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Displaying 1-10 out of 10 total
Shufflets: Shared Mid-level Parts for Fast Object Detection
Found in: 2013 IEEE International Conference on Computer Vision (ICCV)
By Iasonas Kokkinos
Issue Date:December 2013
pp. 1393-1400
We present a method to identify and exploit structures that are shared across different object categories, by using sparse coding to learn a shared basis for the 'part' and 'root' templates of Deformable Part Models (DPMs).Our first contribution consists i...
 
Highly accurate boundary detection and grouping
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Iasonas Kokkinos
Issue Date:June 2010
pp. 2520-2527
In this work we address boundary detection and boundary grouping. We first pursue a learning-based approach to boundary detection. For this (i) we leverage appearance and context information by extracting descriptors around edgels and use them as features ...
 
Scale-invariant heat kernel signatures for non-rigid shape recognition
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Michael M. Bronstein, Iasonas Kokkinos
Issue Date:June 2010
pp. 1704-1711
One of the biggest challenges in non-rigid shape retrieval and comparison is the design of a shape descriptor that would maintain invariance under a wide class of transformations the shape can undergo. Recently, heat kernel signature was introduced as an i...
 
Synergy between Object Recognition and Image Segmentation Using the Expectation-Maximization Algorithm
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Iasonas Kokkinos, Petros Maragos
Issue Date:August 2009
pp. 1486-1501
In this work, we formulate the interaction between image segmentation and object recognition in the framework of the Expectation-Maximization (EM) algorithm. We consider segmentation as the assignment of image observations to object hypotheses and phrase i...
 
Scale invariance without scale selection
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Iasonas Kokkinos, Alan Yuille
Issue Date:June 2008
pp. 1-8
In this work we construct scale invariant descriptors (SIDs) without requiring the estimation of image scale; we thereby avoid scale selection which is often unreliable.
 
Texture Analysis and Segmentation Using Modulation Features, Generative Models, and Weighted Curve Evolution
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Iasonas Kokkinos, Georgios Evangelopoulos, Petros Maragos
Issue Date:January 2009
pp. 142-157
In this work we approach the analysis and segmentation of natural textured images by combining ideas from image analysis and probabilistic modeling. We rely on AM-FM texture models and specifically on the Dominant Component Analysis (DCA) paradigm for feat...
 
Unsupervised Learning of Object Deformation Models
Found in: Computer Vision, IEEE International Conference on
By Iasonas Kokkinos, Alan Yuille
Issue Date:October 2007
pp. 1-8
The aim of this work is to learn generative models of object deformations in an unsupervised manner. Initially, we introduce an Expectation Maximization approach to estimate a linear basis for deformations by maximizing the likelihood of the training set u...
 
Bottom-Up & Top-down Object Detection using Primal Sketch Features and Graphical Models
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Iasonas Kokkinos, Petros Maragos, Alan Yuille
Issue Date:June 2006
pp. 1893-1900
A combination of techniques that is becoming increasingly popular is the construction of part-based object representations using the outputs of interest-point detectors. Our contributions in this paper are twofold: first, we propose a primal-sketch-based s...
 
An Expectation Maximization Approach to the Synergy between Image Segmentation and Object Categorization
Found in: Computer Vision, IEEE International Conference on
By Iasonas Kokkinos, Petros Maragos
Issue Date:October 2005
pp. 617-624
In this work we deal with the problem of modelling and exploiting the interaction between the processes of image segmentation and object categorization. We propose a novel framework to address this problem that is based on the combination of the Expectatio...
 
Dense Segmentation-Aware Descriptors
Found in: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Eduard Trulls,Iasonas Kokkinos,Alberto Sanfeliu,Francesc Moreno-Noguer
Issue Date:June 2013
pp. 2890-2897
In this work we exploit segmentation to construct appearance descriptors that can robustly deal with occlusion and background changes. For this, we downplay measurements coming from areas that are unlikely to belong to the same region as the descriptor's c...
 
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