Use Of Local Binary Patterns Research Articles

Siyam Sinir Ağları ve Yerel İkili Örüntü Kullanılarak Temassız Avuç İçi Doğrulaması

Biyometrik kimlik doğrulama, kişilerin sahip olduğu fizyolojik veya davranışsal özellikler kullanılarak gerçekten iddia ettikleri kişi olup olmadığının teyit edilmesidir. Avuç içi doğrulama, biyometrik doğrulama içinde en yaygın kullanıma sahip yöntemlerden birisidir. 2019 yılının son aylarında ortaya çıkan COVID-19 (Coronavirus Disease 2019) pandemisi insanların ortak kullanıma sahip nesnelere temas konusundaki duyarlılığını artırmıştır. Bu sebeple, temassız şekilde elde edilen görüntülerin kullanıldığı avuç içi doğrulama çalışmalarının yapılması önem kazanmaktadır. Çalışmada, Hong Kong Politeknik Üniversitesi Temassız 3B/2B Veri Seti (Sürüm 1.0) (PolyU Contactless Database 1.0) kullanılmış olup doğrulama için Siyam Sinir Ağlarından (SSA) yararlanılmıştır. SSA eğitimleri 3.540 adet “benzer” ve 31.152 adet “benzemeyen” olmak üzere toplam 34.692 adet görüntü çifti kullanılarak gerçekleştirilmiştir. Çalışmanın test işlemleri ise 885 adet “gerçek” ve 31.152 adet “sahte” olmak üzere toplam 32.037 adet giriş örneği kullanılarak yapılmıştır. Çalışmada, avuç içi görüntüleri doğrudan kullanılarak elde edilen doğrulama sonuçları ile ön işlem olarak Yerel İkili Örüntü (YİÖ) kullanılarak elde edilen doğrulama sonuçları birbirleriyle karşılaştırılmıştır. Çalışma sonuçları ön işlem olarak YİÖ kullanılmasının doğrulama başarısını önemli ölçüde iyileştirdiğini göstermektedir. Çalışmada, avuç içi görüntüleri doğrudan kullanılarak elde edilen Eşit Hata Oranı (EHO) 0,1277 iken ön işlem olarak YİÖ kullanılarak elde edilen EHO 0,0938 olarak gerçekleşmiştir.

AI Driven Innovation in early Detection and Diagnosis of Brain Cancer

The continuous advancement of artificial intelligence (AI) has brought about a significant transformation in the healthcare sector, namely in the domain of early identification and diagnosis of intricate medical ailments. The present study investigates the use of artificial intelligence (AI) in the identification and diagnosis of brain tumors at an early stage. This research capitalizes on a combination of advanced methodologies such as Genetic Algorithms, Local Binary Patterns (LBP), Deep Learning-Based Segmentation, and Support Vector Machines (SVM) to achieve its objectives. Genetic algorithms are utilized in the context of feature selection to optimize the discriminative capability of input data. The use of Local Binary Patterns (LBP) presents a reliable method for doing texture analysis, hence improving the characterization of diseased areas in brain imaging. Deep learning-based segmentation approaches have demonstrated high efficiency in extracting tumor boundaries and accurately distinguishing them from healthy brain tissue, hence enabling precise localization. The Support Vector Machine (SVM) technique, which is highly effective in classification tasks, plays a crucial role in the diagnostic process by accurately distinguishing between benign and malignant tumour cases. The utilisation of an interdisciplinary method not only enhances the precision and dependability of brain tumor diagnosis, but also accelerates the procedure, therefore facilitating prompt intervention and potentially life-preserving therapeutic alternatives for individuals. The research highlights the significant capacity of AI-based approaches in revolutionizing the field of neuroimaging, emphasizing their crucial contribution to augmenting the abilities of healthcare practitioners in the essential undertaking of identifying and diagnosing brain tumors.

Efficient Handwritten Signatures Identification using Machine Learning

Any agreement or contract between two or more parties requires at least one party to employ a signature as evidence of the other parties' identities and as a means of establishing the parties' intent. As a result, more people are curious about Signature Recognition than other biometric methods like fingerprint scanning. Utilizing both Fourier Descriptors and histogram of oriented gradients (HOG) features, this paper presents an efficient algorithms for signature recognition. The use of Local binary patterns (LBP) features in a signature verification technique has been proposed. Using morphological techniques, the signature is encapsulated within a curve that is both symmetrical and a good match. Measured by the frequency with which incorrect patterns are confirmed by a given system, false acceptance rate (FAR) provides an indication of the effectiveness and precision of the proposed system. Using a local dataset of 60 test signature patterns, this investigation found that 10% were incorrectly accepted for FAR of 0.169. Experiments are conducted on signature photos from a local dataset. Verification of signatures has previously made use of KNN classifier. KNN classifier produced higher FARs and recognition accuracies than prior techniques.

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.

Detection of liner surface defects in solid rocket motors using multilayer perceptron neural networks

Debonding problems along the propellant/liner/insulation interface are a critical point to the integrity and one of the major causes of structural failures of solid rocket motors. Current solutions are typically restricted to methods for assessing the integrity of the rocket motors structure and visually inspecting their components. In this context, this paper presents an improved algorithm to detect liner surface defects that may compromise the bonding between the solid propellant and the insulation. The use of Local Binary Patterns (LBP) provides a structural and statistical approach to texture analysis of liner sample images. Along with color information extraction, these two methods allow the representation of image pixels by feature vectors that are further processed by a Multilayer Perceptron (MLP) neural network classifier. The MLP neural network analyzes liner sample images and classifies each pixel into one of three classes: non-defect, foreign object, and defect. Several tests were executed varying different parameters to find the optimal MLP configuration, and as a result, the best classification accuracy of 99.08%, 90.66%, and 99.48% was achieved for the corresponding classes. Moreover, the defect size estimate showed that the MLP classifier correctly identified defects less than 1 mm long, with a relatively small number of training examples. Positive results indicate that the algorithm can identify liner surface defects with a performance similar to human inspectors and has the potential to assist or even automate the liner inspection process of solid rocket motors.

A hybrid fusion of wireless signals and RGB image for indoor positioning

In this article, we propose a new indoor positioning algorithm using smartphones, where wireless signals and images are deeply combined together to improve the positioning performance. Our approach is based on the use of local binary patterns’ feature, which has the advantages of rotation invariance and scale invariance. Moreover, the term “uniform” are fundamental properties of local image textures and their occurrence histogram is proven to be a very powerful texture feature. Besides, the received signal strength acts as a reliable cue on a person’s identity. We first obtain a coarse-grained estimation based on the visualization of wireless signals, which are presented by a vector, making use of fingerprinting methods. Then, we perform a matching process to determine correspondences between two-dimensional pixels and three-dimensional points based on images collected by the smartphone. After being evaluated by experiments, our proposed method demonstrates that the combination of the visual and the wireless data significantly improves the positioning accuracy and robustness. It can be widely applied to smartphones to better analyze human behavior and offer high-accuracy indoor location–based services.

Mapping Arctic Coastal Ecosystems with High Resolution Optical Satellite Imagery Using a Hybrid Classification Approach

ABSTRACTMost mapping methods for Arctic land cover are pixel-based techniques for low resolution data, and have limitations in mapping land cover heterogeneity over complex Arctic polygonal tundra terrain. In this study, we developed a hybrid object-based approach for Arctic coastal tundra mapping using very high resolution optical satellite imagery by combining results from semi-automatic water/land separation, texture analysis based on local binary pattern (LBP), and image classification via Random Forests (RF). The method was applied for coastal land cover mapping in a study site in Tuktoyaktuk, Northwest Territories, Canada using Pleiades satellite data. Results from pixel-based Maximum Likelihood Classifier (MLC), segment-based MLC, pixel-based RF, and segment-based RF were compared with the proposed method. The hybrid method outperformed other approaches and achieved an overall accuracy of 88% for 9 classes. In particular, it has successfully identified unique land cover types of Ice-Wedge Polygons, Wetland (inundated low-lying tundra and marsh with water ponds), with both producer's and user's accuracy over 91%. Results from this study indicate that the developed hybrid method is suitable to be applied for mapping Arctic coastal ecosystems, and confirms the feasibility of proper use of LBP at segment level for mapping complex environment.

Texture analysis of masses malignant in mammograms images using a combined approach of diversity index and local binary patterns distribution

A World Health Organization (WHO) report estimates that in 2015, at least 561 thousand women will die of breast cancer. Although breast cancer is considered a disease of the developed world, nearly 50% of the cases and 58% of the deaths occur in the less developed countries. A mammogram is a way of discovering not just the palpable tumors that cause cancer but also other lesions that are not perceived during the physical examination performed by the expert physician or during self-exams; however, it is known that this exam is targeted for women after the age of 40 because age is one of the factors that can cause great variations in sensitivity during the exam. Besides the patient’s age, the expert’s experience and the quality of the images obtained during the exam are decisive factors in the detection of breast cancer. This work presents two novelties. The first is the use of Local Binary Patterns (LBPs) to generate a representation of a Region of Interest (ROI) image. Over this representation, we generate other representations using techniques such as image histograms, gray-level co-occurrence matrices (GLCMs) and gray-level run-length matrices (GLRLMs). These representations allow texture analysis through several perspectives. The second novelty uses these representations as input to the application of indexes adapted from ecology (Shannon, McIntosh, Simpson, Gleason and Menhinick) as texture descriptors. Based on this strategy, we analyze mammographic image textures to classify regions of these images as benign or malignant using a Support Vector Machine (SVM). The best result achieved was of 88.31% accuracy, 85% sensitivity, 91.89% specificity, a positive probability ratio of 10.48, a negative probability ratio of 0.16, and an area under the Receiver Operating Characteristic (ROC) curve of 0.88, obtained through the Shannon index. We believe that the proposed method, with some adaptations, may also be used for image texture analysis of several different lesions such as lung nodules, glaucoma and prostates. This belief is based on the achieved results and the method’s simplicity.

Texture modelling with nested high-order Markov–Gibbs random fields

Currently, Markov–Gibbs random field (MGRF) image models which include high-order interactions are almost always built by modelling responses of a stack of local linear filters. Actual interaction structure is specified implicitly by the filter coefficients. In contrast, we learn an explicit high-order MGRF structure by considering the learning process in terms of general exponential family distributions nested over base models, so that potentials added later can build on previous ones. We relatively rapidly add new features by skipping over the costly optimisation of parameters.We introduce the use of local binary patterns as features in MGRF texture models, and generalise them by learning offsets to the surrounding pixels. These prove effective as high-order features, and are fast to compute. Several schemes for selecting high-order features by composition or search of a small subclass are compared. Additionally we present a simple modification of the maximum likelihood as a texture modelling-specific objective function which aims to improve generalisation by local windowing of statistics.The proposed method was experimentally evaluated by learning high-order MGRF models for a broad selection of complex textures and then performing texture synthesis, and succeeded on much of the continuum from stochastic through irregularly structured to near-regular textures. Learning interaction structure is very beneficial for textures with large-scale structure, although those with complex irregular structure still provide difficulties. The texture models were also quantitatively evaluated on two tasks and found to be competitive with other works: grading of synthesised textures by a panel of observers; and comparison against several recent MGRF models by evaluation on a constrained inpainting task.

Image authentication using LBP-based perceptual image hashing

Feature extraction is a main step in all perceptual image hashing schemes in which robust features will led to better results in perceptual robustness. Simplicity, discriminative power, computational efficiency and robustness to illumination changes are counted as distinguished properties of Local Binary Pattern features. In this paper, we investigate the use of local binary patterns for perceptual image hashing. In feature extraction, we propose to use both sign and magnitude information of local differences. So, the algorithm utilizes a combination of gradient-based and LBP-based descriptors for feature extraction. To provide security needs, two secret keys are incorporated in feature extraction and hash generation steps. Performance of the proposed hashing method is evaluated with an important application in perceptual image hashing scheme: image authentication. Experiments are conducted to show that the present method has acceptable robustness against perceptual content-preserving manipulations. Moreover, the proposed method has this capability to localize the tampering area, which is not possible in all hashing schemes.

A textural approach for mass false positive reduction in mammography

During the last decade several algorithms have been proposed for automatic mass detection in mammographic images. However, almost all these methods suffer from a high number of false positives. In this paper we propose a new approach for tackling this false positive reduction problem. The key point of our proposal is the use of Local Binary Patterns (LBP) for representing the textural properties of the masses. We extend the basic LBP histogram descriptor into a spatially enhanced histogram which encodes both the local region appearance and the spatial structure of the masses. Support Vector Machines (SVM) are then used for classifying the true masses from the ones being actually normal parenchyma. Our approach is evaluated using 1792 ROIs extracted from the DDSM database. The experiments show that LBP are effective and efficient descriptors for mammographic masses. Moreover, the comparison with current methods illustrates that our proposal obtains a better performance.