scholarly journals A Systematic Survey of ML Datasets for Prime CV Research Areas—Media and Metadata

Data ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 12
Author(s):  
Helder F. Castro ◽  
Jaime S. Cardoso ◽  
Maria T. Andrade
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Qualitative Analysis ◽  
Continuous Growth ◽  
Scientific Databases ◽  
Extensive Survey ◽  
Research Areas ◽  
Current State ◽  
Global View ◽  
Cooperative Production

The ever-growing capabilities of computers have enabled pursuing Computer Vision through Machine Learning (i.e., MLCV). ML tools require large amounts of information to learn from (ML datasets). These are costly to produce but have received reduced attention regarding standardization. This prevents the cooperative production and exploitation of these resources, impedes countless synergies, and hinders ML research. No global view exists of the MLCV dataset tissue. Acquiring it is fundamental to enable standardization. We provide an extensive survey of the evolution and current state of MLCV datasets (1994 to 2019) for a set of specific CV areas as well as a quantitative and qualitative analysis of the results. Data were gathered from online scientific databases (e.g., Google Scholar, CiteSeerX). We reveal the heterogeneous plethora that comprises the MLCV dataset tissue; their continuous growth in volume and complexity; the specificities of the evolution of their media and metadata components regarding a range of aspects; and that MLCV progress requires the construction of a global standardized (structuring, manipulating, and sharing) MLCV “library”. Accordingly, we formulate a novel interpretation of this dataset collective as a global tissue of synthetic cognitive visual memories and define the immediately necessary steps to advance its standardization and integration.

Phytochemical and Pharmacological Review on Two Herbs of Baijiangcao: Patrinia scabiosaefolia Fisch. ex Trev. and Patrinia villosa (Thunb.) Juss

2020 ◽  
Vol 17 (5) ◽  
pp. 496-517
Author(s):  
Yangcheng Liu ◽  
Wei Liu ◽  
Jiaqi Wang ◽  
Yang Liu ◽  
Changlan Chen ◽  
...  
Keyword(s):  
Chemical Constituents ◽  
Volatile Components ◽  
Research Papers ◽  
Pharmacological Activities ◽  
Comprehensive Review ◽  
Scientific Databases ◽  
The Past ◽  
Current State ◽  
Patrinia Scabiosaefolia ◽  
Antiviral Activities

Patrinia scabiosaefolia Fisch. Trev. and Patrinia villosa (Thunb.) Juss, are two species of Patrinia recorded in the Chinese Pharmacopoeia with the same Chinese name “Baijiangcao” and similar therapeutic effect in traditional Chinese medicine. The present article is the first comprehensive review on the chemical composition and pharmacological activities of these herbs. In this review, data on chemical constituents and pharmacological profile of the two herbs are provided. This review discusses all the classes of the 223 compounds (phenylpropanoids, flavonoids, terpenes, saponins and volatile components, etc.) detected in the two herbs providing information on the current state of knowledge of the phytochemicals present in them. In the past three years, our research group has isolated and identified about more than 100 ingredients from the two herbs. Therefore, we published a systematic review of our research papers and studies on the two herbs were carried out using resources such as classic books about Chinese herbal medicine and scientific databases including Pubmed, Web of Science, SciFinder, CNKI. etc. The present review discusses the most thoroughly studied pharmacological activities (antioxidant, anti-inflammatory, immunomodulatory, antimicrobial, antitumor and antiviral activities) of the two herbs. This comprehensive review will be informative for scientists searching for new properties of these herbs and will be important and significant for the discovery of bioactive compounds from the two herbs and in complete utilization of Patrinia scabiosaefolia Fisch. ex Trev. and Patrinia villosa (Thunb.) Juss.

Comparing established visitor monitoring approaches with triggered trail camera images and machine learning based computer vision

2021 ◽  
Vol 35 ◽  
pp. 100387
Author(s):  
Jeroen Staab ◽  
Erica Udas ◽  
Marius Mayer ◽  
Hannes Taubenböck ◽  
Hubert Job
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Trail Camera

scholarly journals Technical Note: A Guide to Annotation of Neurosurgical Intraoperative Video for Machine Learning Analysis and Computer Vision

2021 ◽  
Author(s):  
Dhiraj J. Pangal ◽  
Guillaume Kugener ◽  
Shane Shahrestani ◽  
Frank Attenello ◽  
Gabriel Zada ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Technical Note ◽  
Learning Analysis

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

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3691
Author(s):  
Ciprian Orhei ◽  
Silviu Vert ◽  
Muguras Mocofan ◽  
Radu Vasiu
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Open Source ◽  
Visual Processing ◽  
Research Field ◽  
Learning Models ◽  
Research Activity ◽  
End To End ◽  
Machine Learning Models

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.

scholarly journals Evolutionary Machine Learning for Multi-Objective Class Solutions in Medical Deformable Image Registration

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 99 ◽  
Author(s):  
Kleopatra Pirpinia ◽  
Peter A. N. Bosman ◽  
Jan-Jakob Sonke ◽  
Marcel van Herk ◽  
Tanja Alderliesten
Keyword(s):  
Machine Learning ◽  
Image Registration ◽  
State Of The Art ◽  
Deformable Image Registration ◽  
Optimization Approach ◽  
High Quality ◽  
Trade Off ◽  
Multi Objective ◽  
Current State ◽  
Image Artefacts

Current state-of-the-art medical deformable image registration (DIR) methods optimize a weighted sum of key objectives of interest. Having a pre-determined weight combination that leads to high-quality results for any instance of a specific DIR problem (i.e., a class solution) would facilitate clinical application of DIR. However, such a combination can vary widely for each instance and is currently often manually determined. A multi-objective optimization approach for DIR removes the need for manual tuning, providing a set of high-quality trade-off solutions. Here, we investigate machine learning for a multi-objective class solution, i.e., not a single weight combination, but a set thereof, that, when used on any instance of a specific DIR problem, approximates such a set of trade-off solutions. To this end, we employed a multi-objective evolutionary algorithm to learn sets of weight combinations for three breast DIR problems of increasing difficulty: 10 prone-prone cases, 4 prone-supine cases with limited deformations and 6 prone-supine cases with larger deformations and image artefacts. Clinically-acceptable results were obtained for the first two problems. Therefore, for DIR problems with limited deformations, a multi-objective class solution can be machine learned and used to compute straightforwardly multiple high-quality DIR outcomes, potentially leading to more efficient use of DIR in clinical practice.

scholarly journals GEOBIA 2016: Advances in Object-Based Image Analysis—Linking with Computer Vision and Machine Learning

2019 ◽  
Vol 11 (10) ◽  
pp. 1181 ◽  
Author(s):  
Norman Kerle ◽  
Markus Gerke ◽  
Sébastien Lefèvre
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Image Analysis ◽  
The Netherlands ◽  
Object Based Image Analysis ◽  
Biennial Conference ◽  
Object Based ◽  
The University

The 6th biennial conference on object-based image analysis—GEOBIA 2016—took place in September 2016 at the University of Twente in Enschede, The Netherlands (see www [...]

Sign in / Sign up

Export Citation Format

Share Document