# Interest Point Detection based on Adaptive Ternary Coding

## TL;DR

This paper introduces an adaptive ternary coding-based interest point detector that is robust to noise, illumination changes, and quantization errors, improving feature detection in images.

## Contribution

The paper proposes a novel adaptive ternary coding mechanism for interest point detection that enhances robustness and invariance to contrast and noise.

## Key findings

- Achieves state-of-the-art results on standard datasets.
- Demonstrates robustness in face recognition applications.
- Invariance to illumination and noise demonstrated.

## Abstract

In this paper, an adaptive pixel ternary coding mechanism is proposed and a contrast invariant and noise resistant interest point detector is developed on the basis of this mechanism. Every pixel in a local region is adaptively encoded into one of the three statuses: bright, uncertain and dark. The blob significance of the local region is measured by the spatial distribution of the bright and dark pixels. Interest points are extracted from this blob significance measurement. By labeling the statuses of ternary bright, uncertain, and dark, the proposed detector shows more robustness to image noise and quantization errors. Moreover, the adaptive strategy for the ternary cording, which relies on two thresholds that automatically converge to the median of the local region in measurement, enables this coding to be insensitive to the image local contrast. As a result, the proposed detector is invariant to illumination changes. The state-of-the-art results are achieved on the standard datasets, and also in the face recognition application.