AGalluccio's profile - activity

@GillLevi. Not yet. But i try when i'll use kaze descriptor @Guanta. I'll give a look for sure in case bow (w/out colors) will not give good results. But cnn can be used with binary descriptors/extractors?

Thx a lot to you both guys, you helped me a lot. This app will run on iphone, so i think that CNN is too much (it's like open a door with a bomb :-)). So the next steps i do, will be try to build the IOS framework with the latest version of OpenCV (3.0)(i've already read that it is not so "simply") and the try KAZE+BOW combination. If i will not have good results with this techique also, then i will "put my hands up", and i'll keep the "less worst" combination of descriptor/extractor/matcher.

I will update this post for sure, just for share my experience with all of you, but i'll do in next months (i'm programming the app in the "free" time, that is no so much).

'till then, thx a lot again for your support. Andrea

Hi, check that <projectName>.pch file is filled in this way:

#ifdef __cplusplus
#import <opencv2/opencv.hpp>
#endif

#import <Availability.h>

#ifndef __IPHONE_5_0
#warning "This project uses features only available in iOS SDK 5.0 and later."
#endif

#ifdef __OBJC__
    #import <UIKit/UIKit.h>
    #import <Foundation/Foundation.h>
#endif

Andrea

Hi Gil, all images are of the same size (200X200) (i resize them before start matching) and in grayscale format (converted using std OpenCV method for grayscale). I never read about " Deep convulutional neural nets". are there an OpenCV implementation of such techinque? Or i have to implement it on Knn match results matrix ? Thanks Andrea

Hi, if you include opencv2/highgui/ios.h in your implementation file, then you can use:

UIImageToMat(image, cvImage); // Source UIImage* --> cv::Mat self.matchedImage = MatToUIImage(cvMatchedImage); // cv:Mat --> UIImage*

No method implementations are needed. I always used these methods without any problems. I'm using OpenCV version 2.4.9, Xcode 5.0, IOS 7.0

Andrea

P.s : i use these methods NOT on main thread, and they works well also.

Hi guys, during this week i've made some other tests. The last step i've done was BOW implementation (but i stopped as soon as i read the answer of Guanta about binary descriptors & BOW). I've made some searches on BBOW (Binary Bag of word); i found some good pdfs, but not a code implementation of BBOW.

So i've change my code from BRISK+FREAK+FLANN to ORB+ORB+BFMACTHER(NORM_HAMMING) as described in this good post of stack overflow trying to have (with high hopes) better results http://stackoverflow.com/questions/9539473/opencv-orb-not-finding-matches-once-rotation-scale-invariances-are-introduced

But i have no success (sigh, sob). So i think that the best way to proceed is taking the suggestion of Guanta: download master branch of OpenCV (rel 3.0) and use KAZE+BOW I've load an image that collects all images i'm working on for find image similarities. I've grouped images that for me are similar with a border with different color (for me similar means "same subject in different photos"). http://imagizer.imageshack.us/v2/xq90/855/s8lq.jpg

The best results i got (BEFORE ORB implementation) was that the dog of set1 are correctly matched with himself, but it's matched with one photo of set2 also. And if i use ONLY the three photos of set2 and try to match between them, i never find a good match for anyone (but the subject is the same)

I have better results with images in set 3 and 4, but for the set 3 images, not all images are matched correctly (this means that one of them is not selected as similar). So the question: is KAZE+BOW the better (& only) way to proceed in order to find similar images? Or probably what i'm trying to achieve is not possible?

Thanks a lot again Andrea

Thanks a lot again Guanta for your kind hints and explanations. I will update this post after i've made some tests with this "bag of informations". Andrea

Reading this (your) post http://answers.opencv.org/question/547/using-flann-with-binary-descriptors-brieforb/ what i understand is that a "must-have" to build BOW, is have descriptors in uchar format and then compute manually the Cluster-Centers on that. I read a couple of pdf that describes how to do that but i don't understand too much (too mathematics behind for me...) Could suggest me some likes with pseudo-code of Cluster-Centers (then i will use specific language to implement that). Thanks again Andrea

Hi Guanta, thx a lot for the hint. I'm think the same thing but with AKAZE (that should be part of OpenCV 2.4.9, am i right?). And what you think about keeping BRISK+FREAK,FLANN (to keep speed and light) the convert descriptors to a float CV_32F and then in uchar (as described in this post: http://answers.opencv.org/question/17460/how-to-use-bag-of-words-example-with-brief/ ) and then use these with BOW (to train index and make the match)? Before write implementation, i would like to know what is your think on this way to proceed. I know that could be faster to implement KAZE (or AKAZE) but i read that the matching precision is not equivalent like BRISK or FREAK. Probably i understand wrong?

So i understand very well that i have a problem about "classification" and the only good solution is BOW. But BOW works with float extractor (i understand) and ORB is binary (like BRISK & FREAK). Binary are more fast that float detector (i read). So probably is better use fload descriptor/extractor and use BOW. I'll do some other searches about that (i need algorithm that are not patented because i would like to develop a commercial software). Thank a lot again. I'll post my results as soon as i will able to share my experience with all of you Andrea

Hi Guanta, you're right: are both binary (and not float - it's important to specifiy it, in order to avoid possible misunderstanding for newbies like me :-). I would like thank you also for your help. For this i would like to share with you a very vell done ppt tutorial about BOW (that i found during my long searches): http://www.micc.unifi.it/downloads/tutorial_bow/tutorial_bow_iciap13_1.pdf

Hi Gil, i've wrote a long answer to explain better the problem and answer to you & Guanta both, but the forums doesen't allow to post an answer before 48h. A very long post loosed for two times! So i post a comment instead. You're right: just one image, are better that 1000 words. So in the next days, i will modify my code to produce the matched image (with drawMatches method) to explain better the problem i have. And i would like to thank you also, for your great blogs (i suppose it's yours i.e:http://gilscvblog.wordpress.com/2013/10/04/a-tutorial-on-binary-descriptors-part-3-the-orb-descriptor/) that help me to understand better some very important concepts. Your support is very important for people like me that try to learn and understand new concepts by their own. Thx again! Andrea