A Quantum-Classical Hybrid Method for Image Classification and Segmentation

TL;DR

This paper presents a hybrid quantum-classical pipeline for image classification and segmentation, demonstrating its application to surface crack segmentation within a comprehensive software framework.

Contribution

It introduces a systematic hybrid approach combining quantum and classical processing for image analysis tasks, specifically applied to surface crack segmentation.

Findings

Effective hybrid pipeline demonstrated on crack segmentation

Integration within a Cognitive Model Management framework

Potential for quantum advantage in image processing

Abstract

Enormous activity in the Quantum Computing area has resulted in considering them to solve different difficult problems, including those of applied nature, together with classical computers. An attempt is made in this work to nail down a pipeline consisting of both quantum and classical processing blocks for the task of image classification and segmentation in a systematic fashion. Its efficacy and utility are brought out by applying it to Surface Crack segmentation. Being a sophisticated software engineering task, the functionalities are orchestrated through our in-house Cognitive Model Management framework.