Binary Image Features Proposed to Empower Computer Vision
This work addresses the need for efficient, human-like image analysis in computer vision, particularly for medical applications, but appears incremental as it introduces new features without a major paradigm shift.
The authors tackled the problem of enabling computer vision systems to quickly assess images at a glance, similar to human perception, by proposing three fast, non-pixel-based image features. They tested these features on medical datasets and achieved accuracy in classification, demonstrating their potential for image processing.
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.