Study on Automatic Recognition of Fabric Color and Matching to Standard Color Chip by Computer Vision and Image Analysis Technology

2016 ◽  
Vol 9 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Liqing Xie
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Automatic Recognition ◽  
Color Chip ◽  
Standard Color

scholarly journals Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

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 [...]

scholarly journals Oil Content Fraction in Tortilla Chips During Frying and their Prediction by Image Analysis Using Computer Vision

2013 ◽  
Vol 17 (2) ◽  
pp. 261-272
Author(s):  
Silvia B. Matiacevich ◽  
Olivia C. Henríquez ◽  
Domingo Mery ◽  
Franco Pedreschi
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Oil Content ◽  
Tortilla Chips

Automatic recognition and classification of microseismic waveforms based on computer vision

2022 ◽  
Vol 121 ◽  
pp. 104327
Author(s):  
Jiaming Li ◽  
Shibin Tang ◽  
Kunyao Li ◽  
Shichao Zhang ◽  
Liexian Tang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Automatic Recognition

Use of Image Analysis for Automatic Recognition of Nematodes

1988 ◽  
pp. 293-299 ◽  
Author(s):  
J. Fernández-Valdivia ◽  
P. Castillo ◽  
A. Gómez-Barcina
Keyword(s):  
Image Analysis ◽  
Automatic Recognition
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