Employing histogram of oriented gradient to enhance concrete crack detection performance with classification algorithm and Bayesian optimization

In this study, we present the performance of Support Vector Machine (SVM), Convolution Neural Network (CNN), and Artificial Neural Network (ANN) with Bag of Words (BoW), Histogram of Oriented Gradients (HOG), and Image Pixels (IP) for face recognition. SVM, CNN, and ANN are machine learning approaches and has been used for pattern recognition, especially in face recognition technology. BoW, HOG, and IP are being used for image feature extraction. The testing has been conducted from publicly available AT&T face database. Every individual subject consists of 10 images with different facial expression, different illumination and the dimensions of the images is unified as 92-by-112 pixels with PGM formats. SVM achieved recognition accuracy of 97.00%, 96.00%, and 98.00% with subsequently BoW, HOG, and IP. CNN achieved recognition accuracy of 94.00%, 99.00%, and 99.50% with subsequently BoW, HOG, and IP. ANN achieved recognition accuracy of 96.00%, 99.00%, and 99.50% with subsequently BoW, HOG, and IP. The experimental results show that the IP with ANN approach clearly outperformed the others approaches.

We propose to combine support vector machine (SVM) models learned from different visual features for efficiently classifying fingerprint images. Real datasets of fingerprint images are collected from students at the Can Tho University. The SVM algorithm learns classification models from the handcrafted features such as the scale-invariant feature transform (SIFT) and the bag-of-words (BoW) model, the histogram of oriented gradients (HoG), the deep learning of invariant features Xception, extracted from fingerprint images. Followed which, we propose to train a neural network for combining SVM models trained on these different visual features, making improvements of the fingerprint image classification. The empirical test results show that combining SVM models is more accurate than SVM models trained on any single visual feature type. Combining SVM-SIFT-BoW, SVM-HoG, SVM-Xception improves 11.17%, 14.07%, 10.83% classification accuracy of SVM-SIFT-BoW, SVM-HoG and SVM-Xception, respectively.

Detection of brain tumor is very challenging task in today’s medical world. Nowadays, many doctors prefer ready-made methods to diagnose the tumor from given magnetic resonance imaging (MRI) images. Accurate and less time-consuming methods are used frequently to identify it. This paper introduces new techniques for finding and differentiating brain tumors. The new methods include support vector machine (SVM) and convolution neural network (CNN) with Softmax classifier. The SVM uses histogram of oriented gradients (HOGs) as a feature to label the given dataset. Further, the same feature of the test image is verified against labeled sets and classified. The SVM gives accuracy of 87.33%. In CNN, multilayer structure is used. It contains a convolution layer followed by the pooling layer and finally fully connected layer. In CNN, specifically, Alexnet is preferred for classification. In Alexnet, rectified linear unit (ReLU) is used as an activation function. At the output of it, Softmax is used as activation to classify the images. The CNN with Softmax classifier gives improved accuracy of 98.68%. In addition, the performance measures are also found out such as specificity, sensitivity, and precision. The sensitivity with SVM is 93.18%, and with CNN, it increases up to 100%. Comparing both methods with the obtained results, the CNN with Softmax classifier gives better results. Proposed method with CNN produces only one misclassified image as an advantage over the existing method, while SVM gives nine misclassified images. The methods are tested on 253 MRI images which are detected and classified correctly with Matlab.KeywordsSVMCNNHOGMRI

Face recognition is a process of verifying an individual using facial images and it is widely employed in identifying people on social media platforms, validating identity at ATMs, finding missing persons, controlling access to sensitive areas, finding lost pets, etc. Face recognition is still a trending research area because of various challenges like illumination variations, different poses, and expressions of the person. Here, a novel methodology is introduced for face recognition using Histogram of Oriented Gradients (HOG), histogram of Local Binary Patterns (LBP), and Convolutional Neural Network (CNN). The features from HOG, histogram of LBP, and deep features from the proposed CNN are linearly concatenated to produce the feature space and then classified by Support Vector Machine. The face databases ORL, Extended Yale B, and CMUPIE are used for experimental work and attained a recognition rate of 98.48%, 97.33%, and 97.28% respectively.

Colon cancer is cancer that is present on the inner side of colon walls or the rectum walls in the large intestine. Most of these types of cancer begin as abnormal growth of tissue called as polyp. Colonography uses low dose radiation Computed tomography (CT) scanning to obtain an interior view of the colon making use of special x-ray machine to view the large intestine for cancer and abnormal growths known as polyps. Radiologists examine these images to find polyp like structure using computer tools. As CT Colonography image contain noise such as lungs, small intestine, instruments during image capturing; segmenting colon from noise is the key task. Polyp occurrence can be detected mainly using shape feature; eliminating shapes similar to polyp is challenging. Hence, to tackle above issues, Image processing techniques are used by applying deep learning algorithm - Convolution Neural Network (CNN) and the results are compared with classical machine learning algorithm. In proposed method, each image is pre-processed to filter air filled dark region that includes colon, lungs etc. Next, each pre-processed CT Image separated into fixed number of blocks. Using pre-trained CNN, each block of ROI is classified as Type 1 (Usually Ascending and descending colon), Type 2 (Usually Traversal and sigmoidal colon) and Type3 (Noise such as lungs, instruments) to segment colon blocks by eliminating noise. Classified Blocks is further diagnosed for polyp like structure using pre-trained CNN by classifying each colon block as normal or abnormal. The experiment is setup with classical machine learning algorithms - Random Forest (RF) and k-nearest neighbor (KNN) by extracting texture feature - Local binary pattern (LBP) and shape feature - Histogram oriented gradient (HOG) for comparison. The experiment results showed the accuracy of proposed method for colon segmentation using CNN (87%) outperforms RF (85%) and KNN (83%). In, addition, the polyp detection accuracy of CNN (88%) is better than Random forest (85%) and KNN (80%). Hence, in the proposed method, there is significant accuracy improvement using deep learning algorithm compared to classical machine learning algorithms. It also provide baseline for automated colon cancer diagnosis using Deep learning algorithms for further research.

Cybercriminals use malware or malicious software to cause harm to the victim. Malware is a continuous source of concern for security teams. Malware analysis techniques, including static, dynamic, hybrid, and memory analysis, are used to comprehend the behavior and its impact. The aforementioned malware analysis techniques require domain knowledge to extract the artifacts from suspicious files, which is not always possible. A visualization approach, in which malware files are transformed into images, is one of the recently used techniques by researchers for malware detection and classification. In this paper, we apply four widely used techniques based on the visualization using a new dataset of memory dump files of malware families and benign classes. These visualization techniques include a histogram of oriented gradients (HOG) with multilayer perceptron (MLP), convolutional neural network (CNN) with pretrained weight of visual geometry group 16 (VGG), Transfer learning of VGG16 with support vector machine (SVM), and integration of global image descriptor (GIST) and HOG with SVM. Among the selected techniques, CNN with a pretrained weight of VGG16 outperformed the other techniques in terms of accuracy, precision, recall, and f1-score. Apart from the performance metrics, the results of selected techniques are also analyzed in terms of computational cost and memory utilization.

Palm tree detection using remote sensing images has received increasing attention in recent years, concerning the issues of sustainability, productivity and profitability. There has been significant progress in the research of using machine learning techniques, especially convolutional neural networks (CNNs) for automatic palm tree detection. However, whether CNNs can actually outperform traditional human-engineered approaches in terms of classification accuracy and detection speed is yet unknown. In the present study, we have compared human-engineered features namely histogram of oriented gradients (HOG), local binary pattern (LBP) and scale-invariant feature transform (SIFT) with features extracted using pre-trained AlexNet model for detecting palm trees in high resolution images obtained via unmanned aerial vehicle (UAV). Support vector machines (SVM) with linear and non-linear kernels were used to classify feature vectors obtained by different feature extractors. Haar-like features used in Viola-Jones framework was also tested in the study. Results showed that features extracted from the fifth convolutional layer of AlexNet achieved the highest accuracy of 96.1% using SVM with RBF kernel as classifier, which surpassed the accuracy obtained by fully-connected CNN, namely 95.6%. The results also suggest that SVM classifier with radial basis function (RBF) kernel using LBP as features is the optimal combination as it achieved comparable accuracy but higher detection speed than CNN approaches.

Histogram of oriented gradient (HOG) feature has been widely used in vehicle detection. In this paper, a modified version of HOG is proposed by introducing compass gradient into the HOG calculation. Three different versions of the modified HOG features are used as an input for linear and nonlinear support vector machine (SVM). The modified HOG variants proved to have better classification performance than that of the standard HOG. The classification results of modified HOG and nonlinear SVM are compared to the classification results of YOLO object detector. Finally, a vehicle detection system based on the best performing classifiers is introduced.

The finger vein recognition system uses blood vessels inside the finger of an individual for identity verification. The public is in favor of a finger vein recognition system over conventional passwords or ID cards as the biometric technology is harder to forge, misplace, and share. In this study, the histogram of oriented gradients (HOG) features, which are robust against changes in illumination and position, are extracted from the finger vein for personal recognition. To further increase the amount of information that can be used for recognition, different instances of the finger vein, ranging from the index, middle, and ring finger are combined to form a multi-instance finger vein representation. This fusion approach is preferred since it can be performed without requiring additional sensors or feature extractors. To combine different instances of finger vein effectively, score level fusion is adopted to allow greater compatibility among the wide range of matches. Towards this end, two methods are proposed: Bayesian optimized support vector machine (SVM) score fusion (BSSF) and Bayesian optimized SVM based fusion (BSBF). The fusion results are incrementally improved by optimizing the hyperparameters of the HOG feature, SVM matcher, and the weighted sum of score level fusion using the Bayesian optimization approach. This is considered a kind of knowledge-based approach that takes into account the previous optimization attempts or trials to determine the next optimization trial, making it an efficient optimizer. By using stratified cross-validation in the training process, the proposed method is able to achieve the lowest EER of 0.48% and 0.22% for the SDUMLA-HMT dataset and UTFVP dataset, respectively.

To reduce the occurrence of traffic accidents caused by distraction. a detection method based on histogram of oriented gradient (HOG) and support vector machine (SVM) is proposed for driver’s distraction behavior in this paper. Interest region of driver was detected first from video image, also the image was enhanced, denoised and normalized. Then the histogram of oriented gradient is used to extract the feature of the image. Meanwhile, the cross-validation method is used to optimize parameters in SVM. Finally, the effectiveness of the method is verified by compared with classical SVM algorithm and Local Binary Pattern algorithm (LBP) based on SVM algorithms. The results show that, the proposed method can obtain better classification accuracy.

The classification of benign and malignant masses in mammograms by Computer-Aided Diagnosis (CAD) is one of the most difficult and important tasks in the development of CAD systems. This classification has commonly been automated by extracting a set of handcrafted features from mammograms and relating the responses to breast cancer. Recently, the application of Deep Learning (DL) technology in medical imaging informatics has been attracting extensive research interest. However, limited medical image datasets and feature expression often reduce the performance of DL-based schemes. Therefore, this study aims to develop a new combined feature CAD method based on DL for classifying mammographic masses into three classes: normal, benign and cancer (malignant) masses. Three kinds of breast masses were scored by using Deep Convolution Neural Network (DCNN) as a feature extractor. Then the scoring features are combined with the image texture features as input to the classifier. This features including the scoring features, Gray-Level Co-occurrence Matrix (GLCM) and Histogram of Oriented Gradient (HOT) were employed to extract the breast mass information in mammograms and the classifier of Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) were trained for the classification task. Accuracy (ACC), Precision (Pre), Recall (Rec), F1-score (F1), and Overall Accuracy (Overall ACC) are used to evaluate the performance of the proposed system and the results show that the proposed multi-features combination model performs the best results. The performance of the XGBoost classifier has proved to be better in comparison to the SVM classification algorithms. As a result, when XGBoost was used as a classifier, the correct identification rate of the Overall ACC was 92.80% and that of malignant tumors was 84%, with reasonable and best results. These results indicate that the proposed method may help in more accurately diagnosing cases that are difficult to classify on images.

Objectives/Aim: The present paper deals with building a smart assistant with the aim of assisting the visually impaired people in mobility with confidence by realizing the nearby obstacles and also implement image processing techniques to recognize people. Methods: We use Raspberry Pi 4 which has increased computing performance and is interfaced to the picamera, GPS and GSM modules. Arduino pro mini with buzzer, ultrasonic sensors and vibration motors are used for obstacle detection. Certain libraries important for image processing are used such as OpenCV, Dlib, face detection (Haar cascades, HOG + Linear SVM, or CNNs), espeak for converting text to speech. Programming was implemented through Python and Arduino compiler. Findings: Analysis was carried out using the proposed system for the blind and visually impaired people who could move around comfortably with confidence and were also able to detect objects and recognize people. Hence the proposed system removes the use for the cumbersome white cane in exchange for small and handy modules which can be used in the form of wearables mounted on shoulders, hands and legs to detect the obstacles from multiple directions and provide for a comfortable wear. It was determined that the Raspberry pi 4 was incapable of running CNN detection for which ideally computer GPU was required, so HOG detection method was used instead. Application/Improvements: This project can be implemented mainly in the commercial field of helping visually impaired people with poor eyesight or being completely blind. Industrial applications can be devised and enhanced like robots and machineries along with Security, Identifying and Tracking. Keywords: Face Recognition; Obstacle Detection; Open Source Computer Vision (OpenCV); Deep Learning; Histogram of Oriented Gradients (HOG); Support Vector Machine (SVM); Convolutional Neural Network (CNN)

Objectives: To present an automated computer vision-based traffic sign detection model created to identify roadside signs accurately in different environmental conditions. The goal is to improve detection accuracy using a lightweight architecture suitable for mobile devices. Methods: This study presents CV-TSD model. The technique integrates Histogram of Oriented Gradients (HoG), Local Binary Pattern (LBP), and TensorFlow-based machine learning for image recognition. Traffic sign datasets are collected from various online fora, annotated, and trained using convolutional neural networks. The proposed system uses Histogram of Oriented Gradients (HoG), Local Binary Pattern (LBP), and deep learning with TensorFlow for effective image classification. A custom dataset of traffic sign images was collected from several online sources, manually labelled, and used to retrain a convolutional neural network using transfer learning. Three environmental categories—dark shade, cloudy, and rainy—were used to test classification robustness. We assessed performance with training accuracy, validation accuracy, and cross-entropy loss. Findings: Experimental results show that CV-TSD consistently outperforms traditional HoG and LBP methods across all metrics. The system achieves a maximum training accuracy of 98% and a validation accuracy of 97% with a limited number of image samples. Furthermore, the model keeps stable cross-entropy values, indicating effective learning without overfitting. The lightweight design allows reliable performance even with smaller datasets, making it ideal for mobile and intelligent transportation systems. Novelty: Unlike standard traffic sign detection methods, CV-TSD mimics human visual perception by merging semantic image understanding with deep learning. Its ability to classify accurately with minimal data and low computational demands marks a significant advancement for real-time applications. Keywords: Traffic Sign Detection, Deep Learning, Image Classification, TensorFlow, HoG, LBP, Transfer Learning

Wear debris provides important information about the machine condition that can be used to prevent the loss of expensive machinery. This information is crucial as it portrays the condition of the machines and can be used to predict early failure of the machinery that can prevent a major loss to the industry. Wear debris or particles produced in different parts of machine vary in shape, size, color, and texture. These characteristic features can be utilized to identify the type of wear debris. Human experts are extremely efficient in recognizing such objects; however, wear judgments are occasionally based on their specific perceptions. The goal is to look beyond the personal opinions and bring consistency in judging and recognizing wear particles. Keeping in view the above findings, this study focuses on the identification of wear particles by using shape-based features only. Different shape features, which include the Histogram of Oriented Gradients (HOG), Rotation Scale Translation (RST) invariant features, solidity, aspect ratio, circularity, and Euler number, are extracted and used to train three classification models of Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and discriminant analysis (DA) classifier. The performance of the classifiers are compared with each another and classification of debris based on shape features is analyzed.

Eggshell crack detection using deep convolutional neural networks