Histogram Of Oriented Gradients Method Research Articles

Investigation of Ways to Improve the HOG Method in the Classification of Histological Images by Machine Learning Methods

This study analyzes ways to improve the performance of the histogram of oriented gradients (HOG) method for histological image classification using machine learning methods. We propose applying HOG to each color channel of the image, which improves the extraction of texture features characteristic of histological data. A comparative analysis of the traditional HOG and the improved approach is conducted, including an experimental evaluation of their accuracy and processing time using SVM, RF, DT, KNN, and NB classifiers. The results show that the proposed improvements improve the classification accuracy, which makes the modified HOG method promising for application in biomedical analysis.

Research on the classification of complex noise-mixed microseismic events based on machine vision

Event classification is important for accurately monitoring and warning against rockburst hazards using microseismic technology. Here, we propose an automatic classification method for microseismic events based on machine vision. The method uses Histogram of Oriented Gradient (HOG) integrated with Support Vector Machine (SVM) as the core model (HOG-SVM, HSVM) to classify microseismic events. First, the method uses as input spectrograms generated from microseismic event signals recorded in the field. Next, the HOG method is used to accurately extract the spectral feature information of the useful signals of microseismic events under the interference of noisy signal. Finally, the extracted feature data is used to train SVM, after the training is completed, the SVM is used to classify the microseismic events. The performance of the method for categorizing microseismic events was tested using multiple independent test sets built from data monitored in the field of a mine in Shandong Province. The results show that the method can effectively extract the spectral feature information of useful signals of microseismic events contaminated with noise, with good classification accuracy and robustness to noise. It classifies microseismic events with high accuracy and efficiency compared to well-performing classification methods based on seismic source parameters and typical depth models. The method can provide technical support for the effective classification of microseismic events in complex construction sites, especially in noisy deep underground construction environments.

A Combination of Transfer Learning and Support Vector Machine for Robust Classification on Small Weed and Potato Datasets

Agriculture is the primary sector in Indonesia for meeting people's daily food demands. One of the agricultural commodities that replace rice is potatoes. Potato growth needs to be protected from weeds that compete for nutrients. Spraying using pesticides can cause environmental pollution, affecting cultivated plants. Currently, agricultural technology is being developed using an Artificial Intelligence (AI) approach to classifying crops. The classification process using AI depends on the number of datasets obtained. The number of datasets obtained in this research is not too large, so it requires a particular approach regarding the AI method used. This research aims to use a combination of feature extraction methods with local and deep feature approaches with supervised machine learning to classify of small datasets. The local feature method used in this research is Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG), while the deep feature method used is MobileNet and MobileNetV2. The famous Support Vector Machine (SVM) uses the classification method to separate two data classes. The experimental results showed that the local feature HOG method was the fastest in the training process. However, the most accurate result was using the MobileNetV2 deep feature method with an accuracy of 98%. Deep features produced the best accuracy because the feature extraction process went through many neural network layers. This research can provide insight on how to analyze a small number of datasets by combining several strategies

Advanced imaging system for brain tumor automatic classification from MRI images using HOG and BOF feature extraction approaches

AbstractBrain tumors have recently become a widespread phenomenon that affects almost all age ranges. Magnetic resonance imaging (MRI) is a widely used technique nowadays to diagnose this life‐threatening disease. Therefore, using a computer‐aided diagnosis system is essential for the early identification and classification of brain tumors. This work aims to develop a fully automated classification framework for MRI brain tumor images using machine learning (ML) algorithms. In this article, four ML algorithms such as support vector machines (Cubic SVM), k‐nearest neighbors (fine KNN), decision tree (fine tree), and naive Bayes (Kernel) were implemented with two feature extraction approaches: histogram of oriented gradients (HOG) and a bag of features (BOF) approach. The novelty of this work is based on the original usage of BOF and HOG approaches over different types of ML algorithms for MRI brain tumor image classification. In addition, combining two public datasets that contain three brain tumor types to generalize and improve the proposed work. The results showed that the fine KNN algorithm achieved the greatest success among all algorithms using the HOG method with an accuracy of 98.62% on 6328 MRI images.

A Hybrid Method of Feature Extraction for Signatures Verification Using CNN and HOG a Multi-Classification Approach

The offline signature verification system’s feature extraction stage is regarded as crucial and has a big impact on how well these systems perform because the quantity and calibre of the features that are extracted determine how well these systems can distinguish between authentic and fake signatures. In this study, we introduced a hybrid method for extracting features from signature images, wherein Convolutional Neural Network (CNN) and Histogram of Oriented Gradients (HOG) were used, followed by the feature selection algorithm (Decision Trees) to identify the key features, and finally the CNN and HOG methods were combined. Three classifiers were employed to evaluate the hybrid method’s efficacy (long short-term memory, support vector machine, and K-nearest Neighbor). The experimental findings indicated that our suggested model executed quite satisfactorily in terms of efficiency and predictive ability, obtaining accuracy of (95.4%, 95.2%, and 92.7%) with UTSig dataset, and (93.7%, 94.1%, and 91.3%) with CEDAR dataset. This accuracy is deemed to be a high significance, particularly given that we checked skilled forged signatures that are more difficult to recognize than other forms of forged signatures like (simple or opposite-hand).

Application of neural network and computer vision technologies for image analysis of skin lesion

Opportunity research of using neural networks and computer vision to analyze images of skin lesion and identify features of various pathologies, including oncological neoplasms. A methodology has been developed that makes it possible to evaluate the significance of combinations of color components and spaces in feature extraction using local binary patterns (LBP) and histogram of oriented gradients (HOG) computer vision technologies to extract features of skin changes binary classification of human skin lesions. Optimization of extracted feature makes it possible to more effectively solve the problem of data separability in classification. Research reveals an accessible way to classify skin lesions on a small dataset (less than 1000 images). Research is supposed to be applied to data sequences obtained using a new unique method of multispectral processing of skin lesions. In the course of the work, data from the ISIC-19 and ISIC-20 datasets were used. Samples were formed with a limit of 1000 images for training and validating the models. Additionally, a test sample of 250 images was formed. All images were reduced to 128 × 128 pixels and converted to YCrCb, BGR, Grayscale, HSV color spaces. Features were extracted for each color channel using the HOG and LBP methods. Mathematical models, including neural networks have been used for data classification. The effectiveness of features combinations by color channels and feature extraction methods was evaluated. The preprocessed images were divided into training and validation subsets in a 70/30 ratio. The accuracy, recall, precision and f1-score metrics were used to evaluate the models. The models were evaluated using stratified cross-validation and a test dataset. Optimization of model parameters was carried out based on the loss function represented by the average of cross-validation and evaluation on the validation set. In the process of research, more than 15 000 different optimizations of model parameters were executed. The most stable results on the validation dataset were achieved using ensemble of models, which were trained on a combination of features using local binary patterns (LBP) and histogram of oriented gradients (HOG) technologies. Models which used only local binary patterns technology had the best metrics values, but these models are not recommended to be used in practice without ensemble with stronger models. The results gained can be applied for usage with an ensemble of state-of-the-art convolutional and recurrent neural networks. The proposed approach is universal and applicable both for the analysis of individual images of skin neoplasms and for the analysis of their sequences obtained by the method of multispectral image processing. The technique can be applied to datasets with a limited amount of data. The results obtained will be of interest to specialists in the fields of computer vision and medical images analysis.

Clustered star formation at early evolutionary stages

Context. The process of high-mass star formation during the earliest evolutionary stages and the change over time of the physical and chemical properties of individual fragmented cores are still not fully understood. Aims. We aim to characterize the physical and chemical properties of fragmented cores during the earliest evolutionary stages in the very young star-forming regions ISOSS J22478+6357 and ISOSS J23053+5953. Methods. NOrthern Extended Millimeter Array 1.3 mm data are used in combination with archival mid- and far-infrared Spitzer and Herschel telescope observations to construct and fit the spectral energy distributions of individual fragmented cores. The radial density profiles are inferred from the 1.3 mm continuum visibility profiles, and the radial temperature profiles are estimated from H2CO rotation temperature maps. Molecular column densities are derived with the line fitting tool XCLASS. The physical and chemical properties are combined by applying the physical-chemical model MUlti Stage ChemicaL codE in order to constrain the chemical timescales of a few line-rich cores. The morphology and spatial correlations of the molecular emission are analyzed using the histogram of oriented gradients (HOG) method. Results. The mid-infrared data show that both regions contain a cluster of young stellar objects. Bipolar molecular outflows are observed in the CO 2−1 transition toward the strong millimeter (mm) cores, indicating protostellar activity. We find strong molecular emission of SO, SiO, H2CO, and CH3OH in locations that are not associated with the mm cores. These shocked knots can be associated either with the bipolar outflows or, in the case of ISOSS J23053+5953, with a colliding flow that creates a large shocked region between the mm cores. The mean chemical timescale of the cores is lower (~20 000 yr) compared to that of the sources of the more evolved CORE sample (~60 000 yr). With the HOG method, we find that the spatial emission of species that trace the extended emission and of shock-tracing molecules are well correlated within transitions of these groups. Conclusions. Clustered star formation is observed toward both regions. Comparing the mean results of the density and temperature power-law index with the results of the original CORE sample of more evolved regions, it appears that neither change significantly from the earliest evolutionary stages to the hot molecular core stage. However, we find that the 1.3 mm flux, kinetic temperature, H2 column density, and core mass of the cores increase in time, which can be traced both in the M/L ratio and the chemical timescale, τchem.

Using Texture Feature in Fruit Classification

Recent advances in computer vision have allowed wide-ranging applications in every area of life. One such area of application is the classification of fresh products, but the classification of fruits and vegetables has proven to be a complex problem and needs further development. In recent years, various machine learning techniques have been exploited with many methods of describing the different features of fruit and vegetable classification in many real-life applications. Classification of fruits and vegetables presents significant challenges due to similarities between layers and irregular characteristics within the class.Hence , in this work, three feature extractor/ descriptor which are local binary pattern (LBP), gray level co-occurrence matrix (GLCM) and, histogram of oriented gradient(HoG) has been proposed to extract fruite features , the extracted features have been saved in three feature vectors , then desicion tree classifier has been proposed to classify the fruit types. fruits 360 datasets is used in this work, where 70% of the dataset were used in the training phase while 30% of it used in the testing phase. The three proposed feature extruction methods plus the tree classifier have been used to classifying fruits 360 images, results show that the the three feature extraction methods give a promising results , while the HoG method yielded a poerfull results in which the accuracy obtained is 96%.

Combining Sparse and Dense Features to Improve Multi-Modal Registration for Brain DTI Images.

A new solution to overcome the constraints of multimodality medical intra-subject image registration is proposed, using the mutual information (MI) of image histogram-oriented gradients as a new matching criterion. We present a rigid, multi-modal image registration algorithm based on linear transformation and oriented gradients for the alignment of T2-weighted (T2w) images (as a fixed reference) and diffusion tensor imaging (DTI) (b-values of 500 and 1250 s/mm2) as floating images of three patients to compensate for the motion during the acquisition process. Diffusion MRI is very sensitive to motion, especially when the intensity and duration of the gradient pulses (characterized by the b-value) increases. The proposed method relies on the whole brain surface and addresses the variability of anatomical features into an image stack. The sparse features refer to corners detected using the Harris corner detector operator, while dense features use all image pixels through the image histogram of oriented gradients (HOG) as a measure of the degree of statistical dependence between a pair of registered images. HOG as a dense feature is focused on the structure and extracts the oriented gradient image in the x and y directions. MI is used as an objective function for the optimization process. The entropy functions and joint entropy function are determined using the HOGs data. To determine the best image transformation, the fiducial registration error (FRE) measure is used. We compare the results against the MI-based intensities results computed using a statistical intensity relationship between corresponding pixels in source and target images. Our approach, which is devoted to the whole brain, shows improved registration accuracy, robustness, and computational cost compared with the registration algorithms, which use anatomical features or regions of interest areas with specific neuroanatomy. Despite the supplementary HOG computation task, the computation time is comparable for MI-based intensities and MI-based HOG methods.

Partial Discharge Pattern Recognition Based on 3D Graphs of Phase Resolved Pulse Sequence

Partial discharge (PD) is an important phenomenon that reflects the insulation condition of electrical equipment. In order to protect the safety of power grids, it is of significance to diagnose the type of insulation defects inside the equipment accurately and early through PD pattern recognition. In this article, phase resolved pulse sequence (PRPS) graphs in 3D were constructed by the PD pulse data of the gas-insulated switchgear (GIS) acquired, then the histogram of oriented gradient (HOG) features were extracted directly from the 3D PRPS graphs, and finally the attribute selective Naïve Bayes classifier was used to recognize the discharge pattern. In addition, this method was compared with two traditional methods, i.e., the statistical method and the grayscale gradient co-occurrence matrix method, from three aspects. The result shows that 3D PRPS graphs have different morphology characteristics in vision under different defects, and the similarity among different voltages applied is higher than among different defects, so it is reasonable to use them as the basis for PD pattern recognition. The contrast indicates that the HOG method not only has the highest accuracy with the least requirement for pretreatment and training, but it also has robustness when the voltage applied changes. Consequently, this method has the universality for PD pattern recognition that is based on 3D PRPS graphs.

A Smartphone-Based of Wood Identification Using Image Feature Extraction

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

Facial expression recognition in JAFFE and KDEF Datasets using histogram of oriented gradients and support vector machine

This paper presents the used of histogram of oriented gradient (HOG) for facial expression recognition using support vector machine (SVM). In this work, the facial expression images are firstly preprocessed by face detection and cropped images. Then, HOG method is adopted as feature extraction on facial image. The ability of HOG to preserve the local information and orientation density distribution in facial images suitable as shape descriptor for facial expression. It divides the image into cell or patch that has magnitude and orientations. The extracted HOG was then concatenated into histogram bin to form one feature vector before feed into SVM classifier. Both JAFFE and KDEF datasets were employed to evaluate the performance of proposed method. Based on results, the average recognition rates of JAFFE and KDEF datasets are 76.19% and 80.95% respectively. The results show that the performance of expression surprise has outperformed compared to others expression while expression fear contributes the lowest recognition rate. Thus, utilization of HOG features with SVM classifier have shown the promising results in recognizing facial expression.

Performance analysis on feature extraction using dorsal hand vein image

Personal verification has become a severe and necessary perspective of security access systems. Compared to the traditional authentication codes, the personal identification using biometric feature has attracted more attention these years because the biometric feature is hard to forget and filch and easy to use. Biometrics refers to the identification of the humans by their characteristics or traits. This paper work considers vessels at the back side of the hand called dorsal vein. The features like local binary pattern (LBP), histogram of oriented gradients (HOG) and Weber local descriptor (WLD) are extracted, and performance analysis is done in terms of K-NN classification accuracy. The 10 image of right dorsal hand vein and 10 image of left dorsal hand vein were used from 102 persons, including both male and female. There were 50 males and 52 females. The total number of images is 2040. The WLD method has recognition accuracy up to 98%, the LBP method has 96% of recognition accuracy, and compared with both, the HOG method proclaims the highest recognition accuracy up to 99%.

Analysis of Spectral Bands and Spatial Resolutions for Weed Classification Via Deep Convolutional Neural Network

Weed detection and classification are one of the important and crucial steps for area-specific weed control. This reduces the overall cost and the negative impact of using unnecessary herbicides on human health and crops. As the spectral similarity between weeds and crops is high, patch-based classification approaches are adopted in this letter. Convolutional neural network (CNN) and histogram of oriented gradients (HoG) methods are evaluated and compared. With the advancement in the remote sensing technologies, a large number of sensors are available which provide different number of bands with different spatial resolutions. The effect of these variations on weed identification is investigated. Experimental results show that CNN method extracts more discriminative and powerful features that lead to an accurate classification of different weeds compared to the HoG method. Analysis of the two important parameters provides guidance in choosing correct patch size, spatial resolution, and the number of bands to use when CNN is applied for accurate and efficient weed classification.

An Enhanced Histogram of Oriented Gradient Descriptor for Numismatic Applications

The Histogram of Oriented Gradients (HOG) is one of the most widely used methods to extract the gradient features for object recognition and consistently shows high accuracy rates when compared to other descriptors. The major drawbacks of using the HOG method are the necessity of finding an optimal window size to fit the whole object; and the exhaustive search mechanism represented by a fixed window size sliding through the whole image to locate and recognize objects. This research proposes an efficient and robust Dynamic-HOG as an improvement to the traditional HOG method to locate and recognize structured objects in images. The proposed method works by locating and analyzing the structured objects in images in order to define a dynamic window size w.r.t. each object size. Moreover, the Dynamic-HOG method requires much less processing time by eliminating the exhaustive search mechanism. The method defines the height and width thresholds of objects and bounds each object with a window w.r.t. its size while ignoring non–object edges. It fits structured objects of a close range of heights and widths. This paper considers the characters that are minted on coins of different languages and sizes as the objects to recognize. There are several papers in the literature discussing coin recognition problem and proposing solutions based on various sets of features extracted from the entire coin image. This research also proposes a new method for coin recognition by focusing on recognize coins based on smaller part of the coin image which are the characters. Our method is evaluated on coins from diverse countries with different background complexity. The proposed method achieved precision and recall rates as high as 98.08 and 98.23%, respectively; which demonstrate the effectiveness and robustness of the proposed method.

Detection of License Plate using Sliding Window, Histogram of Oriented Gradient, and Support Vector Machines Method

The license plate recognition usually used as part of system such as parking system. License plate detection considered as the most important step in the license plate recognition system. We propose methods that can be used to detect the vehicle plate on mobile phone. In this paper, we used Sliding Window, Histogram of Oriented Gradient (HOG), and Support Vector Machines (SVM) method to license plate detection so it will increase the detection level even though the image is not in a good quality. The image proceed by Sliding Window method in order to find plate position. Feature extraction in every window movement had been done by HOG and SVM method. Good result had shown in this research, which is 96% of accuracy.

The Excellent Properties of a Dense Grid-Based HOG Feature on Face Recognition Compared to Gabor and LBP

To effectively represent facial features in complex environments, a face recognition method based on dense grid histograms of oriented gradients (HOG) is proposed. First, the face image is divided by numerous dense grids from which the HOG features are extracted. Then, all the grid HOG feature vectors are composed to realize the feature expression of the whole face, and the nearest neighbor classifier is used for recognition. In the FERET face database with complex changes in illumination, time, and environment, we test the gamma illumination correction, the spatial gradient direction, the size of the block, the standardization, and the face image resolution to find and analyze the optimal HOG parameters for face recognition. Finally, we compare our dense grid HOG with the two famous local facial feature extraction methods: the Gabor wavelet and the local binary pattern (LBP) on face recognition. The experimental results show that the dense grid HOG method is more suitable for the variations in time and environment. The feature extraction times of the dense grid HOG and LBP are similar. However, the dense grid HOG method uses fewer dimensions to obtain a better recognition rate than the LBP. Moreover, the dense grid HOG feature extraction time greatly outperforms the Gabor wavelet feature.

Classification of molecular structure images by using ANN, RF, LBP, HOG, and size reduction methods for early stomach cancer detection

Patients who die from stomach cancer are still present. Early diagnosis is crucial in reducing the mortality rate of cancer patients. Therefore, computer aided methods have been developed for early detection in this article. Stomach cancer images were obtained from Fırat University Medical Faculty Pathology Department. The Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) features of these images are calculated. At the same time, Sammon mapping, Stochastic Neighbor Embedding (SNE), Isomap, Classical multidimensional scaling (MDS), Local Linear Embedding (LLE), Linear Discriminant Analysis (LDA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Laplacian Eigenmaps methods are used for dimensional the reduction of the features. The high dimension of these features has been reduced to lower dimensions using dimensional reduction methods. Artificial neural networks (ANN) and Random Forest (RF) classifiers were used to classify stomach cancer images with these new lower feature sizes. New medical systems have developed to measure the effects of these dimensions by obtaining features in different dimensional with dimensional reduction methods. When all the methods developed are compared, it has been found that the best accuracy results are obtained with LBP_MDS_ANN and LBP_LLE_ANN methods.

Deteksi Ketersediaan Slot Parkir Berbasis Pengolahan Citra Digital Menggunakan Metode Histogram of Oriented Gradients dan Support Vector Machine

This research aims to implement method based on digital image processing to inform the status of parking slots at the car parking area by using a feature extraction HOG (Histogram of Oriented Gradients) method in every region of the parking area. Feature extraction results are classified using SVM (Support Vector Machine) by comparing the Linear, RBF (Radial Basis Function), Poly, and Sigmoid kernels. SVM classification results were analyzed using the confusion matrix with accuracy, specificity, sensitivity, and precision parameters. In terms of accuracy, system obtained with Linear kernel in sunny conditions shows 98.0% accuracy; rainy 98.8% accuracy; cloudy 99.2% accuracy. Obtained accuracy using Poly kernel test in sunny conditions shows 99.2%; rainy 98.9%; cloudy 99.4%. Obtained accuracy using RBF kernel in sunny conditions shows 97.9%; rainy 98.7%; cloudy 99.6%. In terms of accuracy using additional data testing obtained with Linear kernel shows accuracy of 97.7%; RBF kernel 97.9% accuracy; Poly kernel 97.4% accuracy. Sigmoid kernel testing can’t be used because the optimal model did not obtained by using default grid.

영상 내 사람의 검출을 위한 에지 기반 방법

Human sensing is an important but challenging technology. Unlike other methods for sensing humans, a vision sensor has many advantages, and there has been active research in automatic human detection in camera images. The combination of Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) is currently one of the most successful methods in vision-based human detection. However, extracting HOG features from an image is computer intensive, and it is thus hard to employ the HOG method in real-time processing applications. This paper describes an efficient solution to this speed problem of the HOG method. Our method obtains edge information of an image and finds candidate regions where humans very likely exist based on the distribution pattern of the detected edge points. The HOG features are then extracted only from the candidate image regions. Since complex HOG processing is adaptively done by the guidance of the simpler edge detection step, human detection can be performed quickly. Experimental results show that the proposed method is effective in various images.