Vaclav Hlavac

Prof. Dr. Vaclav Hlavac

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Research Focus & Metrics

Academic Career Length

36Years (1991-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 January 1993
Verified
Book
Closed access
MSMilan Sonka
Milan Sonka
VHVaclav Hlavac
Vaclav Hlavac
RBRoger Boyle
Roger Boyle

1 June 2008
Verified
Conference Paper
Closed access
CTChristian Thurau
Christian Thurau
VHVaclav Hlavac
Vaclav Hlavac
This paper presents a method for recognizing human actions based on pose primitives. In learning mode, the parameters representing poses and activities are estimated from videos. In run mode, the method can be used both for videos or still images. For recognizing pose primitives, we extend a Histogram of Oriented Gradient (HOG) based descriptor to better cope with articulated poses and cluttered background. Action classes are represented by histograms of poses primitives. For sequences, we incorporate the local temporal context by means of n-gram expressions. Action recognition is based on a simple histogram comparison. Unlike the mainstream video surveillance approaches, the proposed method does not rely on background subtraction or dynamic features and thus allows for action recognition in still images.

12 October 2016 in IEEE Transactions on Robotics
Verified
Journal Article
Closed access
ADAndreas Doumanoglou
Andreas Doumanoglou
JSJan Stria
Jan Stria
GPGeorgia Peleka
Georgia Peleka
IMIoannis Mariolis
Ioannis Mariolis
VPVladimir Petrik
Vladimir Petrik
SMSotiris Malassiotis
Sotiris Malassiotis
This work presents a complete pipeline for folding a pile of clothes using a dual-armed robot. This is a challenging task both from the viewpoint of machine vision and robotic manipulation. The presented pipeline is comprised of the following parts: isolating and picking up a single garment from a pile of crumpled garments, recognizing its category, unfolding the garment using a series of manipulations performed in the air, placing the garment roughly flat on a work table, spreading it, and, finally, folding it in several steps. The pile is segmented into separate garments using color and texture information, and the ideal grasping point is selected based on the features computed from a depth map. The recognition and unfolding of the hanging garment are performed in an active manner, utilizing the framework of active random forests to detect grasp points, while optimizing the robot actions. The spreading procedure is based on the detection of deformations of the garment's contour. The perception for folding employs fitting of polygonal models to the contour of the observed garment, both spread and already partially folded. We have conducted several experiments on the full pipeline producing very promising results. To our knowledge, this is the first work addressing the complete unfolding and folding pipeline on a variety of garments, including T-shirts, towels, and shorts.

1 January 2002
Verified
Book
Closed access
MSMichail I. Schlesinger
Michail I. Schlesinger
VHVaclav Hlavac
Vaclav Hlavac

1 October 2008 in Computer Vision and Image Understanding
Verified
Journal Article
Closed access
ASAlexander Shekhovtsov
Alexander Shekhovtsov
IKIvan Kovtun
Ivan Kovtun
VHVaclav Hlavac
Vaclav Hlavac

1 September 2003 in Pattern Recognition
Verified
Journal Article
Closed access
VFVojtěch Franc
Vojtěch Franc
VHVaclav Hlavac
Vaclav Hlavac