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2013-12-20 06:55:40 -0600 asked a question False positive rate

How can I calculate the false positive rate in haar classifier?
2013-12-17 12:47:33 -0600 commented question My classifier detect the background of the object

Is there a ratio for negative and positive images? Because I read that positive images is good to be less than negatives. Also, I don't have enough images so I create some samples from one image. Is that a problem?
2013-12-17 02:32:19 -0600 asked a question Haar Classifier evaluation

I train my own classifier using haar training. I would like to know how opencv_performance function works. I compare performance results with my program that detect my object and I noticed that some images that my program didn't detect it, the performance evaluation showed that was detected.
2013-12-15 12:15:07 -0600 commented question My classifier detect the background of the object

When I used HSV color space for threshing the result did not good cause the real background has many color, sometimes near to tool's color.

2013-12-15 11:25:26 -0600 commented question My classifier detect the background of the object

@GilLevi I uploaded twoimages
2013-12-14 12:01:35 -0600 asked a question Problem with vec file

I created a vec file with merging 8 16 vector files. The vec file is here When I tried to train my classifier using that vector it returned the follow

Parent node: NULL

*** 1 cluster ***
OpenCV Error: Assertion failed (elements_read == 1) in icvGetHaarTraininDataFromVecCallback, file /home/mcn/opencv-2.4.5/apps/haartraining/cvhaartraining.cpp, line 1859
terminate called after throwing an instance of 'cv::Exception'
  what():  /home/mcn/opencv-2.4.5/apps/haartraining/cvhaartraining.cpp:1859: error: (-215) elements_read == 1 in function icvGetHaarTraininDataFromVecCallback

Aborted

Can anyone tell me what is the problem? I did all of that many times.. I don't know why it happens

2013-12-14 00:51:29 -0600 asked a question Remove image background

I would like to remove the multi color image's backgroung to keep olny the objects of the image. Is that possible?
2013-12-12 13:14:04 -0600 asked a question My classifier detect the background of the object

I trained my own classifier using haar cascade to detect an object but the classifier, also, detect the object's background. Is there any solution for that?

Some images of my tools are https://www.dropbox.com/s/6ncqhnjkekgig5h/grabber_446.bmp https://www.dropbox.com/s/ihh953fheuk05w9/grabber_42.bmp
2013-12-05 12:47:48 -0600 asked a question Haar Training hang after few stages

I executed the follwing command opencv_haartraining -data data6/cascade -vec data6/vector.vec -bg neg.txt -npos 2300 -nneg 1179 -nstages 25 -mem 2000 -mode ALL -w 80 -h 60 –nonsym to train my classifier.

After some stages, the training hanged

    Data dir name: data6/cascade
    Vec file name: data6/vector.vec
    BG  file name: neg.txt, is a vecfile: no
    Num pos: 2300
    Num neg: 1179
    Num stages: 25
    Num splits: 1 (stump as weak classifier)
    Mem: 2000 MB
    Symmetric: TRUE
    Min hit rate: 0.995000
    Max false alarm rate: 0.500000
    Weight trimming: 0.950000
    Equal weights: FALSE
    Mode: ALL
    Width: 80
    Height: 60
    Applied boosting algorithm: GAB
    Error (valid only for Discrete and Real AdaBoost): misclass
    Max number of splits in tree cascade: 0
    Min number of positive samples per cluster: 500
    Required leaf false alarm rate: 2.98023e-08
    Stage 0 loaded
    Stage 1 loaded
    Stage 2 loaded
    Stage 3 loaded
    Stage 4 loaded
    Stage 5 loaded
    Stage 6 loaded
    Stage 7 loaded
    Stage 8 loaded
    Stage 9 loaded
    Stage 10 loaded
    Stage 11 loaded
    Stage 12 loaded
    Stage 13 loaded
    Stage 14 loaded

    Tree Classifier
    Stage
    +---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+
    |  0|  1|  2|  3|  4|  5|  6|  7|  8|  9| 10| 11| 12| 13| 14|
    +---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+

       0---1---2---3---4---5---6---7---8---9--10--11--12--13--14

    Number of features used : 9299021

    Parent node: 14

    *** 1 cluster ***
    POS: 2300 2475 0.929293
      0%

What it happens?

I noticed that it stacked when FA=0. That can be happen when required leaf false alarm rate achieved?
2013-12-04 08:04:59 -0600 commented answer False alarm in haar cascade

ok thank you very much.

2013-12-03 19:36:30 -0600 asked a question False alarm in haar cascade

What is mean FA?

When I trained my classifier I noticed that the FA was 0. I read a lot about that. Some people say that when the FA=0 the classifier is over-train and it can not detect anything. My classifier can detect my object. So what is the problem when FA is 0?
2013-11-24 18:49:55 -0600 asked a question perfomance utility in haar cascade

I create my own classifier and execute the command opencv_performance -data test5.xml -info testing.txt –ni to evaluate the performance of the generated classifier.

The output was 

 +================================+======+======+======+
|            File Name           | Hits |Missed| False|
+================================+======+======+======+
|    0001_0142_0052_0218_0131.jpg|     0|     1|     6|
+--------------------------------+------+------+------+
|    0002_0380_0250_0105_0063.jpg|     1|     0|     1|
+--------------------------------+------+------+------+
|    0003_0173_0237_0497_0298.jpg|     0|     1|    11|
+--------------------------------+------+------+------+
|    0004_0065_0046_0322_0193.jpg|     0|     1|     4|
+--------------------------------+------+------+------+
|    0005_0158_0201_0329_0197.jpg|     0|     1|     6|
+--------------------------------+------+------+------+
|    0006_0141_0258_0134_0080.jpg|     1|     0|     1|
+--------------------------------+------+------+------+
|    0007_0414_0091_0133_0079.jpg|     1|     0|     0|
+--------------------------------+------+------+------+
|    0008_0070_0098_0403_0242.jpg|     1|     0|    14|
+--------------------------------+------+------+------+
|    0009_0154_0062_0144_0086.jpg|     0|     1|     2|
+--------------------------------+------+------+------+
|    0010_0125_0292_0028_0017.jpg|     0|     1|     6|
+--------------------------------+------+------+------+
|    0011_0066_0128_0369_0221.jpg|     0|     1|     7|
+--------------------------------+------+------+------+
|    0012_0102_0132_0205_0123.jpg|     0|     1|     4|
+--------------------------------+------+------+------+
|    0013_0177_0125_0331_0198.jpg|     0|     1|    16|
+--------------------------------+------+------+------+
|    0014_0247_0047_0254_0152.jpg|     1|     0|     1|
+--------------------------------+------+------+------+
|    0015_0086_0063_0397_0238.jpg|     1|     0|     0|
+--------------------------------+------+------+------+
|    0016_0219_0037_0300_0180.jpg|     0|     1|     2|
+--------------------------------+------+------+------+
|    0017_0168_0069_0159_0095.jpg|     0|     1|     3|
+--------------------------------+------+------+------+
|    0018_0252_0190_0151_0090.jpg|     1|     0|     3|
+--------------------------------+------+------+------+
|    0019_0141_0109_0173_0104.jpg|     0|     1|     5|
+--------------------------------+------+------+------+
|    0020_0371_0093_0046_0028.jpg|     1|     0|     2|
+--------------------------------+------+------+------+
|    0021_0315_0101_0242_0145.jpg|     1|     0|     5|
+--------------------------------+------+------+------+
|    0022_0191_0173_0030_0018.jpg|     1|     0|    22|
+--------------------------------+------+------+------+
|    0023_0527_0146_0218_0131.jpg|     0|     1|    33|
+--------------------------------+------+------+------+
|    0024_0424_0238_0064_0038.jpg|     1|     0|     5|
+--------------------------------+------+------+------+
|    0025_0119_0106_0100_0060.jpg|     0|     1|     5|
+--------------------------------+------+------+------+
|    0026_0416_0206_0172_0103.jpg|     0|     1|     4|
+--------------------------------+------+------+------+
|    0027_0369_0056_0168_0101.jpg|     0|     1|    16|
+--------------------------------+------+------+------+
|    0028_0169_0111_0263_0158.jpg|     0|     1|     4|
+--------------------------------+------+------+------+
|    0029_0272_0203_0395_0237.jpg|     0|     1|     9|
+--------------------------------+------+------+------+
|    0030_0181_0034_0249_0149.jpg|     1|     0|     1|
+--------------------------------+------+------+------+
|    0031_0165_0153_0407_0244.jpg|     0|     1|    13|
+--------------------------------+------+------+------+
|    0032_0063_0133_0346_0207.jpg|     1|     0|     1|
+--------------------------------+------+------+------+
|    0033_0211_0291_0157_0094.jpg|     1|     0|     3|
+--------------------------------+------+------+------+
|    0034_0242_0116_0384_0230.jpg|     0|     1|    15|
+--------------------------------+------+------+------+
|    0035_0040_0236_0144_0086.jpg|     1|     0|     1|
+--------------------------------+------+------+------+
|    0036_0111_0151_0414_0248.jpg|     0|     1|     4|
+--------------------------------+------+------+------+
|    0037_0564_0327_0033_0020.jpg|     0|     1|     8|
+--------------------------------+------+------+------+
|    0038_0294_0283_0128_0077.jpg|     0|     1|     3|
+--------------------------------+------+------+------+
|    0039_0145_0267_0250_0150.jpg|     0|     1|    12|
+--------------------------------+------+------+------+
|    0040_0207_0076_0407_0244.jpg|     1|     0|     2|
+--------------------------------+------+------+------+
|    0041_0078_0132_0312_0187.jpg|     0|     1|     2|
+--------------------------------+------+------+------+
|    0042_0173_0296_0234_0140.jpg|     1|     0|     3|
+--------------------------------+------+------+------+
|    0043_0249_0152_0313_0188.jpg|     1|     0|     1|
+--------------------------------+------+------+------+
|    0044_0155_0060_0368_0221.jpg|     1|     0|     2|
+--------------------------------+------+------+------+
|    0045_0148_0177_0046_0027.jpg|     0|     1|     5|
+--------------------------------+------+------+------+
|    0046_0034_0077_0319_0191.jpg|     0|     1|     8|
+--------------------------------+------+------+------+
|    0047_0094_0011_0306_0183.jpg|     0|     1|     3|
+--------------------------------+------+------+------+
|    0048_0122_0094_0212_0127.jpg|     0|     1|     5|
+--------------------------------+------+------+------+
|    0049_0321_0171_0077_0046.jpg|     0|     1|     1|
+--------------------------------+------+------+------+
|    0050_0279_0113_0042_0025.jpg|     0|     1|     0|
+--------------------------------+------+------+------+
|                           Total|    19|    31|   290|
+================================+======+======+======+
Number of stages: 18
Number of weak classifiers: 420
Total time: 9.000000
18
        19      290     0.380000        5.800000
        19      290     0.380000        5.800000
        19      290     0.380000        5.800000
        14      147     0.280000        2.940000
        13      93      0.260000        1.860000
        11      66      0.220000        1.320000
        9       50      0.180000        1.000000
        9       43      0.180000        0.860000
        9       35      0.180000        0.700000
        8       33      0.160000        0.660000
        8       32      0.160000        0.640000
        8       30      0.160000        0.600000
        7       28      0.140000        0.560000
        5       26      0.100000        0.520000
        5       23      0.100000        0.460000
        5       20      0.100000        0.400000
        5       18      0.100000        0.360000
        4       17      0.080000        0.340000
        4       17      0.080000        0.340000
        4       17      0.080000        0.340000
        4       15      0.080000        0.300000
        4       14      0.080000        0.280000
        3       14      0.060000        0.280000
        3       11      0.060000        0.220000
        2       11      0.040000        0.220000
        0       10      0.000000        0.200000
        0       9       0.000000        0.180000
        0       9       0.000000        0.180000
        0       8       0.000000        0.160000
        0       8       0.000000        0.160000
        0       7       0.000000        0.140000
        0       5       0.000000        0.100000
        0       5       0.000000        0.100000
        0 ...
(more)
2013-11-23 12:39:35 -0600 asked a question Traincascade error..

I run the command opencv_traincascade -data data/cascade -vec data/vector.vec -bg neg.txt -numPos 2200 -numNeg 1000 -numStages 25 –featureType LBP -mem 2000 -mode ALL -w 25 -h 15 and returned the follow:

PARAMETERS:
cascadeDirName: data/cascade
vecFileName: data/vector.vec
bgFileName: neg.txt
numPos: 2200
numNeg: 1000
numStages: 25
precalcValBufSize[Mb] : 256
precalcIdxBufSize[Mb] : 256
stageType: BOOST
featureType: HAAR
sampleWidth: 25
sampleHeight: 15
boostType: GAB
minHitRate: 0.995
maxFalseAlarmRate: 0.5
weightTrimRate: 0.95
maxDepth: 1
maxWeakCount: 100
mode: ALL

===== TRAINING 0-stage =====
<BEGIN
POS count : consumed   2200 : 2200
NEG count : acceptanceRatio    1000 : 1
Precalculation time: 18
+----+---------+---------+
|  N |    HR   |    FA   |
+----+---------+---------+
|   1|        1|        1|
+----+---------+---------+
|   2| 0.997727|    0.567|
+----+---------+---------+
|   3| 0.995455|    0.496|
+----+---------+---------+
END>

===== TRAINING 1-stage =====
<BEGIN
OpenCV Error: Bad argument (Can not get new positive sample. The most possible reason is insufficient count of samples in given vec-file.
) in get, file /home/mcn/opencv-2.4.5/apps/traincascade/imagestorage.cpp, line 159
terminate called after throwing an instance of 'cv::Exception'
  what():  /home/mcn/opencv-2.4.5/apps/traincascade/imagestorage.cpp:159: error: (-5) Can not get new positive sample. The most possible reason is insufficient count of samples in given vec-file.
 in function get

Aborted

I see and that http://code.opencv.org/issues/1834 ... So what can I do?
2013-11-23 12:08:05 -0600 commented question Create training samples from one

The problem was the path of infofile.txt
2013-11-23 06:59:48 -0600 commented question opencv_traincascade can not trained

In bg.txt there are 1000 images.

About the parse error, the file has 231 rows and I can not understand why there is a parse error in line 232. I removed the 232 line and executed the command again and the error still exist:/
2013-11-23 06:59:23 -0600 asked a question Create training samples from one

I executed the follow command opencv_createsamples -img positive/rawdata/im5_67.bmp -num 100 -bg negative/infofile.txt -vec data/samples1.vec -bgcolor 0 -bgthresh 0 -w 25 -h 15, in Centos, to create more samples and it returned:

Info file name: (NULL)
Img file name: positive/rawdata/im5_67.bmp
Vec file name: data/samples1.vec
BG  file name: negative/infofile.txt
Num: 100
BG color: 0
BG threshold: 0
Invert: FALSE
Max intensity deviation: 40
Max x angle: 1.1
Max y angle: 1.1
Max z angle: 0.5
Show samples: FALSE
Width: 25
Height: 15
Create training samples from single image applying distortions...
*** glibc detected *** opencv_createsamples: corrupted double-linked list: 0x0000000000657f80 ***
======= Backtrace: =========
/lib64/libc.so.6(+0x760e6)[0x7f9feb6c10e6]
/lib64/libc.so.6(+0x78f01)[0x7f9feb6c3f01]
/lib64/libc.so.6(fclose+0x14d)[0x7f9feb6b174d]
opencv_createsamples[0x405098]
opencv_createsamples[0x40897a]
opencv_createsamples(_Z23cvCreateTrainingSamplesPKcS0_iiS0_iiidddiii+0xe2)[0x40dcc2]
opencv_createsamples(main+0x61a)[0x4046aa]
/lib64/libc.so.6(__libc_start_main+0xfd)[0x7f9feb669cdd]
opencv_createsamples[0x403fc9]
======= Memory map: ========
00400000-0042d000 r-xp 00000000 fd:03 26791                              /usr/local/bin/opencv_createsamples
0062d000-0062e000 rw-p 0002d000 fd:03 26791                              /usr/local/bin/opencv_createsamples
00653000-00674000 rw-p 00000000 00:00 0                                  [heap]
7f9feb367000-7f9feb425000 rw-p 00000000 00:00 0
7f9feb425000-7f9feb44a000 r-xp 00000000 fd:05 10719                      /usr/lib64/libpng12.so.0.49.0
7f9feb44a000-7f9feb64a000 ---p 00025000 fd:05 10719                      /usr/lib64/libpng12.so.0.49.0
7f9feb64a000-7f9feb64b000 rw-p 00025000 fd:05 10719                      /usr/lib64/libpng12.so.0.49.0
7f9feb64b000-7f9feb7d5000 r-xp 00000000 fd:01 313                        /lib64/libc-2.12.so
7f9feb7d5000-7f9feb9d4000 ---p 0018a000 fd:01 313                        /lib64/libc-2.12.so
7f9feb9d4000-7f9feb9d8000 r--p 00189000 fd:01 313                        /lib64/libc-2.12.so
7f9feb9d8000-7f9feb9d9000 rw-p 0018d000 fd:01 313                        /lib64/libc-2.12.so
7f9feb9d9000-7f9feb9de000 rw-p 00000000 00:00 0
7f9feb9de000-7f9feb9f4000 r-xp 00000000 fd:01 2302                       /lib64/libgcc_s-4.4.7-20120601.so.1
7f9feb9f4000-7f9febbf3000 ---p 00016000 fd:01 2302                       /lib64/libgcc_s-4.4.7-20120601.so.1
7f9febbf3000-7f9febbf4000 rw-p 00015000 fd:01 2302                       /lib64/libgcc_s-4.4.7-20120601.so.1
7f9febbf4000-7f9febc77000 r-xp 00000000 fd:01 2196                       /lib64/libm-2.12.so
7f9febc77000-7f9febe76000 ---p 00083000 fd:01 2196                       /lib64/libm-2.12.so
7f9febe76000-7f9febe77000 r--p 00082000 fd:01 2196                       /lib64/libm-2.12.so
7f9febe77000-7f9febe78000 rw-p 00083000 fd:01 2196                       /lib64/libm-2.12.so
7f9febe78000-7f9febf60000 r-xp 00000000 fd:05 1821                       /usr/lib64/libstdc++.so.6.0.13
7f9febf60000-7f9fec160000 ---p 000e8000 fd:05 1821                       /usr/lib64/libstdc++.so.6.0.13
7f9fec160000-7f9fec167000 r--p 000e8000 fd:05 1821                       /usr/lib64/libstdc++.so.6.0.13
7f9fec167000-7f9fec169000 rw-p 000ef000 fd:05 1821                       /usr/lib64/libstdc++.so.6.0.13
7f9fec169000-7f9fec17e000 rw-p 00000000 00:00 0
7f9fec17e000-7f9fec185000 r-xp 00000000 fd:01 2216                       /lib64/librt-2.12.so
7f9fec185000-7f9fec384000 ---p 00007000 fd:01 2216                       /lib64/librt-2.12.so
7f9fec384000-7f9fec385000 r--p 00006000 fd:01 2216                       /lib64/librt-2.12.so
7f9fec385000-7f9fec386000 rw-p 00007000 fd:01 2216                       /lib64/librt-2.12.so
7f9fec386000-7f9fec39d000 r-xp 00000000 fd:01 337                        /lib64/libpthread-2.12.so
7f9fec39d000-7f9fec59d000 ---p 00017000 fd:01 337                        /lib64/libpthread-2.12.so
7f9fec59d000-7f9fec59e000 r--p 00017000 fd:01 337                        /lib64/libpthread-2.12.so
7f9fec59e000-7f9fec59f000 rw-p 00018000 fd:01 337                        /lib64/libpthread-2.12.so
7f9fec59f000-7f9fec5a3000 rw-p 00000000 00:00 0
7f9fec5a3000-7f9fec5a5000 r-xp 00000000 fd:01 2194                       /lib64 ...
(more)
2013-11-22 16:12:12 -0600 commented question opencv_traincascade can not trained

Btw it returns the same problem when I use more images. I notices that when the vec file created there was a parse error. Is that a reason that it can train?
2013-11-22 16:07:33 -0600 commented question opencv_traincascade can not trained

I read that negative images have to be less than possitive.

2013-11-22 14:00:42 -0600 asked a question Segmentation fault using haartraing in opencv

I run the command opencv_haartraining -data data/cascade -vec samples1.vec -bg negative/infofile.txt -npos 231 -nneg 500 -ntages 25 -mem 2000 -mode ALL -w 25 -h 15 in Centos 6 and it returns Segmentation fault.
2013-11-22 12:22:53 -0600 asked a question opencv_traincascade can not trained

I run these command 

opencv_traincascade -data data/cascade -vec samples1.vec -bg negative/infofile.txt -numPos 231 -numNeg 100 -w 25 -h 15

in Centos systems and it returns the follow

PARAMETERS:
cascadeDirName: data/cascade
vecFileName: samples1.vec
bgFileName: negative/infofile.txt
numPos: 231
numNeg: 100
numStages: 20
precalcValBufSize[Mb] : 256
precalcIdxBufSize[Mb] : 256
stageType: BOOST
featureType: HAAR
sampleWidth: 25
sampleHeight: 15
boostType: GAB
minHitRate: 0.995
maxFalseAlarmRate: 0.5
weightTrimRate: 0.95
maxDepth: 1
maxWeakCount: 100
mode: BASIC

===== TRAINING 0-stage =====
<BEGIN
POS count : consumed   231 : 231
Train dataset for temp stage can not be filled. Branch training terminated.
Cascade classifier can't be trained. Check the used training parameters.

Can anyone tell me what is the problem?:/
2013-11-21 06:55:11 -0600 commented question my classifier using haar cascade can not detect anything

ok thanks you both I will follow the advises:)
2013-11-21 04:11:37 -0600 commented answer haar cascade parameters

StevenPuttemans my request is http://answers.opencv.org/question/24241/my-classifier-using-haar-cascade-can-not-detect/
2013-11-21 02:07:41 -0600 received badge ● Student (source)
2013-11-20 18:52:13 -0600 asked a question haar cascade training output

Can anyone explain me the output of the algorithm

N|%SMP|ST.THR|HR|FA|EXP.ERR

and what is represent each stage?

I know that

  • HR is the hit rate: "how many" image detect correctly
  • FA is the false alarm: "how many" image detect wrongly

Can I understand from the output if my classifier have a problem?
2013-11-20 17:58:52 -0600 asked a question my classifier using haar cascade can not detect anything

I create my own classifier using 90 positives samples and 299 negatives to detect doctor's tool.

I run this command createsamples.exe -info positive/info.txt -vec data/vector.vec -num 90 -w 25 -h 15 to create my samples and that haartraining.exe -data data/cascade -vec data/vector.vec -bg negative/infofile.txt -npos 90 -nneg 299 -nstages 25 -mem 1000 -mode ALL -w 25 -h 15 -nonsym to train my classifier.

When the clasifier trained I get the xml file and use it in my program. I notice that it can not detect anything...

Do anyone know what I am doing wrong?:/

Second problem

When I run my command opencv_traincascade -data data/cascade -vec data/vector.vec -bg neg.txt -numPos 2200 -numNeg 1000 -numStages 25 –featureType LBP -mem 2000 -mode ALL -w 25 -h 15 after the first stage it returned the follow

<BEGIN
OpenCV Error: Bad argument (Can not get new positive sample. The most possible reason is insufficient count of samples in given vec-file.
) in get, file /home/mcn/opencv-2.4.5/apps/traincascade/imagestorage.cpp, line 159
terminate called after throwing an instance of 'cv::Exception'
  what():  /home/mcn/opencv-2.4.5/apps/traincascade/imagestorage.cpp:159: error: (-5) Can not get new positive sample. The most possible reason is insufficient count of samples in given vec-file.
 in function get

Aborted

As I read from http://answers.opencv.org/question/776/error-in-parameter-of-traincascade/ I redude the numPos but the problem is still exist in later stages...

2013-11-20 17:45:25 -0600 commented answer haar cascade parameters

StevenPuttemans when I train my classifier and use the xml file, I notice that it can not detect any thing. Do you know what I am doing wrong?
2013-11-20 17:38:00 -0600 commented question Haar classifier

ok thanks you very much :)
2013-11-18 16:47:32 -0600 asked a question haar cascade parameters

In createsamples we use parameters -w and -h. These parameters have to be the size of the object or we can use smaller size?
2013-11-15 05:33:15 -0600 asked a question Haar cascade - problem with xml file

I used 188 positive images and 299 negative images to train my classifier. After 20 stages the process stack. I notices that in previous stage the FA was zero (0). I do not know why happen that so I take the 20 stages to create the xml file. When I use the xml file to my program can not detect any of my object.

Please, can anyone help? I create many xml files and I have the same problem
2013-11-13 19:34:37 -0600 asked a question haar cascade does not detect object

when I create the xml file and use it in my program can not recognize any object. I realize that the problem is 4th param of cvHaarDetectObjects() but i don't know how to choose the correct one so the algorithm can detect my object. CvSeq sign = cvHaarDetectObjects( src, cascade, storage, 1.5, 3, CV_HAAR_DO_CANNY_PRUNING);

Can anyone help me?
2013-11-12 17:21:14 -0600 commented question negative images in haarcascade - training

instrument when burn something cause I want the effect of the operation
2013-11-12 17:03:53 -0600 commented question negative images in haarcascade - training

the bubbles that appear when doctor burn the tissue.
2013-11-12 16:54:18 -0600 commented question negative images in haarcascade - training

sorry I fix it
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