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Related Concept Videos

Perception01:28

Perception

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Perception is a fundamental psychological process that enables individuals to organize, interpret, and consciously experience sensory information. This process is crucial for understanding and interacting with the world around us. It includes both bottom-up and top-down processing, each playing a distinct role in how we perceive our environment.
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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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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Gestalt principles provide a framework for understanding how humans perceive objects as unified wholes within their context. These principles are essential in explaining the cognitive processes that make sense of complex visual stimuli by organizing them into coherent groups. One fundamental principle is proximity, which posits that objects located close to each other are perceived as a collective group. For instance, when dots are positioned near one another, the visual system interprets them...
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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
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Methods for Presenting Real-world Objects Under Controlled Laboratory Conditions
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Integrating planning perception and action for informed object search.

Luis J Manso1, Marco A Gutierrez2, Pablo Bustos2

  • 1RoboLab - Robotics and Artificial Vision Laboratory, Escuela PolitĂ©cnica de Cáceres, Universidad de Extremadura, Avd. de la Universidad s/n, 10071, Badajoz, Spain. lmanso@unex.es.

Cognitive Processing
|August 16, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning method for cognitive robots to efficiently find objects in new environments. It prioritizes container inspection, reducing search time and improving object retrieval success rates.

Keywords:
Active perceptionInformed searchPerception-aware planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Robots often struggle with object search in unmapped environments, leading to inefficient exploration.
  • Current methods lack intelligent prioritization of search locations.

Purpose of the Study:

  • To develop and evaluate a novel machine learning approach for optimizing object search strategies in robots.
  • To reduce the time and improve the success rate of object retrieval for cognitive robots.

Main Methods:

  • A two-process system: passive image analysis to describe container contents and deliberative selection of the most probable container.
  • Utilizes machine learning to predict object locations based on semantic similarity and prior observations.
  • Incorporates a fallback strategy for continued searching upon initial incorrect guesses.

Main Results:

  • The proposed method significantly reduces object search time compared to baseline approaches.
  • Demonstrates improved efficiency in locating target objects in unknown environments.
  • Quantitative results validate the effectiveness of the intelligent search prioritization.

Conclusions:

  • Machine learning-based content description and deliberative search significantly enhance robot object-finding capabilities.
  • The method offers a practical solution for improving robot autonomy and task completion in complex, unorganized spaces.
  • This approach represents a step forward in enabling robots to navigate and interact with unfamiliar environments more effectively.