vectorizing

简明释义

英[ˈvɛktəraɪzɪŋ]美[ˈvɛktəraɪzɪŋ]

向量化

英英释义

单词用法

同义词

反义词

例句

1.And the integrating raster with vector technology predigests the polygon overlaying operation, realizes inner span part vectorizing and speed up the process.

采用矢量与栅格结合的技术进行更新，简化了矢量多边形叠交运算，实现了图斑内部的局部矢量化，提高了处理速度。

2.Image vectorization refers to the process of inputting digitized pictures or drafts to the computer, and then getting CAD file by vectorizing the digital data.

图像矢量化是指将图片或工程图纸用数字化设备输入计算机中，经过矢量化处理形成CAD文件的过程。

3.Image vectorization refers to the process of inputting digitized pictures or drafts to the computer, and then getting CAD file by vectorizing the digital data.

图像矢量化是指将图片或工程图纸用数字化设备输入计算机中，经过矢量化处理形成CAD文件的过程。

4.The processing works after vectorizing topographic maps were discussed, such as diluting, right Angle processing, deleting redundant points and lines.

讨论了地形图矢量化后的几项处理工作，如曲线上点的稀释、直角化处理、重复点线的删除、接边等。

5.The software helps in vectorizing raster images into scalable vector formats.

该软件帮助将光栅图像向量化为可缩放的矢量格式。

6.The process of vectorizing images can significantly reduce their file size.

图像的向量化过程可以显著减小它们的文件大小。

7.In machine learning, vectorizing text data is essential for training algorithms.

在机器学习中，向量化文本数据对于训练算法至关重要。

8.By vectorizing the dataset, we can enhance the performance of our analysis.

通过向量化数据集，我们可以提高分析的性能。

9.We are vectorizing our graphics to improve the scalability of our design.

我们正在向量化我们的图形，以提高设计的可扩展性。

作文

In the realm of computer science and data processing, the term vectorizing refers to the process of converting operations or data into a vector format that can be processed more efficiently by computers. This technique is particularly significant in the field of numerical computing and graphics, where high performance is crucial. The concept of vectorizing allows programmers to optimize their code, enabling it to run faster and utilize modern hardware capabilities effectively.When we talk about vectorizing, we often think of the transformation of scalar operations, which involve single data points, into vector operations that handle multiple data points simultaneously. For instance, instead of processing each element of an array one at a time, vectorizing allows the program to process an entire array at once. This parallel processing capability is what makes vectorizing so powerful, as it significantly reduces the time required for computations.One of the most common applications of vectorizing can be found in image processing. When manipulating images, operations such as filtering, transforming, or enhancing can be applied to entire sets of pixels rather than individually. By vectorizing these operations, developers can leverage the full power of the hardware, resulting in smoother and faster image processing. For example, using libraries that support vectorizing can lead to substantial improvements in performance, especially when dealing with large images or real-time video feeds.Moreover, vectorizing is not limited to just image processing; it plays a crucial role in machine learning and data analysis as well. In these fields, large datasets are common, and the ability to perform operations on entire vectors of data can lead to more efficient algorithms. For example, when training a machine learning model, vectorizing the input data allows the model to learn from multiple examples in parallel, speeding up the training process significantly.However, vectorizing is not without its challenges. Not all algorithms can be easily converted into vector form. Some operations may have dependencies that require sequential processing, making it difficult to achieve the desired performance gains. Furthermore, the initial overhead of vectorizing code can sometimes outweigh the benefits if not done carefully. Therefore, it is essential for developers to analyze their code and identify the parts that would benefit the most from vectorizing.In conclusion, vectorizing is a powerful technique that enhances the efficiency of data processing and computation in various fields, including computer graphics, image processing, and machine learning. By converting scalar operations into vector operations, developers can take advantage of modern hardware capabilities, leading to faster and more efficient programs. As technology continues to evolve, the importance of vectorizing will only increase, making it a vital skill for programmers and data scientists alike. Understanding how to implement vectorizing effectively can unlock new levels of performance and innovation in software development.

在计算机科学和数据处理领域，术语vectorizing指的是将操作或数据转换为向量格式的过程，以便计算机能够更高效地处理。这一技术在数值计算和图形学领域尤为重要，因为高性能至关重要。vectorizing的概念使程序员能够优化他们的代码，使其运行得更快并有效利用现代硬件的能力。当我们谈论vectorizing时，通常会想到将标量操作（涉及单个数据点）转换为向量操作（同时处理多个数据点）。例如，程序可以一次处理整个数组，而不是逐个处理数组中的每个元素。这种并行处理能力就是vectorizing如此强大的原因，因为它显著减少了计算所需的时间。vectorizing最常见的应用之一可以在图像处理领域找到。在操作图像时，诸如过滤、变换或增强等操作可以应用于整组像素，而不是单独处理。通过vectorizing这些操作，开发人员可以充分利用硬件的全部能力，从而实现更流畅、更快速的图像处理。例如，使用支持vectorizing的库可以大幅提高性能，尤其是在处理大型图像或实时视频流时。此外，vectorizing不仅限于图像处理；在机器学习和数据分析中也发挥着至关重要的作用。在这些领域，大型数据集是常态，能够对整个数据向量执行操作可以导致更高效的算法。例如，在训练机器学习模型时，vectorizing输入数据使模型能够并行学习多个示例，从而显著加快训练过程。然而，vectorizing并非没有挑战。并非所有算法都可以轻易转换为向量形式。一些操作可能具有依赖关系，要求顺序处理，这使得实现所需的性能提升变得困难。此外，如果不谨慎处理，vectorizing代码的初始开销有时可能会超过收益。因此，开发人员必须分析他们的代码，识别出最能从vectorizing中受益的部分。总之，vectorizing是一种增强数据处理和计算效率的强大技术，广泛应用于计算机图形学、图像处理和机器学习等领域。通过将标量操作转换为向量操作，开发人员可以利用现代硬件的能力，从而实现更快、更高效的程序。随着技术的不断发展，vectorizing的重要性只会增加，使其成为程序员和数据科学家必备的技能。理解如何有效实施vectorizing可以解锁软件开发中的新性能和创新水平。