Image-based Feature Extraction Research Articles

DeepAVP-TPPred: identification of antiviral peptides using transformed image-based localized descriptors and binary tree growth algorithm.

Despite the extensive manufacturing of antiviral drugs and vaccination, viral infections continue to be a major human ailment. Antiviral peptides (AVPs) have emerged as potential candidates in the pursuit of novel antiviral drugs. These peptides show vigorous antiviral activity against a diverse range of viruses by targeting different phases of the viral life cycle. Therefore, the accurate prediction of AVPs is an essential yet challenging task. Lately, many machine learning-based approaches have developed for this purpose; however, their limited capabilities in terms of feature engineering, accuracy, and generalization make these methods restricted. In the present study, we aim to develop an efficient machine learning-based approach for the identification of AVPs, referred to as DeepAVP-TPPred, to address the aforementioned problems. First, we extract two new transformed feature sets using our designed image-based feature extraction algorithms and integrate them with an evolutionary information-based feature. Next, these feature sets were optimized using a novel feature selection approach called binary tree growth Algorithm. Finally, the optimal feature space from the training dataset was fed to the deep neural network to build the final classification model. The proposed model DeepAVP-TPPred was tested using stringent 5-fold cross-validation and two independent dataset testing methods, which achieved the maximum performance and showed enhanced efficiency over existing predictors in terms of both accuracy and generalization capabilities. https://github.com/MateeullahKhan/DeepAVP-TPPred.

Crack _ PSTU: Crack detection based on the U-Net framework combined with Swin Transformer

Cracks serve as a significant indicator of aging infrastructure. While the surfaces of traditional infrastructures are visually inspected manually, this approach is labor-intensive, highly subjective, and often surpasses the capabilities of available inspection personnel. Although some researchers have employed traditional image processing and machine learning techniques to address these challenges, the presence of irregular crack shapes, complex lighting conditions, and limitations in detecting only a single type of surface crack complicate automatic crack detection. Recent research indicates that deep learning methods are increasingly leading in image-based feature extraction, object detection, and pixel attribute analysis. This paper introduces a method, Crack_PSTU (Pre-trained Swin Transformer U-Net), which leverages the U-Net framework and Swin Transformer model for infrastructure crack classification and detection tasks. Specifically, we utilize a pre-trained Swin Transformer network as the encoder and implement the decoder using convolution, pooling, and other operations, creating a "U"-shaped model architecture. Our experiments encompassed 11,298 crack images from varied scenes, with 9603 designated for training and 1695 for validation. The findings reveal that our approach surpasses other algorithms for this dataset.

DeLiVoTr: Deep and light-weight voxel transformer for 3D object detection

The image-based backbone (feature extraction) networks downsample the feature maps not only to increase the receptive field but also to efficiently detect objects of various scales. The existing feature extraction networks in LiDAR-based 3D object detection tasks follow the feature map downsampling similar to image-based feature extraction networks to increase the receptive field. But, such downsampling of LiDAR feature maps in large-scale autonomous driving scenarios hinder the detection of small size objects, such as pedestrians. To solve this issue we design an architecture that not only maintains the same scale of the feature maps but also the receptive field in the feature extraction network to aid for efficient detection of small size objects. We resort to attention mechanism to build sufficient receptive field and we propose a Deep and Light-weight Voxel Transformer (DeLiVoTr) network with voxel intra- and inter-region transformer modules to extract voxel local and global features respectively. We introduce DeLiVoTr block that uses transformations with expand and reduce strategy to vary the width and depth of the network efficiently. This facilitates to learn wider and deeper voxel representations and enables to use not only smaller dimension for attention mechanism but also a light-weight feed-forward network, facilitating the reduction of parameters and operations. In addition to model scaling, we employ layer-level scaling of DeLiVoTr encoder layers for efficient parameter allocation in each encoder layer instead of fixed number of parameters as in existing approaches. Leveraging layer-level depth and width scaling we formulate three variants of DeLiVoTr network. We conduct extensive experiments and analysis on large-scale Waymo and KITTI datasets. Our network surpasses state-of-the-art methods for detection of small objects (pedestrians) with an inference speed of 20.5 FPS.

Image-based feature extraction for inline quality assurance and wear classification in high-speed blanking processes

Wear is one of the key factors that determine the efficiency of multi-stage processes that include blanking operations. Since wear in these processes not only causes unplanned downtime but also directly affects product quality, inline detection of wear and its effect on product quality is of major importance. However, current quality assurance (QA) methods are limited to manual offline inspection by operators at predefined intervals, so that 100% inspection of the product and description of the state of wear is not found in industrial practice. The aim of this work is therefore to develop an optical system that enables in-line acquisition of product images and the associated control of blanking-specific quality features up to stroke rates of 300 strokes per minute (spm). In order to make the system attractive to small- and medium-sized enterprises (SME), the system is designed to minimize integration and investment costs using commercially available components. By combining the system with a methodology for extracting blanking-specific features, so-called key performance parameters (KPPs), the condition of the blanked surface as a relevant quality parameter is derived directly from the workpiece image. To demonstrate the transferability of the methodology to industrial applications, two use cases are investigated. In the first case, the KPPs are used directly to determine the quality of the blanked workpiece and are compared with reference measurements. Here, the KPPs are quantified with a mean absolute error of 18 μm compared to a ground truth. In the second case, the KPPs are used to build a machine learning (ML) model to estimate the wear of the blanking tool. Here, an accuracy of 92% is achieved in classifying the actual wear state.

Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa.

Plant phenotyping is typically a time-consuming and expensive endeavor, requiring large groups of researchers to meticulously measure biologically relevant plant traits, and is the main bottleneck in understanding plant adaptation and the genetic architecture underlying complex traits at population scale. In this work, we address these challenges by leveraging few-shot learning with convolutional neural networks to segment the leaf body and visible venation of 2,906 Populus trichocarpa leaf images obtained in the field. In contrast to previous methods, our approach (a) does not require experimental or image preprocessing, (b) uses the raw RGB images at full resolution, and (c) requires very few samples for training (e.g., just 8 images for vein segmentation). Traits relating to leaf morphology and vein topology are extracted from the resulting segmentations using traditional open-source image-processing tools, validated using real-world physical measurements, and used to conduct a genome-wide association study to identify genes controlling the traits. In this way, the current work is designed to provide the plant phenotyping community with (a) methods for fast and accurate image-based feature extraction that require minimal training data and (b) a new population-scale dataset, including 68 different leaf phenotypes, for domain scientists and machine learning researchers. All of the few-shot learning code, data, and results are made publicly available.

High-content phenotyping of Parkinson's disease patient stem cell-derived midbrain dopaminergic neurons using machine learning classification.

SummaryCombining multiple Parkinson's disease (PD) relevant cellular phenotypes might increase the accuracy of midbrain dopaminergic neuron (mDAN) in vitro models. We differentiated patient-derived induced pluripotent stem cells (iPSCs) with a LRRK2 G2019S mutation, isogenic control, and genetically unrelated iPSCs into mDANs. Using automated fluorescence microscopy in 384-well-plate format, we identified elevated levels of α-synuclein (αSyn) and serine 129 phosphorylation, reduced dendritic complexity, and mitochondrial dysfunction. Next, we measured additional image-based phenotypes and used machine learning (ML) to accurately classify mDANs according to their genotype. Additionally, we show that chemical compound treatments, targeting LRRK2 kinase activity or αSyn levels, are detectable when using ML classification based on multiple image-based phenotypes. We validated our approach using a second isogenic patient-derived SNCA gene triplication mDAN model which overexpresses αSyn. This phenotyping and classification strategy improves the practical exploitability of mDANs for disease modeling and the identification of novel LRRK2-associated drug targets.

An image-based feature extraction method for fault diagnosis of variable-speed rotating machinery

This paper proposes a new feature extraction method using time–frequency image data for fault diagnosis of variable-speed rotating machinery. Time-frequency representation (TFR) is widely used to analyze time-varying behaviors of rotating machinery. Recently, methods have been developed to extract fault-related features from TFR image data. However, these methods can be only applied to in-phase TFR image data, or have limited sensitivity because they cannot utilize the characteristics of faults in rotating machinery. Therefore, the research outlined in this paper proposes a new fault feature for rotating machinery under variable-speed conditions. The proposed feature enhances sensitivity by exploiting faulty behaviors in the TFR image data. Two experimental case studies are presented to demonstrate the performance of the proposed method: a planetary gearbox and a spur gearbox. From the results, we conclude that the proposed method shows higher fault sensitivity than the previous image-based features, while showing consistent behavior under different phases of TFR image data.

Recognition of Human Emotion using Radial Basis Function Neural Networks with Inverse Fisher Transformed Physiological Signals

Emotion is a complex state of human mind influenced by body physiological changes and interdependent external events thus making an automatic recognition of emotional state a challenging task. A number of recognition methods have been applied in recent years to recognize human emotion. The motivation for this study is therefore to discover a combination of emotion features and recognition method that will produce the best result in building an efficient emotion recognizer in an affective system. We introduced a shifted tanh normalization scheme to realize the inverse Fisher transformation applied to the DEAP physiological dataset and consequently performed series of experiments using the Radial Basis Function Artificial Neural Networks (RBFANN). In our experiments, we have compared the performances of digital image based feature extraction techniques such as the Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP) and the Histogram of Images (HIM). These feature extraction techniques were utilized to extract discriminatory features from the multimodal DEAP dataset of physiological signals. Experimental results obtained indicate that the best recognition accuracy was achieved with the EEG modality data using the HIM features extraction technique and classification done along the dominance emotion dimension. The result is very remarkable when compared with existing results in the literature including deep learning studies that have utilized the DEAP corpus and also applicable to diverse fields of engineering studies.

An Efficient Image Based Feature Extraction and Feature Selection Model for Medical Data Clustering Using Deep Neural Networks

An Efficient Image Based Feature Extraction and Feature Selection Model for Medical Data Clustering Using Deep Neural Networks

Two-dimensional jointly sparse robust discriminant regression

Ridge regression is an important method in feature extraction and has been extended in many different versions. Robust discriminant regression aims to solve the Small Sample Size Problem (SSSP) in ridge regression form by designing a novel regression model and imposing on L2,1-norm as the main metric instead of the regularized term. However, when the dimensions of data are very high, RDR will be faced with the problem of the curse of dimensionality and high memory space cost. The computation cost of RDR will be extremely high in the iterative procedures. To address this problem, we propose an improved method called Two-dimensional jointly sparse RDR (2DJSRDR) for image-based feature extraction. Unlike previous vector-based methods which stretch the data into a high-dimensional vector as input, the proposed 2DJSRDR uses the two-dimensional image matrix directly as the computational unit so that the drawbacks in RDR can be naturally avoided. Besides, we also introduce L2,1-norm as regularization term to obtain jointly sparse projections for feature selection, which is helpful to improve the performance of the model. Experiments on some benchmark datasets demonstrate the superior performance of the proposed method.

Data-Driven Gantry Health Monitoring and Process Status Identification Based on Texture Extraction

Abstract As a critical asset, gantry has wide applications in many fields such as medical image area, infrastructure, and heavy industry. Mostly, the gantry is reliable, however, the loss led by the gantry lockout is inestimable enormous. Moreover, there are limited previous gantry studies concentrated on the statistical quality control to detect the fault not to mention the research that focuses on the algorithms applied to the process status sequence to detect the fault. Time series gantry process status sequence usually consists of categorical values, which makes it hard to obtain features for the task of fault identification. This paper proposes a novel method using texture extraction in image processing to obtain the features of gantry process status sequence. The histogram of oriented gradients (HOG) texture extraction technique is used to the process status sequence. To demonstrate the effectiveness of image-based feature extraction, we applied different machine learning algorithms to the time series gantry process status sequences provided by a leading automobile manufacturer. Results demonstrate that the sequence after the transformation of texture extraction technique has improved the accuracy of machine learning algorithms (i.e., 10% increase on average for KNN, 32% increase for LDA, 26% increase for QDA, and 8% increase for linear SVM).

Automated Harmonic Signal Removal Technique Using Stochastic Subspace-Based Image Feature Extraction.

This paper presents automated harmonic removal as a desirable solution to effectively identify and discard the harmonic influence over the output signal by neglecting any user-defined parameter at start-up and automatically reconstruct back to become a useful output signal prior to system identification. Stochastic subspace-based algorithms (SSI) methods are the most practical tool due to the consistency in modal parameters estimation. However, it will be problematic when applied to structures with rotating machines and the presence of harmonic excitations. Difficulties arise when automating this procedure without any human interaction and the problem is still unresolved because stochastic subspace-based algorithms (SSI) methods still require parameters (the maximum within-cluster distance) that are compulsory to be defined at start-up for each analysis of the dataset. Thus, the use of image-based feature extraction for clustering and classification of harmonic components and structural poles directly from a stabilization diagram and for modal system identification is the focus of the present paper. As a fundamental necessary condition, the algorithm has been assessed first from computed numerical responses and then applied to the experimental dataset with the presence of harmonic excitation. Results of the proposed approach for estimating modal parameters demonstrated very high accuracy and exhibited consistent results before and after removing harmonic components from the response signal.

Region-of-Interest based sparse feature learning method for Alzheimer’s disease identification

Background and ObjectiveIn recent years, some clinical parameters, such as the volume of gray matter (GM) and cortical thickness, have been used as anatomical features to identify Alzheimer’s disease (AD) from Healthy Controls (HC) in some feature-based machine learning methods. However, fewer image-based feature parameters have been proposed, which are equivalent to these clinical parameters, to describe the atrophy of regions-of-interest (ROIs) of the brain. In this study, we aim to extract effective image-based feature parameters to improve the diagnostic performance of AD with magnetic resonance imaging (MRI) data. MethodsA new subspace-based sparse feature learning method is proposed, which builds a union-of-subspace representation model to realize feature extraction and disease identification. Specifically, the proposed method estimates feature dimensions reasonably, at the same time, it protects local features for the specified ROIs of the brain, and realizes image-based feature extraction and classification automatically instead of computing the volume of GM or cortical thickness preliminarily. ResultsExperimental results illustrate the effectiveness and robustness of the proposed method on feature extraction and classification, which are based on the sampled clinical dataset from Peking University Third Hospital of China and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The extracted image-based feature parameters describe the atrophy of ROIs of the brain well as clinical parameters but show better performance in AD identification than clinical parameters. Based on them, the important ROIs for AD identification can be identified even for correlated variables. ConclusionThe extracted features and the proposed identification parameters show high correlation with the volume of GM and the clinical mini-mental state examination (MMSE) score respectively. The proposed method will be useful in denoting the changes of cerebral pathology and cognitive function in AD patients.

QUINT: Workflow for Quantification and Spatial Analysis of Features in Histological Images From Rodent Brain.

Transgenic animal models are invaluable research tools for elucidating the pathways and mechanisms involved in the development of neurodegenerative diseases. Mechanistic clues can be revealed by applying labelling techniques such as immunohistochemistry or in situ hybridisation to brain tissue sections. Precision in both assigning anatomical location to the sections and quantifying labelled features is crucial for output validity, with a stereological approach or image-based feature extraction typically used. However, both approaches are restricted by the need to manually delineate anatomical regions. To circumvent this limitation, we present the QUINT workflow for quantification and spatial analysis of labelling in series of rodent brain section images based on available 3D reference atlases. The workflow is semi-automated, combining three open source software that can be operated without scripting knowledge, making it accessible to most researchers. As an example, a brain region-specific quantification of amyloid plaques across whole transgenic Tg2576 mouse brain series, immunohistochemically labelled for three amyloid-related antigens is demonstrated. First, the whole brain image series were registered to the Allen Mouse Brain Atlas to produce customised atlas maps adapted to match the cutting plan and proportions of the sections (QuickNII software). Second, the labelling was segmented from the original images by the Random Forest Algorithm for supervised classification (ilastik software). Finally, the segmented images and atlas maps were used to generate plaque quantifications for each region in the reference atlas (Nutil software). The method yielded comparable results to manual delineations and to the output of a stereological method. While the use case demonstrates the QUINT workflow for quantification of amyloid plaques only, the workflow is suited to all mouse or rat brain series with labelling that is visually distinct from the background, for example for the quantification of cells or labelled proteins.

Online condition diagnosis for a two-stage gearbox machinery of an aerospace utilization system using an ensemble multi-fault features indexing approach

China manned space station is designed to operate for over ten years. Long-term and sustainable research on space science and technology will be conducted during its operation. The application payloads must meet the “long life and high reliability” mission requirement. Gearbox machinery is one of the essential devices in an aerospace utilization system, failure of which may lead to downtime loss even during some disastrous catastrophes. A fault diagnosis of gearbox has attracted attentions for its significance in preventing catastrophic accidents and guaranteeing sufficient maintenance. A novel fault diagnosis method based on the Ensemble Multi-Fault Features Indexing (EMFFI) approach is proposed for the condition monitoring of gearboxes. Different from traditional methods of signal analysis in the one-dimensional space, this study employs a supervised learning method to determine the faults of a gearbox in a two-dimensional space using the classification model established by training the features extracted automatically from diagnostic vibration signals captured. The proposed method mainly includes the following steps. First, the vibration signals are transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, Speeded-Up Robustness Feature (SURF) is applied to automatically extract the image feature points of the bi-spectrum contour map using a multi-fault features indexing theory, and the feature dimension is reduced by Linear Discriminant Analysis (LDA). Finally, Random Forest (RF) is introduced to identify the fault types of the gearbox. The test results verify that the proposed method based on the multi-fault features indexing approach achieves the target of high diagnostic accuracy and can serve as a highly effective technique to discover faults in a gearbox machinery such as a two-stage one.

Image-Based Feature Extraction Technique for Inclined Crack Quantification Using Pulsed Eddy Current

Existing eddy current non-destructive testing (NDT) techniques generally do not consider the inclination angle of inclined cracks, which potentially harms a larger region of a tested structure. This work proposes the use of 2D scan images generated by using pulsed eddy current (PEC) non-destructive testing (NDT) technique in the quantification of the inclination and depth of inclined cracks. The image-based feature extraction technique effectively identifies the crack axis, which consequently enables extraction of features from the extracted linear scans. The technique extracts linear scans from the images to allow the extraction of three novel image-based features, namely the length of extracted linear scans (LLS), the linear scan skewness (LSS), and the highest value on linear scan (LSmax). The correlation of the three features to surface crack inclination angles and depths were analysed and found to be highly dependent on the crack depths, while only LLS and LSS are correlated to the crack inclination angles.

A URL with Image-based Feature Extraction for Preventing Phishing Attacks

Currently, Phishing is a type of attack in which cyber criminals tricks the victims to steal their personal and financial data. It has become an organized criminal activity. Spoofed emails claiming to be from legitimate source are crafted in a way to lead victims to reveal their personal, financial data by misdirecting them to the counterfeit website. We compared previous password protection solutions, some of the presented solutions use specialized equipment or additional servers to protect passwords. Other solutions are prone to spoofing and phishing attacks as well as introduce usability issues. Also these solutions do not address the challenge of protecting passwords against the adversary who can, for instance, exploit server-side software vulnerabilities. Our goal is enhancing the best solution to prevent phishing by alerting the users from phishing websites when detected based on URL with image-based feature extraction method.

A new fault diagnosis approach for analog circuits based on spectrum image and feature weighted kernel Fisher discriminant analysis.

Analog circuits are one of the most commonly used components in industrial equipment and, therefore, circuit failure may lead to significant causalities and even huge financial losses. To address this problem, this work presents a fault diagnosis method based on spectrum images for analog circuits. Unlike traditional analysis methods in a one-dimensional space, this study employs a computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed approach mainly involves the following steps. First, the sampling signals are converted into spectrum images by utilizing cross-wavelet transform, which can be further processed by the following image-based feature extraction method. Then, Krawtchouk moment is applied to extract both the global and local features of the spectrum images and finally form the feature vector. Feature weighted kernel Fisher discriminant analysis is then introduced for locating faults. Two typical analog circuits, video amplifier circuit and opamp high-pass filter circuit, are chosen to demonstrate the effectiveness of the proposed method. Simulation results show that the proposed approach based on spectrum images achieves a high accuracy, thus providing a highly effective means to fault diagnosis for analog circuits.

Example image-based feature extraction for face recognition

This paper proposes a novel method for recognizing facial images based on the relative distances between an input image and example images. Example facial images can be easily collected online, and a large example database can span new possible facial variations not sufficiently learned during the learning phase. We first extract facial features using a baseline classifier that has a certain degree of accuracy. To achieve a better performance of the proposed method, we divide the collected examples into groups using a clustering method (e.g., k-means), where each clustered group contains examples with similar characteristics. We then hierarchically partition a group formed in the previous level into other groups to analyze more specific facial characteristics, which represent an example pyramid. To describe the characteristics of a group using the clustered examples, we divide the example group into a number of sub-groups. We calculate the averages of the sub-groups and select an example most similar to the average in each sub-group because we assume that the averages of the sub-groups can directly represent their characteristics. Using the selected examples, we build example code words for a novel feature extraction. The example code words are used to measure the distances to an input image and serve as anchors to analyze a facial image in the example domain. The distance values are normalized for each group at all pyramid levels, and are concatenated to form novel features for face recognition. We verified the effectiveness of the proposed example pyramid framework using well-known proposed features, including LBP, HOG, Gabor, and the deep learning method, on the LFW database, and showed that it can yield significant improvements in recognition performance.

Fault Diagnosis for Rotating Machinery: A Method based on Image Processing.

Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery.