Image Processing Capabilities Research Articles

Deep Learning on chromatographic data for Segmentation and Sensitive Analysis

Lateral flow immunoassay (LFIA) is one of the most common methods in point-of-care testing, which is widely applied in some conditions for various applications. Image segmentation is an increasingly popular experimental paradigm to efficiently test the target area in LFIA. However, due to process pollution, and problems related to the experimental operation and irregular structure of the background of the reaction, currently available tools cannot be used to extract correct signals from these images, which affects the accuracy of detection. Machine learning has significantly improved modern biochemical analysis by pushing the limits of traditional techniques for the recognition and processing of images. In this paper, the U-Net, a variant of the convolutional neural network (CNN) is used for the quantitative analysis of LFIA images for the accurate quantification of single- and multi-target images. By graying, binarizing, and labeling different concentrations of test strips, the target area of LFIA images containing the T-/C-lines is extracted and obtained. Then it provides updated trends and directions for the development of LFIA technology. Several indicators are introduced to evaluate the proposed U-Net structure to verify the feasibility and effectiveness of its image processing capability. When the trained U-Net model was used to process images, the peak signal-to-noise ratio was 22.4 dB, significantly higher than prevalent methods in the area that have reported only a 4 dB improvement in the quality of the graphics. The intersection-over-union between samples also increased to above 93%. Our results show that the proposed method has significant potential for detecting a segmented target in an LFIA area, especially weak positive signals and multichannel detection. With other modifications, this deep learning method can be applied as a powerful tool to study rapid detection devices, systems, and biological images.

Research Summary on Light Field Display Technology Based on Projection

In recent years, three-dimensional display technology has developed rapidly, among which light field display technology is a type of technology with broad application prospects that can realize naked-eye stereo display. With the enhancement of digital image processing capabilities and the development of projection technology, projection-based light field display technology has also made great progress. This article analyzes the principle and development of light field display technology based on high-speed projection and projection arrays, briefly introduces the relevant experimental results of the research team at home and abroad, and finally discusses the application prospects of different technical directions.

Ananas comosus crown image thresholding and crop counting using a colour space transformation scheme

The implementation of unmanned aerial vehicle (UAV) technology having image processing capabilities provides an alternative way to observe pineapple crowns captured from aerial images. In the majority of pineapple plantations, an agricultural officer will physically count the crop yield prior to harvesting the Ananas Comosus, also known as pineapple. This process is particularly evident in large plantation areas to accurately identify pineapple numbers. To alleviate this issue, given it is both time-consuming and arduous, automating the process using image processing is suggested. In this study, the possibilities and comparisons between two techniques associated with an image thresholding scheme known as HSV and L*A*B* colour space schemes were implemented. This was followed by determining the threshold by applying an automatic counting (AC) method to count the crop yield. The results of the study found that by applying colour thresholding for segmentation, it improved the low contrast image due to different heights and illumination levels on the acquired colour image. The images that were acquired using a UAV revealed that the best distance for capturing the images was at the height of three (3) metres above ground level. The results also confirm that the HSV colour space provides a more efficient approach with an average error increment of 47.6% when compared to the L*A*B*colour space.

RMSRS: Rover Multi-purpose Surveillance Robotic System

The development of the internet of things (IoT) and the internet of robotics (IoR) are becoming more and more involved with our daily lives. It serves a variety of tasks some of them are essential to us. The main objective of SRR is to develop a surveillance system for detecting suspicious and targeted places for users without any loss of human life. This paper shows the design and implementation of a robotic surveillance platform for real-time monitoring with the help of image processing, which can explorer places of difficult access or high risk. The robotic live streaming is via two cameras, the first one is fixed straight on the road and the second one is dynamic with tilt-pan ability. All cameras have image processing capabilities to analyze, detect and track objects plus few other graphical functions. The components mentioned above built on top of the four-wheel vehicle system with high torque to provide mobility on rough terrain. This work is based on Raspberry Pi and can be controlled over Wi-Fi locally or publicly over the internet. The results show making a high potential, relatively low price robot with lots of features and functions that can perform multiple tasks simultaneously, all are crucial to surveillance and monitoring problems, controlled by a user from far distances and for a long time.

Subject independent emotion recognition system for people with facial deformity: an EEG based approach

Emotion recognition from Electroencephalography (EEG) is a better choice for the people with facial deformity like where facial data is not accurate or not available for example burned or paralyzed faces. This research exploits the image processing capability of convolutional neural network (CNN) and proposes a CNN model to classify different emotions from the scalogram images of EEG data. Scalogram images from EEG obtained by applying continuous wavelet transform used for the study. The proposed model is subject independent where the objective is to extract emotion specific features from EEG data irrespective of the source of the data. The proposed emotion recognition model is evaluated on two benchmark public databases namely DEAP and SEED. In order to show the model as a purely subject independent one, the cross data base criteria is also used for evaluation. The various performance evaluation experiments show that the proposed model is comparable in terms of emotion classification accuracy.

Characterization of fast-growing foams in bottling processes by endoscopic imaging and convolutional neural networks

Regardless of whether the occurrence of foams in industrial processes is desirable or not, the knowledge about the characteristics of their formation and morphology is crucial. This study addresses the measuring of characteristics in foam and the trailing bubbly liquid that result from air bubble entrainment by a plunging jet in the environment of industry-like bottling processes of non-carbonated beverages. Typically encountered during the bottling of fruit juices, this process configuration is characterized by very fast filling speeds with high dynamic system parameter changes. Especially in multiphase systems with a sensitive disperse phase like gas bubbles, the task of its measurement turns out to be difficult. The aim of the study is to develop and employ an image processing capability in real geometries under realistic industrial conditions, e.g. as opposed to a narrow measurement chamber. Therefore, a typically sized test bottle was only slightly modified to adapt an endoscopic measurement technique and to acquire image data in a minimally invasive way. Two convolutional neural networks (CNNs) were employed to analyze irregular non-overlapping bubbles and circular overlapping bubbles. CNNs provide a robust object recognition for varying image qualities and therefore can cover a broad range of process conditions at the cost of a time-consuming training process. The obtained single bubble and population measurements allow approximation, correlation and interpretation of the bubble size and shape distributions within the foam and in the bubbly liquid. The classification of the measured foam morphologies and the influence of operating conditions are presented. The applicability to the described test case as an industrial multiphase process reveals high potential for a huge field of operations for particle size and shape measurement by the introduced method.

Predicting Thermal Runaway in Li-Ion Battery Employing Machine Learning Framework

Failures of lithium-ion batteries (LIBs) arise in both energetic and non-energetic ways. Both modes may occur due to numerous reasons such as cell manufacturing flaws, poor cell design (electrical or mechanical), external abuse of cells (thermal, mechanical, or electrical), defective protection electronics, chargers, etc. In this work, we present a machine learning (ML) study of the most severe energetic failure of the LIBs, which occurs due to the thermal runaway (TRA). In general, the TRA consists of a rapid self-heating of a cell sourced from exothermic (electro-) chemical reactions. During the TRA, the excessive heat has a detrimental influence on the electric characteristics and the state-of-charge (SOC) of the LIBs, which could lead to the accidental explosion of the battery. During the TRA, it is impossible to directly monitor the occurring events in practical operating conditions. However, the change of electric parameters during the TRA like-events could potentially indicate the existence of a failure, thus allowing to foresee the LIBs malfunction.We propose two deep learning techniques to address the problem of predicting the TRA like-events based upon the temporal variation of electric parameters, the SOC and the thermal images in overheating conditions. First, a recurrent neural network (RNN) built upon the long-short-term memory (LSTM) scheme is used to estimate the time dependency of the SOC on the voltage, current and temperature of a cell. In the second method the objective is to predict the presence of the TRA in the LIBs subjected to internal/external heating directly from thermal images collected as sequences of video frames during the battery operation. For this purpose, a Convolutional LSTM (Conv-LSTM) network is constructed and trained to distinguish the recorded thermal images among two classes, specifically the presence or absence of the TRA like-events. Conv-LSTM is a recurrent neural network designed to extract information from sequential images in time series analysis, which combines the image processing capabilities of convolutional neural networks (CNN) and the temporal tracking potential of LSTM. The main advantage of the proposed techniques is that they do not require the physical modeling of the internal battery chemistry, which is cumbersome in the TRA scenario, but they rely only on the experimental/ modeling data and the collected thermal images,

Using Machine Learning, Image Processing & Neural Networks to Sense Bullying in K-12 Schools

We all have heard about bullying and we know that it is an immense challenge that schools have to tackle. Many lives have been ruined due to bullying and the fear it implants into students' mind has caused many of them to go into depression which can lead to suicide. Traditional methods [1] need to be accompanied with modern technology to make the method more effective and efficient. If real time alerts are to school staff, they can identify the perpetuator and extricate the victim swiftly. It this proposed method an AI based solution is implemented to monitor students using standard school surveillance technologies and CCTV to maintain a decorum and safe environment in the school premise. Also the proposed method utilizes other unstructured sources such as attendance records, social media activity and general nature of the students to deliver quick response. Artificial Intelligence (AI) techniques like Convolutional Neural Networks (CNN), which includes image processing capabilities, logistic regression methods, LSTM (Long short-term memory), and pre-trained model Darknet-19 is used for classification. Further, the model also included sentiment analysis to identify commonly used abuse terms and noisy labels to improve overall model accuracy. The model has been trained and validated with the realistic data from all the sources mentioned and has achieved the classification accuracy of 87% for detecting any sign of bullying.

Designing pulse-coupled neural networks with spike-synchronization-dependent plasticity rule: image segmentation and memristor circuit application

Pulse-coupled neural network (PCNN) is a powerful unsupervised learning model with many parameters to be determined empirically. In particular, the weight matrix is invariable in the iterative process, which is inconsistent with the actual biological system. Based on the existing research foundation of biology and neural network, we propose a spike-synchronization-dependent plasticity (SSDP) rule. In this paper, the mathematical model and algorithm of SSDP are presented. Furthermore, a novel memristor-based circuit model of SSDP is designed. Finally, experimental results demonstrate that SSDP has greatly improved the image processing capabilities of PCNN.

MATLAB image processing tool-based GUI for high-throughput image segmentation and analysis to study structure and morphology of skin H&E stained sections

The complex structure of skin tissue can make the analysis of high-throughput data manually inconvenient and leads to inaccurate analysis and time consumption. Therefore, automated system that can segment and detect features which might provide critical information for interesting phenotype is required. User friendly graphical user interface GUI in MATLAB can provide facilities to create a tool to enhance, segment and analyse images without having expert skills in image processing, this can be used in the study of skin morphology phenotyping to find interesting morphological and metabolic phenotypes. Using image processing capability facilitates to develop a tool to analyse a range of different images in term of intensity and quality because of the variation in histology performed in different laboratory. Consequently, develop of automated high-throughput bioimaging tool is considered to be a very important topic in disease diagnosis and drug development. Significant assessment of the morphological features in H&E skin section through the use of GUI MATLAB tool by quantifying all of epidermal and dermal thickness and number and size of adipocyte in subcutaneous. Using our developed tool, we were able to detect interesting epidermis, dermis and adipocyte phenotypes in mice skin sections. The Morphological Bio-imaging Tool provides facilities in the high-throughput analysis of H&E skin section to understand genetic basis of diseases.

Object recognition on patrol ship using image processing and convolutional neural network (CNN)

Autonomous patrol vessels are state border patrol vessels equipped with cameras and image processing capabilities to detect objects around them. This prototype of ship can recognize a detected object; it used an image classification method called Convolutional Neural Network (CNN). So, it will minimize the occurrence of accidents on patrol boats. Input image in the form of RGB will experience feature extraction using a convolution layer. In the classification layer there is an artificial neural network with backpropagation to classify objects against a predetermined dataset. The detection value of the obtained vessel is operated by a predetermined actuator. In the final classification results the object recognition in the form of ships have a quite high accuracy. The average accuracy value is 96.59 percent with a sufficient light condition and RGB image input is taken in real-time.

Holography applications toward medical field: An overview.

Purpose:3D Holography is a commercially available, disruptive innovation, which can be customised as per the requirements and is supporting Industry 4.0. The purpose of this paper is to study the potential applications of 3D holography in the medical field. This paper explores the concept of holography and its significant benefits in the medical field.Methods:The paper is derived through the study of various research papers on Holography and its applications in the medical field. The study tries to identify the direction of research &development and see how this innovative technology can be used effectively for better treatment of patients.Results:Holography uses digital imaging inputs and provides an extensive visualisation of the data for training doctors, surgeons and students. Holography converts information about the body into a digital format and has the potential to inform, promote and entertain the medical students and doctors. However, it needs a large amount of space for data storage and extensive software support for analysis and skills for customising. This technology seems good to solve a variety of medical issues by storing and using patient data in developing 3D holograms, which are useful to assist successful treatment and surgery. It seems useful in providing flexible solutions in the area of medical research. Finally, the paper identifies 13 significant applications of this technology in the medical field and discusses them appropriately.Conclusion:The paper explores holographic applications in medical research due to its extensive capability of image processing. Holographic images are non-contact 3D images having a large field of depth. A physician can now zoom the holographic image for a better view of the medical part. This innovative technology can create advancements in the diagnosis and treatment process, which can improve medical practice. It helps in quick detection of problems in various organs like brain, heart, liver, kidney etc. By using this technology, medical practitioners can see colourful organs at multiple angles with better accuracy. It opens up an innovative way of planning, testing of procedures and diagnosis. With technological developments, compact hardware and software are now available to help medical research and related applications.

IEEE Access Special Section Editorial: Biologically Inspired Image Processing Challenges and Future Directions

Human kind is exposed to large amounts of data. According to statistics, more than 80% of information received by humans comes from the visual system. Therefore, image information processing is not only an important research topic but also a challenging task. The unique information processing mechanism of the human visual system provides it with fast, accurate, and efficient image processing capabilities. At present, many advanced image analysis and processing techniques have been widely used in image communication, geographic information systems, medical image analysis, and virtual reality. However, there is still a large gap between these technologies and the human visual system. Therefore, building an image system research mechanism based on the biological vision system is an attractive but difficult target. Although it is a challenge, it can also be considered as an opportunity which utilizes biologically inspired ideas. Meanwhile, through the integration of neural biology, biological perception mechanisms, and computer science and mathematical science, related research can bridge biological vision and computer vision. Finally, the biologically inspired image analysis and processing system is expected to be built on the basis of further consideration of the learning mechanism of the human brain.

SISTEM CERDAS MENDETEKSI KUALITAS GULA MERAH MENGGUNAKAN METODE NEAREST MEAN CLASSIFIER (NMC)

The development and utilization of digital images has developed rapidly. At present, digital image processing capabilities and techniques make it possible to be used more effectively and efficiently in identifying quality classes of brown sugar. One of them is the concept of Smart Systems with the use of Matlap-based applications so that public recognition of the importance of selecting good quality brown sugar can be a little more efficient. Digital image processing capabilities are supported by the concept of pattern recognition and classification, it is expected that the quality classification of brown sugar based on RGB color variables (Red, Green, Blue) and texture variables (energy, contrast, correlation and homogeneity) with the help of computers can be realized. To get a solution of the problem of classification and determine the accuracy of the classification of the quality of brown sugar into a certain class, then we need a method that is able to classify the quality of brown sugar brown sugar into class A (very good), class B (good) and class C (not good ). The method is expected to also be able to handle the problem of the accuracy of the classification of brown sugar into certain quality classes according to the actual state of brown sugar.

WebCAT: Piloting the Development of a Web Camera Coastal Observing Network for Diverse Applications

Web cameras are transforming coastal environmental monitoring. Improvements in camera technology and image processing capabilities, paired with decreases in cost, enable widespread use of camera systems by researchers, managers and first responders for a growing range of environmental monitoring applications. Applications are related to transportation and commerce, preparedness, risk reduction and response, and stewardship of coastal resources. While web cameras are seemingly ubiquitous, operators often follow unique installation procedures and collect, store, and process imagery data in various ways. These inconsistencies significantly limit the ability for imagery data to be shared and utilized across research and operational disciplines. Similar to the early days of other remote sensing networks like High Frequency Radar, the benefits and downstream application of coastal imagery data can be greatly enhanced through centralized data access and standardization of data collection, analysis and dissemination. The NOAA National Ocean Service Web Camera Applications Testbed (WebCAT) was launched in 2017 in partnership with SECOORA, as a public-private partnership to address this coastal ocean observing standardization need. WebCAT is a pilot project relying on the private sector expertise of Surfline, Inc. to install and operate several web cameras capable of meeting the short-term needs of diverse users including NOAA, USGS, state health agencies, academia and others. The project aims to determine operational imagery collection, storage, processing, access, and archival requirements that will foster collaboration across research and operational user communities. Seven web cameras have been installed at six locations along the southeast U.S. coast (from Florida to North Carolina) for purposes including: counting animals on the beach and migrating right whales, identifying rip currents, validating wave runup models, and understanding human use of natural resources. Here we present a review of the state of coastal imagery data and an overview of the WebCAT project. Goals of an upcoming community workshop will also be presented along with our vision for how WebCAT can motivate a future sustained operational web camera network.

Cell Cytometry: Review and Perspective on Biotechnological Advances.

Cell identification and enumeration are essential procedures within clinical and research laboratories. For over 150 years, quantitative investigation of body fluids such as counts of various blood cells has been an important tool for diagnostic analysis. With the current evolution of point-of-care diagnostics and precision medicine, cheap and precise cell counting technologies are in demand. This article reviews the timeline and recent notable advancements in cell counting that have occurred as a result of improvements in sensing including optical and electrical technology, enhancements in image processing capabilities, and contributions of micro and nanotechnologies. Cell enumeration methods have evolved from the use of manual counting using a hemocytometer to automated cell counters capable of providing reliable counts with high precision and throughput. These developments have been enabled by the use of precision engineering, micro and nanotechnology approaches, automation and multivariate data analysis. Commercially available automated cell counters can be broadly classified into three categories based on the principle of detection namely, electrical impedance, optical analysis and image analysis. These technologies have many common scientific uses, such as hematological analysis, urine analysis and bacterial enumeration. In addition to commercially available technologies, future technological trends using lab-on-a-chip devices have been discussed in detail. Lab-on-a-chip platforms utilize the existing three detection technologies with innovative design changes utilizing advanced nano/microfabrication to produce customized devices suited to specific applications.

A survey on deep-learning-based plant phenotype research in agriculture

Plant phenotype refers to measurable traits of plants, which acts as an observable proxy between gene expression and environmental impact, and is also an important determinant for the yield, quality and stress resistance characteristics of crops. Most of the plant phenotypes can be acquired by digital imaging techniques and processed by image processing algorithms. In recent years, the rapid development of genomics advances the study of plant phenotyping in many aspects, especially in terms of high-precision and high-throughput. Traditional plant phenotype research cannot meet these requirements and revolutions are in urgent need. As a breakthrough in computer science, the emergence of deep learning approaches significantly expands the capability of traditional image processing. For instance, state-of-the-art results in identification and segmentation tasks have been achieved by deep-learning-based methods and the records are continually improved by their variants. It is an interesting topic to study how to incorporate deep-learning techniques into plant phenotype research, and various impactful methods have been proposed in the past few years. The objective of this survey is to provide an overview of the current progress of deep-learning-based plant phenotype research in agriculture. In this survey, we elaborate the work from four different aspects, (i) plant morphology and physiological information extraction, (ii) plant identification and weed detection, (iii) pest detection, and (iv) yield prediction. We also analyze the pros and cons of these methods compared to traditional approaches. The potential future trends of plant phenotyping research are discussed at the end of this survey.

Deep Convolutional Neural Network‐Based Structural Damage Localization and Quantification Using Transmissibility Data

Damage diagnosis has become a valuable tool for asset management, enhanced by advances in sensor technologies that allows for system monitoring and providing massive amount of data for use in health state diagnosis. However, when dealing with massive data, manual feature extraction is not always a suitable approach as it is labor intensive requiring the intervention of domain experts with knowledge about the relevant variables that govern the system and their impact on its degradation process. To address these challenges, convolutional neural networks (CNNs) have been recently proposed to automatically extract features that best represent a system’s degradation behavior and are a promising and powerful technique for supervised learning with recent studies having shown their advantages for feature identification, extraction, and damage quantification in machine health assessment. Here, we propose a novel deep CNN‐based approach for structural damage location and quantification, which operates on images generated from the structure’s transmissibility functions to exploit the CNNs’ image processing capabilities and to automatically extract and select relevant features to the structure’s degradation process. These feature maps are fed into a multilayer perceptron to achieve damage localization and quantification. The approach is validated and exemplified by means of two case studies involving a mass‐spring system and a structural beam where training data are generated from finite element models that have been calibrated on experimental data. For each case study, the models are also validated using experimental data, where results indicate that the proposed approach delivers satisfactory performance and thus being an appropriate tool for damage diagnosis.

Power optimized Hybrid Sorting based Median Filtering

Nowadays, embedded video and image processing capabilities are much more demands with image quality. Digital image noise mostly occurs in a communication channel. The variety of random variation of white and black dots on the surface of an image, seriously degrading the image quality. Median filters are having the excellent image denoising processing capabilities. These filters are particularly reducing the salt-and-pepper noise and increase the throughput with less complexity. In contrast, power efficient remains an untapped area for improvement in the sorting-based network. For this, the filter is designed with hybrid sorting network with intelligent clock gating technique is also presented. The implementation of median filter on ARTIX-7 (90 nm) FPGA. The practical results shows the effectiveness of combined parallel and pipelined with clock gating architecture reduces the dynamic power and complexities in terms of FPGA resource usage and frequency.

A Systematic Review of Intelligence Video Surveillance: Trends, Techniques, Frameworks, and Datasets

Video surveillance systems obtain a great interest as application-oriented studies that have been growing rapidly in the past decade. The most recent studies attempt to integrate computer vision, image processing, and artificial intelligence capabilities into video surveillance applications. Although there are so many achievements in the acquisition of datasets, methods, and frameworks published, there are not many papers that can provide a comprehensive picture of the current state of video surveillance system research. This paper provides a comprehensive and systematic review on the literature from various video surveillance system studies published from 2010 through 2019. Within a selected study extraction process, 220 journal-based publications were identified and analyzed to illustrate the research trends, datasets, methods, and frameworks used in the field of video surveillance, to provide an in-depth explanation about research trends that many topics raised by researchers as a focus in their researches, to provide references on public datasets that are often used by researchers as a comparison and a means of developing a test method, and to give accounts on the improvement and integration of network infrastructure design to meet the demand for multimedia data. In the end of this paper, several opportunities and challenges related to researches in the video surveillance system are mentioned.