Evidence map›Paper›PMID 34073282›Full record

ArticleSensors (Basel, Switzerland)2021

End-To-End Computer Vision Framework: An Open-Source Platform for Research and Education.

Ciprian Orhei, Silviu Vert, Muguras Mocofan, Radu Vasiu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ciprian OrheiDepartment of Communications, Politehnica University of Timișoara, 2, Piata Victoriei, 300006 Timișoara, Romania.ORCID 0000-0002-0071-958X
Silviu VertDepartment of Communications, Politehnica University of Timișoara, 2, Piata Victoriei, 300006 Timișoara, Romania.ORCID 0000-0003-2394-4859
Muguras MocofanDepartment of Communications, Politehnica University of Timișoara, 2, Piata Victoriei, 300006 Timișoara, Romania.ORCID 0000-0002-0203-1519
Radu VasiuDepartment of Communications, Politehnica University of Timișoara, 2, Piata Victoriei, 300006 Timișoara, Romania.ORCID 0000-0003-1185-1997

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Computer Vision is a cross-research field with the main purpose of understanding the surrounding environment as closely as possible to human perception. The image processing systems is continuously growing and expanding into more complex systems, usually tailored to the certain needs or applications it may serve. To better serve this purpose, research on the architecture and design of such systems is also important. We present the End-to-End Computer Vision Framework, an open-source solution that aims to support researchers and teachers within the image processing vast field. The framework has incorporated Computer Vision features and Machine Learning models that researchers can use. In the continuous need to add new Computer Vision algorithms for a day-to-day research activity, our proposed framework has an advantage given by the configurable and scalar architecture. Even if the main focus of the framework is on the Computer Vision processing pipeline, the framework offers solutions to incorporate even more complex activities, such as training Machine Learning models. EECVF aims to become a useful tool for learning activities in the Computer Vision field, as it allows the learner and the teacher to handle only the topics at hand, and not the interconnection necessary for visual processing flow.

Indexed as

benchmarkingComputer VisionComputer Vision Frameworkdeep learningmachine learningneural networkspipeline architecturereproducible research

Identifiers

PMID34073282
PMCPMC8199125

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.