Dor's Image Processing Site

Segmentation example

This program gets an image and the desired number of partitions and finds the means of the different classes and provides a classified image

Mean shift represents a general non-parametric mode finding/clustering procedure. In contrast to the classic K-means clustering approach

This toolbox provides tools to create sophisticated MATLAB graphical user interfaces that resize gracefully. The classes supplied can be used in combination to produce virtually any user interface layout. * Arrange MATLAB user-interface components horizontally, vertically or in grids * Ability to mix fixed size and variable size elements * Dynamic resizing of elements by dragging dividers * Use panels and tabs for switching interface pages

The XML Toolbox converts MATLAB data structures of any level of nesting into an XML string. It also reads most types of XML strings/files and converts these into a Matlab structure.

The VLFeat open source library implements popular computer vision algorithms including SIFT, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, and quick shift. It is written in C for efficiency and compatibility, with interfaces in MATLAB for ease of use, and detailed documentation throughout

OpenCV (Open Source Computer Vision) is a library of programming functions for real time computer vision.

The Lucas - Kanade method is a widely used differential method for optical flow estimation developed by Bruce D. Lucas and Takeo Kanade. It assumes that the flow is essentially constant in a local neighbourhood of the pixel under consideration, and solves the basic optical flow equations for all the pixels in that neighbourhood, by the least squares criterion.

Artical

An implementation of the very classical optical flow method of Horn & Schunck.

this function is doing fit to the function y=A * exp( -(x-mu)^2 / (2*sigma^2) )

The Matlab Toolbox for Dimensionality Reduction contains Matlab implementations of 33 techniques for dimensionality reduction and metric learning.

Clusters data using the Mean Shift Algorithm. testMeanShift shows an example in 2-D. Set plotFlag to true to visualize iterations.

Methods for super-resolution can be broadly classified into two families of methods: (i) The classical multi-image super-resolution (combining images obtained at subpixel misalignments), and (ii) Example-Based super-resolution (learning correspondence between low and high resolution image patches from a database). In this paper we propose a unified framework for combining these two families of methods. We further show how this combined approach can be applied to obtain super resolution from as little as a single image (with no database or prior examples). Our approach is based on the observation that patches in a natural image tend to redundantly recur many times inside the image, both within the same scale, as well as across different scales. Recurrence of patches within the same image scale (at subpixel misalignments) gives rise to the classical super-resolution, whereas recurrence of patches across different scales of the same image gives rise to example-based super-resolution. Our approach attempts to recover at each pixel its best possible resolution increase based on its patch redundancy within and across scales.

This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates.

Seam carving for content-aware image resizing Shai Avidan, Ariel Shamir

The purpose of the technique is to find imperfect instances of objects within a certain class of shapes by a voting procedure.

The Radon transform is widely applicable to tomography, the creation of an image from the scattering data associated to cross-sectional scans of an object.

The whitening transformation is a decorrelation method that converts the covariance matrix S of a set of samples into the identity matrix I. This effectively creates new random variables that are uncorrelated and have the same variances as the original random variables. The method is called the whitening transform because it transforms the input matrix closer towards white noise.

The Canny edge detection operator uses a multi-stage algorithm to detect a wide range of edges in images.

A Tutorial on Support Vector Machines for Pattern Recognition by Christopher J. C. Burges. Data Mining and Knowledge Discovery 2:121???167, 1998

A demosaicing algorithm is a digital image process used to reconstruct a full color image from the incomplete color samples output from an image sensor overlaid with a color lter array (CFA). also known as CFA interpolation or color reconstruction

HIGH-QUALITY LINEAR INTERPOLATION FOR DEMOSAICING OF BAYER-PATTERNED COLOR IMAGES

Camera Characterizations Blog