This literature has proposed three fast and easy computable image features to improve computer vision by offering more human-like vision power. These features are not based on image pixels absolute or relative intensity; neither based on shape or colour. So, no complex pixel by pixel calculation is required. For human eyes, pixel by pixel calculation is like seeing an image with maximum zoom which is done only when a higher level of details is required. Normally, first we look at an image to get an overall idea about it to know whether it deserves further investigation or not. This capacity of getting an idea at a glance is analysed and three basic features are proposed to empower computer vision. Potential of proposed features is tested and established through different medical dataset. Achieved accuracy in classification demonstrates possibilities and potential of the use of the proposed features in image processing.

中文翻译：

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信息计数和分布矩阵：一种基于内容的大脑图像索引的现代方法

该文献提出了三个快速且易于计算的图像特征，通过提供更像人类的视觉能力来改善计算机视觉。这些特征不是基于图像像素的绝对或相对强度；不基于形状或颜色。因此，不需要逐像素计算复杂的像素。对于人眼来说，逐像素计算就像看到最大缩放的图像，只有在需要更高级别的细节时才会这样做。通常情况下，我们首先查看一张图像以获得一个整体的概念，以了解它是否值得进一步研究。分析了这种一目了然的想法的能力，并提出了三个基本特征来增强计算机视觉。通过不同的医学数据集测试和建立提议特征的潜力。