Electronic counting methods and their applications

1975 ◽  
Vol 21 (12) ◽  
pp. 737
Author(s):  
P.J. Fox
Keyword(s):  
Counting Methods ◽  
Electronic Counting

A New System For Optomanual Semiautomatic Quantitative Image Analysis

1977 ◽  
Vol 35 ◽  
pp. 210-211
Author(s):  
H.P. Rohr
Keyword(s):  
Image Analysis ◽  
Volume Density ◽  
List Type ◽  
Distribution Analysis ◽  
Type Number ◽  
Point Counting ◽  
Human Eye ◽  
Quantitative Image ◽  
Point Density ◽  
Electronic Counting

Today, in image analysis the broadest possible rationalization and economization have become desirable. Basically, there are two approaches for image analysis: The image analysis through the so-called scanning methods which are usually performed without the human eye and the systems of optical semiautomatic analysis completely relying on the human eye.The new MOP AM 01 opto-manual system (fig.) represents one of the very promising approaches in this field. The instrument consists of an electronic counting and storing unit, which incorporates a microprocessor and a keyboard for choice of measuring parameters, well designed for easy use.Using the MOP AM 01 there are three possibilities of image analysis:the manual point counting,the opto-manual point counting andthe measurement of absolute areas and/or length (size distribution analysis included).To determine a point density for the calculation of the corresponding volume density the intercepts lying within the structure are scanned with the light pen.

Histomorphometry in Peripheral Nerve Regeneration: Comparison of Different Axon Counting Methods

2021 ◽  
Vol 268 ◽  
pp. 354-362
Author(s):  
Lynn M Orfahli ◽  
Majid Rezaei ◽  
Brian A Figueroa ◽  
Audrey V Crawford ◽  
Michael J Annunziata ◽  
...  
Keyword(s):  
Peripheral Nerve ◽  
Nerve Regeneration ◽  
Peripheral Nerve Regeneration ◽  
Counting Methods

scholarly journals Low-Rank and Sparse Based Deep-Fusion Convolutional Neural Network for Crowd Counting

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Siqi Tang ◽  
Zhisong Pan ◽  
Xingyu Zhou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Regression Method ◽  
Low Rank ◽  
Counting Method ◽  
Direct Integral ◽  
Crowd Counting ◽  
Counting Methods ◽  
Density Map

This paper proposes an accurate crowd counting method based on convolutional neural network and low-rank and sparse structure. To this end, we firstly propose an effective deep-fusion convolutional neural network to promote the density map regression accuracy. Furthermore, we figure out that most of the existing CNN based crowd counting methods obtain overall counting by direct integral of estimated density map, which limits the accuracy of counting. Instead of direct integral, we adopt a regression method based on low-rank and sparse penalty to promote accuracy of the projection from density map to global counting. Experiments demonstrate the importance of such regression process on promoting the crowd counting performance. The proposed low-rank and sparse based deep-fusion convolutional neural network (LFCNN) outperforms existing crowd counting methods and achieves the state-of-the-art performance.

An analysis of simple counting methods for ordering incomplete ordinal data

1977 ◽  
Vol 8 (3) ◽  
pp. 209-227 ◽  
Author(s):  
William V. Gehrlein ◽  
Peter C. Fishburn
Keyword(s):  
Ordinal Data ◽  
Counting Methods

Counting Methods

2019 ◽  
pp. 33-55
Author(s):  
Jim Albert ◽  
Jingchen Hu
Keyword(s):  
Counting Methods

scholarly journals Results Concerning the Evolution of Bacterial Population during the Malolactic Fermentation

1970 ◽  
Vol 66 (2) ◽  
Author(s):  
Ionel POPESCU-MITROI ◽  
Marin GHEORGHIŢĂ ◽  
Felicia STOICA
Keyword(s):  
Bacterial Population ◽  
Alcoholic Fermentation ◽  
Malolactic Fermentation ◽  
Bacterial Density ◽  
Red Wines ◽  
Decline Phase ◽  
Population Evolution ◽  
Counting Methods ◽  
Direct Counting ◽  
Lactic Bacteria

During this experiment, the evolution of inner lactic bacteria microflora was monitored, during a spontaneous and conducted malolactic fermentation developed in the fall of the year 2006 at red wines obtained in Minis – Maderat wine yard. Thereby was monitored the bacterial population evolution, immediately after finishing the alcoholic fermentation (before developing the malolactic fermentation), through standard cultural method and through direct counting methods (counting with Thoma board and counting through Breed method). Results show that wines, at the end of alcoholic fermentation present bacterial loads between 102 and 104 cells/ml, after which in the exponential growing phase of the lactic bacteria registered at 5 days after sowing the selected malolactic bacteria, the bacterial density of wines to grow to 106 – 107 cells/ml, and at the end of malolactic fermentation, which matches the decline phase of lactic bacteria, the bacterial density of wines to get back to 105 cells/ml.

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