no code implementations • CVPR 2022 • Tristan Aumentado-Armstrong, Stavros Tsogkas, Sven Dickinson, Allan Jepson
On the other hand, implicit representations (occupancy, distance, or radiance fields) preserve greater fidelity, but suffer from complex or inefficient rendering processes, limiting scalability.
no code implementations • 27 Feb 2021 • Tristan Aumentado-Armstrong, Stavros Tsogkas, Sven Dickinson, Allan Jepson
In this work, we improve on a prior generative model of geometric disentanglement for 3D shapes, wherein the space of object geometry is factorized into rigid orientation, non-rigid pose, and intrinsic shape.
no code implementations • CVPR 2020 • Charles-Olivier Dufresne Camaro, Morteza Rezanejad, Stavros Tsogkas, Kaleem Siddiqi, Sven Dickinson
We make the following specific contributions: i) we extend the shock graph representation to the domain of real images, by generalizing the shock type definitions using local, appearance-based criteria; ii) we then use the rules of a Shock Grammar to guide our search for medial points, drastically reducing run time when compared to other methods, which exhaustively consider all points in the input image;iii) we remove the need for typical post-processing steps including thinning, non-maximum suppression, and grouping, by adhering to the Shock Grammar rules while deriving the medial axis solution; iv) finally, we raise some fundamental concerns with the evaluation scheme used in previous work and propose a more appropriate alternative for assessing the performance of medial axis extraction from scenes.
no code implementations • ICCV 2019 • Tristan Aumentado-Armstrong, Stavros Tsogkas, Allan Jepson, Sven Dickinson
Representing 3D shape is a fundamental problem in artificial intelligence, which has numerous applications within computer vision and graphics.
2 code implementations • CVPR 2019 • Yukang Wang, Yongchao Xu, Stavros Tsogkas, Xiang Bai, Sven Dickinson, Kaleem Siddiqi
In the present article, we depart from this strategy by training a CNN to predict a two-dimensional vector field, which maps each scene point to a candidate skeleton pixel, in the spirit of flux-based skeletonization algorithms.
Ranked #1 on Object Skeleton Detection on SK-LARGE
no code implementations • CVPR 2019 • Morteza Rezanejad, Gabriel Downs, John Wilder, Dirk B. Walther, Allan Jepson, Sven Dickinson, Kaleem Siddiqi
That is, the medial axis based salience weights appear to add useful information that is not available when CNNs are trained to use contours alone.
1 code implementation • ICCV 2017 • Stavros Tsogkas, Sven Dickinson
We introduce Appearance-MAT (AMAT), a generalization of the medial axis transform for natural images, that is framed as a weighted geometric set cover problem.
no code implementations • ICCV 2015 • Tom Lee, Sanja Fidler, Sven Dickinson
In this paper, we introduce Parametric Min-Loss (PML), a novel structured learning framework for parametric energy functions.
no code implementations • 5 Feb 2015 • Tom Lee, Sanja Fidler, Alex Levinshtein, Cristian Sminchisescu, Sven Dickinson
The role of symmetry in computer vision has waxed and waned in importance during the evolution of the field from its earliest days.
no code implementations • 9 Aug 2014 • Andrei Barbu, Alexander Bridge, Zachary Burchill, Dan Coroian, Sven Dickinson, Sanja Fidler, Aaron Michaux, Sam Mussman, Siddharth Narayanaswamy, Dhaval Salvi, Lara Schmidt, Jiangnan Shangguan, Jeffrey Mark Siskind, Jarrell Waggoner, Song Wang, Jinlian Wei, Yifan Yin, Zhiqi Zhang
We present a system that produces sentential descriptions of video: who did what to whom, and where and how they did it.
no code implementations • CVPR 2013 • Yu Cao, Daniel Barrett, Andrei Barbu, Siddharth Narayanaswamy, Haonan Yu, Aaron Michaux, Yuewei Lin, Sven Dickinson, Jeffrey Mark Siskind, Song Wang
In this paper, we propose a new method that can recognize human activities from partially observed videos in the general case.
no code implementations • NeurIPS 2012 • Sanja Fidler, Sven Dickinson, Raquel Urtasun
We demonstrate the effectiveness of our approach in indoor and outdoor scenarios, and show that our approach outperforms the state-of-the-art in both 2D[Felz09] and 3D object detection[Hedau12].
no code implementations • LREC 2012 • Zoya Gavrilov, Stan Sclaroff, Carol Neidle, Sven Dickinson
A framework is proposed for the detection of reduplication in digital videos of American Sign Language (ASL).