Binary Image Features Proposed to Empower Computer Vision

TL;DR

This paper introduces three novel, computationally simple image features that enhance computer vision by mimicking human-like quick assessment capabilities, tested on medical datasets to demonstrate their effectiveness.

Contribution

It proposes three new image features that do not rely on pixel intensity, shape, or color, enabling faster and more human-like image analysis in computer vision.

Findings

Achieved high classification accuracy on medical datasets.

Features are computationally simple and easy to compute.

Potential to improve real-time image processing applications.

Abstract

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.