Search Results (8)

In the wake of the continuous development of the international strategic petroleum reserve system, the tonnage and quantity of oil tankers have been increasing. This trend has driven the expansion of offshore oil exploration and transportation, resulting in frequent incidents of ship oil spills. Catastrophic impacts have been exerted on the marine environment by these accidents, posing a serious threat to economic development and ecological security. Therefore, there is an urgent need for efficient and reliable methods to detect oil spills in a timely manner and minimize potential losses as much as possible. In response to this challenge, a marine radar oil film segmentation method based on feature fusion and the artificial bee colony (ABC) algorithm is proposed in this study. Initially, the raw experimental data are preprocessed to obtain denoised radar images. Subsequently, grayscale adjustment and local contrast enhancement operations are carried out on the denoised images. Next, the gray level co-occurrence matrix (GLCM) features and Tamura features are extracted from the locally contrast-enhanced images. Then, the generalized least squares (GLS) method is employed to fuse the extracted texture features, yielding a new feature fusion map. Afterwards, the optimal processing threshold is determined to obtain effective wave regions by using the bimodal graph direct method. Finally, the ABC algorithm is utilized to segment the oil films. This method can provide data support for oil spill detection in marine radar images. Full article

Human gesture image recognition is the process of identifying, deciphering, and classifying human gestures in images or video frames using computer vision algorithms. These gestures can vary from the simplest hand motions, body positions, and facial emotions to complicated gestures. Two significant problems affecting the performance of human gesture picture recognition methods are ambiguity and invariance. Ambiguity occurs when gestures have the same shape but different orientations, while invariance guarantees that gestures are correctly classified even when scale, lighting, or orientation varies. To overcome this issue, hand-crafted features can be combined with deep learning to greatly improve the performance of hand gesture image recognition models. This combination improves the model’s overall accuracy and dependability in identifying a variety of hand movements by enhancing its capacity to record both shape and texture properties. Thus, in this study, we propose a hand gesture recognition method that combines Reset50 model feature extraction with the Tamura texture descriptor and uses the adaptability of GAM to represent intricate interactions between the features. Experiments were carried out on publicly available datasets containing images of American Sign Language (ASL) gestures. As Tamura-ResNet50-OptimizedGAM achieved the highest accuracy rate in the ASL datasets, it is believed to be the best option for human gesture image recognition. According to the experimental results, the accuracy rate was 96%, which is higher than the total accuracy of the state-of-the-art techniques currently in use. Full article

As there are few cases of red sandstone rapid antiquing in ancient buildings and as it is difficult to reproduce, this paper carried out an experimental study and effect evaluation assessment on red sandstone rapid antiquing in the restoration of ancient buildings, based on a restoration project of an ancient town in Ganzhou. The method and the implementation process of red sandstone rapid antiquing are proposed by starting from color antiquing and texture antiquing. By controlling the concentration of red mud, grass ash, and carbon black in color coatings as variables, using the HSV (hue, saturation, value) color space and Tamura texture features (roughness, contrast, orientation) to quantitatively analyze the antiquing effect, an analytical model for evaluating the red sandstone antiquing effect based on image processing was established. The results showed that among all the antiquing groups, the group that used white cement, green zeolite, imitation greenery, red clay, grass ash, and 5 mL/L carbon black liquid at the same time had the best effect, with a qualified rate of 90%. The analytical model can improve the evaluation efficiency of red sandstone antiquing and avoid errors caused by subjective factors. With feasibility and practicability, the model is conducive for new red sandstone to meet the requirements of ancient building restoration through rapid antiquing. It provides a scientific basis and technical reference for red sandstone antiquing in stone cultural relics and ancient building restoration. Full article

In modern manufacturing industries with Industry 4.0 capabilities, the automated identification and classification of machined surfaces based on their texture will play a crucial role. Texture analysis through computer vision, image processing, classification using artificial neural networks (ANN), and various machine learning techniques have been prominent research areas in recent decade. Tamura features are very popular in selecting optimum textural features from an image, especially in the medical domain. These textural features correspond to human visual perception and play a significant role in identifying and shortlisting the best features from the photographs. Despite the popularity of Tamura features in the medical domain, their usage in extracting the features from machined surface photographs is seldom reported. Hence, the present study investigates the feasibility of using Tamura features to classify machined surface images produced using turning, milling, grinding, and shaping operations in manufacturing. Photographs of the surfaces produced are obtained using smartphone cameras. Further, the photographs are preprocessed and divided into sixteen different portions. Then, Tamura features are extracted and are given as input to ANN, support vector machines (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), and Random Forest (RF). The result shows that each machine learning (ML) algorithm performs differently while classifying the same set of machined surface images. Amongst the ML algorithms considered in the study, RF classified the photographs of surfaces machined using different machining operations with the highest accuracy. On the other hand, SVM performed poorly. Full article

Malware development has significantly increased recently, posing a serious security risk to both consumers and businesses. Malware developers continually find new ways to circumvent security research’s ongoing efforts to guard against malware attacks. Malware Classification (MC) entails labeling a class of malware to a specific sample, while malware detection merely entails finding malware without identifying which kind of malware it is. There are two main reasons why the most popular MC techniques have a low classification rate. First, Finding and developing accurate features requires highly specialized domain expertise. Second, a data imbalance that makes it challenging to classify and correctly identify malware. Furthermore, the proposed malware classification (MC) method consists of the following five steps: (i) Dataset preparation: 2D malware images are created from the malware binary files; (ii) Visualized Malware Pre-processing: the visual malware images need to be scaled to fit the CNN model’s input size; (iii) Feature extraction: both hand-engineering (Tamura) and deep learning (GoogLeNet) techniques are used to extract the features in this step; (iv) Classification: to perform malware classification, we employed k-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Extreme Learning Machine (ELM). The proposed method is tested on a standard Malimg unbalanced dataset. The accuracy rate of the proposed method was extremely high, making it the most efficient option available. The proposed method’s accuracy rate was outperformed both the Hand-crafted feature and Deep Feature techniques, at 95.42 and 96.84 percent. Full article

In order to evaluate the effect of laser cleaning, a new method of workpiece surface roughness estimation is proposed. First, a Cartesian robot and visible-light camera are used to collect a large number of surface images of a workpiece after laser cleaning. Second, various features including the Tamura coarseness, Alexnet abstract depth, single blind/referenceless image spatial quality evaluator (BRISQUE), and enhanced gray level co-occurrence matrix (EGLCM) are computed from the images above. Third, the improved particle swarm optimization (IPSO) is used to improve the training parameters of support vector regression (SVR). The learning factor of SVR adopts the strategy of dynamic nonlinear asynchronous adaptive adjustment to improve its optimization-processing ability. Finally, both the image features and the IPSO-SVR are considered for the surface roughness estimation. Extensive experiment results show that the accuracy of the IPSO-SVR surface roughness estimation model can reach 92.0%. Full article

Face recognition is one of the emergent technologies that has been used in many applications. It is a process of labeling pictures, especially those with human faces. One of the critical applications of face recognition is security monitoring, where captured images are compared to thousands, or even millions, of stored images. The problem occurs when different types of noise manipulate the captured images. This paper contributes to the body of knowledge by proposing an innovative framework for face recognition based on various descriptors, including the following: Color and Edge Directivity Descriptor (CEDD), Fuzzy Color and Texture Histogram Descriptor (FCTH), Color Histogram, Color Layout, Edge Histogram, Gabor, Hashing CEDD, Joint Composite Descriptor (JCD), Joint Histogram, Luminance Layout, Opponent Histogram, Pyramid of Gradient Histograms Descriptor (PHOG), Tamura. The proposed framework considers image set indexing and retrieval phases with multi-feature descriptors. The examined dataset contains 23,707 images of different genders and ages, ranging from 1 to 116 years old. The framework is extensively examined with different image filters such as random noise, rotation, cropping, glow, inversion, and grayscale. The indexer’s performance is measured based on a distributed environment based on sample size and multiprocessors as well as multithreads. Moreover, image retrieval performance is measured using three criteria: rank, score, and accuracy. The implemented framework was able to recognize the manipulated images using different descriptors with a high accuracy rate. The proposed innovative framework proves that image descriptors could be efficient in face recognition even with noise added to the images based on the outcomes. The concluded results are as follows: (a) the Edge Histogram could be best used with glow, gray, and inverted images; (b) the FCTH, Color Histogram, Color Layout, and Joint Histogram could be best used with cropped images; and (c) the CEDD could be best used with random noise and rotated images. Full article

Recently, texture features have been widely used for historical document image analysis. However, few studies have focused exclusively on feature selection algorithms for historical document image analysis. Indeed, an important need has emerged to use a feature selection algorithm in data mining and machine learning tasks, since it helps to reduce the data dimensionality and to increase the algorithm performance such as a pixel classification algorithm. Therefore, in this paper we propose a comparative study of two conventional feature selection algorithms, genetic algorithm and ReliefF algorithm, using a classical pixel-labeling scheme based on analyzing and selecting texture features. The two assessed feature selection algorithms in this study have been applied on a training set of the HBR dataset in order to deduce the most selected texture features of each analyzed texture-based feature set. The evaluated feature sets in this study consist of numerous state-of-the-art texture features (Tamura, local binary patterns, gray-level run-length matrix, auto-correlation function, gray-level co-occurrence matrix, Gabor filters, Three-level Haar wavelet transform, three-level wavelet transform using 3-tap Daubechies filter and three-level wavelet transform using 4-tap Daubechies filter). In our experiments, a public corpus of historical document images provided in the context of the historical book recognition contest (HBR2013 dataset: PRImA, Salford, UK) has been used. Qualitative and numerical experiments are given in this study in order to provide a set of comprehensive guidelines on the strengths and the weaknesses of each assessed feature selection algorithm according to the used texture feature set. Full article