39 Result(s)
within
Pushmeet Kohli
Computer Science
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Chapter and Conference Paper
Using Strong Shape Priors for Stereo
This paper addresses the problem of obtaining an accurate 3D reconstruction from multiple views. Taking inspiration from the recent successes of using strong prior knowledge for image segmentation, we propose ...
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Chapter and Conference Paper
OBJCUT for Face Detection
This paper proposes a novel, simple and efficient method for face segmentation which works by coupling face detection and segmentation in a single framework. We use the OBJCUT [1] formulation that allows for a...
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Chapter and Conference Paper
Measuring Uncertainty in Graph Cut Solutions – Efficiently Computing Min-marginal Energies Using Dynamic Graph Cuts
In recent years the use of graph-cuts has become quite popular in computer vision. However, researchers have repeatedly asked the question whether it might be possible to compute a measure of uncertainty assoc...
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Chapter and Conference Paper
PoseCut: Simultaneous Segmentation and 3D Pose Estimation of Humans Using Dynamic Graph-Cuts
We present a novel algorithm for performing integrated segmentation and 3D pose estimation of a human body from multiple views. Unlike other related state of the art techniques which focus on either segmentation ...
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Chapter and Conference Paper
Learning CRFs Using Graph Cuts
Many computer vision problems are naturally formulated as random fields, specifically MRFs or CRFs. The introduction of graph cuts has enabled efficient and optimal inference in associative random fields, grea...
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Article
Simultaneous Segmentation and Pose Estimation of Humans Using Dynamic Graph Cuts
This paper presents a novel algorithm for performing integrated segmentation and 3D pose estimation of a human body from multiple views. Unlike other state of the art methods which focus on either segmentation o...
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Article
Robust Higher Order Potentials for Enforcing Label Consistency
This paper proposes a novel framework for labelling problems which is able to combine multiple segmentations in a principled manner. Our method is based on higher order conditional random fields and uses poten...
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Chapter and Conference Paper
Uncertainty Driven Multi-scale Optimization
This paper proposes a new multi-scale energy minimization algorithm which can be used to efficiently solve large scale labelling problems in computer vision. The basic modus operandi of any multi-scale method ...
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Chapter and Conference Paper
Energy Minimization under Constraints on Label Counts
Many computer vision problems such as object segmentation or reconstruction can be formulated in terms of labeling a set of pixels or voxels. In certain scenarios, we may know the number of pixels or voxels wh...
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Chapter and Conference Paper
TriangleFlow: Optical Flow with Triangulation-Based Higher-Order Likelihoods
We use a simple yet powerful higher-order conditional random field (CRF) to model optical flow. It consists of a standard photo-consistency cost and a prior on affine motions both modeled in terms of higher-or...
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Chapter and Conference Paper
Geometric Image Parsing in Man-Made Environments
We present a new parsing framework for the line-based geometric analysis of a single image coming from a man-made environment. This parsing framework models the scene as a composition of geometric primitives s...
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Chapter and Conference Paper
Graph Cut Based Inference with Co-occurrence Statistics
Markov and Conditional random fields (crfs) used in computer vision typically model only local interactions between variables, as this is computationally tractable. In this paper we consider a class of global pot...
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Chapter and Conference Paper
Putting MAP Back on the Map
Conditional Random Fields (CRFs) are popular models in computer vision for solving labeling problems such as image denoising. This paper tackles the rarely addressed but important problem of learning the full ...
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Chapter and Conference Paper
Curvature Prior for MRF-Based Segmentation and Shape Inpainting
Most image labeling problems such as segmentation and image reconstruction are fundamentally ill-posed and suffer from ambiguities and noise. Higher-order image priors encode high-level structural dependencies...
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Chapter and Conference Paper
Relating Things and Stuff by High-Order Potential Modeling
In the last few years, substantially different approaches have been adopted for segmenting and detecting “things” (object categories that have a well defined shape such as people and cars) and “stuff” (object ...
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Chapter and Conference Paper
Learning to Efficiently Detect Repeatable Interest Points in Depth Data
Interest point (IP) detection is an important component of many computer vision methods. While there are a number of methods for detecting IPs in RGB images, modalities such as depth images and range scans hav...
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Chapter and Conference Paper
Indoor Segmentation and Support Inference from RGBD Images
We present an approach to interpret the major surfaces, objects, and support relations of an indoor scene from an RGBD image. Most existing work ignores physical interactions or is applied only to tidy rooms a...
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Chapter and Conference Paper
A Convex Discrete-Continuous Approach for Markov Random Fields
We propose an extension of the well-known LP relaxation for Markov random fields to explicitly allow continuous label spaces. Unlike conventional continuous formulations of labelling problems which assume that...
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Chapter and Conference Paper
Large-Lexicon Attribute-Consistent Text Recognition in Natural Images
This paper proposes a new model for the task of word recognition in natural images that simultaneously models visual and lexicon consistency of words in a single probabilistic model. Our approach combines loca...
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Chapter and Conference Paper
Latent Hough Transform for Object Detection
Hough transform based methods for object detection work by allowing image features to vote for the location of the object. While this representation allows for parts observed in different training instances to...
