Openmv: A Python powered, extensible machine vision camera
Advances in semiconductor manufacturing processes and large scale integration keep pushing demanding applications further away from centralized processing, and closer to the edges of the network (i.e. Edge Computing). It has become possible to perform complex in-network image processing using low-power embedded smart cameras, enabling a multitude of new collaborative image processing applications. This paper introduces OpenMV, a new low-power smart camera that lends itself naturally to wireless sensor networks and machine vision applications. The uniqueness of this platform lies in running an embedded Python3 interpreter, allowing its peripherals and machine vision library to be scripted in Python. In addition, its hardware is extensible via modules that augment the platform with new capabilities, such as thermal imaging and networking modules.
Code (0)
등록된 구현이 없습니다.
Tasks
Edge-computingSimilar Papers 제목 키워드 기반
OpenML-Python: an extensible Python API for OpenML
OpenML is an online platform for open science collaboration in machine learning, used to share datasets and results of machine learning experiments. In this paper we introduce OpenML-Python, a client API for Python, open…
BIG-bench Machine LearningFlexCTC: GPU-powered CTC Beam Decoding With Advanced Contextual Abilities
While beam search improves speech recognition quality over greedy decoding, standard implementations are slow, often sequential, and CPU-bound. To fully leverage modern hardware capabilities, we present a novel open-sour…
Speech RecognitionViser: Imperative, Web-based 3D Visualization in Python
We present Viser, a 3D visualization library for computer vision and robotics. Viser aims to bring easy and extensible 3D visualization to Python: we provide a comprehensive set of 3D scene and 2D GUI primitives, which c…
Badgers: generating data quality deficits with Python
Generating context specific data quality deficits is necessary to experimentally assess data quality of data-driven (artificial intelligence (AI) or machine learning (ML)) applications. In this paper we present badgers, …
Time SeriesEnergy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs
In recent years, the proliferation of unmanned aerial vehicles (UAVs) has increased dramatically. UAVs can accomplish complex or dangerous tasks in a reliable and cost-effective way but are still limited by power consump…
Decision Making