Search results for: automatic target recognition

Authors: Shahpar Yilmaz, Yasir Nadeem, Syed A. Mehdi

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Searching for a record in a dataset is always a frequent task for any data structure-related application. Hence, a fast and efficient algorithm for the approach has its importance in yielding the quickest results and enhancing the overall productivity of the company. Interpolation search is one such technique used to search through a sorted set of elements. This paper proposes a new algorithm, an advancement over interpolation search for the application of search over a sorted array. Pattern Recognition Search or PR Search (PRS), like interpolation search, is a pattern-based divide and conquer algorithm whose objective is to reduce the sample size in order to quicken the process and it does so by treating the array as a perfect arithmetic progression series and thereby deducing the key element’s position. We look to highlight some of the key drawbacks of interpolation search, which are accounted for in the Pattern Recognition Search.

Keywords: array, complexity, index, sorting, space, time

Procedia PDF Downloads 243

Authors: Victor V. Kuzenov, Sergei V. Ryzhkov

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Ongoing experimental and theoretical studies on magneto-inertial confinement fusion (Angara, C-2, CJS-100, General Fusion, MagLIF, MAGPIE, MC-1, YG-1, Omega) and new constructing facilities (Baikal, C-2W, Z300 and Z800) require adequate modeling and description of the physical processes occurring in high-temperature dense plasma in a strong magnetic field. This paper presents a mathematical model, numerical method, and results of the computer analysis of the compression process and the energy transfer in the target plasma, used in magneto-inertial fusion (MIF). The computer simulation of the compression process of the magnetized target by the high-power laser pulse and the high-speed plasma jets is presented. The characteristic patterns of the two methods of the target compression are being analysed.

Keywords: magnetized target, magneto-inertial fusion, mathematical model, plasma and laser beams

Procedia PDF Downloads 296

Authors: Nermeen El Kashef, Yasser Fouad, Khaled Mahar

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No AI-complete system can model the human brain or behavior, without looking at the totality of the whole situation and incorporating a combination of senses. This paper proposes a Pattern Recognition model based on Simulation of Chemical Senses (SCS) for separation and classification of sign language. The model based on human taste controlling strategy. The main idea of the introduced model is motivated by the facts that the tongue cluster input substance into its basic tastes first, and then the brain recognizes its flavor. To implement this strategy, two level architecture is proposed (this is inspired from taste system). The separation-level of the architecture focuses on hand posture cluster, while the classification-level of the architecture to recognizes the sign language. The efficiency of proposed model is demonstrated experimentally by recognizing American Sign Language (ASL) data set. The recognition accuracy obtained for numbers of ASL is 92.9 percent.

Keywords: artificial intelligence, biocybernetics, gustatory system, sign language recognition, taste sense

Procedia PDF Downloads 294

Authors: Silvia Santano Guillén, Luigi Lo Iacono, Christian Meder

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One of the main aims of current social robotic research is to improve the robots’ abilities to interact with humans. In order to achieve an interaction similar to that among humans, robots should be able to communicate in an intuitive and natural way and appropriately interpret human affects during social interactions. Similarly to how humans are able to recognize emotions in other humans, machines are capable of extracting information from the various ways humans convey emotions—including facial expression, speech, gesture or text—and using this information for improved human computer interaction. This can be described as Affective Computing, an interdisciplinary field that expands into otherwise unrelated fields like psychology and cognitive science and involves the research and development of systems that can recognize and interpret human affects. To leverage these emotional capabilities by embedding them in humanoid robots is the foundation of the concept Affective Robots, which has the objective of making robots capable of sensing the user’s current mood and personality traits and adapt their behavior in the most appropriate manner based on that. In this paper, the emotion recognition capabilities of the humanoid robot Pepper are experimentally explored, based on the facial expressions for the so-called basic emotions, as well as how it performs in contrast to other state-of-the-art approaches with both expression databases compiled in academic environments and real subjects showing posed expressions as well as spontaneous emotional reactions. The experiments’ results show that the detection accuracy amongst the evaluated approaches differs substantially. The introduced experiments offer a general structure and approach for conducting such experimental evaluations. The paper further suggests that the most meaningful results are obtained by conducting experiments with real subjects expressing the emotions as spontaneous reactions.

Keywords: affective computing, emotion recognition, humanoid robot, human-robot-interaction (HRI), social robots

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Authors: Yake Zhang, Peng Xu

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The development of building energy models (BEM) required for architectural design and analysis is a time-consuming and complex process, demanding a deep understanding and proficient use of simulation software. To streamline the generation of complex building energy models, this study proposes an automated method for generating building energy models using a large language model and the BEM library aimed at improving the efficiency of model generation. This method leverages a large language model to parse user-specified requirements for target building models, extracting key features such as building location, window-to-wall ratio, and thermal performance of the building envelope. The BEM library is utilized to retrieve energy models that match the target building’s characteristics, serving as reference information for the large language model to enhance the accuracy and relevance of the generated model, allowing for the creation of a building energy model that adapts to the user’s modeling requirements. This study enables the automatic creation of building energy models based on natural language inputs, reducing the professional expertise required for model development while significantly decreasing the time and complexity of manual configuration. In summary, this study provides an efficient and intelligent solution for building energy analysis and simulation, demonstrating the potential of a large language model in the field of building simulation and performance modeling.

Keywords: artificial intelligence, building energy modelling, building simulation, large language model

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Authors: Jinwon Kang, Seonghak Jo, Joohee Ahn, Junghye Choi, Sun-Young Lee

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A morphological priming has proved its importance by showing that segmentation occurs in morphemes when visual words are recognized within a noticeably short time. Regarding Sino-Korean prefixes, this study conducted an experiment on visual masked priming tasks with 57 ms stimulus-onset asynchrony (SOA) to see how individual differences in the amount of morphological knowledge affect morphological priming. The relationship between the prime and target words were classified as morphological (e.g., 미개척 migaecheog [unexplored] – 미해결 mihaegyel [unresolved]), semantical (e.g., 친환경 chinhwangyeong [eco-friendly]) – 무공해 mugonghae [no-pollution]), and orthographical (e.g., 미용실 miyongsil [beauty shop] – 미확보 mihwagbo [uncertainty]) conditions. We then compared the priming by configuring irrelevant paired stimuli for each condition’s control group. As a result, in the behavioral data, we observed facilitatory priming from a group with high morphological knowledge only under the morphological condition. In contrast, a group with low morphological knowledge showed the priming only under the orthographic condition. In the event-related potential (ERP) data, the group with high morphological knowledge presented the N250 only under the morphological condition. The findings of this study imply that individual differences in morphological knowledge in Korean may have a significant influence on the segmental processing of Korean word recognition.

Keywords: ERP, individual differences, morphological priming, sino-Korean prefixes

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Authors: Jayanth C., Anirudh Sai Yetikuri, Kavitha S. N.

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This paper explores the development and application of computer vision and machine learning techniques for real-time pose detection, facial recognition, and number plate extraction. Utilizing MediaPipe for pose estimation, the research presents methods for detecting hand raises and ducking postures through real-time video analysis. Complementarily, facial recognition is employed to compare and verify individual identities using the face recognition library. Additionally, the paper demonstrates a robust approach for extracting and storing vehicle number plates from images, integrating Optical Character Recognition (OCR) with a database management system. The study highlights the effectiveness and versatility of these technologies in practical scenarios, including security and surveillance applications. The findings underscore the potential of combining computer vision techniques to address diverse challenges and enhance automated systems for both individual and vehicular identification. This research contributes to the fields of computer vision and machine learning by providing scalable solutions and demonstrating their applicability in real-world contexts.

Keywords: computer vision, pose detection, facial recognition, number plate extraction, machine learning, real-time analysis, OCR, database management

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Authors: Qiang Wang, Hongyang Yu, MingRong Lai, Miao Luo

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This paper presents a method for accurately localizing and interacting with known surface defects by overlaying patterns onto real-world surfaces using a projection system. Given the world coordinates of the defects, we project corresponding patterns onto the surfaces, providing an intuitive visualization of the specific defect locations. To enable users to interact with and retrieve more information about individual defects, we implement a gesture recognition system based on a pruned and optimized version of YOLOv6. This lightweight model achieves an accuracy of 82.8% and is suitable for deployment on low-performance devices. Our approach demonstrates the potential for enhancing defect identification, inspection processes, and user interaction in various applications.

Keywords: defect localization, projection mapping, gesture recognition, YOLOv6

Procedia PDF Downloads 88

Authors: Fei Gao, Xinyang Dong, Yisu Ge, Shufang Lu, Libo Weng

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Vehicle color recognition plays an important role in traffic accident investigation. However, due to the influence of illumination, weather, and noise, vehicle color recognition still faces challenges. In this paper, a vehicle color classification network based on spatial cluster loss and channel attention mechanism (SCNet) is proposed for vehicle color recognition. A channel attention module is applied to extract the features of vehicle color representative regions and reduce the weight of nonrepresentative color regions in the channel. The proposed loss function, called spatial clustering loss (SC-loss), consists of two channel-specific components, such as a concentration component and a diversity component. The concentration component forces all feature channels belonging to the same class to be concentrated through the channel cluster. The diversity components impose additional constraints on the channels through the mean distance coefficient, making them mutually exclusive in spatial dimensions. In the comparison experiments, the proposed method can achieve state-of-the-art performance on the public datasets, VCD, and VeRi, which are 96.1% and 96.2%, respectively. In addition, the ablation experiment further proves that SC-loss can effectively improve the accuracy of vehicle color recognition.

Keywords: feature extraction, convolutional neural networks, intelligent transportation, vehicle color recognition

Procedia PDF Downloads 183

Authors: Ziyue Zeng

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Bacteriophages are viruses which infect bacteria specifically. Over the past decades, phage λ has been extensively studied, especially its interaction with the Escherichia coli LamB (EcLamB) protein receptor. Nonetheless, despite the enormous numbers and near-ubiquity of environmental phages, aside from phage λ, there is a paucity of information on other phages which target EcLamB as a receptor. In this study, to answer the question of whether there are other EcLamB-targeting phages in the natural environment, a simple and convenient method was developed and used for isolating environmental phages which target a particular surface structure of a particular bacterium; in this case, the EcLamB outer membrane protein. From the enrichments with the engineered bacterial hosts, a collection of EcLamB-targeting phages (ΦZZ phages) were easily isolated. Intriguingly, unlike phage λ, an obligate EcLamB-dependent phage in the Siphoviridae family, the newly isolated ΦZZ phages alternatively recognised EcLamB or E. coli OmpC (EcOmpC) as a receptor when infecting E. coli. Furthermore, ΦZZ phages were suggested to represent new species in the Tequatrovirus genus in the Myoviridae family, based on phage morphology and genomic sequences. Most phages are thought to have a narrow host range due to their exquisite specificity in receptor recognition. With the ability to optionally recognise two receptors, ΦZZ phages were considered relatively promiscuous. Via the heterologous expression of EcLamB on the bacterial cell surface, the host range of ΦZZ phages was further extended to three different enterobacterial genera. Besides, an interesting selection of evolved phage mutants with a broader host range was isolated, and the key mutations involved in their evolution to adapt to new hosts were investigated by genomic analysis. Finally, and importantly, two ΦZZ phages were found to be putative generalised transducers, which could be exploited as tools for DNA manipulations.

Keywords: environmental microbiology, phage, microbe-host interactions, microbial ecology

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Authors: L. Parisi

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A blood pressure monitor or sphygmomanometer can be either manual or automatic, employing respectively either the auscultatory method or the oscillometric method. The manual version of the sphygmomanometer involves an inflatable cuff with a stethoscope adopted to detect the sounds generated by the arterial walls to measure blood pressure in an artery. An automatic sphygmomanometer can be effectively used to monitor blood pressure through a pressure sensor, which detects vibrations provoked by oscillations of the arterial walls. The pressure sensor implemented in this device improves the accuracy of the measurements taken.

Keywords: blood pressure, blood saturation, sensors, actuators, design improvement

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Authors: Marek Maruszczak

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Gamified applications have been used successfully in education for years. The rapid development of technologies such as augmented reality and image recognition increases their availability and reduces their prices. Thus, there is an increasing possibility and need for a wide use of such applications in education. The main purpose of this article is to present the effects of work on a mobile application with augmented reality, the aim of which is to motivate tourists to pay more attention to the attractions and increase the likelihood of moving from one attraction to the next while visiting the Palácio Nacional de Sintra in Portugal. Work on the application was carried out together with the employees of Parques de Sintra from 2019 to 2021. Their effect was the preparation of a mobile application using augmented reality and image recognition. The application was tested on the palace premises by both Parques de Sintra employees and tourists visiting Palácio Nacional de Sintra. The collected conclusions allowed for the formulation of good practices and guidelines that can be used when designing gamified apps for the purpose of cultural education.

Keywords: augmented reality, cultural education, gamification, image recognition, mobile games

Procedia PDF Downloads 190

Authors: Zunjarrao Kadam, Vikas Minchekar

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The facial recognition is an important task in criminal investigation procedure. The security guards-constantly watching the persons-can help to identify the suspected accused. The forensic psychologists are tackled such cases in the criminal justice system. The security personnel may loss their ability to correctly identify the persons due to constant stress while performing the duty. The present study aimed at to identify the effect of experimentally induced stress on facial recognition ability of security personnel’s. For this study 50, security guards from Sangli, Miraj & Jaysingpur city of the Maharashtra States of India were recruited in the experimental study. The randomized two group design was employed to carry out the research. In the initial condition twenty identity card size photographs were shown to both groups. Afterward, artificial stress was induced in the experimental group through the difficultpuzzle-solvingtask in a limited period. In the second condition, both groups were presented earlier photographs with another additional thirty new photographs. The subjects were asked to recognize the photographs which are shown earliest. The analyzed data revealed that control group has ahighest mean score of facial recognition than experimental group. The results were discussed in the present research.

Keywords: experimentally induced stress, facial recognition, cognition, security personnel

Procedia PDF Downloads 261

Authors: Redouane Tlemsani, Redouane, Belkacem Kouninef, Abdelkader Benyettou

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In this paper, our system is a Markovien system which we can see it like a Dynamic Bayesian Networks. One of the major interests of these systems resides in the complete training of the models (topology and parameters) starting from training data. The Bayesian Networks are representing models of dubious knowledge on complex phenomena. They are a union between the theory of probability and the graph theory in order to give effective tools to represent a joined probability distribution on a set of random variables. The representation of knowledge bases on description, by graphs, relations of causality existing between the variables defining the field of study. The theory of Dynamic Bayesian Networks is a generalization of the Bayesians networks to the dynamic processes. Our objective amounts finding the better structure which represents the relationships (dependencies) between the variables of a dynamic bayesian network. In applications in pattern recognition, one will carry out the fixing of the structure which obliges us to admit some strong assumptions (for example independence between some variables).

Keywords: Arabic on line character recognition, dynamic Bayesian network, pattern recognition, networks

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Authors: Hongjian Jia

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A finite-length cylindrical shell with a spherical cap is a typical engineering approximation model of actual underwater targets. The research on the omni-directional acoustic scattering characteristics of this target model can provide a favorable basis for the detection and identification of actual underwater targets. The elastic resonance characteristics of the target are the results of the comprehensive effect of the target length, shell-thickness ratio and materials. Under the conditions of different materials and geometric dimensions, the coincidence resonance characteristics of the target have obvious differences. Aiming at this problem, this paper obtains the omni-directional acoustic scattering field of the underwater hemispherical cylindrical shell by numerical calculation and studies the influence of target geometric parameters (length, shell-thickness ratio) and material parameters on the coincidence resonance characteristics of the target in turn. The study found that the formant interval is not a stable value and changes with the incident angle. Among them, the formant interval is less affected by the target length and shell-thickness ratio and is significantly affected by the material properties, which is an effective feature for classifying and identifying targets of different materials. The quadratic polynomial is utilized to fully fit the change relationship between the formant interval and the angle. The results show that the three fitting coefficients of the stainless steel and aluminum targets are significantly different, which can be used as an effective feature parameter to characterize the target materials.

Keywords: hemispherical cylindrical shell;, fine echo characteristics;, geometric and material parameters;, formant interval

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Authors: Hsiao-Chuan Huang, King-Lung Kuo, Mei-Hsin Lo, Hsiao-Yun Chou, Yusen Lin

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The most robust and economical method for laboratory diagnosis of TB is to identify mycobacterial bacilli (AFB) under acid-fast staining despite its disadvantages of low sensitivity and labor-intensive. Though digital pathology becomes popular in medicine, an automated microscopic system for microbiology is still not available. A new AI-assisted automated microscopic system, consisting of a microscopic scanner and recognition program powered by big data and deep learning, may significantly increase the sensitivity of TB smear microscopy. Thus, the objective is to evaluate such an automatic system for the identification of AFB. A total of 5,930 smears was enrolled for this study. An intelligent microscope system (TB-Scan, Wellgen Medical, Taiwan) was used for microscopic image scanning and AFB detection. 272 AFB smears were used for transfer learning to increase the accuracy. Referee medical technicians were used as Gold Standard for result discrepancy. Results showed that, under a total of 1726 AFB smears, the automated system's accuracy, sensitivity and specificity were 95.6% (1,650/1,726), 87.7% (57/65), and 95.9% (1,593/1,661), respectively. Compared to culture, the sensitivity for human technicians was only 33.8% (38/142); however, the automated system can achieve 74.6% (106/142), which is significantly higher than human technicians, and this is the first of such an automated microscope system for TB smear testing in a controlled trial. This automated system could achieve higher TB smear sensitivity and laboratory efficiency and may complement molecular methods (eg. GeneXpert) to reduce the total cost for TB control. Furthermore, such an automated system is capable of remote access by the internet and can be deployed in the area with limited medical resources.

Keywords: TB smears, automated microscope, artificial intelligence, medical imaging

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Authors: Jutta Hämmerle-Uhl, Georg Penn, Gerhard Pötzelsberger, Andreas Uhl

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Iris codes contain bits with different entropy. This work investigates different strategies to reduce the size of iris code templates with the aim of reducing storage requirements and computational demand in the matching process. Besides simple sub-sampling schemes, also a binary multi-resolution representation as used in the JBIG hierarchical coding mode is assessed. We find that iris code template size can be reduced significantly while maintaining recognition accuracy. Besides, we propose a two stage identification approach, using small-sized iris code templates in a pre-selection satge, and full resolution templates for final identification, which shows promising recognition behaviour.

Keywords: iris recognition, compact iris code, fast matching, best bits, pre-selection identification, two-stage identification

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Authors: John Chan, Jake Linnenbank, Gavin Caird

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Automatic Selective Door Operation (ASDO) systems are increasingly used in railways to provide Correct Side Door Enable (CSDE) protection as well as to protect passenger doors opening off the platform where the train is longer than the platform, or in overshoot or undershoot scenarios. Such ASDO systems typically utilise trackside-installed RFID beacons, such as Eurobalises for odometry positioning purposes. Installing such trackside infrastructure may not be desirable or possible due to various factors such as conflict with existing infrastructure, potential damage from track tamping and jurisdiction constraints. Ultra-wideband (UWB) positioning technology could enable ASDO positioning requirements to be met without requiring installation of equipment directly on track since UWB technology can be installed on adjacent infrastructure such as on platforms. This paper will explore the feasibility of upgrading existing ASDO systems with UWB positioning technology, the feasibility of retrofitting UWB-enabled ASDO systems onto unfitted trains, and any other considerations relating to the use of UWB positioning for ASDO applications.

Keywords: UWB, ASDO, automatic selective door operations, CSDE, correct side door enable

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Authors: Antigoni Panagiotopoulou, Lemonia Ragia

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High-resolution satellite images can provide detailed information about change detection on the earth. In the present work, QuickBird images of spatial resolution 60 cm/pixel and WorldView images of resolution 30 cm/pixel are utilized to perform automatic change detection in urban and suburban areas of Crete, Greece. There is a relative time difference of 13 years among the satellite images. Multiindex scene representation is applied on the images to classify the scene into buildings, vegetation, water and ground. Then, automatic change detection is made possible by pixel-per-pixel comparison of the classified multi-temporal images. The vegetation index and the water index which have been developed in this study prove effective. Furthermore, the proposed change detection approach not only indicates whether changes have taken place or not but also provides specific information relative to the types of changes. Experimentations with other different scenes in the future could help optimize the proposed spectral indices as well as the entire change detection methodology.

Keywords: change detection, multiindex scene representation, spectral index, QuickBird, WorldView

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Authors: Sireesha Tamada, Debraj Bhattacharjee, Pranab K. Dan, Prabha Bhola

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The most important component in an automotive transmission system is the gearbox which controls the speed of the vehicle. In an automatic transmission, the right positioning of actuators ensures efficient transmission mechanism embodiment, wherein the challenge lies in formulating the number of actuators associated with modelling a gearbox. Data with respect to actuation and gear shifting sequence has been retrieved from the available literature, including patent documents, and has been used in this proposed heuristics based methodology for modelling actuation sequence in a gear box. This paper presents a methodological approach in designing a gearbox for the purpose of obtaining an optimal shifting sequence. The computational model considers factors namely, the number of stages and gear teeth as input parameters since these two are the determinants of the gear ratios in an epicyclic gear train. The proposed transmission schematic or stick diagram aids in developing the gearbox layout design. The number of iterations and development time required to design a gearbox layout is reduced by using this approach.

Keywords: automatic transmission, gear-shifting, multi-stage planetary gearbox, rank ordered clustering

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Authors: I-En Lin, Feng-Jung Yang

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In addition to kidney transplant, renal replacement therapy involves hemodialysis and peritoneal dialysis (PD). PD possesses advantages such as maintaining stable physiological blood status and blood pressure, alleviating anemia, and improving mobility, which make it an ideal method for at-home dialysis treatment. However, potential danger still exists despite the numerous advantages of PD, particularly when patients require dialysis exchange four to five times a day, during which improper operation can easily lead to peritonitis. The process of draining and filling is called an exchange and takes about 30 to 40 minutes. Connecting the transfer set requires sterile technique. Transfer set may require a new cap each time that it disconnects from the bag after an exchange. There are many chances to get infection due to unsafe behavior (ex: hand tremor, poor eyesight and weakness, cap fall-down). The proposed semi-automatic connection mechanism used in the PD can greatly reduce infection chances. This light-weight connection device is portable. The device also does not require using throughout the entire process. It is capable of significantly improving quality of life. Therefore, it is very promising to adopt in home care application.

Keywords: automatic connection, catheter, glomerulonephritis, peritoneal dialysis

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Authors: Keyi Wang

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Similar to the touchscreen, hand gesture based human-computer interaction (HCI) is a technology that could allow people to perform a variety of tasks faster and more conveniently. This paper proposes a training method of an image-based hand gesture image and video clip recognition system using a CNN (Convolutional Neural Network) with a dataset. A dataset containing 6 hand gesture images is used to train a 2D CNN model. ~98% accuracy is achieved. Furthermore, a 3D CNN model is trained on a dataset containing 4 hand gesture video clips resulting in ~83% accuracy. It is demonstrated that a Cozmo robot loaded with pre-trained models is able to recognize static and dynamic hand gestures.

Keywords: deep learning, hand gesture recognition, computer vision, image processing

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Authors: P. Shrivastava, A. Shukla, K. Verma, S. Rungta

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Parkinson's disease is a chronic neurological disorder that directly affects human gait. It leads to slowness of movement, causes muscle rigidity and tremors. Gait serve as a primary outcome measure for studies aiming at early recognition of disease. Using gait techniques, this paper implements efficient binary bat algorithm for an early detection of Parkinson's disease by selecting optimal features required for classification of affected patients from others. The data of 166 people, both fit and affected is collected and optimal feature selection is done using PSO and Bat algorithm. The reduced dataset is then classified using neural network. The experiments indicate that binary bat algorithm outperforms traditional PSO and genetic algorithm and gives a fairly good recognition rate even with the reduced dataset.

Keywords: parkinson, gait, feature selection, bat algorithm

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Authors: Carlos Gonzalez, Neil Slatcher, Marcus Properzi, Kan Seah

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In many underground mining operations, shotcrete is used for permanent rock support. Shotcrete thickness is a critical measure of the success of this process. 3D Laser Mapping, in conjunction with Jetcrete, has developed a 3D laser scanning system specifically for measuring the thickness of shotcrete. The system is mounted on the shotcrete spraying machine and measures the rock faces before and after spraying. The calculated difference between the two 3D surface models is measured as the thickness of the sprayed concrete. Typical work patterns for the shotcrete process required a rapid and automatic system. The scanning takes place immediately before and after the application of the shotcrete so no convergence takes place in the interval between scans. Automatic alignment of scans without targets was implemented which allows for the possibility of movement of the spraying machine between scans. Case studies are presented where accuracy tests are undertaken and automatic audit reports are calculated. The use of 3D imaging data for the calculation of shotcrete thickness is an important tool for geotechnical engineers and contract managers, and this could become the new state-of-the-art methodology for the mining industry.

Keywords: 3D imaging, shotcrete, surface model, tunnel stability

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Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life of the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or Covidi-19 induced pneumonia. The early prediction and classification of such lung diseases help to reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans have pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publically available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scan, Covid-19, deep learning, image processing, lung disease classification

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Authors: Rabab M. Ramadan, Elaraby A. Elgallad

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With extensive application, the performance of unimodal biometrics systems has to face a diversity of problems such as signal and background noise, distortion, and environment differences. Therefore, multimodal biometric systems are proposed to solve the above stated problems. This paper introduces a bimodal biometric recognition system based on the extracted features of the human palm print and iris. Palm print biometric is fairly a new evolving technology that is used to identify people by their palm features. The iris is a strong competitor together with face and fingerprints for presence in multimodal recognition systems. In this research, we introduced an algorithm to the combination of the palm and iris-extracted features using a texture-based descriptor, the Scale Invariant Feature Transform (SIFT). Since the feature sets are non-homogeneous as features of different biometric modalities are used, these features will be concatenated to form a single feature vector. Particle swarm optimization (PSO) is used as a feature selection technique to reduce the dimensionality of the feature. The proposed algorithm will be applied to the Institute of Technology of Delhi (IITD) database and its performance will be compared with various iris recognition algorithms found in the literature.

Keywords: iris recognition, particle swarm optimization, feature extraction, feature selection, palm print, the Scale Invariant Feature Transform (SIFT)

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Authors: N. N. Mosola, S. J. Molete, L. S. Masoebe, M. Letsae

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Hand gesture recognition is a technique used to locate, detect, and recognize a hand gesture. Detection and recognition are concepts of Artificial Intelligence (AI). AI concepts are applicable in Human Computer Interaction (HCI), Expert systems (ES), etc. Hand gesture recognition can be used in sign language interpretation. Sign language is a visual communication tool. This tool is used mostly by deaf societies and those with speech disorder. Communication barriers exist when societies with speech disorder interact with others. This research aims to build a hand recognition system for Lesotho’s Sesotho and English language interpretation. The system will help to bridge the communication problems encountered by the mentioned societies. The system has various processing modules. The modules consist of a hand detection engine, image processing engine, feature extraction, and sign recognition. Detection is a process of identifying an object. The proposed system uses Canny pruning Haar and Haarcascade detection algorithms. Canny pruning implements the Canny edge detection. This is an optimal image processing algorithm. It is used to detect edges of an object. The system employs a skin detection algorithm. The skin detection performs background subtraction, computes the convex hull, and the centroid to assist in the detection process. Recognition is a process of gesture classification. Template matching classifies each hand gesture in real-time. The system was tested using various experiments. The results obtained show that time, distance, and light are factors that affect the rate of detection and ultimately recognition. Detection rate is directly proportional to the distance of the hand from the camera. Different lighting conditions were considered. The more the light intensity, the faster the detection rate. Based on the results obtained from this research, the applied methodologies are efficient and provide a plausible solution towards a light-weight, inexpensive system which can be used for sign language interpretation.

Keywords: canny pruning, hand recognition, machine learning, skin tracking

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Authors: Yufeng Li, Yinan Ma, Jing Wu, Chengnian Long

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Infrared object detection aims at separating small and dim target from clutter background and its capabilities extend beyond the limits of visible light, making it invaluable in a wide range of applications such as improving safety, security, efficiency, and functionality. However, existing methods are usually sensitive to the noise of the input infrared image, leading to a decrease in target detection accuracy and an increase in the false alarm rate in high-noise environments. To address this issue, an infrared small target detection algorithm called YOLO-IR is proposed in this paper to improve the robustness to high infrared noise. To address the problem that high noise significantly reduces the clarity and reliability of target features in infrared images, we design a soft-threshold coordinate attention mechanism to improve the model’s ability to extract target features and its robustness to noise. Since the noise may overwhelm the local details of the target, resulting in the loss of small target features during depth down-sampling, we propose a deep and shallow feature fusion neck to improve the detection accuracy. In addition, because the generalized Intersection over Union (IoU)-based loss functions may be sensitive to noise and lead to unstable training in high-noise environments, we introduce a Wasserstein-distance based loss function to improve the training of the model. The experimental results show that YOLO-IR achieves a 5.0% improvement in recall and a 6.6% improvement in F1-score over existing state-of-art model.

Keywords: infrared small target detection, high noise, robustness, soft-threshold coordinate attention, feature fusion

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Authors: Endrick Barnacin, Jean-Luc Henry, Jimmy Nagau, Jack Molinie

Abstract:

Palynology is a field of interest in many disciplines due to its multiple applications: chronological dating, climatology, allergy treatment, and honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time consuming task that requires the intervention of experts in the field, which are becoming increasingly rare due to economic and social conditions. That is why the need for automation of this task is urgent. A lot of studies have investigated the subject using different standard image processing descriptors and sometimes hand-crafted ones.In this work, we make a comparative study between classical feature extraction methods (Shape, GLCM, LBP, and others) and Deep Learning (CNN, Autoencoders, Transfer Learning) to perform a recognition task over 80 regional pollen species. It has been found that the use of Transfer Learning seems to be more precise than the other approaches

Keywords: pollens identification, features extraction, pollens classification, automated palynology

Procedia PDF Downloads 136

Authors: Abdullah A. AlShaher

Abstract:

In this paper, we demonstrate how regression curves can be used to recognize 2D non-rigid handwritten shapes. Each shape is represented by a set of non-overlapping uniformly distributed landmarks. The underlying models utilize 2^nd order of polynomials to model shapes within a training set. To estimate the regression models, we need to extract the required coefficients which describe the variations for a set of shape class. Hence, a least square method is used to estimate such modes. We then proceed by training these coefficients using the apparatus Expectation Maximization algorithm. Recognition is carried out by finding the least error landmarks displacement with respect to the model curves. Handwritten isolated Arabic characters are used to evaluate our approach.

Keywords: character recognition, regression curves, handwritten Arabic letters, expectation maximization algorithm

Procedia PDF Downloads 145