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Related Concept Videos

Movement Joints in Buildings01:27

Movement Joints in Buildings

Movement joints in buildings are essential design elements that accommodate inevitable motions caused by various factors such as temperature changes, moisture content variations, and structural deflections. These motions, if not considered in design and construction, can lead to unsightly or dangerous damage. Movement joints are incorporated in different forms to manage these stresses and allow materials to move without causing distress.
The simplest type of movement joints, working joints, are...
Functional Classification of Joints01:09

Functional Classification of Joints

Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An immobile...

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Related Experiment Video

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An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor
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Categorization of indoor places using the Kinect sensor.

Oscar Martinez Mozos1, Hitoshi Mizutani, Ryo Kurazume

  • 1Faculty of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan. omozos@irvs.ait.kyushu-u.ac.jp

Sensors (Basel, Switzerland)
|July 11, 2012
PubMed
Summary
This summary is machine-generated.

This study presents a robot vision method for categorizing indoor places like offices and labs. Using depth and grayscale images, the system accurately identifies different environments for service robots.

Keywords:
Kinect sensorplace categorizationservice robots

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Service robots require environmental understanding for effective human interaction.
  • Categorizing indoor spaces is crucial for navigation and task execution in robotics.

Purpose of the Study:

  • To develop and evaluate a method for automated indoor place categorization using a mobile robot.
  • To compare the efficacy of Support Vector Machines (SVM) and Random Forests (RF) for this task.

Main Methods:

  • Utilized a mobile robot equipped with a Kinect camera to capture depth and grayscale images.
  • Transformed image data into histograms of Local Binary Patterns (LBPs) with dimensionality reduction.
  • Combined LBPs into a feature vector for supervised classification using SVM and RF.

Main Results:

  • The developed method successfully categorized five distinct indoor place types: corridors, laboratories, offices, kitchens, and study rooms.
  • High accuracy was achieved in distinguishing between these different environments.
  • Both SVM and RF classifiers demonstrated strong performance in place categorization.

Conclusions:

  • The proposed approach provides an effective solution for indoor place recognition in service robotics.
  • The LBP-based feature extraction and supervised classification method is robust and accurate.
  • This technique enhances the environmental awareness of mobile robots, enabling more sophisticated human-robot interaction.