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相关概念视频

Perceptual Constancy01:12

Perceptual Constancy

384
Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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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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Self-Discrepancy Theory02:45

Self-Discrepancy Theory

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One influential perspective on what motivates people's behavior is detailed in Tory Higgin's self-discrepancy theory (Higgins, 1987). He proposed that people hold disagreeing internal representations of themselves that lead to different emotional states.  
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Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
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Prediction Intervals01:03

Prediction Intervals

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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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The Representativeness Heuristic02:13

The Representativeness Heuristic

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The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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感知意识 纹理 类似性 预测

Weibo Wang, Xinghui Dong

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    此摘要是机器生成的。

    这项研究引入了一种新的感知感知纹理相似性预测网络 (PATSP-Net),以改进细粒度纹理分析. 该网络通过将算法预测与人类视觉感知对齐以获得纹理相似性来实现卓越的性能.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 人与计算机的交互

    背景情况:

    • 纹理相似性对于材料识别至关重要,但算法预测往往与人类感知有所不同.
    • 由于表示和指标不一致,现有的方法与感知上一致的细粒度纹理相似性作斗争.

    研究的目的:

    • 开发一个新的网络,感知感知纹理相似性预测网络 (PATSP-Net),该网络解决了对纹理相似性的算法和人类感知之间的差异.
    • 引入一种新的方法来学习感知意识的纹理表示和相似度指标.

    主要方法:

    • 介绍了感知感知纹理相似性预测网络 (PATSP-Net).
    • 开发了一种双线侧向注意力变压器网络 (BiLAViT),结合了罗特征提取子网络 (SFEN) 和度量学习子网络 (MLN).
    • 提出了一个新的排名和缩放损失函数 (RSLoss),用于测量排名和缩放差异.

    主要成果:

    • 在三个不同的细粒度纹理相似性预测任务上,PATSP-Net与现有方法相比表现优越或可比.
    • 拟议的双线侧向注意力变压器网络 (BiLAViT) 和RSLoss被证明对纹理相似性任务是有效的.
    • 该网络成功地学习了感知知觉的纹理表示和相似度指标.

    结论:

    • 在PATSP-Net中,BiLAViT和RSLoss的联合使用使得能够学习感知感知的纹理表示和相似度量.
    • PATSP-Net为感知上一致的细粒度纹理相似性预测提供了一个有希望的解决方案.
    • 这项工作通过弥合计算和人类感知之间的差距来推进纹理分析.