Preindication Mining for Predicting Pedestrian Action Change

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.

Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases.

Each wood species has their special characteristics which can be differentiated based on their anatomical structures through wood identification. One of the methods is by detecting macroscopic wood image using computer vision. This method is more rapid and accurate to identify wood species compared to the conventional method. In previous work, we have developed a computer vision technique for wood identification by combining Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM). As smartphone usage increasing worldwide, capturing wood structures using this smart device is very easy to do and can replace the use of digital microscopes. This paper propose a technique for extraction the wood species on smartphone using HOG method as well as the classification method using SVM on android smartphone. SVM was used to classify the extracted wood textures from the HOG features. In our experiments, wood images of 7 wood species were used i.e Mimusops_-elengi, Melanorrhoea wallichii, Acer niveum, Cratoxylon formosum, Agathis endertii, Dyera costulata and Knema glauca. Each species has a total of 100 training images and 100 testing images. The highest accuracy is obtained by Melanorrhoea wallichii and Agathis endertii species with 84.00% score. The Agathis endertii species has the highest sensitivity and the value reaches 86%. Moreover, the Melanorrhoea wallichii species has a highest score for specificity and precision

Edge is a very important type of feature for human detection and Histogram of Oriented Gradient (HOG) is the most popular method to encode edge information since proposed. Because HOG detects edges based on intensity gradients, it is not invariant with respect to image illumination. In this paper, we propose three new types of features based on local phase: Local Phase based symmetry (LPS), Local Phase based Asymmetry (LPA), and Histogram of Oriented Asymmetry (HOA) for human detection. The LPA and HOA are similar with gradient magnitude and HOG features, but from different perspective. The key idea is the intensity around an edge point in an image is always asymmetry. Thus we can detect edges by measuring the asymmetry of the local structure at every point in the image. This is achieved by analyzing the phase of its constituent frequency components. This asymmetry measurement is invariant with respect to image contrast. After the asymmetry is computed, this value could be distributed to different orientation bins according to gradient orientation. We also measure symmetry around each point which yields LPS. This is useful to detect torso and limbs. These local phase induced features are combined with the classical Aggregated Channel Features (ACF) and are fed into the boosted decision tree (BDT) framework. Experiment shows that the proposed features are complementary to the ACF features and will increase the detection accuracy.

This paper describes different approaches for the face authentication from the features and classification abilities point of view. Authors compare two types of features - Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) including their combination. These parameters are classified using Multilayer Neural Network (MLNN) and Support Vector Machines (SVM). Face authentication consists of several steps. The first step contains Viola-Jones algorithm for face detection. Authors resize the detected face for a fixed vector and afterwards, it is converted into grayscale. Next, feature extraction with a simple Min-Max normalization is applied. Obtained parameters are evaluated by classifiers and for each detected face, authors get posterior probability as the output of the classifier. Different approaches for face authentication are compared with each other using False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER), Receiver Operating Characteristic (ROC) and Detection Error Tradeoff (DET) curves. The results are verified with AR Face Database and elaborated in a feature extraction and classifier design point of view. Best results were achieved by HOG feature for SVM classifier. Detailed results are listed in the text below.

This research focuses on pedestrian detection using infrared thermal imager. The purpose is to locate the pedestrians from studying thermal imagery. Based on HOG (Histograms of Oriented Gradients), Adaboost algorithm is used as a way to perform the detection. The system is divided into three sections, to extract the features of the pedestrians, to train the Adaboost classifier, and to detect the pedestrian. To get the features of the pedestrians, data is gathered from inserted images. The features allow the detection to work well. The feature extraction includes image segmentation, ROI selection, and feature extraction. We have successfully located the positions of the pedestrians with the methods mentioned above. This can be applied to the development of the intelligent driver assistance system, giving more road traffic situations to the drivers throughout the night.

In recent years, pedestrian detection has attracted more attention in many practical applications. In this paper, a novel pedestrian detection method using quaternion histograms of oriented gradients (QHOG) was proposed, which is integrated the advantages of both the quaternionic representation and HOG feature of a color image. Firstly, the quaternionic representation was performed on the color image in the sliding window, and then the histograms of oriented gradients (HOG) feature was extracted over the quaternionic representation map. After that, the QHOG histogram was constructed to represent the sliding window. Two groups of experiments are performed on two popular pedestrian datasets, INRIA dataset and Daimler Chrysler (DC) dataset, respectively. The experimental results show that our proposed QHOG detector performs better than the HOG detector, HOG-LBP detector and MWLD detector.

Face Recognition plays a very important role in numerous occasions based on visual security in recent days. The current methods of face recognition are to extract the different features of different faces to distinguish from others, so feature extraction has become a vital step in face recognition. For this reason, this paper presents a new fusion of local difference binary (LDB) and histogram of oriented gradients (HOG) for face recognition. We use LDB descriptor to extract the local pattern features of a face image. At the same time, the edge features of the original image are extracted by using HOG descriptor. The proposed new fusion of features improves the shortcomings of the low accuracy and avoids the problems that the dimension of the general fusion of features is too high. The experimental results on ORL and Yale face database verify the validity of the proposed fusion of features.

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

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.

Automatic classification of severe and mild wear in worn surface images using histograms of oriented gradients as descriptor

Tread pattern image classification is an important means in providing useful clues in traffic accident control and crime case solving. This paper proposes an effective tread pattern classification algorithm with feature fusion based on transfer learning. The algorithm consists of four parts, (1) Transfer the knowledge of a pre-trained CNN model on ImageNet dataset to produce a new model for the task of tread pattern classification by fine-tuning the model parameters using tread pattern image data. (2) Extract features from multiple fully-connected layers as high-level features of tread pattern images. The concept of transfer learning solves the problem of lacking large training dataset. (3) Histogram of Oriented Gradient (HOG) is calculated as the low-level feature of the tread pattern images. (4) The features from the CNN model are combined with HOG as fusion feature, which is used to train SVM classifier for tread pattern image classification. Experimental results demonstrated the outstanding performance of the proposed algorithm for tread pattern image classification.

Forthcoming surveys such as the Large Synoptic Survey Telescope (LSST) and Euclid necessitate automatic and efficient identification methods of strong lensing systems. We present a strong lensing identification approach that utilizes a feature extraction method from computer vision, the Histogram of Oriented Gradients (HOG), to capture edge patterns of arcs. We train a supervised classifier model on the HOG of mock strong galaxy–galaxy lens images similar to observations from the Hubble Space Telescope (HST) and LSST. We assess model performance with the area under the curve (AUC) of a Receiver Operating Characteristic (ROC) curve. Models trained on 10,000 lens and non-lens containing images exhibit an AUC of 0.975 for an HST-like sample, 0.625 for one exposure of LSST, and 0.809 for 10 yr mock LSST observations. Performance appears to continually improve with the training set size. Models trained on fewer images perform better in the absence of the lens galaxy light. However, with larger training data sets, information from the lens galaxy actually improves model performance, indicating that HOG captures much of the morphological complexity of the arc-finding problem. We test our classifier on data from the Sloan Lens ACS Survey and find that small-scale image features reduce the efficiency of our trained model. However, these preliminary tests indicate that some parameterizations of HOG can compensate for differences between observed mock data. One example best-case parameterization results in an AUC of 0.6 in the F814 filter image, with other parameterization results equivalent to random performance.

Vehicle classification is vital to an intelligent transport system. To obtain a high accuracy, it is the most crucial process to extract reliable and distinguishable features of vehicles. A feature extraction method using a lightweight convolutional network for vehicle classification is proposed. The main contributions are threefold: (1) a lightweight network named LWNet with two convolution layers is proposed to extract the features of the vehicles; (2) Hu moment is integrated with spatial location information to improve its own describing and distinguishing ability; and (3) histogram of oriented gradient (HOG) feature is extracted from the complete image, and then the above two kinds of features are combined with HOG to form the vector. And then, a support vector machine is trained to obtain the classification model. Vehicles are classified into six categories, i.e., large bus, car, motorcycle, minibus, truck, and van. The experimental results have demonstrated that the classification accuracy can achieve 97.39%, which is 3.81% higher than that obtained from the conventional methods. In addition, for this vehicle classification task, the proposed lightweight convolutional network can achieve comparable or even higher performance compared to the deep convolutional neural networks, while the proposed method does not need the support of a graphics processing unit and has much lower complexity without the training process.

Recognizing pedestrians in traffic scenarios is an important task for any smart vehicle application. Within the context of a real-time stereo based driving assistance system, this paper presents a novel method for recognizing pedestrians. We have designed a meta- classification scheme composed of a mixture of Bayesian and boosted classifiers that learn the discriminant features of a pedestrian space partitioned into attitudes like pedestrian standing and pedestrian running. Our experiments show that the mixture of classifiers proposed outperforms a single classifier trained on the whole un-partitioned object space. For classification we have used a probabilistic approach based on Bayesian Networks and Adaptive Boosting. Two types of features were extracted from the image: anisotropic gaussians and histograms of gradient orientations (HOG).KeywordsPedestrian recognitionclassificationanisotropic gaussianshistogram of gradient orientations