| AI (Artificial Intelligence) |
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Research at The Robotics Institute
Most of the Institute's research can be categorized into several broad areas: basic robotics technologies, automation and computer-integrated manufacturing, robotics for hazardous environments, and autonomous mobile robots. We have over 200 projects ongoing at any one time.
http://www.ri.cmu.edu/general/research.html
2006-06-21 18:38:58 |
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Computer Vision Source Code
before a link means the link points to a binary file, not a readable page ...
http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/txtv-source.html
2006-06-21 18:42:09 |
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Automatic Text Detection and Tracking in Digital Video (1998) - Huiping Li, David Doermann, Omid Kia
Abstract: Text which either appears in a scene or is graphically added to video can provide an important supplemental source of index information as well as clues for decoding the video's structure and for classification. In this paper we present algorithms for detecting and tracking text components that appear within digital video frames. Our system implements a scale-space feature extractor that feeds an artificial neural processor to extract textual regions and track their movement over time. The...
http://citeseer.ist.psu.edu/42875.html
2006-06-21 18:44:54 |
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Automatic text detection and tracking in digital video
Text that appears in a scene or is graphically added to video can provide an important supplemental source of index information as well as clues for decoding the video's structure and for classification. In this work, we present algorithms for detecting and tracking text in digital video. Our system implements a scale-space feature extractor that feeds an artificial neural processor to detect text blocks. Our text tracking scheme consists of two modules: a sum of squared difference (SSD) based module to find the initial position and a contour-based module to refine the position. Experiments conducted with a variety of video sources show that our scheme can detect and track text robustly
http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=817607
2006-06-21 18:46:42 |
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Webpage for Marius Bulacu
Computer vision, statistical pattern recognition, biometrics, document analysis and recognition, intelligent robots.
http://www.ai.rug.nl/~bulacu/
2006-06-21 18:49:00 |
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Edge Detection Tutorial - Author: Bill Green (2002)
This tutorial assumes the reader knows:
(1) How to develop source code to read BMP header and info header (i.e. width, height & # of colors).
(2) How to develop source code to read raster data
http://www.pages.drexel.edu/~weg22/edge.html
2006-06-21 18:56:04 |
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EDGE DETECTION -
Grégoire Malandain
Principles
Example: 2D edge detection versus 3D edge detection
Code
Examples obtained with the code
Other uses of the code
http://www-sop.inria.fr/epidaure/personnel/malandain/segment/edges.html
2006-06-21 18:59:09 |
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Image Processing Khorosware: Edge Detection I
Given an X-bit per pixel image, slicing the image at different planes (bit-planes) plays an important role in image processing. An application of this technique is also in data compression. In general, 8-bit per pixel images are processed. We can slice an image into the following bit-planes. Zero is the least significant bit (LSB) and 7 is the most significant bit (MSB)
http://www.ee.bgu.ac.il/~greg/graphics/special.html
2006-06-21 19:01:10 |
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Vision Systems
Vision Systems
Dr A D Marshall
Room M 1.38
World Scientific 1992
David Marshall 1994
http://www.cs.cf.ac.uk/Dave/Vision_lecture/Vision_lecture_caller.html
2006-06-21 19:03:32 |
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Basic Image Processing Demos (for EECS20)
These are (MATLAB programmed) demos showing some basic image processing filters: thresholding, Gaussian filter, and Canny edge detector. MATLAB codes and correspondent demo results of each filter are given below. If you want to run them by yourself, you should download these codes and just save them as .m files or functions(if you know how to use MATLAB, it is very easy). Of course, you could then try them on the other (gray) images you have other than lena.gif.(The black and white image "lena.gif" we used here was obtained by translating from a color lena.tiff by using MATLAB. So it might not be the standard black and white "lena".)
http://robotics.eecs.berkeley.edu/~mayi/imgproc/index.html
2006-06-21 19:09:27 |
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Image Processing
Contents :
General Algorithms
Image Registration
Mathematical Techniques
http://www.efg2.com/Lab/Library/ImageProcessing/Algorithms.htm
2006-06-21 19:11:06 |
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Feature-Based Image Metamorphosis
A new technique is presented for the metamorphosis of one digital image into another. The approach gives the animator high-level control of the visual effect by providing natural feature-based specification and interaction. When used effectively, this technique can give the illusion that the photographed or computer generated subjects are transforming in a fluid, surrealistic, and often dramatic way. Comparisons with existing methods are drawn, and the advantages and disadvantages of each are examined. The new method is then extended to accommodate keyframed transformations between image sequences for motion image work. Several examples are illustrated with resulting images.
http://www.hammerhead.com/thad/morph.html
2006-06-21 19:12:20 |
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AIST
banyak PDF nya lhooo...
http://unit.aist.go.jp/itri/itri-rwig/cie/ari/Pub-E/Conference.html
2006-06-22 17:23:46 |
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Character Recognition by Feature Point Extraction
Abstract
The ability to identify machine printed characters in an automated or a semi-automated manner has obvious applications in numerous fields. Since creating an algorithm with a one hundred percent correct recognition rate is quite probably impossible in our world of noise and different font styles, it is important to design character recognition algorithms with these failures in mind so that when mistakes are inevitably made, they will at least be understandable and predictable to the person working with the program. This paper explores one such algorithm and tests it on two different fonts using a third font as a reference. The results are discussed and several improvements are suggested.
http://www.ccs.neu.edu/home/feneric/charrec.html
0106-11-02 03:56:02 |
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Vision Systems
Vision Systems
Dr A D Marshall
Room M 1.38
tex2html_wrap_inline2984 World Scientific 1992
tex2html_wrap_inline2984 David Marshall 1994
http://www.cs.cf.ac.uk/Dave/Vision_lecture/Vision_lecture_caller.html
0106-11-30 16:20:20 |
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Image Processing Fundamentals
Fundamentals of Image Processing
I.T. Young
J.J. Gerbrands
L.J. van Vliet
* Introduction
* Digital Image Definitions
* Tools
* Perception
* Image Sampling
* Noise
* Cameras
* Displays
* Algorithms
* Techniques
* Acknowledgments
* References
* Contents
http://www.ph.tn.tudelft.nl/Courses/FIP/noframes/fip.html
0106-11-30 16:26:40 |
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Morphology
Contents
Dilation - grow image regions
Erosion - shrink image regions
Opening - structured removal of image region boundary pixels
Closing - structured filling in of image region boundary pixels
Hit and Miss Transform - image pattern matching and marking
Thinning - structured erosion using image pattern matching
Thickening - structured dilation using image pattern matching
Skeletonization/Medial Axis Transform - finding skeletons of binary regions
http://homepages.inf.ed.ac.uk/rbf/HIPR2/morops.htm
0106-11-30 16:32:05 |
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Pattern Recognition
1. Pattern Recognition Course on the Web (by Richard O. Duda)
2. Introduction to Machine Learning (by Nils J. Nilsson)
3. Image Processing Course
4. Classification Society of North America
5. The Pattern Recognition Files
6. Pattern Recognition Journals
7. Machine Learning Resources
8. Morphing Bibliography of Mark Grundland
9. Neural Network Information
10. Neural Network FAQ's
11. Applets for Neural Networks
12. Face Recognition Home Page
13. Handwriting Recognition
14. Java Demos for Handwriting Recognition
15. Multivariate Analysis
16. Iris Data
17. Software and Hardware for Pattern Recognition Research
18. Typography
19. Music Meter Recognition (PS file)
20. Combinatorial Geometric Problems in Pattern Recognition (compressed PS file)
http://cgm.cs.mcgill.ca/~godfried/teaching/pr-web.html
0106-11-30 16:50:45 |
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Automatic number plate recognition
Automatic number plate recognition (ANPR; see also other names below) is a mass surveillance method that uses optical character recognition on images to read the licence plates on vehicles. As of 2006, systems can scan number plates at around one per second on cars travelling up to 100 mph (160 km/h). They can use existing closed-circuit television or road-rule enforcement cameras, or ones specifically designed for the task. They are used by various police forces and as a method of electronic toll collection on pay-per-use roads, and monitoring traffic activity such as red light adherence in an intersection.
http://en.wikipedia.org/wiki/Automatic_number_plate_recognition
0106-12-03 22:01:37 |
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License Plate Recognition - A Tutorial
Contents:
1. What is LPR?
2. Technology Highlights
3. Other Names
4. Plates and Images
5. What's in an image
6. Does it work?
7. Elements of typical LPR systems
8. How does it Work?
9. Typical applications
10. More Information and links
http://www.licenseplaterecognition.com/
0106-12-03 22:14:32 |
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Web-enabled image processing operators
Below you find web implementations of various biologically motivated image processing operators, which you can use with your own image material.
http://matlabserver.cs.rug.nl/
0106-12-22 14:20:44 |
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