scholarly journals Tensor Methods in Computer Vision and Deep Learning

2021 ◽  
Vol 109 (5) ◽  
pp. 863-890
Author(s):  
Yannis Panagakis ◽  
Jean Kossaifi ◽  
Grigorios G. Chrysos ◽  
James Oldfield ◽  
Mihalis A. Nicolaou ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Tensor Methods

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 An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 343
Author(s):  
Kim Bjerge ◽  
Jakob Bonde Nielsen ◽  
Martin Videbæk Sepstrup ◽  
Flemming Helsing-Nielsen ◽  
Toke Thomas Høye
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Vision System ◽  
Low Cost ◽  
Light Trap ◽  
Automatic Monitoring ◽  
Light Sources ◽  
Monitoring Methods ◽  
Computer Vision System ◽  
Substantial Investment

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

Image based species identification of Globodera quarantine nematodes using computer vision and deep learning

2021 ◽  
pp. 106058
Author(s):  
Romain Thevenoux ◽  
Van Linh LE ◽  
Heloïse Villessèche ◽  
Alain Buisson ◽  
Marie Beurton-Aimar ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Species Identification

Deep learning computer vision for the separation of Cast- and Wrought-Aluminum scrap

2021 ◽  
Vol 172 ◽  
pp. 105685
Author(s):  
Dillam Díaz-Romero ◽  
Wouter Sterkens ◽  
Simon Van den Eynde ◽  
Toon Goedemé ◽  
Wim Dewulf ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Aluminum Scrap ◽  
Wrought Aluminum

Real-time Drowsiness and Distraction Detection using Computer Vision and Deep Learning

2020 ◽  
Author(s):  
Phakawat Pattarapongsin ◽  
Bipul Neupane ◽  
Jirayus Vorawan ◽  
Harit Sutthikulsombat ◽  
Teerayut Horanont
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Real Time

scholarly journals Implementation of conventional communication system in deep learning

2017 ◽  
Vol 7 (1.1) ◽  
pp. 696
Author(s):  
Satyanarayana P ◽  
Charishma Devi. V ◽  
Sowjanya P ◽  
Satish Babu ◽  
N Syam Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Communication Network ◽  
Deep Learning ◽  
Communication System ◽  
Communication Systems ◽  
Physical Layer ◽  
Learning Capacity ◽  
Constrained Learning ◽  
Complex Channel

Machine learning (ML) has been broadly connected to the upper layers of communication systems for different purposes, for example, arrangement of cognitive radio and communication network. Nevertheless, its application to the physical layer is hindered by complex channel conditions and constrained learning capacity of regular ML algorithms. Deep learning (DL) has been as of late connected for some fields, for example, computer vision and normal dialect preparing, given its expressive limit and advantageous enhancement ability. This paper describes about a novel use of DL for the physical layer. By deciphering a communication system as an auto encoder, we build up an essential better approach to consider communication system outline as a conclusion to-end reproduction undertaking that tries to together enhance transmitter and receiver in a solitary procedure. This DL based technique demonstrates promising execution change than traditional communication system.  

scholarly journals Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Rama K. Vasudevan ◽  
Maxim Ziatdinov ◽  
Lukas Vlcek ◽  
Sergei V. Kalinin
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Bayesian Methods ◽  
Deep Neural Networks ◽  
Applied Research ◽  
Modern Science ◽  
Generative Models ◽  
Knowledge Development ◽  
Physical Constraints

AbstractDeep neural networks (‘deep learning’) have emerged as a technology of choice to tackle problems in speech recognition, computer vision, finance, etc. However, adoption of deep learning in physical domains brings substantial challenges stemming from the correlative nature of deep learning methods compared to the causal, hypothesis driven nature of modern science. We argue that the broad adoption of Bayesian methods incorporating prior knowledge, development of solutions with incorporated physical constraints and parsimonious structural descriptors and generative models, and ultimately adoption of causal models, offers a path forward for fundamental and applied research.

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