Texture Algorithms Research Articles

A Transfer Learning-Based Framework for Classifying Lymph Node Metastasis in Prostate Cancer Patients.

Background: Prostate cancer is the second most common new cancer diagnosis in the United States. It is usually slow-growing, and when it is low-grade and confined to the prostate gland, it can be treated either conservatively (through active surveillance) or with surgery. However, if the cancer has spread beyond the prostate, such as to the lymph nodes, then that indicates a more aggressive cancer, and surgery may not be adequate. Methods: The challenge is that it is often difficult for radiologists reading prostate-specific imaging such as magnetic resonance images (MRIs) to differentiate malignant lymph nodes from non-malignant ones. An emerging field is the development of artificial intelligence (AI) models, including machine learning and deep learning, for medical imaging to assist in diagnostic tasks. Earlier research focused on implementing texture algorithms to extract imaging features used in classification models. More recently, researchers began studying the use of deep learning for both stand-alone feature extraction and end-to-end classification tasks. In order to tackle the challenges inherent in small datasets, this study was designed as a scalable hybrid framework utilizing pre-trained ResNet-18, a deep learning model, to extract features that were subsequently fed into a machine learning classifier to automatically identify malignant lymph nodes in patients with prostate cancer. For comparison, two texture algorithms were implemented, namely the gray-level co-occurrence matrix (GLCM) and Gabor. Results: Using an institutional prostate lymph node dataset (42 positives, 84 negatives), the proposed framework achieved an accuracy of 76.19%, a sensitivity of 79.76%, and a specificity of 69.05%. Using GLCM features, the classification achieved an accuracy of 61.90%, a sensitivity of 74.07%, and a specificity of 42.86%. Using Gabor features, the classification achieved an accuracy of 65.08%, a sensitivity of 73.47%, and a specificity of 52.50%. Conclusions: Our results demonstrate that a hybrid approach, i.e., using a pre-trainined deep learning model for feature extraction, followed by a machine learning classifier, is a viable solution. This hybrid approach is especially useful in medical-imaging-based applications with small datasets.

Capacity and capability of remote sensing to inform invasive plant species management in the Pacific Islands region.

The Pacific Islands region is home to several of the world's biodiversity hotspots, yet its unique flora and fauna are under threat because of biological invasions. These invasions are likely to proliferate as human activity increases and large-scale natural disturbances unfold, exacerbated by climate change. Remote sensing data and techniques provide a feasible method to map and monitor invasive plant species and inform invasive plant species management across the Pacific Islands region. We used case studies taken from literature retrieved from Google Scholar, 3 regional agencies' digital libraries, and 2 online catalogs on invasive plant species management to examine the uptake and challenges faced in the implementation of remote sensing technology in the Pacific region. We synthesized remote sensing techniques and outlined their potential to detect and map invasive plant species based on species phenology, structural characteristics, and image texture algorithms. The application of remote sensing methods to detect invasive plant species was heavily reliant on species ecology, extent of invasion, and available geospatial and remotely sensed image data. However, current mechanisms that support invasive plant species management, including policy frameworks and geospatial data infrastructure, operated in isolation, leading to duplication of efforts and creating unsustainable solutions for the region. For remote sensing to support invasive plant species management in the region, key stakeholders including conservation managers, researchers, and practitioners; funding agencies; and regional organizations must invest, where possible, in the broader geospatial and environmental sector, integrate, and streamline policies and improve capacity and technology access.

Deep Learning based Smart Image Search Engine

Abstract: This paper introduces a new reverse search engine integration into content-based image retrieval (CBIR) systems that employs convolutional neural networks (CNNs) for feature extraction. It generates global descriptors using pre-trained CNN architectures such as ResNet50, InceptionV3, and InceptionResNetV2. It retrieves visually similar images without depending on linguistic annotations. Comparative analysis against existing methods, such as Gabor Wavelet, CNN-SVM, Metaheuristic Algorithm, etc., has been tested, and it proves the superiority of the proposed algorithm, the Cartoon Texture Algorithm, in CBIR. As the Internet sees an exponential growth of different data types, the importance of CBIR continues to grow. In order to efficiently retrieve images, solely relying on image features while ignoring metadata is exactly what we need. As such, this paper is a reminder of the need for CBIR in this changing world. They showed that CBIR continues to be quite effective in the age of the Internet. Their proposed model for CBIR, which integrates ResNet-50-based feature extraction, a neural network model trained on different image datasets, and clustering techniques to make retrieval fast, provides a significant improvement in accuracy and efficiency for content-dependent image retrieval. This methodology is likely to be very useful as we work with the increasingly huge data of vision and beyond on the Internet. It provides a good basis for an effective image search and retrieval system

Mathematical Model Design of the Traditional Dress Recognition Algorithm Based on Digital Watermarking Technology

According to the characteristics of traditional clothing, clothing identification is studied, and clothing identification and clothing culture learning are effectively combined to find a new method for the inheritance of national culture and strive to make contributions to the inheritance of national culture. According to the requirement of the traditional garment identification watermark monitoring system, a self-synchronous digital watermarking algorithm is designed and implemented. Watermark is embedded in the time domain, and feature information is extracted from traditional clothing by means of mean filtering and replaced by watermark to achieve the purpose of embedding information. Blind detection is realized without the participation of the original image. The difference between the traditional costume embedded with watermark and the original traditional costume is almost imperceptible. It can effectively resist synchronous attacks including clipping and time shifting, showing good robustness. Imperceptibility and robustness can be adjusted freely by embedding strength. The HOG + SVM algorithm is applied to minority clothing classification and recognition. By comparing different classifiers, it is concluded that the classifier trained by the support vector machine algorithm has the best classification effect on ethnic clothing. In order to improve the classification effect, the classical algorithm of color, texture, and shape feature extraction was combined with SVM to conduct experiments on the clothing database collected and sorted out in Yunnan ethnic minority communities, and finally, we verified that the HOG feature combined with the SVM classification algorithm achieved good results in the classification of ethnic clothing.

An investigation of woven fabric density measurement using image analysis based on RTV-SFT

To overcome the shortcomings of time-consuming manual measurement methods of fabric density, a novel density measurement method of woven fabrics using image analysis technology via the relative total variation model based on the view of structure extraction from texture (RTV-SFT) is developed. Firstly, a fabric image acquisition system of multi-zoom portable digital microscope is constructed to obtain color dyed fabric images. Secondly, creatively from the perspective of analyzing periodic structure information of image texture information, a new structure extraction from texture algorithm, namely RTV-SFT is proposed to process fabric images to obtain images without non-periodic texture information aiming to achieve accurate yarn segmentation. Finally, the woven fabric density is calculated by effectively adopting the gray level projection method with the improved Gaussian filter algorithm. In the experiment, compared with manual method, the accuracy of the proposed density measurement method is above 93.3%.

A novel pixel’s fractional mean-based image enhancement algorithm for better image splicing detection

Image manipulation has become widely accessible to the masses over the past years due to the sophisticated image editing tools which are readily-available and easy to use. As a result, image forgery has increased such that it has become infeasible to discriminate authentic from tampered images with the naked eye. Image forgery plays a prominent role in the spread of misinformation, which might be criminalized under certain jurisdictions. Image splicing is a common type of image manipulation and constitutes one of the most widespread image tampering methods on the internet. Efforts have been made to tackle the implications of image forgery by developing computer algorithms that can discriminate tampered images, however, more research is needed to keep up with the advancements of image editing tools. Previously, we have explored fractional calculus in other image processing applications. In this study, we propose a novel pixel’s fractional mean (PFM) algorithm to enhance images prior to classification for better detection of image splicing forgery based on texture features. The proposed PFM enhances each pixel separately depending on the occurrence number of the pixel’s intensity. Two sets of texture algorithms are used to extract essential features from suspected spliced images. These features are then used with the “support vector machine” (SVM) classifier to classify authentic and spliced images. The proposed model demonstrated an accuracy rate of 97% when evaluated with the publicly-available image splicing dataset “CASIA v2.0”. With a relatively low dimension feature vector, the proposed model demonstrates high accuracy and efficiency, which corroborate the benefit of using fractional calculus in image processing algorithms.

The Portilla-Simoncelli Texture Model: towards Understanding the Early Visual Cortex

Texture synthesis is a prolific subarea in computer vision where statistical methods are often successful. The Portilla and Simoncelli (PS) texture algorithm is one of such methods that became very popular and has influenced visual perception studies. For many reasons it can still be considered as a state-of-the art texture synthesis algorithm: (i) it generates textures that are often indistinguishable from the original without scrutiny; (ii) it relies on few parameters compared to recent deep learning methods; (iii) recent algorithms often compare to it. Here, we review the scientific impact of this algorithm and give a detailed explanation. Briefly, the PS algorithm synthesizes a new texture by iteratively imposing to a Gaussian white noise image a set of high-order statistics of wavelet coefficients precomputed on a texture example. After few iterations the initial white noise image is transformed into a texture that is similar to the texture example. We provide a fast C++ implementation, evaluate the effect of the algorithm parameters and illustrate its capabilities with many synthesis examples. In addition, we propose two notable new features to the original implementation: (i) the possibility to interpolate between two textures; (ii) the possibility to handle non-periodicity using the 'periodic+smooth' decomposition.

Retinal Blood Vessel Segmentation on Style-augmented Images

The average human lifespan increased dramatically in the second half of 20th century. It was mainly due to technological improvements, which were driven by the continuous war preparations, and while humans have got another 20 years to live, unfortunately there are some sad side effects added to the elderly life. Various diseases can attack the eye, our major organ responsible for receiving information, therefore many researches were devoted to examine these diseases, their early signs, and how could they be stopped. From the start of 21th century, methods aided by computer were more and more involved in these processes, up to the current trend of using Convolutional Neural Networks (CNNs). While supervised methods, CNNs do achieve accuracy which can be compared to a skilled ophtalmologist, they require a tremendous amount of labeled data which is sparse in medical fields because the amount of time and resources needed to create them. One natural solution is to augment the data present, that is, copying the distribution while adding a small variety, like coloring an image differently. That is, what our paper aims to explore, whether a texturing algorithm, the Neural Style Transfery can be used to make a data set richer, and therefore helping a classifier CNN to achieve better results.

Automatic Positional Accuracy Assessment of Imagery Segmentation Processes: A Case Study

There are many studies related to Imagery Segmentation (IS) in the field of Geographic Information (GI). However, none of them address the assessment of IS results from a positional perspective. In a field in which the positional aspect is critical, it seems reasonable to think that the quality associated with this aspect must be controlled. This paper presents an automatic positional accuracy assessment (PAA) method for assessing this quality component of the regions obtained by means of the application of a textural segmentation algorithm to a Very High Resolution (VHR) aerial image. This method is based on the comparison between the ideal segmentation and the computed segmentation by counting their differences. Therefore, it has the same conceptual principles as the automatic procedures used in the evaluation of the GI’s positional accuracy. As in any PAA method, there are two key aspects related to the sample that were addressed: (i) its size—specifically, its influence on the uncertainty of the estimated accuracy values—and (ii) its categorization. Although the results obtained must be taken with caution, they made it clear that automatic PAA procedures, which are mainly applied to carry out the positional quality assessment of cartography, are valid for assessing the positional accuracy reached using other types of processes. Such is the case of the IS process presented in this study.

Optimization of the image acquisition procedure in low-field MRI for non-destructive analysis of loin using predictive models.

The use of low-field magnetic resonance imaging (LF-MRI) scanners has increased in recent years. The low economic cost in comparison to high-field (HF-MRI) scanners and the ease of maintenance make this type of scanner the best choice for nonmedical purposes. However, LF-MRI scanners produce low-quality images, which encourages the identification of optimization procedures to generate the best possible images. In this paper, optimization of the image acquisition procedure for an LF-MRI scanner is presented, and predictive models are developed. The MRI acquisition procedure was optimized to determine the physicochemical characteristics of pork loin in a nondestructive way using MRI, feature extraction algorithms and data processing methods. The most critical parameters (relaxation times, repetition time, and echo time) of the LF-MRI scanner were optimized, presenting a procedure that could be easily reproduced in other environments or for other purposes. In addition, two feature extraction algorithms (gray level co-occurrence matrix (GLCM) and one point fractal texture algorithm (OPFTA)) were evaluated. The optimization procedure was validated by using several evaluation metrics, achieving reliable and accurate results (r > 0.85; weighted absolute percentage error (WAPE) lower than 0.1%; root mean square error of prediction (RMSEP) lower than 0.1%; true standard deviation (TSTD) lower than 2; and mean absolute error (MAE) lower than 2). These results support the high degree of feasibility and accuracy of the optimized procedure of LF-MRI acquisition. No other papers present a procedure to optimize the image acquisition process in LF-MRI. Eventually, the optimization procedure could be applied to other LF-MRI systems.

Use of Magnetic Resonance Imaging to Analyse Meat and Meat Products Non-destructively

ABSTRACT The methodology traditionally used to analyse the quality parameters of meat and meat products involves the destruction of the samples. Moreover, these techniques are tedious, time and solvent consuming. As an alternative, the use of different techniques based on images are being evaluated to be proposed as methods to analyse the quality parameters of meat and meat products non-destructively. For that, this study reviews the use of MRI to analyse meat and meat products, being specially centered on the used procedures and main achievements. High-Field and Low-Field MRI scanners have been used for analysing meat products. These studies have been mainly focused on classification purposes and prediction of different quality parameters of hams and loins. The usual procedure consists of three steps: image acquisition, image analysis, and data analysis. The most applied methodology have been SE T1, texture algorithms, and classification (PCA, PLS) and prediction (MLR) techniques, respectively. MRI publications have shown accurate results, indicating the appropriateness of this technique to be proposed as an alternative methodology to analyse meat and meat products with high reliability in a non-destructive way. However, improvements in the image acquisition and analysis as well as in the process automation are desired.

Diagnosis of Neem Leaf Diseases Using Fuzzy-HOBINM and ANFIS Algorithms

This paper proposes an approach to detecting diseases in neem leaf that uses a Fuzzy-Higher Order Biologically Inspired Neuron Model (F-HOBINM) and adaptive neuro classifier (ANFIS). India exports USD 0.28-million worth of neem leaf to the UK, USA, UAE, and Europe in the form of dried leaves and powder, both of which help reduce diabetes-related issues, cardiovascular problems, and eye disorders. Diagnosing neem leaf disease is difficult through visual interpretation, owing to similarity in their color and texture patterns. The most common diseases include bacterial blight, Colletotrichum and Alternaria leaf spot, blight, damping-off, powdery mildew, Pseudocercospora leaf spot, leaf web blight, and seedling wilt. However, traditional color and texture algorithms fail to identify leaf diseases due to irregular lumps and surfaces, and rough ridges, as the classification time involved takes as long as a week. The proposed F-HOBINM algorithm recognizes the leaf intensity through the leaky capacitor, and uses subjective intensity and physical stimulus to interpret the diagnosis. Further, the processed leaf images from the HOBINM algorithm are applied to the ANFIS classifier to identify neem leaf diseases. The experimental results show 92.18% accuracy from a database of 1,462 neem leaves.

Utilizing Advance Texture Features for Rapid Damage Detection of Built Heritage Using High-Resolution Space Borne Data: A Case Study of UNESCO Heritage Site at Bagan, Myanmar

Heritage sites are vulnerable to damage due to social, anthropological and environmental factors. Major Earthquakes are followed by damage to cultural heritage buildings. The assessment of such building damage is a critical problem. Earth Observation data, owing to its property of being non-contact, cost effective, synoptic view and high repeatability, has a significant role to play in estimation of damage in the earthquake affected areas. Currently, several and varied types of remote sensing data have become available, and therefore, appropriate methods for rapid assessment and analysis of the data need to be developed. Rapid damage assessment is critical to minimize loss in terms of life and property. In case of cultural monuments, rapid assessment can minimize damage and help in the conservation of monument. This research focuses on evolving a robust method for rapid identification and extraction of damaged heritage building structures, especially those affected by disasters such as earthquakes. In this study, we propose to examine the utility of advance texture algorithms such as Gabor, fractal and semi-variogram for rapid damage detection in heritage building structures. The methodology attempts to automatically highlight damaged portions of the structure through a knowledge driven rule set. The technique was able to extract the damaged area from the heritage building structure with the use of high-resolution space borne data. It is observed that feature extraction algorithms based on fractal and variogram provide better results than the Gabor based textures and are very useful in the case of high-resolution satellite imagery. Both the methods are able to extract damaged features in both shadowed and non-shadowed regions of the image. Hence the problems posed by shadowed dead grounds on EO data can be effectively resolved. However, it is also observed that the advance texture feature extraction algorithms are useful only in case of high-spatial resolution dataset and has limited use for rapid damage assessment from medium and low resolution datasets.

Local feature extraction based facial emotion recognition: a survey

Notwithstanding the recent technological advancement, the identification of facial and emotional expressions is still one of the greatest challenges scientists have ever faced. Generally, the human face is identified as a composition made up of textures arranged in micro-patterns. Currently, there has been a tremendous increase in the use of local binary pattern based texture algorithms which have invariably been identified to being essential in the completion of a variety of tasks and in the extraction of essential attributes from an image. Over the years, lots of LBP variants have been literally reviewed. However, what is left is a thorough and comprehensive analysis of their independent performance. This research work aims at filling this gap by performing a large-scale performance evaluation of 46 recent state-of-the-art LBP variants for facial expression recognition. Extensive experimental results on the well-known challenging and benchmark KDEF, JAFFE, CK and MUG databases taken under different facial expression conditions, indicate that a number of evaluated state-of-the-art LBP-like methods achieve promising results, which are better or competitive than several recent state-of-the-art facial recognition systems. Recognition rates of 100%, 98.57%, 95.92% and 100% have been reached for CK, JAFFE, KDEF and MUG databases, respectively.

Radial textures: a new algorithm to analyze meat quality on MRI

Magnetic Resonance Imaging (MRI) was introduced as an alternative to destructive methods for determining the quality parameters of meat products, since MRI is a non-destructive, non-ionizing, and innocuous technique. The use of MRI is linked to the use of computer vision algorithms and data mining techniques. Thus, the success of MRI could not be understood without referring to the implementation of powerful algorithms to handle all data generated. In this study, a new texture algorithm was developed in order to obtain new features based on the radial distribution of the textures from MRI. This new algorithm is called Radial Texture Algorithm (RTA) and it was compared with four classical texture features approaches. The results obtained allow describing the semantic context of the texture features for this new algorithm and the relationship among the features from this algorithm and the classical approaches. Besides, the results obtained by means of these computer vision algorithms were correlated with the results obtained by means of physico-chemical and sensory parameters of the Iberian loins. All of computer vision algorithms achieved correlation coefficients higher than 0.500, noting that RTA reached the highest correlation coefficients in almost all cases. These high correlation coefficients confirm the proposed algorithm as an alternative to the other computational texture features approaches in order to compute the physico-chemical and sensory parameters of meat products in an accurate, fast, non-destructive and efficient way.

A Textural Approach to Improving Snow Depth Estimates in the Weddell Sea

The snow depth on Antarctic sea ice is critical to estimating the sea ice thickness distribution from laser altimetry data, such as from Operation IceBridge or ICESat-2. Snow redistributed by wind collects around areas of deformed ice and forms a wide variety of features on sea ice; the morphology of these features may provide some indication of the mean snow depth. Here, we apply a textural segmentation algorithm to classify and group similar textures to infer the distribution of snow using snow surface freeboard measurements from Operation IceBridge campaigns over the Weddell Sea. We find that texturally-similar regions have similar snow/ice ratios, even when they have different absolute snow depth measurements. This allows for the extrapolation of nadir-looking snow radar data using two-dimensional surface altimetry scans, providing a two-dimensional estimate of the snow depth with ∼22% error. We show that at the floe scale (∼180 m), snow depth can be directly estimated from the snow surface with ∼20% error using deep learning techniques, and that the learned filters are comparable to standard textural analysis techniques. This error drops to ∼14% when averaged over 1.5 km scales. These results suggest that surface morphological information can improve remotely-sensed estimates of snow depth, and hence sea ice thickness, as compared to current methods. Such methods may be useful for reducing uncertainty in Antarctic sea ice thickness estimates from ICESat-2.

Multi-Hypothesis Approach for Efficient Human Detection

Detection of human beings in a complex background environment is a challenging task in computer vision. Most of the time no single feature algorithm is rich enough to capture all the relevant information available in the image. In this paper, we propose a new feature extraction technique that combines three types of visual information; shape, color, and texture, and is named as the Color space Phase features with Gradient and Texture (CPGT) algorithm. Gradient concept and the phase congruency in color domain are used to localize the shape features. The Center-Symmetric Local Binary Pattern (CSLBP) approach is used to extract the texture information of the image. Fusing of these complementary features yields to capture a broad range of the human appearance details that improves the detection performance. The proposed features are formed by computing the gradient magnitude and CSLBP values for each pixel in the image with respect to its neighborhood in addition to the phase congruency of the three-color channels. Only the maximum phase congruency magnitudes are selected from the corresponding color channels. The histogram of oriented phase and gradients as well as the histogram of CSLBP values for the local regions of the image are determined and concatenated to construct the proposed descriptor. Principal Component Analysis (PCA) is performed to reduce the dimensionality of the resultant features. Several experiments were conducted to evaluate the performance of the proposed descriptor. The experimental results show that the proposed approach yields promising performance and has lower error rates when compared to several state of the art feature extraction methodologies. We observed a miss rate of 2.23% in the INRIA dataset and 2.6% in the NICTA dataset.

A fast particle filter pedestrian tracking method based on colour, texture and corresponding space information

In order to track pedestrians accurately, a fast particle filter pedestrian tracking method based on colour, texture and corresponding space information is proposed. In this algorithm, firstly, we extract space information of object pedestrian and disintegrate it into three local regions. In addition, employ the improved texture and colour information extract algorithm to get the joint texture and colour information from the corresponding sub-region. Finally, determine the position of object by colour-texture similarity indicator based on space division, and get the result of accurately track. In consideration of the multi thread information fusion algorithm need a larger number of particles, this factor could reduce the computational efficiency. Therefore, a wave integral histogram algorithm is proposed for improving arithmetic speed. The experiment carried out on videos, result indicates the effectiveness and efficiency of the proposed method, which can achieve higher accuracy than other tow state of the art algorithms in the actual traffic scene, and the real-time performance also has been improved considerably.

MIDAS projection

The visual appearance of an object can be disguised by projecting virtual shading as if overwriting the material. However, conventional projection-mapping methods depend on markers on a target or a model of the target shape, which limits the types of targets and the visual quality. In this paper, we focus on the fact that the shading of a virtual material in a virtual scene is mainly characterized by surface normals of the target, and we attempt to realize markerless and modelless projection mapping for material representation. In order to deal with various targets, including static, dynamic, rigid, soft, and fluid objects, without any interference with visible light, we measure surface normals in the infrared region in real time and project material shading with a novel high-speed texturing algorithm in screen space. Our system achieved 500-fps high-speed projection mapping of a uniform material and a tileable-textured material with millisecond-order latency, and it realized dynamic and flexible material representation for unknown objects. We also demonstrated advanced applications and showed the expressive shading performance of our technique.

Medical Image Retrieval Approach by Texture Features Fusion Based on Hausdorff Distance

Medical images play an important role in the hospital diagnosis and treatment, which include a lot of valuable medical information. Manually annotated viewing is obviously not effective in managing large amounts of medical imaging data. Hence it is an important task to establish an efficient and accurate medical image retrieval system. In this paper, a medical image retrieval approach based on Hausdorff distance combining Tamura texture features and wavelet transform algorithm is proposed. The combination of Tamura texture features and wavelet transform features can extract the texture features of medical images more effectively, and Hausdorff distance can reflect the overall similarity of medical image feature set. In this paper, 6 group experiments of brain MRI database and the lung CT database were conducted separately. Experiments show that the proposed approach has higher accuracy than a single feature texture algorithm and is also higher than the approach of Tamura texture features and wavelet transform features combined with Euclidean distance.