J
Jezekiel Ben-Arie
Researcher at University of Illinois at Chicago
Publications - 91
Citations - 1160
Jezekiel Ben-Arie is an academic researcher from University of Illinois at Chicago. The author has contributed to research in topics: Filter (signal processing) & Edge detection. The author has an hindex of 16, co-authored 91 publications receiving 1149 citations. Previous affiliations of Jezekiel Ben-Arie include Technion – Israel Institute of Technology & University of Illinois at Urbana–Champaign.
Papers
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Human activity recognition using multidimensional indexing
TL;DR: A novel method for view-based recognition of human action/activity from videos that uses a sequence-based voting approach to recognize the activity invariant to the activity speed and finds that the probability of false alarm drops exponentially with the increased number of sampled body poses.
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Optimal edge detection using expansion matching and restoration
TL;DR: A family of optimal DSNR edge detectors based on the expansion filter for several edge models is introduced and the optimal step expansion filter (SEF) is compared with the widely used Canny edge detector (CED).
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The probabilistic peaking effect of viewed angles and distances with application to 3-D object recognition
TL;DR: It is concluded that in most cases, the values of angles and distances are being altered only slightly by the imaging process, and they can still serve as a strong cue for model-based recognition.
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Pictorial recognition of objects employing affine invariance in the frequency domain
Jezekiel Ben-Arie,Zhiqian Wang +1 more
TL;DR: An efficient approach to pose invariant pictorial object recognition employing spectral signatures of image patches that correspond to object surfaces which are roughly planar based on singular value decomposition (SVD).
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A volumetric/iconic frequency domain representation for objects with application for pose invariant face recognition
Jezekiel Ben-Arie,Dibyendu Nandy +1 more
TL;DR: A novel method for representing 3D objects that unifies viewer and model centered object representations is presented, which encapsulates both the spatial structure of the object and a continuum of its views in the same data structure.