convolve

简明释义

英[kənˈvɒlv]美[kənˈvɑːlv]

v. 使卷曲，使缠绕；卷积

第 三 人 称 单 数 c o n v o l v e s

现 在 分 词 c o n v o l v i n g

过 去 式 c o n v o l v e d

过 去 分 词 c o n v o l v e d

英英释义

单词用法

同义词

反义词

例句

1.First, gradient image is constructed by using direction templates convolve with image.

首先利用方向模板对图像进行卷积，求得梯度图像。

2.The HVS function, HVS function is inversely Fourier transformed in frequency domain, and the method is realized by calculating some value of the function in spacial domain as convolve nucleus.

根据HVS函数的特点，对频域中HVS函数进行反傅立叶变换，将空间域中该函数的部分函数值作为特定的模板参数，并与原图像进行卷积来实现边缘检测。

3.Two-value iris template will be achieved by compare every point of convolve image with zero.

对虹膜区域进行二值化编码，获得虹膜特征模板。

4.We then convolve x_image with the weight tensor, add the bias, apply the ReLU function, and finally max pool.

我们把x_image和权值向量进行卷积，加上偏置项，然后应用ReLU激活函数，最后进行max pooling。

5.They could be used directly as a projection basis to reduce dimensionality, but it needs to consider how to convolve them properly with the data you want to project.

他们可以直接使用作为预测的基础上，以减少维数，但它需要考虑如何卷积的数据要妥善项目。

6.The HVS function, HVS function is inversely Fourier transformed in frequency domain, and the method is realized by calculating some value of the function in spacial domain as convolve nucleus.

根据HVS函数的特点，对频域中HVS函数进行反傅立叶变换，将空间域中该函数的部分函数值作为特定的模板参数，并与原图像进行卷积来实现边缘检测。

7.When you convolve 卷积 two probability distributions, you get the distribution of the sum of two independent random variables.

当你对两个概率分布进行卷积 卷积时，你会得到两个独立随机变量之和的分布。

8.In signal processing, we often convolve 卷积 the input signal with a filter to enhance certain features.

在信号处理中，我们经常将输入信号与滤波器卷积 卷积以增强某些特征。

9.To blur an image, you can convolve 卷积 it with a Gaussian kernel.

要模糊图像，可以用高斯核对其进行卷积 卷积。

10.The mathematician explained how to convolve 卷积 two functions to find their combined effect.

数学家解释了如何对两个函数进行卷积 卷积以找出它们的综合效果。

11.In machine learning, we use convolutional layers that convolve 卷积 the input data to extract features.

在机器学习中，我们使用卷积层对输入数据进行卷积 卷积以提取特征。

作文

In the realm of mathematics and signal processing, the term convolve refers to a mathematical operation that combines two functions to produce a third function. This process is fundamental in various applications, particularly in engineering and data analysis. To convolve two functions, one typically integrates the product of the two functions after one has been flipped and shifted. This operation is not just a theoretical concept; it has practical implications in fields such as audio processing, image filtering, and even in the analysis of time-series data.For instance, consider a scenario in audio processing where a sound engineer wants to apply an effect to a recording. By using a filter, which can be represented as a function, the engineer can convolve this filter with the original audio signal. The result is a new audio signal that incorporates the desired effects, such as reverb or echo. This illustrates how convolve serves as a bridge between raw data and processed outcomes, enabling more complex transformations and enhancements.Similarly, in image processing, the concept of convolve is vital for tasks such as edge detection and blurring. An image can be represented as a matrix of pixel values, and by convolveing this matrix with a kernel (a smaller matrix designed for a specific effect), one can achieve various visual modifications. For example, applying a Gaussian blur involves convolveing the image with a Gaussian function, resulting in a smoother appearance that reduces noise and detail.Moreover, the convolve operation is not limited to two-dimensional data. In the context of time-series analysis, for instance, economists often convolve historical data with a moving average to identify trends and patterns over time. This technique allows analysts to smooth out fluctuations and better understand underlying behaviors in economic indicators.Understanding the mechanics behind convolve is essential for anyone working in technical fields that rely on data processing. It requires a solid grasp of calculus and linear algebra, as well as the ability to visualize how functions interact with one another. The beauty of convolve lies in its versatility; it can be applied across a multitude of disciplines, from physics to machine learning.In conclusion, the operation to convolve is a powerful tool in mathematics and engineering, facilitating a wide range of applications. Whether it is used in audio effects, image processing, or economic trend analysis, convolve enables professionals to manipulate and enhance data effectively. As technology continues to advance, the importance of understanding such mathematical operations will only grow, making it imperative for students and practitioners alike to master the concept of convolve and its applications.

在数学和信号处理中，术语convolve指的是一种将两个函数结合以产生第三个函数的数学运算。这个过程在各种应用中是基础性的，特别是在工程和数据分析中。要convolve两个函数，通常会在一个函数翻转并偏移之后，对两个函数的乘积进行积分。这一操作不仅仅是一个理论概念；它在音频处理、图像滤波，甚至时间序列数据分析等领域都有实际的应用。例如，考虑一个音频处理的场景，其中一位声音工程师想要对录音应用效果。通过使用一个可以被表示为函数的滤波器，工程师可以将这个滤波器与原始音频信号进行convolve。结果是一个新的音频信号，包含了所需的效果，例如混响或回声。这说明了convolve如何作为原始数据与处理结果之间的桥梁，使得更复杂的变换和增强成为可能。同样，在图像处理中，convolve的概念对于边缘检测和模糊等任务至关重要。一幅图像可以被表示为像素值的矩阵，通过将这个矩阵与一个内核（一个设计用于特定效果的小矩阵）进行convolve，可以实现各种视觉修改。例如，应用高斯模糊涉及到将图像与高斯函数进行convolve，从而得到一种更平滑的外观，减少噪声和细节。此外，convolve操作并不仅限于二维数据。在时间序列分析的背景下，例如，经济学家经常将历史数据与移动平均进行convolve，以识别趋势和模式。这种技术使分析师能够平滑波动，更好地理解经济指标中的潜在行为。理解convolve背后的机制对于任何在依赖数据处理的技术领域工作的人来说都是至关重要的。它需要对微积分和线性代数有扎实的掌握，以及能够可视化函数之间如何相互作用的能力。convolve的美在于它的多功能性；它可以跨多个学科应用，从物理学到机器学习。总之，convolve的操作是数学和工程中的一种强大工具，促进了广泛的应用。无论是在音频效果、图像处理还是经济趋势分析中，convolve使专业人士能够有效地操控和增强数据。随着技术的不断进步，理解这种数学运算的重要性只会增加，使得学生和从业者都必须掌握convolve的概念及其应用。