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3 edition of Uncertain information fusion in active object recognition found in the catalog.

Uncertain information fusion in active object recognition

Hermann Borotschnig

Uncertain information fusion in active object recognition

by Hermann Borotschnig

  • 171 Want to read
  • 8 Currently reading

Published by Österreichische Computer Gesellschaft in [Wien] .
Written in English

    Subjects:
  • Computer vision.,
  • Pattern recognition systems.

  • Edition Notes

    StatementHermann Borotschnig ; editor, Axel Pinz.
    SeriesSchriftenreihe der Österreichischen Computer Gesellschaft ;, Bd. 127, Computer vision and graphics dissertations ;, vol. 11
    ContributionsPinz, Axel.
    Classifications
    LC ClassificationsTA1634 .B65 1999
    The Physical Object
    Pagination197 p. :
    Number of Pages197
    ID Numbers
    Open LibraryOL133917M
    ISBN 103854031270
    LC Control Number99513964

    Bin Fang,Fuchun Sun, Huaping Liu, Chuanqi Tan, Di Guo, A glove-based system for object recognition via visual-tactile fusion, Science China Information Sciences, vol, no.5, , . List of computer science publications by Fuchun Sun. Fuchun Sun, Huaping Liu, Dewen Hu: Cognitive Systems and Signal Processing - 4th International Conference, ICCSIP , Beijing, China, November 29 - December 1, , Revised Selected Papers, Part I. Communications in Computer and Information .

    Object recognition problems in computer vision are often based on single image data processing. In various applications this processing can be extended to a complete sequence of images, usually received passively. In contrast, we propose a method for active object recognition, where a camera is selectively moved around a considered by:   Active 6D Multi-Object Pose Estimation in Cluttered Scenarios with Deep Reinforcement Learning. 10/19/ ∙ by Juil Sock, et al. ∙ 25 ∙ share. In this work, we explore how a strategic selection of camera movements can facilitate the task of 6D multi-object .

    Since, the trackers are able to benefit from the complimentary information provided by other cameras. Here, we extend the single-camera AOT to a Collaborative Multi-Camera Active Object Tracking(CMC-AOT) problem, which aims at coordinating multiple cameras in one system to improve the performance of the active . Robust feature selection for object recognition using uncertain 2D image data Abstract: The use of a small set of features is recurrent in the object recognition literature. If the image data is perfect with no sensor uncertainty and there are not incorrect feature correspondences between the model and the image, then the pose of the object .


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Uncertain information fusion in active object recognition by Hermann Borotschnig Download PDF EPUB FB2

Uncertain Information Fusion in Active Object Recognition - Free ebook download as PDF File .pdf), Text File .txt) or read book online for free.

PhD Thesis, Comparison of Fuzzy, Evidence Theory based and Probabilistic Active Recognition Strategies, Definition of Uncertainty Measures, Eigenspace Appearance based Recognition. Today's computer vision applications often have to deal with multiple, uncertain, and incomplete visual information.

In this paper, we apply a new method, termed 'active fusion', to the problem of generic object by: 9. In this context, the purpose of this work is to demonstrate the possibility of obtaining tokens on the detected object nature in a standard domestic environment from uncertain data and gaps in the information: the objects Author: C.

Vasiljevic, E. Connessons-Vergne. The realization of a fusion / control unit of such an active fusion module for the efficient control of complicated processes in image understanding seems feasible.

The presented framework will be implemented to carry out experiments in active object recognition. Keywords: image understanding, active information fusion. Vol. Uncertain Information Fusion in Active Object Recognition, Hermann Borotschnig, ISBN; Vol. FSP Dmitry Chetverikov, Tamás Szirányi, ISBNECDL – European Computer Driving Licence.

OCG tries to play an active. Object recognition by active fusion Object recognition by active fusion Prantl, Manfred Today's computer vision applications often have to deal with multiple, uncertain, and incomplete visual information.

In this paper, we apply a new method, termed 'active fusion', to the problem of generic object : Prantl, Manfred. We introduce a general framework, called `active fusion', that actively selects and combines information from multiple sources in order to arrive at a reliable result at reasonable costs.

The paper is laid out as follows: Section 2 describes the generation of object hypotheses in the appearance based context, Section 3 outlines the fusion of sequential beliefs, reinforcement learning for optimizing recognition Cited by: The major modules involved in active object recognition.

A query triggers the first action. The image data is processed and object hypotheses are established. While various approaches for passive object recognition exist (see e.g.

[1] and references therein), active object recog-nition still is in its early stages. One of the first approaches to active object recognition can be found in [2], where the object.

An active object recognition system is built using three different uncertainty calculi: probability theory, possibility theory (or fuzzy logic) and Dempster-Shafer theory of evidence. The system is allowed. One major goal of active object recognition systems is to extract useful information from multiple measurements.

We compare three frameworks for information fusion and view-planning using different uncertainty calculi Cited by: Information theoretic concepts have been investigated recently for active vision and action selection.

Examples are active localization of robots [9], active view point selection for object recognition [1], and sensor planning for active object search. Visual sensors provide exclusively uncertain and partial knowledge of a scene. In this article, we present a suitable scene knowledge representation that makes integration and fusion of new, uncertain Cited by: 6.

specifically on the object recognition task. We take a probabilistic approach to fuse such information. We introduce MultiRank, an information fusion algorithm that uses the label probabilities for bounding boxes obtained from computer vision (CV) as priors and computes posteri-ors based on object.

Object Recognition: History and Overview Slides adapted from Fei-Fei Li, Rob Fergus, Antonio Torralba, and Jean Ponce Visual learning and recognition of 3-d objects from appearance, IJCV Limitations of global appearance • Recognition of flat textured objects (CD covers, book covers etc)book.

Object recognition is performed by humans in around ms. If contours are deleted at a vertex they can be restored, as long as there is no accidental filling-in. The greater disruption from vertex deletion is expected on the basis of their importance as Lecture 7 Introduction to Object Recognition File Size: 6MB.

in object recognition and visual cognition. Despite this, there is an alarming absence of a comprehensive account of object recognition. Rather, as outlined above, most theorists have more File Size: 4MB.

Feature Fusion in Improving Object Class Recognition 1Noridayu Manshor, 2Amir Rizaan Abdul Rahiman, 3Mandava Rajeswari and 3Dhanesh Ramachandram 1Department of Computer Science, 2Department of Multimedia, Faculty of Computer Science and Information.

Probabilistic fusion of multiple algorithms for object recognition at information level Abstract: Reliable object recognition is a mandatory prerequisite for service robots that operate in everyday environments. Typical approaches run a single classifier for the purpose of object recognition.

The experiments show that the recognition Cited by: 4. An active object recognition system is proposed in, which makes use of saccades, i.e., small camera movements, to provide more useful information from a dynamic vision sensor (DVS) for the object recognition Author: Pourya Hoseini, Janelle Blankenburg, Mircea Nicolescu, Monica N.

Nicolescu, David Feil-Seifer.to the choice of object pose estimator and we propose a fusion function to incorporate multi-view information.

Active vision, object detection and poses. We review recent work on the active vision that aims to either improve the detection of objects Cited by: 2. Purchase Three-Dimensional Object Recognition Systems, Volume 1 - 1st Edition.

Print Book. ISBN Book Edition: 1.