Image Pattern Recognition Techniques Research Articles

Computer Interactive Gesture Recognition Model Based on Improved YOLOv5 Algorithm

The expansion of virtual and augmented reality, intelligent assistance technology, and other fields has led to an increased demand for human–computer interaction methods that are more natural and intuitive. Gesture recognition has become an important research direction. Traditional gesture recognition methods are mainly based on image processing and pattern recognition techniques. However, there are certain challenges to its accuracy and robustness in complex backgrounds. In addition, the temporal correlation and spatial information in gesture sequences have not been fully utilized, which limits the performance of gesture recognition systems. In response to the above issues, this study first utilizes the Ghost module for feature extraction based on the You Only Look Once version 5 (YOLOv5) algorithm. Then drawing inspiration from the idea of densely connected networks, feature map stitching is carried out, and a human–machine interactive gesture recognition algorithm is designed by combining it with a hybrid attention mechanism. The experimental results showed that the average accuracy of the algorithm tended to converge after 160 iterations, and the final MAP value converged to 92.19%. Compared to the standard YOLOv5 algorithm, its iteration speed had been improved by 12.5%, and the MAP value had been improved by 4.63%. The designed human–computer interaction gesture recognition algorithm has higher accuracy and smaller error, and has certain application potential in the field of machine vision.

Unsupervised Learning for Public Transport Delay Pattern Analysis

To analyze inherent and diverse patterns within line-based public transport daily delay occurrences, we introduce a data-driven exploratory analysis focused on the spatial-temporal distribution of these delays. Our approach relies on the utilization of the image pattern recognition technique and k-means clustering algorithm. We extract daily punctuality information from the automatic vehicle location data for a singular public transport route. This information is then translated into a visual representation through aggregated daily delay distribution profile images, offering insights into the spatial and temporal distribution of delays. The delay distribution finds expression in the arrangement of pixels within these profile images. The essence of these images is further distilled through image pattern recognition using the neural network architecture of ResNet50. Employing the k-means algorithm, we cluster these images based on their similarity, revealing five distinct daily delay patterns. The analysis of these patterns offers insight into their unique characteristics, yielding noteworthy outcomes. These findings hold the potential to provide public transport operators with an enriched comprehension of the dynamics of delays occurring on a specific line.

Developing a Neural Network-based Model for Identifying Medicinal Plant Leaves using Image Recognition Techniques

Herbal plants play a vital role in human health and the environment, as they can provide both medical benefits and oxygen. Many herbal plants contain valuable therapeutic elements that can be passed down to future generations. Traditional methods of identifying plant species, such as manual measurement and examination of characteristics, are labor-intensive and time-consuming. There has been a push to develop more efficient methods using technology, such as digital image processing and pattern recognition techniques to address this. The proper identification of plant methods using computer vision and neural network techniques has been proposed. This approach involves neural network models such as CNN, ALexnet and ResNet for identifying the medical plants based on their respective features. Classification metrics give the 96.82 average accuracies. These results have been promising, and further research will involve using a larger dataset and going more into deep-learning neural networks to improve the accuracy of medicinal plant identification. It is hoped that a web or mobile-based system for automatic plant identification can help increase knowledge about medicinal plants, improve species identification techniques, and protect endangered species.

Developing A Neural Network-Based Model for Identifying Medicinal Plant Leaves Using Image Recognition Techniques

Herbal plants contribute an important role in people's health and the environment, as they can provide both medical benefits and oxygen. Many herbal plants contain valuable therapeutic elements that can be passed down to future generations. Traditional methods of identifying plant species, such as manual measurement and examination of characteristics, are labor-intensive and time-consuming. To address this, there has been a push to develop more efficient methods using technology, such as digital image processing and pattern recognition techniques. The exact recognition of plants uses methodologies like computer vision and neural networks, which have been proposed earlier. This approach involves neural network models such as CNN, ALexnet, and ResNet for identifying the medical plants based on their respective features. Classification metrics give the 96.82 average accuracies. These results have been promising, and further research will involve using a larger dataset and going more into deep-learning neural networks to improve the accuracy of medicinal plant identification. It is hoped that a web or mobile-based system for automatic plant identification can help increase knowledge about medicinal plants, improve techniques for species recognition, and participate in the preservation of species that are considered ad endangered.

Applying Deep Learning and Machine Learning Algorithms for The Identification of Medicinal Plant Leaves Based on Their Spectral Characteristics

The study and consideration of medicinal plants have been ongoing throughout history due to their significant role in maintaining the well-being of mammals. Although identifying medicinal plants can be a valuable skill, it is often time-consuming, tedious, and requires the expertise of a specialist. The project works on the technique of image processing, which identifies the various medicinal plants. There has been a strong emphasis on improving efficiency through the application of technology, with a focus on incorporating digital image processing and pattern recognition techniques. To ensure accurate plant identification, proposals involving the application of computer vision neural network techniques have been advanced. This approach involves neural network models such as CNN, SVM, KNN, and Navie Bay for identifying the medical plants based on their respective features. After the validation step, the project provides a classification of 92.3 precision and 90.56 F1 score.

Cervical Cancer Detection Techniques: A Chronological Review.

Cervical cancer is known as a major health problem globally, with high mortality as well as incidence rates. Over the years, there have been significant advancements in cervical cancer detection techniques, leading to improved accuracy, sensitivity, and specificity. This article provides a chronological review of cervical cancer detection techniques, from the traditional Pap smear test to the latest computer-aided detection (CAD) systems. The traditional method for cervical cancer screening is the Pap smear test. It consists of examining cervical cells under a microscope for abnormalities. However, this method is subjective and may miss precancerous lesions, leading to false negatives and a delayed diagnosis. Therefore, a growing interest has been in shown developing CAD methods to enhance cervical cancer screening. However, the effectiveness and reliability of CAD systems are still being evaluated. A systematic review of the literature was performed using the Scopus database to identify relevant studies on cervical cancer detection techniques published between 1996 and 2022. The search terms used included "(cervix OR cervical) AND (cancer OR tumor) AND (detect* OR diagnosis)". Studies were included if they reported on the development or evaluation of cervical cancer detection techniques, including traditional methods and CAD systems. The results of the review showed that CAD technology for cervical cancer detection has come a long way since it was introduced in the 1990s. Early CAD systems utilized image processing and pattern recognition techniques to analyze digital images of cervical cells, with limited success due to low sensitivity and specificity. In the early 2000s, machine learning (ML) algorithms were introduced to the CAD field for cervical cancer detection, allowing for more accurate and automated analysis of digital images of cervical cells. ML-based CAD systems have shown promise in several studies, with improved sensitivity and specificity reported compared to traditional screening methods. In summary, this chronological review of cervical cancer detection techniques highlights the significant advancements made in this field over the past few decades. ML-based CAD systems have shown promise for improving the accuracy and sensitivity of cervical cancer detection. The Hybrid Intelligent System for Cervical Cancer Diagnosis (HISCCD) and the Automated Cervical Screening System (ACSS) are two of the most promising CAD systems. Still, deeper validation and research are required before being broadly accepted. Continued innovation and collaboration in this field may help enhance cervical cancer detection as well as ultimately reduce the disease's burden on women worldwide.

A review on optimization techniques for medical image analysis

SummaryData mining of medical imaging approaches makes it difficult to determine their value in the disease's insight, analysis, and diagnosis. Image classification presents a significant difficulty in image analysis and plays a vital part in computer‐aided diagnosis. This task concerned the use of optimization techniques for the utilization of image processing, pattern recognition, and classification techniques, as well as the validation of image classification results in medical expert reports. The primary intention of this study is to analyze the performance of optimization techniques explored in the area of medical image analysis. For this motive, the optimization techniques employed in existing literature from 2012 to 2021 are reviewed in this study. The contribution of optimization‐based medical image classification and segmentation utilized image modalities, data sets, and tradeoffs for each technique are also discussed in this review study. Finally, this review study provides the gap analysis of optimization techniques used in medical image analysis with the possible future research direction.

A Study on Automatic Extraction of Retinal Blood Vessels in Fundus Images

Medical imaging is the practice of visualizing internal human body parts in order to make diagnoses. The discipline of medicine now relies heavily on medical imaging as a tool. Digital image processing and pattern recognition techniques in medical imaging aid in the detection of eye disorders. Changes in retinal Blood Vessels (BVs) are used in retinal image analysis to help diagnose retinal vascular illnesses such Diabetic Retinopathy (DR), Macular Edema, and Glaucoma. The retinal blood vessels are given a brief introduction in this review paper, along with a detailed description of the methods/techniques utilised to segment the retinal blood vessels. This paper also discusses the difficulties in segmenting BVs.

UAV Landing Platform Recognition Using Cognitive Computation Combining Geometric Analysis and Computer Vision Techniques

Unmanned aerial vehicles (UAVs) are excellent tools with extensive demand. During the last phase of landing, they require additional support to that of GPS. This can be achieved through the UAV’s perception system based on its on-board camera and intelligence, and with which decisions can be made as to how to land on a platform (target). A cognitive computation approach is proposed to recognize this target that has been specifically designed to translate human reasoning into computational procedures by computing two probabilities of detection which are combined considering the fuzzy set theory for proper decision-making. The platform design is based on: (1) spectral information in the visible range which are uncommon colors in the UAV’s operating environments (indoors and outdoors) and (2) specific figures in the foreground, which allow partial perception of each figure. We exploit color image properties from specific-colored figures embedded on the platform and which are identified by applying image processing and pattern recognition techniques, including Euclidean Distance Smart Geometric Analysis, to identify the platform in a very efficient and reliable manner. The test strategy uses 800 images captured with a smartphone onboard a quad-rotor UAV. The results verify the proposed method outperforms existing strategies, especially those that do not use color information. Platform recognition is also possible even with only a partial view of the target, due to image capture under adverse conditions. This demonstrates the effectiveness and robustness of the proposed cognitive computing-based perception system.

CAD of BCD from Thermal Mammogram Images Using Machine Learning

Lump in the breast, discharge of blood from the nipple, and deformation of the nipple/breast and its texture are the symptoms of breast cancer. Though breast cancer is very common in women, men can also get breast cancer. In the early stages, BCD makes use of Thermal Mammograms Breast Images (TMBI). The cost of treatment can be severely reduced in the early stages of detection. Based on the techniques of segmentation, the Breast Cancer Detection (BCD) works. Moreover, by providing a balanced, reliable and appropriate second opinion, a tremendous role has been played by ML in medical practices due to enhanced Information and Communication Technology (ICT). For the purpose of making the whole detection process of Malignant Tumor (MT)/Benign Tumor (BT) very resourceful and time-efficient, there is now a possibility to form an automated and precise Computer-Aided Diagnosis System (CADs). Several Image Pattern Recognition Techniques were used to classify breast cancer using Thermal Mammograms Image Processing Techniques (TMIPT) in the present investigation. Presenting a new model to classify the BCD with the help of TMIPT, thermal imaging, and smart devices is the aim of this research article. Using well-designed experiments like Intensive Preoperative Radio Therapy (IPRT) and BCD, the implementation and valuation of a concrete application are carried out. This proposed method is for the automatic classification of TMBI of a similar standard so that the thermal camera of FLIR One Gen 3 One 3rd Generation (FLIR One Gen 3) that can be attached to the smart devices are capable of capturing BCD using Machine Learning (ML) algorithms. To imitate the behaviour of human Artificial Intelligence (AI), designing drug formulations, helping in clinical diagnosis and robotic surgery systems, finding medical statistical datasets, and decoding human diseases’ wireless network model as well as cancer are the reasons for the ML to empower the computer and robots. The outperformance of the ML models against all other classifiers and scoring impressively across heterogeneous performance metrics like 98.44% of Precision, 98.83% of Accuracy, and 100% of Recall are observed from the comparative analysis.

Breast Cancer Diagnosis by Convolutional Neural Network and Advanced Thermal Exchange Optimization Algorithm.

A common gynecological disease in the world is breast cancer that early diagnosis of this disease can be very effective in its treatment. The use of image processing methods and pattern recognition techniques in automatic breast detection from mammographic images decreases human errors and increments the rapidity of diagnosis. In this paper, mammographic images are analyzed using image processing techniques and a pipeline structure for the diagnosis of the cancerous masses. In the first stage, the quality of mammogram images and the contrast of abnormal areas in the image are improved by using image contrast improvement and a noise decline. A method based on color space is then used for image segmentation that is followed by mathematical morphology. Then, for feature image extraction, a combined gray-level cooccurrence matrix (GLCM) and discrete wavelet transform (DWT) method is used. At last, a new optimized version of convolutional neural network (CNN) and a new improved metaheuristic, called Advanced Thermal Exchange Optimizer, are used for the classification of the features. A comparison of the simulations of the proposed technique with three different techniques from the literature applied on the MIAS mammogram database is performed to show its superiority. Results show that the accuracy of diagnosing cancer cases for the proposed method and applied on the MIAS database is 93.79%, and sensitivity and specificity are obtained 96.89% and 67.7%, respectively.

Kinerja Pendekatan Convolutional Neural Network dan Dense Network dalam Klasifikasi Citra Malaria

Indonesia is an archipelago, which three of its five main island consists mainly, or dense tropical rainforest. This rainforest is main breeding ground for malaria disease that mostly affect regions near said forest. In an effort to treat malaria disease, a diagnostic process is performed to correctly identify the disease. Several image pattern recognition technique been developed and have potential to be utilized as malaria diagnostic tool. In this research, a method is described on designing neural network to detect a blood cell parasitized by malaria. The method consists of utilizing a dense network, and a convolutional neural network, to be trained using publicly available training dataset. Both models’ performance is then compared and analyzed. Before the data is used, a process of padding is performed to resize the input image into 200 x 200 pixels. The resized input data is then used to train both models. From the training and testing, it is found that the dense network achiever 64.78% accuracy. On the other hand, model based on convolutional neural network achiever 94.32%. From analysis, it is found that the size of the model being used is not big enough to achieve better performance. Hence, it is suggested for future research to increase the model size in terms of network width and depth.

Optimized convolutional neural network model for plant species identification from leaf images using computer vision

In recent works of computer science, especially in the fields of image processing and pattern recognition techniques with machine learning, considerable focus is given to plant taxonomy which enhances the abilities of people to recognize plant species. This paper presents a method that analyzes color images of leaves using a type of Convolutional Neural Network to recognize plant species. The proposed Neural Network consists of four convolutional layers followed by two Fully-Connected layers and a final soft-max layer to offer a feature representation for different plant species. Four max-pooling layers are performed over a 2 × 2 pixel window with stride 2. Results on five plant datasets viz. Leaf snap (52 plant species), UCI leaf (40 plant species), PlantVillage (38 plant species), Flavia (32 plant species) and Swedish (15 plant species) are tabulated that demonstrate the remarkable performance of the proposed deep neural network when compared to the state of art methods.

Non-invasive detection of diabetic complications via pattern analysis of temporal facial colour variations

Background and Objective: Diabetes mellitus is a common disorder amounting to 400 million patients worldwide. It is often accompanied by a number of complications, including neuropathy, nephropathy, and cardiovascular diseases. For example, peripheral neuropathy is present among 20–30% of diabetics before the diagnosis is substantiated. For this reason, a reliable detection method for diabetic complications is crucial and attracts a lot of research attention. Methods: In this paper, we introduce a non-invasive detection framework for patients with diabetic complications that only requires short video recordings of faces from a standard commercial camera. We employed multiple image processing and pattern recognition techniques to process video frames, extract relevant information, and predict the health status. To evaluate our framework, we collected a dataset of 114 video files from diabetic patients, who were diagnosed with diabetes for years and 60 video files from the control group. Extracted features from videos were tested using two conceptually different classifiers. Results: We found that our proposed framework correctly identifies patients with diabetic complications with 92.86% accuracy, 100% sensitivity, and 80% specificity. Conclusions: Our study brings a novel perspective on diagnosis procedures in this field. We used multiple techniques from image processing, pattern recognition, and machine learning to robustly process video frames and predict the health status of our subjects with high efficiency.

Automatic Detection of the Cracks on the Concrete Railway Sleepers

A vision-based method for detecting the cracks in the concrete sleepers of the railway tracks will be introduced in this paper. The method is able to detect and partially classify the cracks of the concrete sleepers in two successive steps based on the image processing and pattern recognition techniques. The method has been implemented on the acquired image data frames followed by the analysis, experimental, comparison results and evaluation. The presented results are reasonable which indicates the goodness of the introduced method. The preliminary results of this work have been presented in [A. Delforouzi, A. H. Tabatabaei, M. H. Khan and M. Grzegorzek, A vision-based method for automatic crack detection in railway sleepers, in Kurzynski, M., Wozniak, M., Burduk, R. (eds.), Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017, Polanica Zdroj, Poland. CORES 2017. Advances in Intelligent Systems and Computing, Vol. 578 (Springer, Cham, 2018), pp. 130–139, doi: 10.1007/978-3-319-59162-9_14].

DEEP LEARNING AND STATISTICAL MODELS FOR DETECTION OF WHITE STEM BORER DISEASE IN ARABICA COFFEE

Abstract. Early detection of crop pest and disease is very critical for taking up suitable control measures to reduce the loss of economic yield. Coffee is an important commercial crop in India which is affected by pests and diseases every year resulting in major yield loss. White stem borer (Xylotrechus quadripes) is the most serious pest of coffee (Arabica sp.) in India causing substantial loss of yield every year. Detection of the infestation in its early stage is quite challenging. In this regard, image pattern recognition techniques offer cost effective and scalable solutions. An image library was created representing different stages of the plant infestation using camera/mobile devices. Our Convolutional Neural Network (CNN) models use these images of healthy and infested plants for early detection of white stem borer infestation. The overall methodology included image processing, machine learning, supervised transfer learning and unsupervised auto-encoding techniques to solve the problem of early detection and severity of the infestation. Using the Inception v3 transfer learning model, we obtained average accuracy of 85.5% which is quite encouraging with limited image datasets. We explore Unsupervised Autoencoder models, which can work with limited image datasets. In addition, statistical analysis of long-term climatic factors such as temperature, rainfall, humidity and luminescence is explored for reliable detection and diagnosis of the infestation. Based on the encouraging results, a mobile application is proposed for near real time monitoring of WSB infestation to help the coffee planter’s community.

Real-time demographic profiling from face imagery with Fisher vectors

In the last decade, demographic profiling from facial imagery has grown in its importance in the computer vision field. For demographic profiling, we usually mean gender, ethnicity, and age identification from face images. In this paper, we propose an efficient and effective profiling framework and we assess the quality of the proposed approach comparing the results obtained by our system with those achieved by other recently published methods on large datasets of facial images with different age, gender, and ethnicity. These results show how a carefully engineered pipeline of efficient image analysis and pattern recognition techniques leads to state-of-the-art results at 20 FPS using a single thread on a 1.6 GHZ i5-2467M processor.

Motor Oil Classification Using Color Histograms and Pattern Recognition Techniques.

Motor oil classification is important for quality control and the identification of oil adulteration. In this work, we propose a simple, rapid, inexpensive and nondestructive approach based on image analysis and pattern recognition techniques for the classification of nine different types of motor oils according to their corresponding color histograms. For this, we applied color histogram in different color spaces such as red green blue (RGB), grayscale, and hue saturation intensity (HSI) in order to extract features that can help with the classification procedure. These color histograms and their combinations were used as input for model development and then were statistically evaluated by using linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM) techniques. Here, two common solutions for solving a multiclass classification problem were applied: (1) transformation to binary classification problem using a one-against-all (OAA) approach and (2) extension from binary classifiers to a single globally optimized multilabel classification model. In the OAA strategy, LDA, QDA, and SVM reached up to 97% in terms of accuracy, sensitivity, and specificity for both the training and test sets. In extension from binary case, despite good performances by the SVM classification model, QDA and LDA provided better results up to 92% for RGB-grayscale-HSI color histograms and up to 93% for the HSI color map, respectively. In order to reduce the numbers of independent variables for modeling, a principle component analysis algorithm was used. Our results suggest that the proposed method is promising for the identification and classification of different types of motor oils.

Classification of malignant and benign lung nodules using taxonomic diversity index and phylogenetic distance.

Lung cancer presents the highest cause of death among patients around the world, in addition of being one of the smallest survival rates after diagnosis. Therefore, this study proposes a methodology for diagnosis of lung nodules in benign and malignant tumors based on image processing and pattern recognition techniques. Mean phylogenetic distance (MPD) and taxonomic diversity index (Δ) were used as texture descriptors. Finally, the genetic algorithm in conjunction with the support vector machine were applied to select the best training model. The proposed methodology was tested on computed tomography (CT) images from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), with the best sensitivity of 93.42%, specificity of 91.21%, accuracy of 91.81%, and area under the ROC curve of 0.94. The results demonstrate the promising performance of texture extraction techniques using mean phylogenetic distance and taxonomic diversity index combined with phylogenetic trees. Graphical Abstract Stages of the proposed methodology.

Classification of patterns of benignity and malignancy based on CT using topology-based phylogenetic diversity index and convolutional neural network

Lung cancer has been recognized as the primary global cause of death among cancer patients. This work is intended to develop a methodology for diagnosis of lung nodules using images from the Lung Image Database Consortium and Image Database Resource Initiative. The proposed method uses image processing and pattern recognition techniques. To differentiate the patterns of malignant and benign forms, we used index basic taxic weights and standardized taxic weights. Finally, we applied a convolutional neural network for classification. In the test stage, we applied the proposed methodology to 50,580 (14,184 malignant and 36,396 benign) nodules from the image database. The proposed method presents promising results for the diagnosis of malignancy and benignity, achieving an accuracy of 92.63%, sensitivity of 90.7%, specificity of 93.47%, and receiver operating characteristic curve of 0.934. These results are promising and demonstrate a real rate of correct detections using the texture features. Because precocious detection allows faster therapeutic intervention, and thus a more favorable prognosis for the patient, we propose herein a methodology that contributes to the field in this aspect.