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相关概念视频

Color Vision01:24

Color Vision

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Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
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Vision01:24

Vision

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Storage01:23

Storage

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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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Colors and Magnetism03:02

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Color in Coordination Complexes
When atoms or molecules absorb light at the proper frequency, their electrons are excited to higher-energy orbitals. For many main group atoms and molecules, the absorbed photons are in the ultraviolet range of the electromagnetic spectrum, which cannot be detected by the human eye. For coordination compounds, the energy difference between the d orbitals often allows photons in the visible range to be absorbed and emitted, which is seen as colors by the human...
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Predicting Molecular Geometry02:27

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VSEPR Theory for Determination of Electron Pair Geometries
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Prediction Intervals01:03

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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基于计算机视觉测量牛肉颜色和预测储存时间的方法的研究.

Yixuan Chen1, Jinghao Zhou2, Fatih Oz3

  • 1State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China; School of Food Science and Technology, Jiangnan University, Wuxi 214122, China.

Meat science
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概括

这项研究引入了一种计算机视觉和机器学习方法,通过测量表面颜色来评估牛肉的新鲜度. 该技术准确地预测了储存时间和氧米球蛋白水平,提供了一种非破坏性的方法来监测牛肉质量.

关键词:
牛肉的颜色是牛肉的颜色计算机视觉 计算机视觉 计算机视觉卷积神经网络是一种卷积神经网络.测量了肌球蛋白的测量结果.

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科学领域:

  • 食品科学 食品科学 食品科学
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 牛肉质量评估传统上依赖于主观方法或破坏性测试.
  • 需要客观,非破坏性的方法来准确确定牛肉的新鲜度和保质期.

研究的目的:

  • 开发一种使用计算机视觉和机器学习测量牛肉表面颜色的非破坏性方法.
  • 准确预测牛肉的储存时间和氧基球蛋白含量.
  • 为了将计算机视觉衍生色度数据与传统色度数据进行比较.

主要方法:

  • 获得长胸肌 (LT) 肌肉的图像.
  • 使用GrabCut和Otsu二元化对红肌区域进行细分.
  • 提取RGB值并将其转换为CIE L*,a*,b*色彩空间.
  • 开发和培训一个卷积神经网络 (CNN) 模型.

主要成果:

  • 与传统色度计相比,计算机视觉对牛肉颜色的时间变化具有更高的灵敏度.
  • 该CNN模型实现了高精度 (R2=0.926用于存储时间,R2=0.893用于氧化球蛋白).
  • 开发的方法为评估牛肉新鲜度提供了一个强大的框架.

结论:

  • 计算机视觉与机器学习相结合,为牛肉质量评估提供了准确而非破坏性的方法.
  • 这种方法可以更好地反映牛肉的外观和新鲜度.
  • 这项研究为预测牛肉储存时间和氧化球蛋白水平建立了一个科学稳健的框架.