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INRIA, France

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5 Papers

PROJECTOR CALIBRATION USING A MARKERLESS PLANE

Topics: Image-Based Modeling and 3D Reconstruction; Motion and Tracking; Stereo Vision and Structure from Motion; Multi-View Geometry; Active and Robot Vision

Authors: Jamil Draréni ¹ , Sébastien Roy ¹ and Peter Sturm ²

Affiliations: ¹ DIRO, Universite de Montreal, Canada , ² INRIA Rhone-Alpes, France

Digital Object Identifier: 10.5220/0001559603770382

Proceedings of VISAPP 2009/VISIGRAPP, ISBN: 978-989-8111-69-2

Quick Abstract

In this paper we address the problem of geometric video projector calibration using a markerless planar surface (wall) and a partially calibrated camera. Instead of using control points to infer the camera-wall orientation, we find such relation by efficiently sampling the hemisphere of possible orientations. This process is so fast that even the focal of the camera can be estimated during the sampling process. Hence, physical grids and full knowledge of camera parameters are no longer necessary to calibrate a video projector.

Related Ontology Subjects/Areas/Topics (15)

Active and Robot Vision	Image Formation and Preprocessing	Multi-View Geometry
Applications	Image-Based Modeling	Pattern Recognition
Computer Vision, Visualization and Computer Graphics	Methodologies and Methods	Physiological Computing Systems
Geometry and Modeling	Motion and Tracking	Software Engineering
Human-Computer Interaction	Motion, Tracking and Stereo Vision	Stereo Vision and Structure from Motion

HYBRID MATCHING OF UNCALIBRATED OMNIDIRECTIONAL AND PERSPECTIVE IMAGES

Topic: Vision, Recognition and Reconstruction

Authors: Luis Puig ¹ , J. J. Guerrero ¹ and Peter Sturm ²

Affiliations: ¹ DIIS-I3A, University of Zaragoza, Spain , ² INRIA Rhône-Alpes, France

Digital Object Identifier: 10.5220/0001494901250128

Proceedings of ICINCO 2008, ISBN: 978-989-8111-31-9

Quick Abstract

This work presents an automatic hybrid matching of central catadioptric and perspective images. It is based on the hybrid epipolar geometry. The goal is to obtain correspondences between an omnidirectional image and a conventional perspective image taken from different points of view. Mixing both kind of images has multiple applications, since an omnidirectional image captures many information and perspective images are the simplest way of acquisition. Scale invariant features with a simple unwrapping are considered to help the initial putative matching. Then a robust technique gives an estimation of the hybrid fundamental matrix, to avoid outliers. Experimental results with real image pairs show the feasibility of that hybrid and difficult matching problem.

Related Ontology Subjects/Areas/Topics (3)

Informatics in Control, Automation and Robotics

Robotics and Automation

Vision, Recognition and Reconstruction

SEMI-SUPERVISED DIMENSIONALITY REDUCTION USING PAIRWISE EQUIVALENCE CONSTRAINTS

Topics: Feature Extraction; Recognition and Indexing; Segmentation and Grouping; Visual Learning

Authors: Hakan Cevikalp , Jakob Verbeek , Frédéric Jurie and Alexander Kläser

Affiliation: Inria Rhone-Alpes, France

Digital Object Identifier: 10.5220/0001070304890496

Proceedings of VISAPP 2008/VISIGRAPP, ISBN: 978-989-8111-21-0

Quick Abstract

To deal with the problem of insufficient labeled data, usually side information – given in the form of pairwise equivalence constraints between points – is used to discover groups within data. However, existing methods using side information typically fail in cases with high-dimensional spaces. In this paper, we address the problem of learning from side information for high-dimensional data. To this end, we propose a semi-supervised dimensionality reduction scheme that incorporates pairwise equivalence constraints for finding a better embedding space, which improves the performance of subsequent clustering and classification phases. Our method builds on the assumption that points in a sufficiently small neighborhood tend to have the same label. Equivalence constraints are employed to modify the neighborhoods and to increase the separability of different classes. Experimental results on high-dimensional image data sets show that integrating side information into the dimensionality reduction improves the clustering and classification performance.

Related Ontology Subjects/Areas/Topics (7)

Computer Vision, Visualization and Computer Graphics	Image and Video Analysis	Segmentation and Grouping
Feature Extraction	Informatics in Control, Automation and Robotics	Signal Processing, Sensors, Systems Modeling and Control
Features Extraction

CATEGORY LEVEL OBJECT SEGMENTATION - Learning to Segment Objects with Latent Aspect Models

Topics: Recognition and Indexing; Object, Event and Scene Recognition, Retrieval and Indexing; Segmentation and Grouping

Authors: Diane Larlus and Frédéric Jurie

Affiliation: LEAR Group, INPG-CNRS, INRIA Rhône-Alpes, France

Digital Object Identifier: 10.5220/0002050201220127

Proceedings of VISAPP 2007, ISBN: 978-972-8865-74-0

Quick Abstract

We propose a new method for learning to segment objects in images. This method is based on a latent variables model used for representing images and objects, inspired by the LDA model. Like the LDA model, our model is capable of automatically discovering which visual information comes from which object. We extend LDA by considering that images are made of multiple overlapping regions, treated as distinct documents, giving more chance to small objects to be discovered. This model is extremely well suited for assigning image patches to objects (even if they are small), and therefore for segmenting objects. We apply this method on objects belonging to categories with high intra-class variations and strong viewpoint changes.

Related Ontology Subjects/Areas/Topics (3)

Computer Vision, Visualization and Computer Graphics

Image and Video Analysis

Segmentation and Grouping

AN AUTOMATIC APPROACH FOR PARAMETER SELECTION IN SELF-ADAPTIVE TRACKING

Topics: Cognitive Models for Interpretation, Integration and Control; Model Acquisition and Validation; Visual Learning

Authors: Daniela Hall , Rémi Emonet and James L. Crowley

Affiliation: INRIA Rhône-Alpes, France

Digital Object Identifier: 10.5220/0001372600200026

Proceedings of VISAPP 2006, ISBN: 972-8865-40-6

Quick Abstract

In this article we propose an automatic approach for parameter selection of a tracking system. We show that such a self-adaptive tracking system achieves better tracking performance than a system with manually tuned parameters. Our approach requires little supervision by a user which makes this approach ideally suited for commercial applications. The self-adaptive component makes the system less sensitive to changing environmental conditions. Components for tracking, auto-critical evaluation and automatic parameter regulation serve to detect performance drops that trigger the parameter regulation process. The self-adaptive components require a quality measure based on a statistical scene reference model. We propose an automatic approach for the generation of such a reference model and compare several learning approaches. The experiments show that the auto-regulation of parameters significantly enhances the performance of the tracking system.

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