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相关概念视频

Structural Classification of Joints01:20

Structural Classification of Joints

3.4K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.4K
Functional Classification of Joints01:09

Functional Classification of Joints

4.1K
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...
4.1K
Force Classification01:22

Force Classification

1.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.2K
Dot Product: Problem Solving01:21

Dot Product: Problem Solving

379
The dot product is a powerful tool in problem-solving involving vectors, given that the dot product of two vectors is the product of their magnitudes and the cosine of the angle between them measured anti-clockwise. Solving problems involving the dot product requires understanding its properties and developing a step-by-step process to solve them. Here are the main steps to follow when solving any general problem involving the dot product:
Identify the problem: Start by reading the problem and...
379
Design Example: Measuring Distance Between Two Points with Obstructions01:10

Design Example: Measuring Distance Between Two Points with Obstructions

39
When measuring distances in areas with physical obstructions, such as a lake in a field, surveyors must employ techniques to calculate accurate lengths without direct line measurements. One effective method is the offset technique, which allows for precise distance estimation over inaccessible stretches.In this scenario, a surveyor must measure a side of an area that crosses a lake. Since the measuring tape cannot span the lake, the surveyor begins by establishing a baseline that aligns with...
39
Aggregates Classification01:29

Aggregates Classification

326
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
326

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相关实验视频

Updated: Jul 6, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

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概率交叉跨联盟用于训练和评估定向物体探测器.

Jeffri Murrugarra-Llerena, Lucas N Kirsten, Luis Felipe Zeni

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |January 8, 2024
    PubMed
    概括

    本研究引入了用于定向物体检测的高斯界限框 (GBB),提供了一个称为概率交叉对联 (ProbIoU) 的可微分损失函数,可以改进训练和评估.

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 深度学习 (Deep Learning) 是一种深度学习.

    背景情况:

    • 定向物体检测是一个具有挑战性的问题,现有的方法经常适应水平界限盒 (HBB) 检测器.
    • 当前定向界限框 (OBB) 方法面临复杂的配方,定制反向传播和不规则或圆形对象的模两可的表示的困难.

    研究的目的:

    • 开发一种新的,统一的方法来培训,代表和评估面向物体探测器.
    • 解决传统OBB的局限性,特别是对模两可的物体形状.

    主要方法:

    • 介绍了高斯边界框 (GBB) 作为面向对象的模糊表示.
    • 提出了基于GBB之间的Hellinger距离相似度指标,产生了局部化损失的可微分闭式表达式.
    • 证明GBBs自然地映射到圆边界盒 (EBBs),解决圆形物体的模糊性.

    主要成果:

    • 拟议的度量,称为概率的跨欧盟交叉点 (ProbIoU),与相应的EBB的跨欧盟交叉点 (IoU) 密切相关.
    • 使用ProbIoU作为回归损失,可以在没有额外的超参数或自定义实现的情况下获得与最先进的方法相比具有竞争力的结果.
    • 作为面向物体探测器的评估指标,ProbIoU显示出有希望的结果.

    更多相关视频

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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    相关实验视频

    Last Updated: Jul 6, 2025

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

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    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

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    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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    结论:

    • 高斯边界框 (GBB) 为定向对象提供了强大而灵活的表示方式.
    • 概率交叉对联 (ProbIoU) 度量为训练和评估定向物体探测器提供了有效和简单的解决方案.