Computer Vision with a Superpixelation Camera

2026-03-27

AI Summary

This work proposes SuperCam, a novel camera architecture designed to address the inefficiency of conventional cameras in resource-constrained settings, where they generate excessive redundant data that hinders downstream visual tasks. SuperCam uniquely integrates adaptive superpixel segmentation directly into the hardware pipeline, enabling online, lightweight data compression and preservation of critical visual information at the point of capture. By synergizing this design with edge computing, the system substantially reduces memory footprint and bandwidth requirements. Experimental results demonstrate that SuperCam consistently outperforms traditional approaches across multiple vision tasks—including semantic segmentation, object detection, and monocular depth estimation—thereby validating its feasibility and superiority for efficient perception under stringent resource limitations.

Abstract

Conventional cameras generate a lot of data that can be challenging to process in resource-constrained applications. Usually, cameras generate data streams on the order of the number of pixels in the image. However, most of this captured data is redundant for many downstream computer vision algorithms. We propose a novel camera design, which we call SuperCam, that adaptively processes captured data by performing superpixel segmentation on the fly. We show that SuperCam performs better than current state-of-the-art superpixel algorithms under memory-constrained situations. We also compare how well SuperCam performs when the compressed data is used for downstream computer vision tasks. Our results demonstrate that the proposed design provides superior output for image segmentation, object detection, and monocular depth estimation in situations where the available memory on the camera is limited. We posit that superpixel segmentation will play a crucial role as more computer vision inference models are deployed in edge devices. SuperCam would allow computer vision engineers to design more efficient systems for these applications.

Problem

• resource-constrained
• redundant data
• computer vision
• edge devices
• memory-limited

Innovation

• superpixel segmentation
• edge computing
• memory-constrained vision
• adaptive camera design
• efficient computer vision