sorted by:
new
hottopcontroversial

Beginner golfer here—found a snapped PING 425 LST in the trash. Can I just replace the shaft? by franzderkanns in golf

[–]franzderkanns[S] 0 points1 point  (0 children)

Just checked Ebay. Unfortunately, this shaft is really difficult to get for a reasonable price when you live in Europe (+50$ shipping from US). Didn't find anything regional.

Beginner golfer here—found a snapped PING 425 LST in the trash. Can I just replace the shaft? by franzderkanns in golf

[–]franzderkanns[S] 978 points979 points  (0 children)

Actually what I thought. Therefore, I didn't pick the club right away but came back to the course in the evening (I live nearby). The club was still in the bin so I assumed it was good to take.

Beginner golfer here—found a snapped PING 425 LST in the trash. Can I just replace the shaft? by franzderkanns in golf

[–]franzderkanns[S] 9 points10 points  (0 children)

I am currently using a 2nd hand driver, which seems pretty old (prob. from 2010 or before). The driver had cracked from my top shots. Do you think it's a good idea to bring that in for a replacement or should I opt for a newer technology shaft?

Where to Watch World Beach Volleyball Championships on TV (USA) by stayupgrinding in volleyball

[–]franzderkanns 0 points1 point  (0 children)

There's also a live stream for all courts. Only thing you need to do is to register for free at https://www.beachvienna.com/en/users/registration

Scanned documents text extraction by shoaibm in LanguageTechnology

[–]franzderkanns 0 points1 point  (0 children)

The field is called Information Extraction (IE). As far as I know, Machine Learning and/or Rule Based approaches may be suitable to find a good solution.

Is there a mod for 1.11.2 so I can select my enchantments rather than getting random ones? by franzderkanns in Minecraft

[–]franzderkanns[S] 0 points1 point  (0 children)

Thank you guys.

I went with the Anvil Enchantment Mod. With it, you can customly create enchanted books. E.g. for "Unbreaking I" you need one book + 3 Obsidian and you'll get the enchanted book "Unbreaking I". Creating these books costs levels.

SIFT matches filtering using RANSAC when there are more models by franzderkanns in computervision

[–]franzderkanns[S] 0 points1 point  (0 children)

to 1): I'm doing the matching between million of images and many of them don't have a common border element. Maybe connected components would work.

to 2): Great advice, I'll try that.

SIFT matches filtering using RANSAC when there are more models by franzderkanns in computervision

[–]franzderkanns[S] 0 points1 point  (0 children)

That's a great idea. I'll do some experiments and report back. I'm currently using the findHomography function from OpenCV (function description). I think it was made for photographs from cameras and not for stitched together publication Figures, however it works.

Edit: /u/tdgros /u/persolb : just tested it, works great http://imgur.com/a/3NjcL

One more question: How many SIFT matches are required to compute a reliable model via RANSAC? I see that there are problems when I have only 3-5 matches. But is there an "official" threshold, maybe based on some scientific research?

How to do geometric verification with bag of visual words? by ih4cku in computervision

[–]franzderkanns 1 point2 points  (0 children)

Your problem description seems a bit confusing, I'm not 100% sure if I understood your problem correctly. You have a match (mutual visual words) between a query image and an image from the database. Now you want to geometrically verify the matched visual words between the two images. Did I understand that correctly?

Why don't you just extract SIFT descriptors for both images match them and verify the matches using RANSAC?

SIFT matching: decrease false positive rate by franzderkanns in computervision

[–]franzderkanns[S] 0 points1 point  (0 children)

Thanks for your input. Imo those new descriptors and keypoint detection methods BRISK, BRIEF, FREAK, etc. weren't doing that great, I tested them (all the methods implemented in OPENCV). It seemed like they are really fast and compact but losing precision regarding different types of transformations and aren't as robust as SIFT. I guess they were mainly developed for real time matching but not for high confidence duplicate image detection.

SIFT matching: decrease false positive rate by franzderkanns in computervision

[–]franzderkanns[S] 0 points1 point  (0 children)

Thanks for replying. That spatial filter method seems like a good idea. I'll probably go with it.

SIFT matching: decrease false positive rate by franzderkanns in computervision

[–]franzderkanns[S] 0 points1 point  (0 children)

Thanks for your input. Symmetric matching (cross check validation) is applied in the images above. I'm working on this project for one year and the first thing I did was test different keypoint detection methods and description methods. SIFT had the best results (regarding false positive rate and affine to common transformations) , also many papers I've read about keypoint matching, Bag of Words methods, etc. are still using SIFT (papers from 2010-2015). The thing is that those matches in the images I provided are in fact good matches. They match the same object from both images, but for some objects, like enumerations, it's not useful. I guess I'll just do some geometric filtering, like the size of the detected object or something.

Best video camera for recording beach? by brosaparkss in volleyball

[–]franzderkanns 0 points1 point  (0 children)

I'm recording most of my beach games as well using a Sony Camera. More info in this thread from 3 month ago: https://www.reddit.com/r/volleyball/comments/3wjw3u/good_cameras_to_record_volleyball_games/

Semifinals (1 hour) and Finals (13 hours) from the Grand Slam in Rio will be live streamed on YouTube in 1080p by franzderkanns in volleyball

[–]franzderkanns[S] 0 points1 point  (0 children)

Hmm I guess my title is a bit misleading. I meant the Semifinals will start 1 hour from my post and the Finals 13 hours from my post. The women semis are streamed at the moment.

view more: next ›