Contrast Limited Adaptive Histogram Equalization (CLAHE)

CLAHE is a useful tool for preprocessing images (or video) for computer vision/pattern recognition tasks. It more or less helps you "see" areas of the image that are in shadows.

There are many available implementations of this but I like the one in open CV:

http://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_imgproc/py_histograms/py_histogram_equalization/py_histogram_equalization.html

Note: it is usually better to convert images to HSV colorspace first.

Before

After

Additional info:

http://fiji.sc/wiki/index.php/Enhance_Local_Contrast_(CLAHE)

Corner Detector

Detecting corners is often a good first step in computer vision.  If you can match corners from two images you are well on your way to figuring out how they fit together for example.

Corner detection is an approach used within computer vision systems to extract certain kinds of features and infer the contents of an image. Corner detection is frequently used in motion detection, image registration, video tracking, image mosaicing, panorama stitching, 3D modelling and object recognition. Corner detection overlaps with the topic of interest point detection.

http://en.wikipedia.org/wiki/Corner_detection


Lecture slide decks:


http://www.cse.psu.edu/~rcollins/CSE486/lecture06.pdf

http://courses.cs.washington.edu/courses/cse577/05sp/notes/harris.pdf

Tutorials:

Python/OpenCV

http://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_feature2d/py_features_harris/py_features_harris.html

YouTube video:

https://www.youtube.com/watch?v=vkWdzWeRfC4

Matlab code:

http://www.mathworks.com/matlabcentral/fileexchange/9272-harris-corner-detector

Multi-scale Oriented Patches (MOPS)

MOPS is a useful tool for many computer vision and related applications. They key is that it gives you a way to generate features that are (mostly) invariant to rotation and scale to use as input for machine learning/artificial intelligence algorithms.

http://www.cs.bath.ac.uk/brown/mops/mops.html


Technical report from Microsoft Research:

http://research.microsoft.com/pubs/70120/tr-2004-133.pdf

Nice slide deck on MOPS:

http://www.csie.ntu.edu.tw/~cyy/courses/vfx/08spring/lectures/handouts/lec06_feature2_4up.pdf


Example app from Microsoft Research:

http://research.microsoft.com/en-us/um/redmond/groups/ivm/PhotoTours/

VLFeat open source library computer vision algorithms

The VLFeat open source library implements popular computer vision algorithms specializing in image understanding and local featurexs extraction and matching. Algorithms incldue Fisher Vector, VLAD, SIFT, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, SLIC superpixes, quick shift superpixels, large scale SVM training, and many others. It is written in C for efficiency and compatibility, with interfaces in MATLAB for ease of use, and detailed documentation throughout. It supports Windows, Mac OS X, and Linux.

http://www.vlfeat.org/

Viola Jones object detection framework

The Viola–Jones object detection framework is the first object detection framework to provide competitive object detection rates in real-time proposed in 2001 by Paul Viola and Michael Jones. Although it can be trained to detect a variety of object classes, it was motivated primarily by the problem of face detection. This algorithm is implemented in OpenCV as cvHaarDetectObjects().

YouTube video explaining Viola Jones face detection:

https://www.youtube.com/watch?v=WfdYYNamHZ8

This is a slide deck explaining Viola Jones face detection:

http://www.slideshare.net/wolf/avihu-efrats-viola-and-jones-face-detection-slides/


Haar features are not an AI or ML algorithm themselves, but instead are often a useful tool for transforming data into a format that an AI or ML algorithm can use.

The Wikipedia article only talks about them with respect to object recognition in visible light images.  However, they can be used with images from any spectrum or even any type of data that can be represented as X,Y,Z such as a digital elevation map of terrain.

http://en.wikipedia.org/wiki/Haar-like_features

This pdf has a good explanation of how to use Haar features:

http://nichol.as/papers/Wilson/Facial%20feature%20detection%20using%20Haar.pdf

Decision Forests for Computer Vision and Medical Image Analysis book from Microsoft Research

This is the best resource I have found for understanding and using decision tree based machine learning algorithms.  It is very thorough on both theory and practical use with comparisons with other algorithms such as SVM's and AdaBoost.

There are also a bunch of supplemental materials available for free including a very nice PowerPoint with great explanations.  The supplemental materials include C++ and C# code.

Decision Forests - Microsoft Research

Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning

This technical report from Microsoft Research is an A-Z tutorial on how decision tree machine learning algorithms work.  It includes in depth explanations of random forests, extra tree classifiers, random ferns and other variations for both classification and regression.

It is in report format and compares decision forests to other types of machine learning algorithms such as SVM.  Some simple toy problems give the basics and some real life applications such as body position recognition and medical image are included.

There is also an accompanying PowerPoint with some nice animations.

http://research.microsoft.com/pubs/155552/decisionForests_MSR_TR_2011_114.pdf is not available

Texturecam: Autonomous Image Analysis For Astrobiology Survey

This is a paper about a project to include software on robotic rover spacecraft that uses a random forest algorithm to allow the rover to autonomously classify rocks by texture.  This helps the rover to search for signs of life.

ml.jpl.nasa.gov/papers/thompson/thompson-2012-lpsc.pdf