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相关概念视频

Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

305
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
305
Convolution Properties II01:17

Convolution Properties II

240
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
240
Convolution Properties I01:20

Convolution Properties I

190
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:
190
Deconvolution01:20

Deconvolution

201
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
201
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

352
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
352
Line, Surface, and Volume Integrals01:15

Line, Surface, and Volume Integrals

2.4K
A line integral for a vector field is defined as the integral of the dot product of a vector function with an infinitesimal displacement vector along a prescribed path. If the prescribed path is closed, the integrals reduce to a closed-line integral. The closed-contour integral of the vector field is referred to in terms of the circulation of the vector field around the closed path. A vector with zero circulation around every closed path is called a conservative field, while one with non-zero...
2.4K

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相关实验视频

Updated: Jul 26, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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网状卷积与连续波器用于3D表面解析.

Huan Lei, Naveed Akhtar, Mubarak Shah

    IEEE transactions on neural networks and learning systems
    |June 13, 2023
    PubMed
    概括

    这项研究介绍了毕加索,这是一个用于在三角形网格上学习3D几何特征的新工具包. 它可以在3D表面上进行层次深度学习,改善形状分析和场景细分.

    科学领域:

    • 计算机图形 计算机图形
    • 三维计算机视觉 3D计算机视觉
    • 几何深度学习 几何深度学习

    背景情况:

    • 对于3D表面的深度学习,由于缺乏高效的操作,在层次模型中面临着挑战.
    • 现有的方法难以从复杂的3D网状数据中有效地提取几何特征.

    研究的目的:

    • 提出从3D三角形网格中增强几何特征学习的新型模块化运算.
    • 开发一个层次神经网络,用于对3D表面的感知分析.
    • 为更广泛的研究可访问性提供开源实现.

    主要方法:

    • 开发了新的网状卷积,利用球体波器为连续波器.
    • 实现了GPU加速网格消灭,用于即时处理分批网格.
    • 引入了用于处理上抽样/下抽样网格特征的网格 (非) 聚合操作.
    • 将这些操作集成到一个层次的神经网络中,PicassoNet++.

    主要成果:

    • 毕加索操作使3D表面的有效等级建模成为可能.
    • 在3D形状分析基准上,PicassoNet++表现出极具竞争力的表现.
    • 该系统在3D场景细分任务中取得了最先进的结果.

    更多相关视频

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    Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales
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    Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales
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    结论:

    • 拟议的Picasso操作和PicassoNet++显著提升了用于3D表面分析的深度学习能力.
    • 该开源工具包为未来的几何深度学习和3D计算机视觉研究提供了便利.
    • 这项工作解决了计算机图形和视觉应用的层次式3D网格处理的关键局限性.