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相关概念视频

Somatosensation01:33

Somatosensation

36.7K
The somatosensory system relays sensory information from the skin, mucous membranes, limbs, and joints. Somatosensation is more familiarly known as the sense of touch. A typical somatosensory pathway includes three types of long neurons: primary, secondary, and tertiary. Primary neurons have cell bodies located near the spinal cord in groups of neurons called dorsal root ganglia. The sensory neurons of ganglia innervate designated areas of skin called dermatomes.
36.7K
Somatosensory, Motor, and Association Cortex01:24

Somatosensory, Motor, and Association Cortex

562
The somatosensory cortex in the parietal lobes is crucial for interpreting sensory data such as touch, temperature, and proprioception. The somatosensory cortex, situated in the parietal lobes, plays a vital role in interpreting sensory information like touch, temperature, and proprioception—awareness of body position. This specialized brain region features an organized structure wherein neurons at the top primarily process sensations originating from the lower body. In contrast, those at...
562
Association Areas of the Cortex01:21

Association Areas of the Cortex

5.5K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
5.5K
Tactile and Chemical Senses01:27

Tactile and Chemical Senses

325
Tactile senses encompass touch, temperature, and pain, each mediated by specific receptors. Touch receptors detect mechanical energy or pressure against the skin. Sensory fibers from these receptors enter the spinal cord and relay information to the brain stem. Here, most fibers cross over to the opposite side of the brain. The touch information then moves to the thalamus, which projects a map of the body's surface onto the somatosensory areas of the parietal lobes in the cerebral cortex.
325
Convolution Properties I01:20

Convolution Properties I

184
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
184
Sensory Perception: Organization of the Somatosensory System01:11

Sensory Perception: Organization of the Somatosensory System

3.1K
The somatosensory system is the central and peripheral nervous system component that senses and processes touch, pressure, pain, temperature, and body position or proprioception. The process of sensation takes place at three levels:
The receptor level:
The receptor level is the first stage of sensation. It involves the detection of a stimulus by specialized sensory receptors. The stimulus must arrive within the receptor's receptive field. Next, the receptor converts the energy of the...
3.1K

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Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
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建立触觉纹理属性空间,并使用1D-CNN从图像特征预测触觉属性.

Waseem Hassan1, Joolekha Bibi Joolee1, Seokhee Jeon2

  • 1Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do, South Korea.

Scientific reports
|July 19, 2023
PubMed
概括
此摘要是机器生成的。

研究人员开发了一个标准化的触觉属性空间来分类纹理表面. 一个1D-CNN模型准确地预测纹理属性,使触觉纹理的可扩展识别.

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科学领域:

  • 触觉学是指触觉学,是指触觉学.
  • 计算机视觉 计算机视觉
  • 心理物理学的精神物理.

背景情况:

  • 对触觉纹理的客观表征是具有挑战性的.
  • 现有的方法缺乏触觉纹理表示的标准化框架.
  • 对于触觉纹理,需要一个类似于RGB颜色的标准化模型.

研究的目的:

  • 为纹理表面建立一个标准化的四维触觉属性空间.
  • 开发一个可扩展的模型来表示和识别触觉纹理.
  • 评估1D-CNN模型在预测触觉属性的性能.

主要方法:

  • 进行了一项心理物理实验,人类参与者对100个现实表面的纹理进行了评分.
  • 定义了一个四维的触觉属性空间:粗-光滑,平-,粘-滑,硬-软.
  • 从纹理样本中训练了一种1D-CNN模型,使用心理物理数据和图像特征.

主要成果:

  • 1D-CNN模型展示了对触觉纹理属性的强大预测准确性.
  • 拟议的模型表现优于其他机器学习和深度学习算法.
  • 开发的触觉属性空间为纹理识别提供了一个可扩展的框架.

结论:

  • 已建立的触觉属性空间为纹理分类提供了一个标准化的方法.
  • 1D-CNN模型提供了一个可扩展和准确的方法来预测触觉属性.
  • 这项研究为客观和一致的触觉纹理分析铺平了道路.