adjacent pixels

简明释义

1. 相邻象元; 2. 相邻象素;

英英释义

例句

1.The edge detection technique relies heavily on the differences between adjacent pixels 相邻像素 to identify boundaries.

边缘检测技术在很大程度上依赖于相邻像素 adjacent pixels 之间的差异来识别边界。

2.The segmentation algorithm groups adjacent pixels 相邻像素 based on color similarity to isolate objects.

分割算法根据颜色相似性将相邻像素 adjacent pixels 分组以隔离对象。

3.In image processing, algorithms often analyze the values of adjacent pixels 相邻像素 to enhance image quality.

在图像处理过程中，算法通常分析相邻像素 adjacent pixels 的值以提高图像质量。

4.When applying a blur effect, the software averages the colors of adjacent pixels 相邻像素 to create a smoother appearance.

在应用模糊效果时，软件会平均相邻像素 adjacent pixels 的颜色，以创建更平滑的外观。

5.In computer vision, detecting features often involves examining adjacent pixels 相邻像素 for patterns.

在计算机视觉中，检测特征通常涉及检查相邻像素 adjacent pixels 的模式。

作文

In the realm of digital imaging and computer graphics, the term adjacent pixels refers to the individual picture elements that are next to each other in a grid layout. Each pixel in an image carries color and intensity information, and the relationship between adjacent pixels plays a crucial role in how we perceive images on screens. Understanding this concept is essential for various applications, including image processing, computer vision, and graphic design.When we consider an image made up of thousands or millions of pixels, it becomes clear that the way these adjacent pixels interact can significantly affect the overall quality and clarity of the image. For example, in a photograph, if two adjacent pixels have very different colors, the transition between them may appear harsh and unnatural. On the other hand, if the colors of adjacent pixels are similar, the image will have a smoother gradient, resulting in a more visually appealing output.In image processing, algorithms often analyze adjacent pixels to perform tasks such as edge detection, noise reduction, and image segmentation. Edge detection algorithms, for instance, evaluate the differences in intensity between adjacent pixels to identify boundaries within an image. When there is a significant change in pixel values between two adjacent pixels, the algorithm recognizes this as an edge, which can be crucial for understanding the structure of the scene depicted in the image.Moreover, the concept of adjacent pixels is also important in the context of compression techniques. In lossy compression methods, such as JPEG, the relationships between adjacent pixels are leveraged to reduce the amount of data needed to represent an image. By analyzing blocks of adjacent pixels, the algorithm can discard less important visual information while preserving the overall appearance of the image. This process allows for significant file size reduction without a noticeable loss in quality, making it easier to store and transmit images over the internet.In graphic design, artists often manipulate adjacent pixels to create effects such as gradients, shadows, and textures. By carefully adjusting the colors of adjacent pixels, designers can achieve a sense of depth and realism in their work. For example, when creating a shadow effect, a designer might gradually change the color of adjacent pixels from a darker shade to a lighter one, simulating the way light interacts with surfaces in the real world.Furthermore, machine learning models used in computer vision heavily rely on the analysis of adjacent pixels. These models learn to recognize patterns based on the relationships between adjacent pixels in training images. For instance, convolutional neural networks (CNNs) utilize filters that scan through images, focusing on groups of adjacent pixels to extract features that help in tasks like object detection and image classification.In conclusion, the significance of adjacent pixels in digital imaging cannot be overstated. Whether in image processing, graphic design, or machine learning, the interactions between these neighboring elements are foundational to how we create, manipulate, and understand visual information. As technology continues to advance, our ability to work with adjacent pixels will only become more refined, leading to even more sophisticated imaging techniques and applications in the future.

在数字成像和计算机图形学领域，术语相邻像素指的是在网格布局中彼此相邻的单个图像元素。图像中的每个像素都携带颜色和亮度信息，而相邻像素之间的关系在我们如何感知屏幕上的图像中起着至关重要的作用。理解这一概念对于图像处理、计算机视觉和图形设计等各种应用至关重要。当我们考虑由成千上万或数百万个像素组成的图像时，很明显，这些相邻像素之间的互动方式会显著影响图像的整体质量和清晰度。例如，在一张照片中，如果两个相邻像素的颜色差异很大，则它们之间的过渡可能看起来生硬且不自然。另一方面，如果相邻像素的颜色相似，则图像将具有更平滑的渐变，从而产生更具视觉吸引力的输出。在图像处理过程中，算法通常分析相邻像素以执行边缘检测、噪声减少和图像分割等任务。例如，边缘检测算法评估相邻像素之间的亮度差异，以识别图像中的边界。当两个相邻像素之间的像素值发生显著变化时，算法将其识别为边缘，这对于理解图像中所描绘场景的结构至关重要。此外，相邻像素的概念在压缩技术中也很重要。在有损压缩方法（例如JPEG）中，利用相邻像素之间的关系来减少表示图像所需的数据量。通过分析相邻像素的块，算法可以丢弃不太重要的视觉信息，同时保留图像的整体外观。这个过程允许显著减小文件大小，而不会明显降低质量，使得在互联网上存储和传输图像变得更加容易。在图形设计中，艺术家通常操纵相邻像素以创建渐变、阴影和纹理等效果。通过仔细调整相邻像素的颜色，设计师可以在作品中实现深度和真实感。例如，在创建阴影效果时，设计师可能会逐渐将相邻像素的颜色从较暗的色调改变为较亮的色调，从而模拟光线与现实世界表面交互的方式。此外，计算机视觉中使用的机器学习模型在很大程度上依赖于对相邻像素的分析。这些模型通过训练图像中相邻像素之间的关系来学习识别模式。例如，卷积神经网络（CNN）利用扫描图像的滤波器，专注于相邻像素的组，以提取帮助进行对象检测和图像分类的特征。总之，相邻像素在数字成像中的重要性不容小觑。无论是在图像处理、图形设计还是机器学习中，这些相邻元素之间的互动都是我们创建、操纵和理解视觉信息的基础。随着技术的不断进步，我们与相邻像素的工作能力只会变得更加精细，从而在未来导致更复杂的成像技术和应用。