Histogram Of Oriented Gradients Features Research Articles

Influence of VR-Assisted College Dance on College Students' Physical and Mental Health and Comprehensive Quality

With the development and the popularization of sports dance, sports dance teaching has become a required elective course in universities. Sports dance can not only improve students' comprehensive quality, but also affect college students' healthy psychology. The use of VR (Virtual Reality) technology in dance education will definitely develop and promote dance education. This paper studies an effective feature extraction method for the characteristics of dance movements based on VR. The edge features of all video images in each segment are accumulated into one image, and the directional gradient histogram features are extracted from it. The results show that compared with the current robust regression method and cascade regression method, our method has higher positioning accuracy on the pollution test set, and more than 75% of the sample errors in this method are within 0.1. This also verifies the effectiveness of this motion recognition algorithm for dance motion recognition. Dance can effectively resist the psychological barriers of college students and improve their comprehensive quality.

A novel low-resource consumption and high-speed hardware implementation of HOG feature extraction on FPGA for human detection

In today’s increasingly complex traffic environment, pedestrian detection has become increasingly important. The Histogram of Oriented Gradients (HOG) algorithm has been proven to be highly efficient in pedestrian detection. This paper proposes a low-resource consumption, high-speed hardware implementation for HOG algorithm. In the case of a slight sacrifice in accuracy, it increases computational speed and reduces resource consumption. Experimental results demonstrate that the implementation achieves a speed of 0.933 pixels per clock cycle and consumes 4117 look-up tables and 4.5 Kbits of block RAMs while its accuracy decreases by 1.2% on the INRIA dataset and by 0.11% on the MIT dataset.

Application of histogram of oriented gradient features and support vector machine models in stage lighting control in drama

SummaryThe lighting control of the theatrical stage is directly related to the presentation effect of the program and the audience's experience. With the development of intelligent control technology, the control of lighting effects has been continuously improved. However, the existing stage lighting control lacks intelligence, resulting in poor control effect and weak energy‐saving performance. Based on this, a human contour image classification model using support vector machines (SVM) based on histogram of oriented gradient (HOG) feature extraction method is constructed to achieve stage lighting control. The results show that the revised HOG feature extraction approach achieves recognition performance of 99.6% when the ratio is 0.8. The HOG‐SVM model proposed in the study has the best recognition performance, reaching 99.6%. This indicates that the stage lighting control system based on HOG‐SVM feature extraction constructed in the research can meet the actual needs of stage program performance, which can be applied to stage lighting control to effectively reduce energy consumption.

Application of Ensemble Learning in CXR Classification for Improving COVID-19 Diagnosis

This study delves into the vital task of classifying chest X-ray (CXR) samples, particularly those related to respiratory ailments, using advanced clinical image analysis and computer-aided radiology techniques. Its primary focus is on developing a classifier to accurately identify COVID-19 cases. Through the application of machine learning and computer vision methodologies, the research aims to enhance the precision of COVID-19 detection. It investigates the effectiveness of Histogram of Oriented Gradients (HOG) feature extraction techniques in conjunction with various classifiers, such as Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), K-nearest neighbor (KNN), and Tree Bagger (TB), alongside an innovative ensemble learning approach. Results indicate impressive accuracy rates, with KNN, SVM, DT, NB, and TB all surpassing the 90% mark. However, the ensemble learning method emerges as the standout performer. By leveraging HOG features extracted from CXR images, this approach presents a robust solution for COVID-19 diagnosis, offering a powerful tool to address the diagnostic challenges posed by the pandemic.

Automatic Diagnosis of Parkinson’s Disease using Handwriting Patterns

Parkinson's Disease (PD) is a neuro-degenerative syndrome characterized by motor and non-motor signs, and early detection is crucial for effective intervention. This paper presents a novel approach for PD detection using computer vision and machine learning techniques applied to Spiral-Wave handwriting analysis. The dataset comprises frontal handwritten images obtained through the Spiral-Wave test, capturing subtle motor control differences. Our methodology involves resizing images to a standardized 200x200 pixels, converting them to grayscale, and applying thresholding for improved feature abstraction. Histogram of Oriented Gradients (HOG) is employed to capture shape and texture information. The development of a strong approach for deriving significant features from Spiral-Wave handwriting patterns and the usage of machine learning classifiers for precise PD analysis are the two main goals of this work. The emphasis is on using Random Forest and K-Nearest Neighbours (KNN) classifiers for Spiral and Wave pictures, respectively, in conjunction with the Histogram of Oriented Gradients (HOG) approach for feature extraction. For Spiral images, a Random Forest Classifier is utilized, achieving an accuracy of 86.67%. The classifier's interpretability is enhanced through an analysis of feature importance, revealing critical HOG features for distinguishing between healthy and PD-afflicted patterns. The Wave images are classified using a K-Nearest Neighbours (KNN) model, attaining an accuracy of 76.67%. Performance metrics, including precision, recall, and F1-score, offer a nuanced assessment of the KNN model's capabilities.

Application of Ensemble Learning in CXR Classification for Enhancing COVID-19 Diagnosis

This study delves into the vital task of classifying chest X-ray (CXR) samples, particularly those related to respiratory ailments, using advanced clinical image analysis and computer-aided radiology techniques. Its primary focus is on developing a classifier to accurately identify COVID-19 cases. Through the application of machine learning and computer vision methodologies, the research aims to enhance the precision of COVID-19 detection. It investigates the effectiveness of Histogram of Oriented Gradients (HOG) feature extraction techniques in conjunction with various classifiers, such as Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), K-nearest neighbor (KNN), and Tree Bagger (TB), alongside an innovative ensemble learning approach. Results indicate impressive accuracy rates, with KNN, SVM, DT, NB, and TB all surpassing the 90% mark. However, the ensemble learning method emerges as the standout performer. By leveraging HOG features extracted from CXR images, this approach presents a robust solution for COVID-19 diagnosis, offering a powerful tool to address the diagnostic challenges posed by the pandemic.

Using the Support Vector Machine method with the HOG feature for classification of orchid types

Orchids are the most species-rich flowering plants, with approximately 750 genera and 43,000 types of orchids in the world, of which about 5,000 species have been recorded in several provinces in Indonesia. Orchids have beautiful flowers with attractive colors, making them ornamental plants that many people like. From plant morphology, orchid plants can be differentiated based on the morphology of flowers, leaves, fruit, stems, and roots. The leaves of orchid plants have their characteristics for each type of orchid, such as long, round, or lanceolate. All orchids have veins that run parallel to their leaves. The individual shapes of orchid leaves can be classified using a Support Vector Machine (SVM) and Histogram of Gradient (HOG). In this research, five types of orchids that are popular among orchid lovers were used, namely Dendrobium, Cattleya, Oncidium, Phalaenopsis, and Vanda orchids, which were taken from public data. The accuracy of this method in classifying orchid species based on leaf morphology can be measured using a confusion matrix that measures precision, recall, and accuracy. From five tests, the Oncidium orchid had the highest average accuracy with a value of 98%, the Vanda orchid had the highest average precision of 99.80%, and the Cattleya orchid had the highest average recall of 100%.

Subcutaneous fat predicts bone metastasis in breast cancer: A novel multimodality-based deep learning model.

This study explores a deep learning (DL) approach to predicting bone metastases in breast cancer (BC) patients using clinical information, such as the fat index, and features like Computed Tomography (CT) images. CT imaging data and clinical information were collected from 431 BC patients who underwent radical surgical resection at Harbin Medical University Cancer Hospital. The area of muscle and adipose tissue was obtained from CT images at the level of the eleventh thoracic vertebra. The corresponding histograms of oriented gradients (HOG) and local binary pattern (LBP) features were extracted from the CT images, and the network features were derived from the LBP and HOG features as well as the CT images through deep learning (DL). The combination of network features with clinical information was utilized to predict bone metastases in BC patients using the Gradient Boosting Decision Tree (GBDT) algorithm. Regularized Cox regression models were employed to identify independent prognostic factors for bone metastasis. The combination of clinical information and network features extracted from LBP features, HOG features, and CT images using a convolutional neural network (CNN) yielded the best performance, achieving an AUC of 0.922 (95% confidence interval [CI]: 0.843-0.964, P< 0.01). Regularized Cox regression results indicated that the subcutaneous fat index was an independent prognostic factor for bone metastasis in breast cancer (BC). Subcutaneous fat index could predict bone metastasis in BC patients. Deep learning multimodal algorithm demonstrates superior performance in assessing bone metastases in BC patients.

Vision-Based On-Road Nighttime Vehicle Detection and Tracking Using Improved HOG Features.

The lack of discernible vehicle contour features in low-light conditions poses a formidable challenge for nighttime vehicle detection under hardware cost constraints. Addressing this issue, an enhanced histogram of oriented gradients (HOGs) approach is introduced to extract relevant vehicle features. Initially, vehicle lights are extracted using a combination of background illumination removal and a saliency model. Subsequently, these lights are integrated with a template-based approach to delineate regions containing potential vehicles. In the next step, the fusion of superpixel and HOG (S-HOG) features within these regions is performed, and the support vector machine (SVM) is employed for classification. A non-maximum suppression (NMS) method is applied to eliminate overlapping areas, incorporating the fusion of vertical histograms of symmetrical features of oriented gradients (V-HOGs). Finally, the Kalman filter is utilized for tracking candidate vehicles over time. Experimental results demonstrate a significant improvement in the accuracy of vehicle recognition in nighttime scenarios with the proposed method.

A hybridized feature extraction for COVID-19 multi-class classification on computed tomography images

COVID-19 has killed more than 5 million individuals worldwide within a short time. It is caused by SARS-CoV-2 which continuously mutates and produces more transmissible new different strains. It is therefore of great significance to diagnose COVID-19 early to curb its spread and reduce the death rate. Owing to the COVID-19 pandemic, traditional diagnostic methods such as reverse-transcription polymerase chain reaction (RT-PCR) are ineffective for diagnosis. Medical imaging is among the most effective techniques of respiratory disorders detection through machine learning and deep learning. However, conventional machine learning methods depend on extracted and engineered features, whereby the optimum features influence the classifier's performance. In this study, Histogram of Oriented Gradient (HOG) and eight deep learning models were utilized for feature extraction while K-Nearest Neighbour (KNN) and Support Vector Machines (SVM) were used for classification. A combined feature of HOG and deep learning feature was proposed to improve the performance of the classifiers. VGG-16 + HOG achieved 99.4 overall accuracy with SVM. This indicates that our proposed concatenated feature can enhance the SVM classifier's performance in COVID-19 detection.

Identification of Adulterants in Chili Powder Based on the Histogram of Oriented Gradients Algorithm by Using an Electronic Nose

Driven by economic interests, adding adulterations in chili powder is a problem which threatens people’s health. Thanks to its nondestructive, rapid, and portable advantages, electronic nose has more potential to be used for adulteration detection than the traditional methods. An approach for identifying the adulterants in chili powder was proposed in this paper. Firstly, an electronic nose system with 10 gas sensors was designed, and then the response images were drawn based on the response signals of the electronic nose. Afterwards, gas features were extracted from those response images by using a histogram of oriented gradients (HOG) algorithm. Finally, an SVM-based identification model was constructed to achieve the identification of plant adulterants in chili powder. The experimental results showed that the identification accuracy of the adulterant categories (almond shell, red beetroot, and tomato peel) based on the HOG features could reach up to 98.3%, and the identification results for adulterant content were 94.2%, 93.3%, and 95%, respectively. Furthermore, in order to compare the efficiency of the proposed identification approach, the widely used model AlexNet was also investigated and discussed.

Neutrosophic Fuzzy Logic based SVM approach for Enhanced Skin Cancer Prediction

In this study, a thorough methodology is used to present a unique way for improving skin cancer prediction accuracy. The research uses sophisticated preprocessing methods, such as the Frost filter for noise reduction and histogram equalization for contrast enhancement, to boost contrast on dermoscopic pictures from various sources, using an ISIC 2020 dataset. These actions greatly raise the dermoscopic pictures’ overall quality and usefulness for diagnosis. Utilizing labeled data for training, we offer a Fuzzy-based C-means clustering technique based on Neutrosophic Logic during the segmentation phase. In order to overcome ambiguities in skin lesion segmentation, the neutrosophic set—a groundbreaking idea in philosophy—is used. The suggested model enhances the accuracy of segmentation by modifying the neutrosophic set functions. For precise prediction, the approach combines Support Vector Machine (SVM) classification with Histogram of Oriented Gradient (HOG) feature extraction. While SVM, a supervised learning algorithm, diagnoses skin lesions based on the collected features, HOG features capture gradient information. To improve object recognition and classification, the HOG-SVM architecture is made to methodically collect and quantify essential information using dermoscopic pictures. The use of Neutrosophic Fuzzy Logic, which combines the benefits of fuzzy clustering with neutrosophic sets to produce more precise and nuanced predictions, sets the suggested method apart. The integration of different approaches into a holistic solution for skin cancer prediction is what makes the proposed study innovative. Findings and performance analysis show of the HOG-SVM method exhibits an outstanding accuracy of 98.69%, outperforming LR, KNN, and GNB methods. Python software is used to accomplish the suggested approach. This discovery opens up a possible path for better skin cancer diagnosis and advances the rapidly developing fields of dermatology and medical image processing.

Comparative analysis of mature tomato detection by feature extraction and machine learning for autonomous greenhouse robots

Accurate detection of tomatoes grown in greenhouses is important for timely harvesting. In this way, it is ensured that mature tomatoes are collected by distinguishing them from the unripe ones. Insufficient light, occlusion, and overlapping adversely affect the detection of mature tomatoes. In addition, it is time consuming for people to detect mature tomatoes at certain periods in large greenhouses. For these reasons, high-performance automatic detection of tomatoes by greenhouse robots has become an increasingly studied area today. In this paper, two feature extraction methods, histogram of oriented gradients (HOG) and local binary patterns (LBP), which are effective in object recognition, and two important and commonly used classifiers of machine learning, support vector machines (SVM) and k-nearest neighbor (kNN), are comparatively used to detect and count tomatoes. The HOG and LBP features are classified separately and together by SVM or kNN, and the success of each case are compared. Performance of the detection is improved by eliminating false positive results at the postprocessing stage using color information.

Multi-Method Analysis of Histopathological Image for Early Diagnosis of Oral Squamous Cell Carcinoma Using Deep Learning and Hybrid Techniques.

Oral cancer is a fatal disease and ranks seventh among the most common cancers throughout the whole globe. Oral cancer is a type of cancer that usually affects the head and neck. The current gold standard for diagnosis is histopathological investigation, however, the conventional approach is time-consuming and requires professional interpretation. Therefore, early diagnosis of Oral Squamous Cell Carcinoma (OSCC) is crucial for successful therapy, reducing the risk of mortality and morbidity, while improving the patient's chances of survival. Thus, we employed several artificial intelligence techniques to aid clinicians or physicians, thereby significantly reducing the workload of pathologists. This study aimed to develop hybrid methodologies based on fused features to generate better results for early diagnosis of OSCC. This study employed three different strategies, each using five distinct models. The first strategy is transfer learning using the Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201 models. The second strategy involves using a pre-trained art of CNN for feature extraction coupled with a Support Vector Machine (SVM) for classification. In particular, features were extracted using various pre-trained models, namely Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201, and were subsequently applied to the SVM algorithm to evaluate the classification accuracy. The final strategy employs a cutting-edge hybrid feature fusion technique, utilizing an art-of-CNN model to extract the deep features of the aforementioned models. These deep features underwent dimensionality reduction through principal component analysis (PCA). Subsequently, low-dimensionality features are combined with shape, color, and texture features extracted using a gray-level co-occurrence matrix (GLCM), Histogram of Oriented Gradient (HOG), and Local Binary Pattern (LBP) methods. Hybrid feature fusion was incorporated into the SVM to enhance the classification performance. The proposed system achieved promising results for rapid diagnosis of OSCC using histological images. The accuracy, precision, sensitivity, specificity, F-1 score, and area under the curve (AUC) of the support vector machine (SVM) algorithm based on the hybrid feature fusion of DenseNet201 with GLCM, HOG, and LBP features were 97.00%, 96.77%, 90.90%, 98.92%, 93.74%, and 96.80%, respectively.

A novel fast hybrid face recognition approach using convolutional Kernel extreme learning machine with HOG feature extractor

The ability to identify a person's face from a digitized photo or video frame against a database of faces is known as facial recognition. In the past few years, algorithms that use deep learning to recognize faces have become more popular. The majority of them are predicated on extremely accurate but complicated Convolutional Neural Networks (CNNs), which require a lot of computational power, storage space, and a number of training epochs before they provide satisfying results, and are notably difficult to implement. In an effort to reduce the training time by reducing the number of epochs and increase accuracy, this paper introduces a novel fast hybrid face recognition approach HOG-CKELM, based on CNN that makes use of Kernel based Extreme Learning Machines (KELM) and Histogram of Oriented Gradients (HOG) as facial feature extractor. The effectiveness of the proposed hybrid face recognition technique is evaluated using AT & T, Yale, and JAFFE datasets. When compared to traditional HOG-CNN based techniques, the experimental evaluation indicates that the proposed method for face recognition is capable of achieving excellent performance in terms of accuracy and training time.

Utilization of information from CNN feature maps for offline word-level writer identification

This paper proposes a text-independent method for authorship identification using handwritten word images. Our method is text-independent and imposes no limitations on the size of the input word images being analyzed. To begin with, the SIFT algorithm is utilized to extract key-point regions (fragments) across different levels of abstraction, encompassing allographs, characters, or character combinations. These fragments are subsequently processed through a CNN network, yielding feature maps corresponding to convolution layers. The information in these maps is then transformed into a fixed-dimension representation using a modified version of the HOG feature descriptor.The noteworthy contribution of our proposal lies in harnessing additional cues from CNN’s feature maps for writer identification. We propose a measure to gauge the importance or ‘saliency’ of feature maps within a CNN layer during training. This measure originates from applying Sparse Principal Component Analysis (SPCA) to Histogram Of Gradient (HOG) features. Once saliency values are obtained, we combine them with HOG representations to create writer descriptors tailored to a CNN layer. The derived descriptors are then passed through a set of SVM classifiers that are scored at two levels to determine the identity of the handwritten word image.The efficacy of our system has been demonstrated on the word-level data of the CVL, IAM and CERUG-EN datasets with an accuracy of 82.10%, 87.68%, and 75.80% respectively. The results obtained are shown to be promising when compared with previous works.

Face and Facial Expressions Recognition System for Blind People Using ResNet50 Architecture and CNN

Many blind individuals have difficulties in recognizing people’s facial expression which may impact their social interaction. With the recognition, the blind individuals can accurately interpret and respond to the emotions. There is a lack in the existing application with the combination of face and facial expressions recognition. The blind individuals have to rely on multiple applications to accomplish the same task, making it difficult and time-consuming for them to use. The paper aims to recognize faces and facial expressions for blind individuals and provides feedback in real-time. Three face detection algorithms of Haar Cascade Classifier, Dlib, and RetinaFace are compared. Dlib is chosen to process with Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM). It loads the pre-trained model, computes the HOG features, slide the window scanning at different scales, classify the windows using the SVM classifier, generate bounding boxes, and applying non-maximum suppression. ResNet50 architecture is employed to recognize face and Convolutional Neural Networks (CNN) is applied to recognize facial expression. The training accuracy is 70% and validation accuracy is 60%.

Two-Level Feature-Fusion Ship Recognition Strategy Combining HOG Features with Dual-Polarized Data in SAR Images

Due to the inherent characteristics of synthetic aperture radar (SAR) imaging, SAR ship features are not obvious and the category distribution is unbalanced, which makes the task of ship recognition in SAR images quite challenging. To address the above problems, a two-level feature-fusion ship recognition strategy combining the histogram of oriented gradients (HOG) features with the dual-polarized data in the SAR images is proposed. The proposed strategy comprehensively utilizes the features extracted by the HOG operator and the shallow and deep features extracted by the Siamese network in the dual-polarized SAR ship images, which can increase the amount of information for the model learning. First, the Siamese network is used to extract the shallow and deep features from the dual-polarized SAR images, and then the HOG feature of the dual-polarized SAR images is also extracted. Furthermore, the bilinear transformation layer is used for fusing the HOG features from dual-polarized SAR images, and the grouping bilinear pooling process is used for fusing the dual-polarized shallow feature and deep feature extracted by the Siamese network, respectively. Finally, the catenation operation is used for fusing the dual-polarized HOG features and dual-polarized shallow feature and deep feature, respectively, which are used for the recognition of the SAR ship targets. Experimental results tested on the OpenSARShip2.0 dataset demonstrate the correctness and effectiveness of the proposed strategy, which can effectively improve the recognition performance of the ship targets by fusing the different level features of the dual-polarized SAR images.

Performance Analysis of Naive Bayes Variation Method in Spice Image Classification Using Histogram of Gradient Oriented (HOG) Feature Extraction

Indonesia has a lot of natural wealth of spices. The diversity of spices is an inseparable aspect of Indonesian history. Spices and seasonings are biological resources that have long played an important role in human life. Indonesian spices have almost the same color and shape. The purpose of this study was to analyze the performance of the Naïve Bayes variation method in classifying spices using a Histogram Of Oriented Gradient (HOG) feature extraction. Based on 3 tests, the performance of the four Naïve Bayes variation methods carried out in this study, it can be seen that testing 5 types of spices using the Gaussian Naïve Bayes method obtained the best performance with an accuracy of 0.946, a precision of 0.95, a recall of 0.945, f1 score of 0.947, f beta score of 0.946, and Jaccard score of 0.90. Where as using the Complement Naïve Bayes method gets the lowest performance. From the results of this study it can be concluded that by utilizing HOG feature extraction and the Naïve Bayes variation method, maximum classification results are obtained in classifying spices. To obtain more accurate classification results, consider using other methods and other feature extraction

A computer-aided diagnosis system for brain tumors based on artificial intelligence algorithms.

The choice of treatment and prognosis evaluation depend on the accurate early diagnosis of brain tumors. Many brain tumors go undiagnosed or are overlooked by clinicians as a result of the challenges associated with manually evaluating magnetic resonance imaging (MRI) images in clinical practice. In this study, we built a computer-aided diagnosis (CAD) system for glioma detection, grading, segmentation, and knowledge discovery based on artificial intelligence algorithms. Neuroimages are specifically represented using a type of visual feature known as the histogram of gradients (HOG). Then, through a two-level classification framework, the HOG features are employed to distinguish between healthy controls and patients, or between different glioma grades. This CAD system also offers tumor visualization using a semi-automatic segmentation tool for better patient management and treatment monitoring. Finally, a knowledge base is created to offer additional advice for the diagnosis of brain tumors. Based on our proposed two-level classification framework, we train models for glioma detection and grading, achieving area under curve (AUC) of 0.921 and 0.806, respectively. Different from other systems, we integrate these diagnostic tools with a web-based interface, which provides the flexibility for system deployment.