基于超像素化相机的计算机视觉
Computer Vision with a Superpixelation Camera
把超像素分割前移到相机端做在线自适应压缩，为边缘设备上的分割、检测、单目深度估计提供低内存输入；做端侧视觉系统的人可以借鉴这种「传感器-算法协同」的降数据量思路，替代在图像域事后压缩。
原文依据 SuperCam performs better than current state-of-the-art superpixel algorithms under memory-constrained situations
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论传统相机会产生大量数据，在资源受限的应用中难以处理。通常，相机产生的数据流规模与图像像素数处于同一量级。然而，对于许多下游计算机视觉算法而言，所采集数据中的大部分是冗余的。我们提出一种新颖的相机设计，称为 SuperCam，它通过即时执行超像素分割来自适应地处理采集到的数据。我们表明，在内存受限的情形下，SuperCam 的表现优于当前最优的超像素算法。我们还比较了将压缩后的数据用于下游计算机视觉任务时 SuperCam 的表现。结果表明，在相机可用内存有限的情况下，所提出的设计在图像分割、目标检测和单目深度估计上都能提供更优的输出。我们断言，随着越来越多的计算机视觉推理模型部署到边缘设备上，超像素分割将发挥关键作用。SuperCam 将使计算机视觉工程师能够为这些应用设计更高效的系统。
英文原文摘要 1144 字符
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.