文件列表:

IMAGES (0, 2001-06-12)
IMAGES\breast_cell.pbm (51570, 1998-02-24)
IMAGES\eggs.pbm (40901, 1998-02-24)
IMAGES\plates.pbm (46923, 1998-02-24)
Makefile (734, 1998-03-04)
include (0, 2001-06-12)
include\globals.h (1256, 1998-03-01)
include\hough.h (14316, 1998-03-03)
include\rht.h (990, 1998-02-24)
src (0, 2001-06-12)
src\Xdraw_func.c (85066, 1998-02-24)
src\geo_sym_ellipse.c (53519, 1998-02-24)
src\image.c (17686, 1998-03-01)
src\init_X_stuff.c (66883, 1998-03-01)
src\init_hough.c (1335, 1998-02-24)
src\lin_algebra.c (46146, 1998-03-01)
src\line.c (2924, 1998-02-24)
src\menu_callback_functions.c (12956, 1998-03-01)
src\misc.c (2358, 1998-02-24)
src\node.c (13639, 1998-03-01)
src\noise.c (1831, 1998-02-24)
src\pht_ellipse.c (39741, 1998-03-01)
src\point.c (7705, 1998-02-24)
src\recog.c (15549, 1998-03-03)
src\rht.c (55106, 1998-02-24)
src\rht_data_structures.c (8534, 1998-02-24)
src\sht_ellipse.c (43070, 1998-03-01)
src\sht_line.c (19393, 1998-02-24)

Program name: ------------- recog Author: ------- Robert McLaughlin (ram@ee.uwa.edu.au) How to compile: --------------- type: make If compiling on a machine running Linux or SunOS, uncomment the appropriate lines in the Makefile. By default, the program uses the compiler gcc. To change this to the compiler cc, read the Makefile. The program was written on a Unix machine in ANSI C, and should compile on a Unix machine which has X11 installed. The program was not written for Windows 95 machines. The program has been tested on SGI and Linux machines using both cc and gcc compilers. It has been tested on SunOS machines using the gcc compiler. Usage: ------ recog - begin with a blank image recog filename.pbm - load an edge-detected pbm image. Edges should be approx. 2 pixels thick. e.g. recog IMAGES/eggs.pbm This program has been tested with pbm images generated by xv. The xv program is available from: http://www.trilon.com/xv/ Note that the Standard Hough Transform and Probabilistic Hough Transform for Ellipse detection are VERY SLOW algorithms. I strongly recommend that the first time you use these algorithms, just draw a single ellipse on the screen and then run the algorithm. Then try it with two ellipses. The computation time required for the algorithms is roughly related to the square of the number of black pixels on the screen. All algorithms degrade rapidly when subject to speckle noise. Also, computation times tend to increase rapidly under speckle noise. If you choose to add speckle noise to an image, I strongly recommend that you begin with a value like 0.5 or 1. To change the size of the default window (which is 750 x 550) you need to change the #define statements for WINDOW_SIZE_X and WINDOW_SIZE_Y in the file include/hough.h Description ----------- The program allows the user to hand draw simple shapes using the mouse. The program will then perform a recognition upon the image. Four ellipse detection algorithms have been implemented: 1. Randomized Hough Transform: Details have been submitted in a paper to Pattern Recognition Letters: "Randomized Hough Transform: Improved Ellipse Detection with Comparison", R. A. McLaughlin, Pattern Recognition Letters, 19***. For details, check out the web site: http://ciips.ee.uwa.edu.au/Papers/Journal_Papers/19***/01/Index.html 2. Standard Hough Transform A standard ellipse detection method which makes use of the Hough Transform. This is an implementation of the algorithm described in the paper: "Detecting Partially Occluded Ellipses Using the Hough Transform", H. K. Yuen, J. Illingworth and J. Kittler, Image and Vision Computing, Vol. 7, No. 1, 1***9, pp 31-37. 3. Probabilistic Hough Transform A probabilistic implementation of the above standard algorithm 4. Geometric Symmetry An implementation of the method detailed in the paper: "A Fast Ellipse/Circle Detector Using Geometric Symmetry", Chun-Ta Ho and Ling-Hwei Chen, Pattern Recognition, Vol. 28, No. 1, 1995, pp 117-124. A brief explanation of each algorithm can be found in the paper: "Randomized Hough Transform: Improved Ellipse Detection with Comparison", R. A. McLaughlin, Pattern Recognition Letters, 19***. http://ciips.ee.uwa.edu.au/Papers/Journal_Papers/19***/01/Index.html IMPORTANT NOTE: --------------- Implementations of the Standard Hough Transform and the Probabilistic Hough Transform have been improved since the journal paper was written. Thus both algorithms will achieve slightly better results with the real-world images than was published in the paper. The implementation of the Randomized Hough Transform may give different results when run several times on the same image. This can be seen if you process the test image IMAGE/breast_cell.pbm several times. This is a side-effect of the tree structure used to store points in the Hough parameter space. The program assumes that edges in images are approximately 2 pixels thick. Bugs and Disclaimer ------------------- The documentation is not fully detailed and some minors bugs remain. No support is provided for users to use or modify this software. We can not and will not provide any technical support for using this software. Licence Information ------------------- This software may be freely used for research and scholarly purposes. No part of this software may be used as part of a commercial software product without obtaining the prior written consent of the author, Robert McLaughlin. Release Dates: -------------- Version 1.0 18 August, 1996 Version 1.1 25 May, 1997 Bug fixes Version 1.2 18 August, 1997 Bug fixes Version 1.3 24 January, 19*** Organised code into src, include and IMAGES subdirectories. Simplified Makefile. Bug fixes. Version 1.4 4 March, 19*** Implement code to handle displays with different color depths. Robert McLaughlin 4 March, 19***

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