Laboratorij za strojni vid

28/01/2015

Ko roboti zavladajo zemlji ... ;)

Roboti so zavladali zemlji, pravi Matej Kristan iz Laboratorija za Umetne Vizualne Spoznavne Sisteme Fakultete za računalništvo in informatiko Univerze v Ljubljani. Tudi sam se ukvarja z raziskovanjem in razvojem robotov, ne zemeljskih, pa tudi ne zračnih, ampak morskih. Takih, ki plujejo ob obali i…

25/02/2014

Googlov prispevek k računalniškemu vidu in robotiki: http://www.youtube.com/watch?v=Qe10ExwzCqk

Project Tango is an exploration into giving mobile devices a human-scale understanding of space and motion. What if you never found yourself lost in a new bu...

27/11/2013

Še en lep dosežek MITa.

Device could be used in medical imaging, collision-avoidance detectors for cars, and interactive gaming.

21/11/2013

Računalniška različica vseživljenjskega učenja B)

PITTSBURGH—A computer program called the Never Ending Image Learner (NEIL) is running 24 hours a day at Carnegie Mellon University, searching the Web for images, doing its best to understand them on its own and, as it builds a growing visual database, gathering common sense on a massive scale.

23/10/2013

Nekaj utrinkov s terenskega dela v Kopru, v sodelovanju s Harpha Sea: testiranje metod računalniškega vida za uporabo na avtonomnem plovilu.

18/07/2013

Pravo vreme za open air testiranje :-)

18/06/2013

Zakaj je detekcija objektov tezka?

http://www.findthecat.net/

http://wafflesatnoon.com/2011/11/22/find-the-cat-in-the-garbage-facebook-picture-revealed/

There is a missing cat in all of these pictures! Your goal is to find the cat.

04/06/2013

Robotsko plovilo z racunalniskim vidom

17/05/2013

Naša katedra uspešno dočakala abrahama - it's torta time! :P

15/05/2013

Epska bitka članov laboratorija končana :)
I am pleased to confirm that your paper "Towards commoditized smart-camera design" has been accepted for publication in Journal of Systems Architecture.

08/05/2013

Danes, 8.5. od 12. ure do 15. ure v P8 drugi sklop predavanj prof. Vaclava Hlavaca. Vabljeni!

Lecture 4 ‒ CLASSIFIER (Task formulation; Perceptron, its learning, and beyond; Support Vector Machines (SVM), learning; Nonlinear classifiers

Lecture 5 ‒ LEARNING IN PATTERN RECOGNITION (Behaviorism, beginning of 20th century, no breakthrough; Learning by examples, knowledge engineering paradox; Feedbacks in learning -supervised, semisupervised,reinforcement, unsupervised; Four substitutive quality criteria in the related optimization
task

Lecture 6 ‒ PRACTICAL EXAMPLES FROM OUR RESEARCH

09/04/2013

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