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Saturday, July 18, 2015
Orioles Highlight: Chris Tillman allows leadoff single, no other hits in 8 IP, Manny Machado 20th HR in win over Tigers (ESPN)
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Jamaica beats Haiti 1-0, will play US in Gold Cup semifinal
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I have a new follower on Twitter
spanish words
learn new spanish words of the day
Phoenix
Following: 1438 - Followers: 1258
July 18, 2015 at 08:48PM via Twitter http://twitter.com/spaniishhh
Anonymous on Twitter: "SHOT THE WRONG MAN??? http://ift.tt/1HBK3p8;
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Dempsey's 3 goals lift US over Cuba 6-0, into Gold Cup semis
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Bartomeu elected for 2nd term as Barcelona president
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[FD] Information Exposure Vulnerability in WordPress Mobile Pack Wordpress Plugin v2.1.2 and below
Should be invisible<\/p>\n","content":"
Should be invisible<\/p>\n","comment_status":"open","no_comments":0,"show_avatars":true,"require_name_email":true,"category_id":1,"category_name":"Uncategorized","related_posts":"","related_web_posts":"","zemanta":false}}) ## Solution: Upgrade to v2.1.3. Users who installed v2.1.3 before June 3, 2015 should re-download and re-install the package. ## Disclosure Timeline: 2015-06-01 - Discovered. Contacted developer on support forums. 2015-06-03 - Mailed report to developer. 2015-06-03 - Updated v2.1.3 released. 2015-07-18 - Publishing disclosure on FD mailing list. ## Disclaimer: This disclosure is purely meant for educational purposes. I will in no way be responsible as to how the information in this disclosure is used.
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Re: [FD] weblogin software cross site request
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Re: [FD] OpenSSH keyboard-interactive authentication brute force vulnerability (MaxAuthTries bypass)
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Pele has back surgery, leaving Sao Paulo hospital next week
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Banned FIFA official pleads not guilty to racketeering
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Dempsey, Johannsson start up top for US against Cuba
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Brazil soccer great Pele is hospitalized
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Message to @Beneful, @Purina, @Nestle :: We are #Anonymous
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I have a new follower on Twitter
About My World
Your smile makes my world shine like a star and every thought of you makes my problems disappear.
Following: 2707 - Followers: 2533
July 18, 2015 at 12:14PM via Twitter http://twitter.com/Aboutmyworldd
Hertha Berlin signs Czech midfielder Darida from Freiburg
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FIFA leaders return to Zurich, ready to pick election date
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WhatsApp, Viber and Skype Internet Calls may No Longer be FREE in India
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Like It Or Not... You Can't Disable Windows 10 Automatic Updates
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Nasri goal gives Manchester City 1-0 win over Melbourne City
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Anonymous confessions about hockey, from the Whisper App
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Brazil's soccer chief decides not to travel to Switzerland
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Charon
Friday, July 17, 2015
Food Addicts in Recovery Anonymous Meeting
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Don't Wanna Die Anonymous
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Orioles Video: Chris Davis robbed by another leaping catch at wall, Ubaldo Jimenez allows 7 R in loss to Tigers (ESPN)
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US forward Sydney Leroux needs ankle surgery
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US forward Morgan has knee surgery
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Cuba says 4 players absent for Gold Cup quarterfinal vs US
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President of Bolivia's soccer federation arrested
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Judge in California dismisses soccer concussion lawsuit
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[FD] weblogin software cross site request
[FD] UDID+ v2.5 iOS - Mail Command Inject Vulnerability
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[FD] OpenSSH keyboard-interactive authentication brute force vulnerability (MaxAuthTries bypass)
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[FD] Remote file upload vulnerability in mailcwp v1.99 wordpress plugin
"; • echo $result; • echo "
"; • ?> • Advisory: http://ift.tt/1I9jICg
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[FD] 1503A - Chrome - ui::AXTree::Unserialize use-after-free
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Frimpong sent off in Russia after apparent racist abuse
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Delph makes U-turn, leaves Aston Villa for Man City
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Prosecutor confirms FIFA official in US to face charges
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Hacker Earns 1.25 Million Free Frequent Flyer Miles On United Airlines
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I have a new follower on Twitter
Jewish Quotes
Funny Jewish sayings have been around a long time. We are going to look at some of these sayings, some funny quotes ..
Following: 1382 - Followers: 1203
July 17, 2015 at 11:30AM via Twitter http://twitter.com/JewishQuotess
Spain government honours football greats Casillas, Xavi
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[FD] AirDroid ID - Client Side JSONP Callback Vulnerability
JSONP Call
Vulnerable Source: JSONP Call samir({"code":"1","result":{"id":"9731220","nickname":"Hadji+Samir","mail":"info.dimanet@gmail.com","create_date":"2015-07-06 06:18:40","data_flow_total":"0","vip":"0","vip_starttime":null,"vip_endtime":null,"from_type":"","read_new":"1","mail_verify":"0","avatar_url":"","last_update_avatar":"2015-07-06 06:18:40","country":"DZ","isPremium":-1,"is_recurring":0,"has_device":"1","device":[{"id":"10257826","name":"htc HTC T528w","deviceId":"3cacf266733309329510a4d2477ace37","channelToken":"2785903c941c8450ebf816b47dab1164","logicKey":"6227d20a5103046b92d118d5db9e2e67","manu":"htc","model":"HTC T528w","model_pic":"http:\/\/img.airdroid.com\/devices\/default","osVersion":"4.1.1","sdkApiLevel":"16","netOpts":{"ip":"192.168.1.4","port":8888,"socket_port":8889,"ssl_port":8890,"usewifi":"true","file_port":8765},"appVer":"20142","gcmId":"","is_default":"0","imsi":"0","create_date":"2015-07-06 06:20:18","account_id":"9731220","push_token":"20a2a64bd6cb1608cb2fc1c1bb1ed18b","support_plugin_vnc":0,"plugin_vnc_versions":0,"plugin_vnc_url":"","plugin_vnc_log":"","plugin_vnc_update_from_url":"false","phone_versions":0,"pc_versions":"","mac_versions":"","addon_package_name":""}],"app_last_modify":"1436177702","token":"","avatar":[],"push_ws_sub_url":"ws:\/\/54.227.249.159:443","push_tcp_sub_url":"54.227.249.159:80","push_pub_url":"http:\/\/push.airdroid.com","pc_push_token":"99eb3edebbbc30883679e563d0ed2d1f","web_push_token":"b585e6763f41df6c8fcf1961f38c6d74","fmp_push_token":"d15b2f78e335b68186bab0664c027520","account_type":2,"is_unlock":0,"max_file_size":31457280,"lan_trans_folder":0,"unlock_starttime":"","unlock_expired":"","server_timestamp":14362728935628},"msg":"success!"}) Reference(s): http://ift.tt/JCvqri http://ift.tt/1fPT8Sv Solution - Fix & Patch: ======================= Parse in the jsonp GET method request the vulnerable callback value to prevent client-side script code injection attacks. Restrict the callback input by a whitelist and disallow special chars on server-side or client-side GET method requests. Security Risk: ============== The security risk of the vulnerability in the android id login web-application is estimated as high. (CVSS 5.6) Credits & Authors: ================== Vulnerability Laboratory [Research Team] - Hadji Samir [samir@evolution-sec.com] Disclaimer & Information: ========================= The information provided in this advisory is provided as it is without any warranty. Vulnerability Lab disclaims all warranties, either expressed or implied, including the warranties of merchantability and capability for a particular purpose. Vulnerability-Lab or its suppliers are not liable in any case of damage, including direct, indirect, incidental, consequential loss of business profits or special damages, even if Vulnerability-Lab or its suppliers have been advised of the possibility of such damages. Some states do not allow the exclusion or limitation of liability for consequential or incidental damages so the foregoing limitation may not apply. We do not approve or encourage anybody to break any vendor licenses, policies, deface websites, hack into databases or trade with fraud/stolen material. Domains: http://ift.tt/1jnqRwA - www.vuln-lab.com - http://ift.tt/1kouTut Contact: admin@vulnerability-lab.com - research@vulnerability-lab.com - admin@evolution-sec.com Section: magazine.vulnerability-db.com - http://ift.tt/1zNuo47 - http://ift.tt/1wo6y8x Social: http://twitter.com/#!/vuln_lab - http://ift.tt/1kouSqa - http://youtube.com/user/vulnerability0lab Feeds: http://ift.tt/1iS1DH0 - http://ift.tt/1kouSqh - http://ift.tt/1kouTKS Programs: http://ift.tt/1iS1GCs - http://ift.tt/1iS1FyF - http://ift.tt/1kouSqp Any modified copy or reproduction, including partially usages, of this file requires authorization from Vulnerability Laboratory. Permission to electronically redistribute this alert in its unmodified form is granted. All other rights, including the use of other media, are reserved by Vulnerability-Lab Research Team or its suppliers. All pictures, texts, advisories, source code, videos and other information on this website is trademark of vulnerability-lab team & the specific authors or managers. To record, list (feed), modify, use or edit our material contact (admin@vulnerability-lab.com or research@vulnerability-lab.com) to get a permission. Copyright © 2015 | Vulnerability Laboratory - [Evolution Security GmbH]™Source: Gmail -> IFTTT-> Blogger
ISS Daily Summary Report – 07/16/15
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Eleven Anonymous Turkish Muslims Baptized Orthodox Christians
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[FD] FoxyCart Bug Bounty #1 - Filter Bypass & Persistent Vulnerability
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Dortmund heads to Belarus or Austria in Europa League
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Liverpool beats Brisbane Roar 2-1 on late James Milner goal
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Monaco draws Young Boys in Champions League qualifiers
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NSA Releases Open Source Network Security Tool for Linux
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problem with anonymous function in executable
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How to Crack RC4 Encryption in WPA-TKIP and TLS
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Pellegrini won't play Sterling in 1st Australian match
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50 Miles on Pluto
Thursday, July 16, 2015
Optimizing the computation of overriding. (arXiv:1507.04630v1 [cs.AI])
We introduce optimization techniques for reasoning in DLN---a recently introduced family of nonmonotonic description logics whose characterizing features appear well-suited to model the applicative examples naturally arising in biomedical domains and semantic web access control policies. Such optimizations are validated experimentally on large KBs with more than 30K axioms. Speedups exceed 1 order of magnitude. For the first time, response times compatible with real-time reasoning are obtained with nonmonotonic KBs of this size.
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Black-Box Policy Search with Probabilistic Programs. (arXiv:1507.04635v1 [stat.ML])
In this work, we explore how probabilistic programs can be used to represent policies in sequential decision problems. In this formulation, a probabilistic program is a black-box stochastic simulator for both the problem domain and the agent. We relate classic policy gradient techniques to recently introduced black-box variational methods which generalize to probabilistic program inference. We present case studies in the Canadian traveler problem, Rock Sample, and a benchmark for optimal diagnosis inspired by Guess Who. Each study illustrates how programs can efficiently represent policies using moderate numbers of parameters.
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Overeaters Anonymous
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Uruguayan soccer legend Alcides Ghiggia dies at 88
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Uruguayan soccer great Alcides Ghiggia dies at 88
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French defender Kaboul joins Sunderland from Tottenham
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Anonymous on Twitter: "Anonymous takes down @DenverPolice union website (@ColoradoFOP ...
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[FD] Broken, Abandoned, and Forgotten Code, Part 11
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Re: [FD] 15 TOTOLINK router models vulnerable to multiple RCEs
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[FD] SEC Consult SA-20150716-0 :: Permanent Cross-Site Scripting in Oracle Application Express
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Brazilian club Palmeiras signs Paraguayan striker Barrios
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Janson Media to Bring Bob Ross to Digital Platforms Worldwide
Janson Media to Bring Bob Ross to Digital Platforms Worldwide
Bob Ross Inc. is pleased to announce an agreement with Janson Media that will introduce The Joy of Painting with Bob Ross — the most-recognized, most-watched TV art show in history — to digital platforms worldwide. The series has already begun its rollout on Amazon Instant Video and Amazon Prime.
You’ve see Bob Ross before. He is the soft-spoken guy painting happy clouds, mountains, trees in about 26 television minutes, using big housepainting-type brushes and encouraging his audiences with his gentle reminder, “you can do it.”
The Joy of Painting continues to air on television networks and channels worldwide, making The Joy of Painting the most popular art show of all time.
“We are thrilled — no, ‘happy’ actually — to announce this new agreement,” said Jesse Janson, Director of Acquisitions and Digital Media at Janson Media. “We believe The Joy of Painting, long a staple on U.S. public television stations nationwide, will find important new audiences on all of the major digital platforms worldwide, where episodes will be available to Bob Ross fans 24 hours a day, seven days a week, 365 days a year.”
Click here to access: http://bit.ly/BobRossAmazonPrime
For more information, send an email to info@bobross.com or call 1-800-262-7677
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FIFA ethics panel wants to lift strict secrecy rules
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Where did SIFT and SURF go in OpenCV 3?
If you’ve had a chance to play around with OpenCV 3 (and do a lot of work with keypoint detectors and feature descriptors) you may have noticed that the SIFT and SURF implementations are no longer included in the OpenCV 3 library by default.
Unfortunately, you probably learned this lesson the hard way by opening up a terminal, importing OpenCV, and then trying to instantiate your favorite keypoint detector, perhaps using code like the following:
$ python >>> import cv2 >>> detector = cv2.FeatureDetector_create("SIFT") Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: 'module' object has no attribute 'FeatureDetector_create'
Oh no! There is no longer a
cv2.FeatureDetector_createmethod!
The same is true for our
cv2.DescriptorExtractor_createfunction as well:
>>> extractor = cv2.DescriptorExtractor_create("SIFT") Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: 'module' object has no attribute 'DescriptorExtractor_create'
Furthermore,
cv2.SIFT_createand
cv2.SURF_createwill fail as well:
>>> cv2.SIFT_create() Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: 'module' object has no attribute 'SIFT_create' >>> cv2.SURF_create() Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: 'module' object has no attribute 'SURF_create'
I’ll be honest — this had me scratching my head at first. How am I supposed to access SIFT, SURF, and my other favorite keypoint detectors and local invariant descriptors if
cv2.FeatureDetector_createand
cv2.DescriptorExtractor_createhave been removed?
The
cv2.FeatureDetector_createand
cv2.DescriptorExtractor_createwere (and still are) methods I used all the time. And personally, I really liked the OpenCV 2.4.X implementation. All you needed to do was pass in a string and the factory method would build the instantiation for you. You could then tune the parameters using the getter and setter methods of the keypoint detector or feature descriptor.
Furthermore, these methods have been part of OpenCV 2.4.X for many years. Why in the world were they removed from the default install? And where were they moved to?
In the remainder of this blog post, I’ll detail why certain keypoint detectors and local invariant descriptors were removed from OpenCV 3.0 by default. And I’ll also show you where you can find SIFT, SURF, and other detectors and descriptors in the new version of OpenCV.
Why were SIFT and SURF removed from the default install of OpenCV 3.0?
SIFT and SURF are examples of algorithms that OpenCV calls “non-free” modules. These algorithms are patented by their respective creators, and while they are free to use in academic and research settings, you should technically be obtaining a license/permission from the creators if you are using them in a commercial (i.e. for-profit) application.
With OpenCV 3 came a big push to move many of these “non-free” modules out of the default OpenCV install and into the opencv_contrib package. The
opencv_contribpackages contains implementations of algorithms that are either patented or in experimental development.
The algorithms and associated implementations in
opencv_contribare not installed by default and you need to explicitly enable them when compiling and installing OpenCV to obtain access to them.
Personally, I’m not too crazy about this move.
Yes, I understand including patented algorithms inside an open source library may raise a few eyebrows. But algorithms such as SIFT and SURF are pervasive across much of computer vision. And more importantly, the OpenCV implementations of SIFT and SURF are used by academics and researchers daily to evaluate new image classification, Content-Based Image Retrieval, etc. algorithms. By not including these algorithms by default, more harm than good is done (at least in my opinion).
How do I get access to SIFT and SURF in OpenCV 3?
To get access to the original SIFT and SURF implementations found in OpenCV 2.4.X, you’ll need to pull down both the opencv and opencv_contrib repositories from GitHub and then compile and install OpenCV 3 from source.
Luckily, compiling OpenCV from source is easier than it used to be. I have gathered install instructions for Python and OpenCV for many popular operating systems over on the OpenCV 3 Tutorials, Resources, and Guides page — just scroll down the Install OpenCV 3 and Python section and find the appropriate Python version (either Python 2.7+ or Python 3+) for your operating system.
How do I use SIFT and SURF with OpenCV 3?
So now that you have installed OpenCV 3 with the
opencv_contribpackage, you should have access to the original SIFT and SURF implementations from OpenCV 2.4.X, only this time they’ll be in the
xfeatures2dsub-module through the
cv2.SIFT_createand
cv2.SURF_createfunctions.
To confirm this, open up a shell, import OpenCV, and execute the following commands (assuming you have an image named
test_image.jpgin your current directory, of course):
$ python >>> import cv2 >>> image = cv2.imread("test_image.jpg") >>> gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) >>> sift = cv2.xfeatures2d.SIFT_create() >>> (kps, descs) = sift.detectAndCompute(gray, None) >>> print("# kps: {}, descriptors: {}".format(len(kps), descs.shape)) # kps: 274, descriptors: (274, 128) >>> surf = cv2.xfeatures2d.SURF_create() >>> (kps, descs) = surf.detectAndCompute(gray, None) >>> print("# kps: {}, descriptors: {}".format(len(kps), descs.shape)) # kps: 393, descriptors: (393, 64)
If all goes well, you should be able to instantiate the SIFT and SURF keypoint detectors and local invariant descriptors without error.
It’s also important to note that by using
opencv_contribyou will not be interfering with any of the other keypoint detectors and local invariant descriptors included in OpenCV 3. You’ll still be able to access KAZE, AKAZE, BRISK, etc. without an issue:
>>> kaze = cv2.KAZE_create() >>> (kps, descs) = kaze.detectAndCompute(gray, None) >>> print("# kps: {}, descriptors: {}".format(len(kps), descs.shape)) # kps: 359, descriptors: (359, 64) >>> akaze = cv2.AKAZE_create() >>> (kps, descs) = akaze.detectAndCompute(gray, None) >>> print("# kps: {}, descriptors: {}".format(len(kps), descs.shape)) # kps: 192, descriptors: (192, 61) >>> brisk = cv2.BRISK_create() >>> (kps, descs) = brisk.detectAndCompute(gray, None) >>> print("# kps: {}, descriptors: {}".format(len(kps), descs.shape)) # kps: 361, descriptors: (361, 64)
Summary
In this blog post we learned that OpenCV has removed the
cv2.FeatureDetector_createand
cv2.DescriptorExtractor_createfunctions from the library. Furthermore, the SIFT and SURF implementations have also been removed from the default OpenCV 3 install.
The reason for SIFT and SURF removal is due to what OpenCV calls “non-free” algorithms. Both SIFT and SURF are patented algorithms, meaning that you should technically be getting permission to use them in commercial algorithms (they are free to use for academic and research purposes though).
Because of this, OpenCV has made the decision to move patented algorithms (along with experimental implementations) to the opencv_contrib package. This means that to obtain access to SIFT and SURF, you’ll need to compile and install OpenCV 3 from source with
opencv_contribsupport enabled. Luckily, this isn’t too challenging with the help of my OpenCV 3 install guides.
Once you have installed OpenCV 3 with
opencv_contribsupport you’ll be able to find your favorite SIFT and SURF implementations in the
xfeatures2dpackage through the
cv2.xfeatures2d.SIFT_create()and
cv2.xfeatures2d.SURF_create()functions.
The post Where did SIFT and SURF go in OpenCV 3? appeared first on PyImageSearch.
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Barcelona posts over 600 million euros in revenue
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ISS Daily Summary Report – 07/15/15
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Ex-Cardiff manager Mackay cleared by English FA over texts
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Malware And Hacking Forum Seized, Dozens Arrested
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Leverkusen signs Jonathan Tah from Hamburger SV
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Swiss extradite detained FIFA official to the United States
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Swiss authorities say 1 of 7 detained FIFA officials has been extradited to the United States
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I have a new follower on Twitter
Martin Coleman
I help customers #accelerate #cloud, improve customer service, shrink #datacentre, slash #TCO. Over 20 years in #data #storage, at EMC, NetApp, Violin.
Melbourne, Australia
http://t.co/SNpXyO2ZG7
Following: 685 - Followers: 892
July 16, 2015 at 05:17AM via Twitter http://twitter.com/flashstor
[FD] Backdoor and RCE found in 8 TOTOLINK router models
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[FD] Backdoor credentials found in 4 TOTOLINK router models
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[FD] 4 TOTOLINK router models vulnerable to CSRF and XSS attacks
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[FD] 15 TOTOLINK router models vulnerable to multiple RCEs
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[FD] Capstone disassembly engine 3.0.4 is out!
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why does it keep telling me i didnt pass an anonymous function into the each method call and i am ...
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Pluto Resolved
Wednesday, July 15, 2015
I have a new follower on Twitter
To Be Motivated...
Everyday is a second chance.
Following: 281 - Followers: 103
July 15, 2015 at 08:41PM via Twitter http://twitter.com/tobemtivated
Evidential relational clustering using medoids. (arXiv:1507.04091v1 [cs.AI])
In real clustering applications, proximity data, in which only pairwise similarities or dissimilarities are known, is more general than object data, in which each pattern is described explicitly by a list of attributes. Medoid-based clustering algorithms, which assume the prototypes of classes are objects, are of great value for partitioning relational data sets. In this paper a new prototype-based clustering method, named Evidential C-Medoids (ECMdd), which is an extension of Fuzzy C-Medoids (FCMdd) on the theoretical framework of belief functions is proposed. In ECMdd, medoids are utilized as the prototypes to represent the detected classes, including specific classes and imprecise classes. Specific classes are for the data which are distinctly far from the prototypes of other classes, while imprecise classes accept the objects that may be close to the prototypes of more than one class. This soft decision mechanism could make the clustering results more cautious and reduce the misclassification rates. Experiments in synthetic and real data sets are used to illustrate the performance of ECMdd. The results show that ECMdd could capture well the uncertainty in the internal data structure. Moreover, it is more robust to the initializations compared with FCMdd.
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Solomonoff Induction Violates Nicod's Criterion. (arXiv:1507.04121v1 [cs.LG])
Nicod's criterion states that observing a black raven is evidence for the hypothesis H that all ravens are black. We show that Solomonoff induction does not satisfy Nicod's criterion: there are time steps in which observing black ravens decreases the belief in H. Moreover, while observing any computable infinite string compatible with H, the belief in H decreases infinitely often when using the unnormalized Solomonoff prior, but only finitely often when using the normalized Solomonoff prior. We argue that the fault is not with Solomonoff induction; instead we should reject Nicod's criterion.
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On the Computability of Solomonoff Induction and Knowledge-Seeking. (arXiv:1507.04124v1 [cs.AI])
Solomonoff induction is held as a gold standard for learning, but it is known to be incomputable. We quantify its incomputability by placing various flavors of Solomonoff's prior M in the arithmetical hierarchy. We also derive computability bounds for knowledge-seeking agents, and give a limit-computable weakly asymptotically optimal reinforcement learning agent.
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Revisiting AdaBoost for Cost-Sensitive Classification. Part I: Theoretical Perspective. (arXiv:1507.04125v1 [cs.CV])
Boosting algorithms have been widely used to tackle a plethora of problems. In the last few years, a lot of approaches have been proposed to provide standard AdaBoost with cost-sensitive capabilities, each with a different focus. However, for the researcher, these algorithms shape a tangled set with diffuse differences and properties, lacking a unifying analysis to jointly compare, classify, evaluate and discuss those approaches on a common basis. In this series of two papers we aim to revisit the various proposals, both from theoretical (Part I) and practical (Part II) perspectives, in order to analyze their specific properties and behavior, with the final goal of identifying the algorithm providing the best and soundest results.
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Revisiting AdaBoost for Cost-Sensitive Classification. Part II: Empirical Analysis. (arXiv:1507.04126v1 [cs.CV])
A lot of approaches, each following a different strategy, have been proposed in the literature to provide AdaBoost with cost-sensitive properties. In the first part of this series of two papers, we have presented these algorithms in a homogeneous notational framework, proposed a clustering scheme for them and performed a thorough theoretical analysis of those approaches with a fully theoretical foundation. The present paper, in order to complete our analysis, is focused on the empirical study of all the algorithms previously presented over a wide range of heterogeneous classification problems. The results of our experiments, confirming the theoretical conclusions, seem to reveal that the simplest approach, just based on cost-sensitive weight initialization, is the one showing the best and soundest results, despite having been recurrently overlooked in the literature.
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Learning Action Models: Qualitative Approach. (arXiv:1507.04285v1 [cs.LG])
In dynamic epistemic logic, actions are described using action models. In this paper we introduce a framework for studying learnability of action models from observations. We present first results concerning propositional action models. First we check two basic learnability criteria: finite identifiability (conclusively inferring the appropriate action model in finite time) and identifiability in the limit (inconclusive convergence to the right action model). We show that deterministic actions are finitely identifiable, while non-deterministic actions require more learning power-they are identifiable in the limit. We then move on to a particular learning method, which proceeds via restriction of a space of events within a learning-specific action model. This way of learning closely resembles the well-known update method from dynamic epistemic logic. We introduce several different learning methods suited for finite identifiability of particular types of deterministic actions.
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Massively Parallel Methods for Deep Reinforcement Learning. (arXiv:1507.04296v1 [cs.LG])
We present the first massively distributed architecture for deep reinforcement learning. This architecture uses four main components: parallel actors that generate new behaviour; parallel learners that are trained from stored experience; a distributed neural network to represent the value function or behaviour policy; and a distributed store of experience. We used our architecture to implement the Deep Q-Network algorithm (DQN). Our distributed algorithm was applied to 49 games from Atari 2600 games from the Arcade Learning Environment, using identical hyperparameters. Our performance surpassed non-distributed DQN in 41 of the 49 games and also reduced the wall-time required to achieve these results by an order of magnitude on most games.
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Bayesian Modeling with Gaussian Processes using the GPstuff Toolbox. (arXiv:1206.5754v6 [stat.ML] UPDATED)
Gaussian processes (GP) are powerful tools for probabilistic modeling purposes. They can be used to define prior distributions over latent functions in hierarchical Bayesian models. The prior over functions is defined implicitly by the mean and covariance function, which determine the smoothness and variability of the function. The inference can then be conducted directly in the function space by evaluating or approximating the posterior process. Despite their attractive theoretical properties GPs provide practical challenges in their implementation. GPstuff is a versatile collection of computational tools for GP models compatible with Linux and Windows MATLAB and Octave. It includes, among others, various inference methods, sparse approximations and tools for model assessment. In this work, we review these tools and demonstrate the use of GPstuff in several models.
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Cuba advances with 1-0 Gold Cup win over Guatemala
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Southampton signs Jordy Clasie from Feyenoord
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FIFA urged to let public figures lead change after scandals
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2 Cuban soccer players are no-shows at Gold Cup match in US
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Rowdies of 2nd-tier NASL agree to contract with Freddy Adu
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US soccer chief felt 'discomfort' during soccer proceedings
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Anonymous gossip app Yik Yak will now let you post photos — but selfies aren't allowed
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Anonymous on Twitter: "For context on recent events in #Denver: https://t.co/YUZq73O4xT ...
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US soccer chief felt 'discomfort' during FIFA proceedings
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Barcelona, Juventus players head UEFA Best Player shortlist
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ISS Daily Summary Report – 07/14/15
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Sanfrecce Hiroshima leads J-League after beating Yamaga 6-0
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Moggi cleared of defaming ex-Inter president Facchetti
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1 million offered by anonymous fan for stolen Wizard of Oz ruby slippers
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How Hacking Team and FBI planned to Unmask A Tor User
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Polish coach Henry Kasperczak takes over at Tunisia
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Stoke signs Moha El Ouriachi from Barcelona
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Nigeria confirms Sunday Oliseh as new coach
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