scholarly journals A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches

2021 ◽  
Author(s):  
Jiawei Zhang ◽  
Chen Li ◽  
Md Mamunur Rahaman ◽  
Yudong Yao ◽  
Pingli Ma ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Deep Learning ◽  
Learning Approaches ◽  
Comprehensive Review ◽  
Analysis Methods ◽  
Classical Image

scholarly journals Analysis of the Nosema Cells Identification for Microscopic Images

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3068
Author(s):  
Soumaya Dghim ◽  
Carlos M. Travieso-González ◽  
Radim Burget
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
The Other ◽  
Support Vector ◽  
Learning Approaches ◽  
Microscopic Images ◽  
Trained Neural Network ◽  
Nosema Disease

The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.

scholarly journals A Review on Deep Image Contrast Enhancement

2020 ◽  
Vol 6 (1) ◽  
pp. 4
Author(s):  
Puspad Kumar Sharma ◽  
Nitesh Gupta ◽  
Anurag Shrivastava
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Image Enhancement ◽  
Research Work ◽  
Atmospheric Condition ◽  
Input Image ◽  
Learning Approaches ◽  
Sensing Applications ◽  
Image Contrast Enhancement

In image processing applications, one of the main preprocessing phases is image enhancement that is used to produce high quality image or enhanced image than the original input image. These enhanced images can be used in many applications such as remote sensing applications, geo-satellite images, etc. The quality of an image is affected due to several conditions such as by poor illumination, atmospheric condition, wrong lens aperture setting of the camera, noise, etc [2]. So, such degraded/low exposure images are needed to be enhanced by increasing the brightness as well as its contrast and this can be possible by the method of image enhancement. In this research work different image enhancement techniques are discussed and reviewed with their results. The aim of this study is to determine the application of deep learning approaches that have been used for image enhancement. Deep learning is a machine learning approach which is currently revolutionizing a number of disciplines including image processing and computer vision. This paper will attempt to apply deep learning to image filtering, specifically low-light image enhancement. The review given in this paper is quite efficient for future researchers to overcome problems that helps in designing efficient algorithm which enhances quality of the image.

scholarly journals Democratized image analytics by visual programming through integration of deep models and small-scale machine learning

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Primož Godec ◽  
Matjaž Pančur ◽  
Nejc Ilenič ◽  
Andrej Čopar ◽  
Martin Stražar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Data Analysis ◽  
Protein Localization ◽  
Visual Programming ◽  
Developmental Competence ◽  
Image Clustering ◽  
Small Scale ◽  
Learning Approaches

Abstract Analysis of biomedical images requires computational expertize that are uncommon among biomedical scientists. Deep learning approaches for image analysis provide an opportunity to develop user-friendly tools for exploratory data analysis. Here, we use the visual programming toolbox Orange (http://orange.biolab.si) to simplify image analysis by integrating deep-learning embedding, machine learning procedures, and data visualization. Orange supports the construction of data analysis workflows by assembling components for data preprocessing, visualization, and modeling. We equipped Orange with components that use pre-trained deep convolutional networks to profile images with vectors of features. These vectors are used in image clustering and classification in a framework that enables mining of image sets for both novel and experienced users. We demonstrate the utility of the tool in image analysis of progenitor cells in mouse bone healing, identification of developmental competence in mouse oocytes, subcellular protein localization in yeast, and developmental morphology of social amoebae.

scholarly journals A comprehensive review on accuracy in ultrasonic flow measurement using reconfigurable systems and deep learning approaches

AIP Advances ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 105221
Author(s):  
Senthil Kumar J ◽  
Kamaraj A ◽  
Kalyana Sundaram C ◽  
Shobana G ◽  
Kirubakaran G
Keyword(s):  
Deep Learning ◽  
Flow Measurement ◽  
Reconfigurable Systems ◽  
Learning Approaches ◽  
Comprehensive Review

scholarly journals Fashion Product Classification through Deep Learning and Computer Vision

2019 ◽  
Vol 9 (7) ◽  
pp. 1385 ◽  
Author(s):  
Luca Donati ◽  
Eleonora Iotti ◽  
Giulio Mordonini ◽  
Andrea Prati
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Feature Extraction ◽  
Deep Learning ◽  
Template Matching ◽  
Learning Approaches ◽  
Visual Classification ◽  
Product Classification ◽  
Processing Techniques

Visual classification of commercial products is a branch of the wider fields of object detection and feature extraction in computer vision, and, in particular, it is an important step in the creative workflow in fashion industries. Automatically classifying garment features makes both designers and data experts aware of their overall production, which is fundamental in order to organize marketing campaigns, avoid duplicates, categorize apparel products for e-commerce purposes, and so on. There are many different techniques for visual classification, ranging from standard image processing to machine learning approaches: this work, made by using and testing the aforementioned approaches in collaboration with Adidas AG™, describes a real-world study aimed at automatically recognizing and classifying logos, stripes, colors, and other features of clothing, solely from final rendering images of their products. Specifically, both deep learning and image processing techniques, such as template matching, were used. The result is a novel system for image recognition and feature extraction that has a high classification accuracy and which is reliable and robust enough to be used by a company like Adidas. This paper shows the main problems and proposed solutions in the development of this system, and the experimental results on the Adidas AG™ dataset.

scholarly journals A NOVEL METHOD FOR INSPECTION DEFECTS IN COMMERCIAL EGGS USING COMPUTER VISION

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 809-816
Author(s):  
Y. M. Valencia ◽  
J. J. Majin ◽  
V. B. Taveira ◽  
J. D. Salazar ◽  
M. E. Stivanello ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Visual Inspection ◽  
Working Hours ◽  
Inspection System ◽  
Learning Approaches ◽  
Visual Fatigue ◽  
Long Working Hours ◽  
Novel Method ◽  
Classical Image

Abstract. The objective of this work is to compare the use of classical image processing approaches with deep learning approaches in a visual inspection system for defects in commercial eggs. Currently, many industries perform the detection of defects in eggs manually, this implies a large number of workers with long working hours who are exposed to visual fatigue and physical and mental discomfort. As a solution, this work proposes to develop an automatic inspection technique for defects in eggs using computer vision, capable of being operable in the industry. Different image processing approaches were evaluated in order to determine the best solution in terms of performance and processing time.

scholarly journals RANSAC FOR OUTLIER DETECTION

2012 ◽  
Vol 31 (3) ◽  
pp. 83-87 ◽  
Author(s):  
Birutė Ruzgienė ◽  
Wolfgang Förstner
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Matching ◽  
Data Sets ◽  
Digital Photogrammetry ◽  
Huge Data ◽  
Straight Line ◽  
Sample Data ◽  
Random Sample Consensus ◽  
Classical Image

Up-to-date digital photogrammetry involves operations on huge data sets, and with classical image processing procedures it might be time consuming to find out the best solution. One of the key tasks is to detect outliers in given data, eg for curve fitting or image matching. The problem is hard as the number of outliers is usually large, possibly larger than 50%, thus powerful estimation techniques are needed. We demonstrate one of these techniques, namely Random Sample Consensus (RANSAC), for fitting a model to sample data, especially for fitting a straight line through a set of given points. Experiments with up to 80% outliers prove the efficiency of RANSAC. The results are representative for image analysis in digital photogrammetry

Introducing the Deep Learning for Digital Age

2020 ◽  
pp. 317-333
Author(s):  
Shaila S. G. ◽  
Sunanda Rajkumari ◽  
Vadivel Ayyasamy
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Language Processing ◽  
Vital Role ◽  
Learning Ability ◽  
Learning Approaches ◽  
Data Set ◽  
Use Of Data ◽  
Repetitive Learning

Deep learning is playing vital role with greater success in various applications, such as digital image processing, human-computer interaction, computer vision and natural language processing, robotics, biological applications, etc. Unlike traditional machine learning approaches, deep learning has effective ability of learning and makes better use of data set for feature extraction. Because of its repetitive learning ability, deep learning has become more popular in the present-day research works.

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