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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
07:36

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

Published on: November 30, 2018

Visual-context boosting for eye detection.

Mingli Song1, Dacheng Tao, Zhuo Sun

  • 1College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China. brooksong@ieee.org

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 23, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new two-step eye detection method using visual-context patterns (VCP) and semisupervised boosting. This approach offers effective and efficient eye detection for practical applications.

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Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Eye detection is crucial for numerous applications.
  • Existing methods may require extensive human labeling.

Purpose of the Study:

  • To develop a novel, efficient, and robust two-step eye detection scheme.
  • To reduce the need for manual data labeling in eye detection.

Main Methods:

  • Modeling eyes using a novel visual-context pattern (VCP).
  • Extracting context features via integral images.
  • Applying semisupervised boosting with VCP and Haar-like features for precise detection.

Main Results:

  • The proposed method demonstrates effectiveness and robustness on standard face datasets.
  • Achieved precise eye detection with reduced labeling effort.
  • Validated efficiency for real-world applications.

Conclusions:

  • The novel two-step eye detection scheme is effective, robust, and efficient.
  • The approach minimizes human labeling, making it practical.
  • This method is suitable for deployment in real-world applications.