Study on rotation-invariant texture feature extraction for tire pattern retrieval

Automatic segmentation of colorectal polyps from endoscopic images forms an interesting challenge in computer vision. The method proposed in this paper intends to segment colorectal polyp (abnormal) regions from normal regions from a given endoscopic image. Due to lack of any regular texture patterns in this kind of images and apparent visual similarity in background and foreground pixels, conventional texture feature extraction and classification methods do not always yield good results. Hence, active contour based method has been explored to automatically segment out probable abnormal region(s). Our aim is to automatically detect the probable polyp region(s) and then verify the results with respect to the ground truth. Due to lack of very definitive edge criteria along the boundaries of a polyp, we used "active contour without edges" instead of classical active contour.

Vehicle tire tread pattern is an important part of vehicle tire and also an important object concerned by the police workers who want to deduce or identify the suspected vehicle tire through the tire pattern impression left on the crime scene, which is routinely reflected by tire pattern impression on the surface tire rolled through. In this paper, the characteristics of vehicle tire pattern and its application in forensic science was analyzed and summarized in accordance with cases, based on the analysis of the process of production and using. The research indicated that the tire pattern not only has type characteristics corresponding to vehicle type, but also may have some new individual characteristics in the process of production and using in special condition. The morphological characteristics of tire pattern can be reflected more or less by the tire impression which left on road, clothes or body skin. With the development of trace extraction and enhancement technology, the morphological characteristics of tire pattern in tire impression will be used to deduce vehicle type or identify the suspected vehicle tire, which will play an important role in forensic evidence.

Data-driven prognostic methods typically make use of observer signals reflective of the system health combined with machine learning methods to predict the Remaining Useful Life (RUL) of the system. Currently, majority of feature extraction methods developed for prognostics focused on extracting features from regular time series applications. However, events-data collected during occurrence of an event are stochastic in nature with irregular sampling frequency, which is challenging for current methods. For most prognostic applications, the RUL is closely correlated with changes in data trend exhibited in the observer signals. Motivated by this phenomenon, this paper proposes a novel Time Series-Histogram of Features method, which extracts features describing the local degradation features exhibited by observer signals in a moving time window. The proposed method is illustrated via a case study on a benchmark simulated aero-engine dataset. Results indicate that the proposed methodology performs as well as or better than conventional feature extraction methods on the same time window of information. Furthermore, it is also shown that the proposed method extracts information complementary to conventional feature extraction techniques, thus resulting in superior performance by combining these feature extraction techniques.

Automotive industries in the last decade are demanding for lightweight, corrosion resistance and improved fatigue performance materials. Composites having all such properties quickly gain popularity. Due to complex fabrication methods, composites are known for various process defects. Thus, monitoring and characterizing the composite component before assembly is necessary to maintain the overall structural integrity. Further a rapid and reliable inspection is much needed addition in most automotive industries to save time.
 Non-Destructive Evaluation (NDE) is popular for component testing in the automobile industry because they do not cause any permanent alteration to components. Some NDE techniques require sensors (Piezo Electric transducers, PVDF films, Optical fiber, etc.) that need to be bonded to the structure for testing. Some of these sensors are expensive and cannot be reused once detached. This work aims at the reusability of sensors. Further, the reusable sensors can be deployed in an array configuration for multi-purpose NDE. SMART Skin shall be explained as a Multiple-Transmitter-Multiple-Receiver (MTMR) Piezo-ceramic based sensor (PZT) array which is embedded to a conformable skin. The bottom layer of the skin is coated with pressure-sensitive adhesive to be attached to most curved and non-curved structural surfaces. Each PZT sensor nodes are individually controlled by a MATLAB code that actuates and receive the GW waves signals.
 In this work, we have validated the ability of rapid material characterization using isotropic materials.
 Using experimental data collected by the proposed methodology, we have developed an automated material classification algorithm using various machine learning algorithms. A novel application of functional data analysis (fDA) which converts discrete samples into continuous curves is used. The curves are represented as linear combinations of basis functions. The advantage of this fDA method is that we use the shape of the signal instead of extracting features from the signal. Several choices of basis coefficients such as fourier series, wavelet and B-spline are considered of which spline basis gives the best results. These basis coefficients are then feed into machine learning models like Support Vector Machine (SVM), Random Forest (RF), k nearest neighbor (knn) for classification. The efficacy of this novel method is compared with conventional feature extraction techniques such as zero-crossing coefficients, absolute maximum value, different statistical features like mean and variance, energy of the signal. Encouraging results are obtained that shows the fDA methodology is efficient over the conventional feature extraction methods as it improves the prediction performance on the classifier and result in a faster and cost-effective model by reducing the predictor dimensionality.

In recent years, deep learning-based remote sensing object detection has achieved remarkable progress, yet the detection of tiny objects remains a significant challenge. Tiny objects in remote sensing images typically occupy only a few pixels, resulting in low contrast, poor resolution, and high sensitivity to localization errors. Their diverse scales and appearances, combined with complex backgrounds and severe class imbalance, further complicate the detection tasks. Conventional spatial feature extraction methods often struggle to capture the discriminative characteristics of tiny objects, especially in the presence of noise and occlusion. To address these challenges, we propose a frequency-aware attention-based tiny-object detection network with two plug-and-play modules that leverage frequency-domain information to enhance the targets. Specifically, we introduce a Multi-Scale Frequency Feature Enhancement Module (MSFFEM) to adaptively highlight the contour and texture details of tiny objects while suppressing background noise. Additionally, a Channel Attention-based RoI Enhancement Module (CAREM) is proposed to selectively emphasize high-frequency responses within RoI features, further improving object localization and classification. Furthermore, to mitigate sample imbalance, we employ multi-directional flip sample augmentation and redundancy filtering strategies, which significantly boost detection performance for few-shot categories. Extensive experiments on public object detection datasets, i.e., AI-TOD, VisDrone2019, and DOTA-v1.5, demonstrate that the proposed FANet consistently improves detection performance for tiny objects, outperforming existing methods and providing new insights into the integration of frequency-domain analysis and attention mechanisms for robust tiny-object detection in remote sensing applications.

Leveraging on few-shot learning for tire pattern classification in forensics

Fault detection in rotatory machines is an important necessity to provide system operational reliability and lower unexpected downtime in industrial systems. Conventional feature extraction methods, although good in controlled situations, tend to fall short in noisy situations that mirror real-world operation situations. To fulfill this gap, this work explores machine learning classifiers-Random Forest (RF), Gradient Boosting (GB), XGBoost (XGB), and LightGBM (LGBM)-with two different feature sets: statistical features and Recurrence Quantification Analysis (RQA) features. First, we systematically validate the proposed framework by benchmarking recurrence-based dynamical features against conventional statistical features on a synthetic nonlinear benchmark composed of four well-established dynamical systems: the Lorenz system, the Rössler system, the Hénon map, and the Duffing oscillator. This controlled evaluation enables a methodological comparison across distinct nonlinear regimes before applying the framework to industrial fault data. Then, using experimental rotary machines data, we further examine the robustness of the classifiers under two different operation situations: noise-free and noise-included situations. In a noise-free situation, all classifiers resulted near perfect classification with all statistical features obtaining 99%, whereas RQA features resulted similarly around 93%. Yet, when a Gaussian noise is added (15%-75% of signal magnitude), statistical features marked a considerable decline to 85% accuracy, whereas RQA features remained highly efficient above 94%; consistent results were obtained when other types of noise were added, namely, Brownian noise and impulsive noise. These results present a demonstration of RQA-based feature superiority in long-term dynamics capture and cite its potential to provide a more reliable fault detection in industrial practice where the use of noise and variability of environments is an inescapable consequence.

Learning from dyadic and relational data is a fundamental problem for IR and KDD applications in web and social media domain. Basic behaviors and characteristics of users and documents are typically described by a collection of dyads, i.e., pairs of entities. Discriminative features extracted from such data are essential in exploratory and discriminatory analyses. Relational properties of the entities reflect pair-wise similarities and their collective community structure which are also valuable for discriminative learning. A challenging aspect of learning from the relational data in many domains, is that the generative process of relational links appears noisy and is not well described by a stochastic model.In this paper, we present a principled approach for learning discriminative features from heterogeneous sources of dyadic and relational data. We propose an information-theoretic framework called Latent Feature Encoding (LFE) which projects the entities and the links to a latent feature space in the analogy of -encoding. Projection is formalized as a maximization of the mutual information preserved in the latent features, regularized by the compression rate of encoding. The regularization is emphasized over more probable links to account for the noisiness of the observation. An empirical evaluation of the proposed method using text and social media datasets is presented. Performances in supervised and unsupervised learning tasks are compared with those of conventional latent feature extraction methods.

Although pulse signal is quasiperiodic, most feature extraction methods usually consider it as a whole or only use a single cycle, neglecting the variations between pulse cycles. To characterize both the inter- and intra-cycle variations, in this chapter we propose three feature extraction methods, i.e., simple combination, multi-scale entropy, and complex network. The simple combination method is a direct extension of conventional single-cycle feature extraction method by concatenating features from multiple cycles. The multi-scale entropy method measures the inter- and intra-cycle variations using entropies of different scales. The complex network method transforms the pulse signal from time domain to network domain and measures the inter-cycle variations using the statistical properties on complex network. Experimental results show that the presented features are effective in characterizing both inter- and intra-cycle variations and can obtain better performance in pulse diagnosis.

The steady-state visual evoked potential (SSVEP), which is a kind of event-related potential in electroencephalograms (EEGs), has been applied to brain–computer interfaces (BCIs). SSVEP-based BCIs currently perform the best in terms of information transfer rate (ITR) among various BCI implementation methods. Canonical component analysis (CCA) or spectrum estimation, such as the Fourier transform, and their extensions have been used to extract features of SSVEPs. However, these signal extraction methods have a limitation in the available stimulation frequency; thus, the number of commands is limited. In this paper, we propose a complex valued convolutional neural network (CVCNN) to overcome the limitation of SSVEP-based BCIs. The experimental results demonstrate that the proposed method overcomes the limitation of the stimulation frequency, and it outperforms conventional SSVEP feature extraction methods.

In this paper, a novel feature extraction method is proposed; Genetic Programming (GP) is used to discover features, while the Fisher criterion is employed to provide fitness values. This produces nonlinear features for both two-class and multi-class recognition problems by revealing the discriminating information between classes. The proposed approach is experimentally compared to conventional nonlinear feature extraction methods, including kernel generalised discriminant analysis (KGDA), kernel principal component analysis (KPCA). Results demonstrate the capability of the proposed approach to transform information from the high dimensional feature space into a single dimensional space by automatically discovering the relationships among data.

A novel local texture feature extraction algorithm is proposed based on histogram of oriented gradient domain texture tendency (HOGTT). Classical HOG descriptor proves to be sensitive to rotation transformation, though it is insensitive to illumination and scale changes. The intrinsic texture tendency of tyre tread image has obvious consistency and is robust to different types of images transformation. This paper mainly makes two contributions: First, the scale and circular region constraint on tyre pattern in local feature extraction highly improves the representation precision of the extracted HOG feature. Second, the HOG feature matrix is modified by texture tendency to obtain the rotation invariance property. Experimental results on tyre tread dataset demonstrate the effectiveness of HOGTT in tyre pattern retrieval.

The method of texture feature extraction and classification of pork loin B ultrasound image is proposed, which can be applied to the computer-aided judgment the pork loin fat content of pork loin. 5 texture features which is based on the texture of the co-occurrence matrix are extracted from the B ultrasound image of pork loin according to the digital image processing algorithm. Using the correlation analysis method to select the key texture extraction in the first step. Then,the classification is realized based on the BP neural network. The train set and test set are randomly chosen from 135 cases. Tests performed show that the proposed method result in a high classification accuracy, which will provide the researcher a valuable opinion on the pork fat content detection.

The aim of this research was to determine an acceptable method of texture feature extraction that could be used for a visual inspection of ceramic tiles quality. Traditional extraction of statistical features has been carried out as well as feature extraction based on local binary pattern operators. For the purpose of testing the proposed methods good and defective ceramic tiles have been identified. The obtained results indicate a possibility for developing a system of a visual inspection of ceramic tiles quality and point out local binary pattern operators as a powerful tool applicable in this particular case.

This paper presents a novel feature extraction algorithm based on particle swarms for processing hyperspectral imagery data. Particle swarm optimization, originally developed for global optimization over continuous spaces, is extended to deal with the problem of feature extraction. A formulation utilizing two swarms of particles was developed to optimize simultaneously a desired performance criterion and the number of selected features. Candidate feature sets were evaluated on a regression problem. Artificial neural networks were trained to construct linear and nonlinear models of chemical concentration of glucose in soybean crops. Experimental results utilizing real-world hyperspectral datasets demonstrate the viability of the method. The particle swarms-based approach presented superior performance in comparison with conventional feature extraction methods, on both linear and nonlinear models.