Interest Point Detection based on Adaptive Ternary Coding
This work addresses the problem of reliable interest point detection for computer vision tasks, particularly in noisy or varying illumination conditions, representing an incremental improvement over existing methods.
The paper tackled interest point detection in images by proposing an adaptive ternary coding mechanism, resulting in a detector that is robust to noise, quantization errors, and illumination changes, achieving state-of-the-art results on standard datasets and in face recognition applications.
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.