scholarly journals Object detection networks and augmented reality for cellular detection in fluorescence microscopy

2020 ◽  
Vol 219 (10) ◽  
Author(s):  
Dominic Waithe ◽  
Jill M. Brown ◽  
Katharina Reglinski ◽  
Isabel Diez-Sevilla ◽  
David Roberts ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Fluorescence Microscopy ◽  
Object Detection ◽  
High Performance ◽  
Back Projection ◽  
Detection Algorithms ◽  
High Classification Accuracy ◽  
Fast Processing ◽  
Automate Detection ◽  
Very High

Object detection networks are high-performance algorithms famously applied to the task of identifying and localizing objects in photography images. We demonstrate their application for the classification and localization of cells in fluorescence microscopy by benchmarking four leading object detection algorithms across multiple challenging 2D microscopy datasets. Furthermore we develop and demonstrate an algorithm that can localize and image cells in 3D, in close to real time, at the microscope using widely available and inexpensive hardware. Furthermore, we exploit the fast processing of these networks and develop a simple and effective augmented reality (AR) system for fluorescence microscopy systems using a display screen and back-projection onto the eyepiece. We show that it is possible to achieve very high classification accuracy using datasets with as few as 26 images present. Using our approach, it is possible for relatively nonskilled users to automate detection of cell classes with a variety of appearances and enable new avenues for automation of fluorescence microscopy acquisition pipelines.

scholarly journals Object Detection Networks and Augmented Reality for Cellular Detection in Fluorescence Microscopy Acquisition and Analysis

2019 ◽  
Author(s):  
D Waithe ◽  
JM Brown ◽  
K Reglinski ◽  
I Diez-Sevilla ◽  
D Roberts ◽  
...  
Keyword(s):  
Biological Sciences ◽  
Augmented Reality ◽  
Fluorescence Microscopy ◽  
Object Detection ◽  
High Performance ◽  
Cell Types ◽  
Detection Algorithms ◽  
Fast Processing ◽  
Automate Detection ◽  
The Cost

AbstractIn this paper we demonstrate the application of object detection networks for the classification and localization of cells in fluorescence microscopy. We benchmark two leading object detection algorithms across multiple challenging 2-D microscopy datasets as well as develop and demonstrate an algorithm which can localize and image cells in 3-D, in real-time. Furthermore, we exploit the fast processing of these algorithms and develop a simple and effective Augmented Reality (AR) system for fluorescence microscopy systems. Object detection networks are well-known high performance networks famously applied to the task of identifying and localizing objects in photography images. Here we show their application and efficiency for localizing cells in fluorescence microscopy images. Object detection algorithms are typically trained on many thousands of images, which can be prohibitive within the biological sciences due to the cost of imaging and annotating large amounts of data. Through taking different cell types and assays as an example, we show that with some careful considerations it is possible to achieve very high performance with datasets with as few as 26 images present. Using our approach, it is possible for relatively non-skilled users to automate detection of cell classes with a variety of appearances and enable new avenues for automation of conventionally manual fluorescence microscopy acquisition pipelines.

Two-photon excitation fluorescence microscopy in living systems

1993 ◽  
Vol 51 ◽  
pp. 154-155 ◽  
Author(s):  
David W. Piston
Keyword(s):  
Confocal Microscopy ◽  
Fluorescence Microscopy ◽  
Singlet State ◽  
Excited Electronic State ◽  
Input Power ◽  
Two Photon ◽  
Simultaneous Absorption ◽  
Photon Excitation ◽  
Very High ◽  
Two Photon Excitation

Two-photon excitation fluorescence microscopy provides attractive advantages over confocal microscopy for three-dimensionally resolved fluorescence imaging. Two-photon excitation arises from the simultaneous absorption of two photons in a single quantitized event whose probability is proportional to the square of the instantaneous intensity. For example, two red photons can cause the transition to an excited electronic state normally reached by absorption in the ultraviolet. In our fluorescence experiments, the final excited state is the same singlet state that is populated during a conventional fluorescence experiment. Thus, the fluorophore exhibits the same emission properties (e.g. wavelength shifts, environmental sensitivity) used in typical biological microscopy studies. In practice, two-photon excitation is made possible by the very high local instantaneous intensity provided by a combination of diffraction-limited focusing of a single laser beam in the microscope and the temporal concentration of 100 femtosecond pulses generated by a mode-locked laser. Resultant peak excitation intensities are 106 times greater than the CW intensities used in confocal microscopy, but the pulse duty cycle of 10−5 maintains the average input power on the order of 10 mW, only slightly greater than the power normally used in confocal microscopy.

KEMPER C688

Alloy Digest ◽  
2017 ◽  
Vol 66 (12) ◽  
Keyword(s):  
Corrosion Resistance ◽  
Physical Properties ◽  
Tensile Properties ◽  
Copper Alloy ◽  
High Performance ◽  
High Conductivity ◽  
Bend Strength ◽  
Very High

Abstract Alloy C688 is a high-performance copper alloy with very high conductivity. This datasheet provides information on composition, physical properties, hardness, elasticity, tensile properties, and bend strength. It also includes information on corrosion resistance as well as forming and joining. Filing Code: Cu-867. Producer or source: Gebr. Kemper GmbH + Company KG Metallwerke.

KEMPER KHP7025

Alloy Digest ◽  
2017 ◽  
Vol 66 (10) ◽  
Keyword(s):  
Corrosion Resistance ◽  
Physical Properties ◽  
Tensile Properties ◽  
Copper Alloy ◽  
High Performance ◽  
High Conductivity ◽  
Bend Strength ◽  
Very High

Abstract Alloy KHP 7025 (UNS C70250) is a high-performance copper alloy with very high conductivity. Uses include connector springs, tabs, contact springs, switches, relays, and leadframes. This datasheet provides information on composition, physical properties, hardness, elasticity, tensile properties, and bend strength. It also includes information on corrosion resistance as well as forming, machining, and joining. Filing Code: Cu-865. Producer or source: Gebr. Kemper GmbH + Company KG Metallwerke.

Use of activated carbon to remove undesirable residual amylase from refinery streams

2017 ◽  
pp. 96-103 ◽  
Author(s):  
Gillian Eggleston ◽  
Isabel Lima ◽  
Emmanuel Sarir ◽  
Jack Thompson ◽  
John Zatlokovicz ◽  
...  
Keyword(s):  
Activated Carbon ◽  
High Temperature ◽  
High Performance ◽  
Activated Carbons ◽  
Amylase Activity ◽  
Sugar Industry ◽  
End User ◽  
Customer Complaints ◽  
Carry Over ◽  
Very High

In recent years, there has been increased world-wide concern over residual (carry-over) activity of mostly high temperature (HT) and very high temperature (VHT) stable amylases in white, refined sugars from refineries to various food and end-user industries. HT and VHT stable amylases were developed for much larger markets than the sugar industry with harsher processing conditions. There is an urgent need in the sugar industry to be able to remove or inactivate residual, active amylases either in factory or refinery streams or both. A survey of refineries that used amylase and had activated carbon systems for decolorizing, revealed they did not have any customer complaints for residual amylase. The use of high performance activated carbons to remove residual amylase activity was investigated using a Phadebas® method created for the sugar industry to measure residual amylase in syrups. Ability to remove residual amylase protein was dependent on the surface area of the powdered activated carbons as well as mixing (retention) time. The activated carbon also had the additional benefit of removing color and insoluble starch.

A dual U-Net algorithm for automating feature extraction from satellite imagery

2021 ◽  
pp. 154851292098354
Author(s):  
Samuel Humphries ◽  
Trevor Parker ◽  
Bryan Jonas ◽  
Bryan Adams ◽  
Nicholas J Clark
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Satellite Images ◽  
Detection Algorithm ◽  
Military Operations ◽  
Detection Algorithms ◽  
Us Military ◽  
Standard Product ◽  
Different Types ◽  
Road Intersections

Quick identification of building and roads is critical for execution of tactical US military operations in an urban environment. To this end, a gridded, referenced, satellite images of an objective, often referred to as a gridded reference graphic or GRG, has become a standard product developed during intelligence preparation of the environment. At present, operational units identify key infrastructure by hand through the work of individual intelligence officers. Recent advances in Convolutional Neural Networks, however, allows for this process to be streamlined through the use of object detection algorithms. In this paper, we describe an object detection algorithm designed to quickly identify and label both buildings and road intersections present in an image. Our work leverages both the U-Net architecture as well the SpaceNet data corpus to produce an algorithm that accurately identifies a large breadth of buildings and different types of roads. In addition to predicting buildings and roads, our model numerically labels each building by means of a contour finding algorithm. Most importantly, the dual U-Net model is capable of predicting buildings and roads on a diverse set of test images and using these predictions to produce clean GRGs.

scholarly journals Fractional Order Processing of Satellite Images

2021 ◽  
Vol 11 (11) ◽  
pp. 5288
Author(s):  
Manuel Henriques ◽  
Duarte Valério ◽  
Rui Melicio
Keyword(s):  
Edge Detection ◽  
Fractional Order ◽  
High Performance ◽  
Satellite Images ◽  
Automatic Detection ◽  
Automatic Processing ◽  
Order Processing ◽  
Detection Algorithms ◽  
Grey Scale ◽  
Fixed Set

Nowadays, satellite images are used in many applications, and their automatic processing is vital. Conventional integer grey-scale edge detection algorithms are often used for this. This study shows that the use of color-based, fractional order edge detection may enhance the results obtained using conventional techniques in satellite images. It also shows that it is possible to find a fixed set of parameters, allowing automatic detection while maintaining high performance.

scholarly journals Augmented Reality and Machine Learning Incorporation Using YOLOv3 and ARKit

2021 ◽  
Vol 11 (13) ◽  
pp. 6006
Author(s):  
Huy Le ◽  
Minh Nguyen ◽  
Wei Qi Yan ◽  
Hoa Nguyen
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
Object Detection ◽  
Feature Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Data Annotation ◽  
Machine Learning Model ◽  
Potential Benefits ◽  
Feature Detection And Tracking

Augmented reality is one of the fastest growing fields, receiving increased funding for the last few years as people realise the potential benefits of rendering virtual information in the real world. Most of today’s augmented reality marker-based applications use local feature detection and tracking techniques. The disadvantage of applying these techniques is that the markers must be modified to match the unique classified algorithms or they suffer from low detection accuracy. Machine learning is an ideal solution to overcome the current drawbacks of image processing in augmented reality applications. However, traditional data annotation requires extensive time and labour, as it is usually done manually. This study incorporates machine learning to detect and track augmented reality marker targets in an application using deep neural networks. We firstly implement the auto-generated dataset tool, which is used for the machine learning dataset preparation. The final iOS prototype application incorporates object detection, object tracking and augmented reality. The machine learning model is trained to recognise the differences between targets using one of YOLO’s most well-known object detection methods. The final product makes use of a valuable toolkit for developing augmented reality applications called ARKit.

scholarly journals A Fast and Lightweight Method with Feature Fusion and Multi-Context for Face Detection

2018 ◽  
Vol 10 (8) ◽  
pp. 80
Author(s):  
Lei Zhang ◽  
Xiaoli Zhi
Keyword(s):  
Face Detection ◽  
Graphics Processing Units ◽  
High Performance ◽  
Feature Fusion ◽  
Local Context ◽  
Data Set ◽  
Global Context ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Benchmark Datasets

Convolutional neural networks (CNN for short) have made great progress in face detection. They mostly take computation intensive networks as the backbone in order to obtain high precision, and they cannot get a good detection speed without the support of high-performance GPUs (Graphics Processing Units). This limits CNN-based face detection algorithms in real applications, especially in some speed dependent ones. To alleviate this problem, we propose a lightweight face detector in this paper, which takes a fast residual network as backbone. Our method can run fast even on cheap and ordinary GPUs. To guarantee its detection precision, multi-scale features and multi-context are fully exploited in efficient ways. Specifically, feature fusion is used to obtain semantic strongly multi-scale features firstly. Then multi-context including both local and global context is added to these multi-scale features without extra computational burden. The local context is added through a depthwise separable convolution based approach, and the global context by a simple global average pooling way. Experimental results show that our method can run at about 110 fps on VGA (Video Graphics Array)-resolution images, while still maintaining competitive precision on WIDER FACE and FDDB (Face Detection Data Set and Benchmark) datasets as compared with its state-of-the-art counterparts.

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