# 4X4 Census Transform

## TL;DR

This paper introduces a 4X4 Census Transform (4X4CT) that uses a larger, overlapping pixel kernel to improve image textural detail and contrast in computer vision applications.

## Contribution

The paper presents a novel 4X4 Census Transform that extends the traditional 3X3 kernel to enhance image feature extraction.

## Key findings

- Enhanced image textural crispness and contrast
- Potential for more meaningful solutions in visual computing
- Preliminary results show improved feature representation

## Abstract

This paper proposes a 4X4 Census Transform (4X4CT) to encourage further research in computer vision and visual computing. Unlike the traditional 3X3 CT which uses a nine pixels kernel, the proposed 4X4CT uses a sixteen pixels kernel with four overlapped groups of 3X3 kernel size. In each overlapping group, a reference input pixel profits from its nearest eight pixels to produce an eight bits binary string convertible to a grayscale integer of the 4X4CT's output pixel. Preliminary experiments demonstrated more image textural crispness and contrast than the CT as well as alternativeness to enable meaningful solutions to be achieved.

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Source: https://tomesphere.com/paper/1907.12891