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Related Concept Videos

Visual Agnosia01:12

Visual Agnosia

Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round end"...

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Active visual segmentation.

Ajay K Mishra1, Yiannis Aloimonos, Loong-Fah Cheong

  • 1Department of Computer Science, University of Maryland, College Park, MD 20742, USA. mishraka@umiacs.umd.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|March 3, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel object segmentation method using visual attention fixation points. It finds optimal contours in polar space, overcoming scale challenges for general visual systems.

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Area of Science:

  • Computer Vision
  • Human Visual System
  • Image Segmentation

Background:

  • Visual attention guides human eye fixations to salient scene regions.
  • Fixation points identify objects or object parts within arbitrary regions.
  • Object segmentation remains challenging due to scale variations.

Purpose of the Study:

  • To propose a new method for automatic object segmentation based on visual attention.
  • To address the scale problem in object segmentation using polar space.
  • To develop a general-purpose visual system for automatic segmentation.

Main Methods:

  • Combined visual cues to generate a probabilistic boundary edge map.
  • Identified the optimal closed contour around a fixation point in polar space.
  • Implemented a two-step segmentation process with feedback between region and edge cues.

Main Results:

  • Demonstrated a novel approach to object segmentation using fixation points.
  • Successfully avoided scale issues inherent in Cartesian space segmentation.
  • Showcased a segmentation refinement process through feedback mechanisms.

Conclusions:

  • The proposed method offers a promising framework for automatic object segmentation.
  • The polar space contour finding effectively handles scale variations.
  • The feedback mechanism enhances segmentation accuracy in general visual systems.