and Gerard Medioni University of Southern California. Their application sounds familiar. Video surveillance Sensors with pan-tilt and zoom Sensors mounted on moving airborne platforms

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Announcements

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Convolution and Edge Detection

Convolution and Edge Detection. 15-463: Computational Photography Alexei Efros, CMU, Fall 2005. Some slides from Steve Seitz. Fourier spectrum . Fun and games with spectra. Gaussian filtering. A Gaussian kernel gives less weight to pixels further from the center of the window

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A fast local descriptor for dense matching

A fast local descriptor for dense matching. Engin Tola, Vincent Lepetit, Pascal Fua Ecole Polytechnique Federale de Lausanne, Switzerland. Wide baseline stereo. Stereo is called wide baseline if there are significant rotations of the camera translation between camera centers

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Image analysis with EBImage

Image analysis with EBImage. Gregoire Pau, EMBL Heidelberg gregoire.pau@embl.de. Introduction. EBImage Set of quantitative image processing tools Image reading/writing/rendering Image processing (linear operators, matrix algebra, morphology) Object detection Features extraction Goal

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Convolution

Convolution. A convolution operation is a cross-correlation where the filter is flipped both horizontally and vertically before being applied to the image: It is written: Suppose H is a Gaussian or mean kernel. How does convolution differ from cross-correlation?. Continuous filtering.

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Convolution

Convolution. A convolution operation is a cross-correlation where the filter is flipped both horizontally and vertically before being applied to the image: It is written: Suppose H is a Gaussian or mean kernel. How does convolution differ from cross-correlation?. Continuous filtering.

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Automatic Tracing of Vocal Fold Motion in High Speed Laryngeal Video

Automatic Tracing of Vocal Fold Motion in High Speed Laryngeal Video. Erik Bieging. Vocal Folds. Glottis. Vocal Fold Imaging. Human vocal folds oscillate at 100 to 400 Hz during normal phonation High-speed digital imaging (4000 frames/sec) is used to study the motion of the vocal folds

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