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Journal articles on the topic 'Modified Local Binary pattern (MOD-LBP)'

Author: Grafiati

Published: 3 June 2025

Last updated: 19 July 2025

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1

S. Sathiya, Devi. "Texture classification with modified rotation invariant local binary pattern and gradient boosting." International Journal of Knowledge-based and Intelligent Engineering Systems 26, no. 2 (2022): 125–36. http://dx.doi.org/10.3233/kes220012.

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Since texture is prominent low level feature of an image, most of the image processing and computer vision applications rely on this feature for efficient extraction, retrieval, visualization and classification of the images. Hence, the texture analysis method mainly concentrates on efficient feature extraction and representation of the image. The images captured and analyzed in many of the applications are not in same (or) similar scale, orientation and illumination and also texture has regular, stochastic, periodic, homogeneous (or) inhomogeneous and directional in nature. To address these i

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Majed, O. Dwairi. "A modified symmetric local binary pattern for image features extraction." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 3 (2020): 1224–28. https://doi.org/10.12928/TELKOMNIKA.v18i3.14256.

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The process of identifying images and patterns is one of the most important processes of digital image processing, which is used in many applications such as fingerprint recognition, face recognition and pattern recognition. Due to the large size of the image, the process of identifying the image requires a great time, which in turn leads us to extract some characteristics of the magnitude of the volume, which can be used as an identifier to retrieve the image or recognize it and thus we have devoted a lot of time to identify the image. In this research paper, a modified symmetric local binary

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Arun Kumar H. D. and Prabhakar C. J. "Moving Vehicles Detection in Traffic Video Using Modified SXCS-LBP Texture Descriptor." International Journal of Computer Vision and Image Processing 5, no. 2 (2015): 14–34. http://dx.doi.org/10.4018/ijcvip.2015070102.

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Background modeling and subtraction based method for moving vehicle's detection in traffic video using a novel texture descriptor called as Modified Spatially eXtended Center Symmetric Local Binary Pattern (Modified SXCS-LBP) descriptor. The XCS-LBP texture descriptor is sensitive to noise because in order to generate binary code, the value of center pixel value is used as the threshold directly, and it does not consider temporal motion information. In order to solve this problem, this paper proposed a novel texture descriptor called as Modified SXCS-LBP descriptor for moving vehicle detection

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Alizadeh, Sayyad, Hossein B. Jond, Vasif V. Nabiyev, and Cemal Kose. "Automatic Retrieval of Shoeprints Using Modified Multi-Block Local Binary Pattern." Symmetry 13, no. 2 (2021): 296. http://dx.doi.org/10.3390/sym13020296.

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A shoeprint is a valuable clue found at a crime scene and plays a significant role in forensic investigations. In this paper, in order to maintain the local features of a shoeprint image and place a pattern in a block, a novel automatic method was proposed, referred to as Modified Multi-Block Local Binary Pattern (MMB-LBP). In this method, shoeprint images are divided into blocks according to two different models. The histograms of all blocks of the first and second models are separately measured and stored in the first and second feature matrices, respectively. The performance evaluations of

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Sunil, S. Harakannanavar, Sapnakumari C, C. Ramachandra A, Pramodhini R, and R. Prashanth C. "Performance Evaluation of Fusion Based Efficient Algorithm for Facial Expression Recognition." Indian Journal of Science and Technology 16, no. 4 (2023): 266–76. https://doi.org/10.17485/IJST/v16i4.1891.

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ABSTRACT <strong>Objectives:</strong> To develop face expression recognition system using JAFFE database and to evaluate the performance of the face expression recognition models. <strong>Methods:</strong> This study used the FER model based on modified-HoG (Histogram of oriented gradient), LBP (Local Binary Patterns) and Fast Key point detector and BRIEF descriptor (FKBD) to extract the significant features of JAFFE dataset. The features extracted using HoG, LBP and FKBD techniques form a feature vector. Then, the fusion of all the features is carried out at the feature level.

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Madhusudhan, K. N., and P. Sakthivel. "Combining Digital Signature with Local Binary Pattern-Least Significant Bit Steganography Techniques for Securing Medical Images." Journal of Medical Imaging and Health Informatics 10, no. 6 (2020): 1288–93. http://dx.doi.org/10.1166/jmihi.2020.3015.

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The image authentication is generally based on two different types of techniques: watermarking and digital signature. In watermarking methods, embedded watermarking is often imperceptible and it contains either a specific ID of producer or codes related to content that are used for authentication. Normally a separate file is stored, digital signature is a non-repudiation and encrypted version of the information extracted from the data. A digital signature can be attached to the data to prove the originality and integrity. The proposed work presents a new approach to steganography of medical im

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Alapati, Anusha, and Dae-Seong Kang. "An Efficient Approach to Face Recognition Using a Modified Center-Symmetric Local Binary Pattern (MCS-LBP)." International Journal of Multimedia and Ubiquitous Engineering 10, no. 8 (2015): 13–22. http://dx.doi.org/10.14257/ijmue.2015.10.8.02.

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Huang, Rong Bing, Hong Zhang, and Chang Ming Shu. "Multi-View Face Detection Based on Multi-Features AdaBoost Collaborative Learning Algorithm." Advanced Materials Research 998-999 (July 2014): 884–88. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.884.

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In View of the Multi-View Face Detection Problem under Complex Background, an Improved Face Detection Method Based on Multi-Features Boosting Collaborative Learning Algorithm Integrating Local Binary Pattern (LBP) is Presented. Firstly, Facial Skin Color Information is Used to Exclude most of the Background Regions. then, Haar-like Feature and LBP Feature are Extracted from Facial Candidate Regions and Inputted into a Modified Adaboost Algorithm to Obtain a Strong Classifier. Lastly, in Order to Improve the Detection Speed, Pyramid Classifier System Structure is Adopted to Determine the Face.

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Mustafa, Ahmed A., and Ahmed AK Tahir. "Improving the Performance of Finger-Vein Recognition System Using A New Scheme of Modified Preprocessing Methods." Academic Journal of Nawroz University 9, no. 3 (2020): 397. http://dx.doi.org/10.25007/ajnu.v9n3a855.

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This paper aims at improving the performance of finger-vein recognition system using a new scheme of image preprocessing. The new scheme includes three major steps, RGB to Gray conversion, ROI extraction and alignment and ROI enhancement. ROI extraction and alignment includes four major steps. First, finger-vein boundaries are detected using two edge detection masks each of size (4 x 6). Second, the correction for finger rotation is done by calculating the finger base line from the midpoints between the upper and lower boundaries using least square method. Third, ROI is extracted by cropping a

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Roy, Kaushik, Brian O'Connor, Foysal Ahmad, and Mohamed S. Kamel. "Multibiometric System Using Level Set, Modified LBP and Random Forest." International Journal of Image and Graphics 14, no. 03 (2014): 1450013. http://dx.doi.org/10.1142/s0219467814500132.

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Multibiometric systems alleviate some of the shortcomings possessed by the unimodal biometrics and provide better recognition performance. This paper presents a multibiometric system that integrates the iris and face features based on the fusion at the feature level. The proposed multibiometric system has three novelties as compared to the previous works. First, distance regularized level-set evolution (DRLSE) technique is utilized to localize the iris and pupil boundary from an iris image. The DRLSE maintains the regularity of the level set function intrinsically during the curve evolution pr

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K., Anitha, Radhika S., Kavitha C., Wen-Chen Lai, S. R. Srividhya, and Naresh K. "A Modified LBP Operator-Based Optimized Fuzzy Art Map Medical Image Retrieval System for Disease Diagnosis and Prediction." Biomedicines 10, no. 10 (2022): 2438. http://dx.doi.org/10.3390/biomedicines10102438.

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Medical records generated in hospitals are treasures for academic research and future references. Medical Image Retrieval (MIR) Systems contribute significantly to locating the relevant records required for a particular diagnosis, analysis, and treatment. An efficient classifier and effective indexing technique are required for the storage and retrieval of medical images. In this paper, a retrieval framework is formulated by adopting a modified Local Binary Pattern feature (AvN-LBP) for indexing and an optimized Fuzzy Art Map (FAM) for classifying and searching medical images. The proposed ind

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Farmadi, Andi, and Muliadi Muliadi. "DETEKSI PENYAKIT TANAMAN PADI MENGGUNAKAN EKSTRAKSI FIRUR LBP DAN KLASIFIKASI MODIFIED KNN." Jurnal Komputasi 11, no. 2 (2023): 129–37. http://dx.doi.org/10.23960/komputasi.v11i2.13238.

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Daun dan batang padi merupakan bagian utama dalam pemantauan investigasi tanaman padi yang memberikan informasi mengenai status kesehatan tanaman yang mempengaruhi kualitas dan kuantitas hasil tanaman padi. Pemantauan melalaui hasil digitasi daun dan batang dapat mengklasifikasikan penyakit tanaman padi sebagai jenis kelas penyakit berdasarkan data yang diperoleh dari repositori basis data citra pertanian. Data penyakit pada yang digunakan sebanyak 300 data dengan 3 kelas penyakit, yaitu Brown Spot, Hispa, dan Leaf Blast. Digunakan metode analisis tekstur gambar (citra) dengan menggunakan mode

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Wiharta, Dewa Made. "PARTICLE FILTER-BASED OBJECT TRACKING USING JOINT FEATURES OF COLOR AND LOCAL BINARY PATTERN HISTOGRAM FOURIER." Kursor 8, no. 2 (2016): 79. http://dx.doi.org/10.28961/kursor.v8i2.64.

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Object tracking is defined as the problem of estimating object location in image sequences. In general, the problems of object tracking in real time and complex environtment are affected by many uncertainty. In this research we use a sequensial Monte Carlo method, known as particle filter, to build an object tracking algorithm. Particle filter, due to its multiple hypotheses, is known to be a robust method in object tracking task. The performances of particle filter is defined by how the particles distributed. The role of distribution is regulated by the system model being used. In this resear

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Safarov, Furkat, Alpamis Kutlimuratov, Ugiloy Khojamuratova, Akmalbek Abdusalomov, and Young-Im Cho. "Enhanced AlexNet with Gabor and Local Binary Pattern Features for Improved Facial Emotion Recognition." Sensors 25, no. 12 (2025): 3832. https://doi.org/10.3390/s25123832.

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Facial emotion recognition (FER) is vital for improving human–machine interactions, serving as the foundation for AI systems that integrate cognitive and emotional intelligence. This helps bridge the gap between mechanical processes and human emotions, enhancing machine engagement with humans. Considering the constraints of low hardware specifications often encountered in real-world applications, this study leverages recent advances in deep learning to propose an enhanced model for FER. The model effectively utilizes texture information from faces through Gabor and Local Binary Pattern (LBP) f

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Julianto, Veri, Ahmad Rusadi Arrahimi, Oky Rahmanto, and Mohammad Sofwat Aldi. "Modified Particle Swarm Optimization on Feature Selection for Palm Leaf Disease Classification." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 8, no. 6 (2024): 846–52. https://doi.org/10.29207/resti.v8i6.6049.

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Palm oil plantations in Indonesia face challenges in enhancing productivity and profitability, notably due to pest attacks that reduce production. Early identification and classification of plant conditions, particularly palm oil leaves, are crucial for mitigating losses. This study explores the application of artificial intelligence, specifically computer vision and machine learning, for disease detection. Various machine learning techniques, including Local Binary Pattern (LBP), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), have been used in different studies with varying accu

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Devi, Bhagyashri, and M. Mary Synthuja Jain Preetha. "Automatic Face Emotion Recognition With the Aid of Probability-Based Bird Swarm-Trained Neural Network." International Journal of Swarm Intelligence Research 12, no. 4 (2021): 1–24. http://dx.doi.org/10.4018/ijsir.2021100101.

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This paper intends to develop a novel FER model, which consists of four stages: (1) face detection, (2) feature extraction, (3) dimension reduction, and (4) classification. In this context, the face detection is done using Viola Jones method (VJ). It is the first object recognition model to offer better recognition rates in real-time. Further, features extraction techniques like local binary pattern (LBP) and discrete wavelet transform (DWT) are used for extracting the features from face detected images. Moreover, the dimension reduction of features is done using principal component analysis (

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Lee, Hansung, So-Hee Park, Jang-Hee Yoo, Se-Hoon Jung, and Jun-Ho Huh. "Face Recognition at a Distance for a Stand-Alone Access Control System." Sensors 20, no. 3 (2020): 785. http://dx.doi.org/10.3390/s20030785.

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Although access control based on human face recognition has become popular in consumer applications, it still has several implementation issues before it can realize a stand-alone access control system. Owing to a lack of computational resources, lightweight and computationally efficient face recognition algorithms are required. The conventional access control systems require significant active cooperation from the users despite its non-aggressive nature. The lighting/illumination change is one of the most difficult and challenging problems for human-face-recognition-based access control appli

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Arjun, B. C., and N. Prakash H. "Multimodal Biometric Recognition: Fusion of Modified Adaptive Bilinear Interpolation Data Samples of Face and Signature using Local Binary Pattern Features." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 3111–20. https://doi.org/10.35940/ijeat.C6117.029320.

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Biometric based authentication systems use particular person characteristics which might be based on either behavior like voice, signature etc. or body structure like face, iris, palm print, fingerprint, etc. The performance of any unimodal biometric arrangement is depending on elements like surroundings, atmosphere, and sensor precision. Also, there are numerous trait unique demanding situations which include pose, expression, growing old and so forth for face reputation, occlusion and acquisition related problems for iris and terrible high-quality and social popularity related troubles for f

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Nie, Ting, Xiyu Han, Bin He, Xiansheng Li, Hongxing Liu, and Guoling Bi. "Ship Detection in Panchromatic Optical Remote Sensing Images Based on Visual Saliency and Multi-Dimensional Feature Description." Remote Sensing 12, no. 1 (2020): 152. http://dx.doi.org/10.3390/rs12010152.

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Ship detection in panchromatic optical remote sensing images is faced with two major challenges, locating candidate regions from complex backgrounds quickly and describing ships effectively to reduce false alarms. Here, a practical method was proposed to solve these issues. Firstly, we constructed a novel visual saliency detection method based on a hyper-complex Fourier transform of a quaternion to locate regions of interest (ROIs), which can improve the accuracy of the subsequent discrimination process for panchromatic images, compared with the phase spectrum quaternary Fourier transform (PQF

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Bayazitov, M., A. Liashenko, D. Bayazitov, T. Stoeva, and T. Godlevska. "Machine learning with different digital images classification in laparoscopic surgery." Journal of Education, Health and Sport 12, no. 3 (2022): 295–304. http://dx.doi.org/10.12775/jehs.2022.12.03.025.

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The evaluation of the effectiveness of the automatic computer diagnostic (ACD) systems developed based on two classifiers – HAAR features cascade and AdaBoost for the laparoscopic diagnostics of appendicitis and ovarian cysts in women with chronic pelvic pain is presented. The training of HAAR features cascade, and AdaBoost classifiers were performed with images/ frames, which have been extracted from video gained in laparoscopic diagnostics. Both gamma-corrected RGB and RGB converted into HSV frames were used for training. Descriptors were extracted from images with the method of Local Binary

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Sharma, Bhanu Prakash, and Ravindra Kumar Purwar. "Ensemble Boosted Tree based Mammogram image classification using Texture features and extracted smart features of Deep Neural Network." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 10, no. 4 (2022): 419–34. http://dx.doi.org/10.14201/adcaij2021104419434.

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     This work proposes a technique of breast cancer detection from mammogram images. It is a multistage process which classifies the mammogram images into benign or malignant category. During preprocessing, images of Mammographic Image Analysis Society (MIAS) database are passed through a couple of filters for noise removal, thresholding and cropping techniques to extract the region of interest, followed by augmentation process on database to enhance its size. Features from Deep Convolution Neural Network (DCNN) are merged with texture features to form final feat

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Mostafiz, Rafid, Mohammad Rahman, A. Islam, and Saeid Belkasim. "Focal Liver Lesion Detection in Ultrasound Image Using Deep Feature Fusions and Super Resolution." Machine Learning and Knowledge Extraction 2, no. 3 (2020): 172–91. http://dx.doi.org/10.3390/make2030010.

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This research presents a machine vision approach to detect lesions in liver ultrasound as well as resolving some issues in ultrasound such as artifacts, speckle noise, and blurring effect. The anisotropic diffusion is modified using the edge preservation conditions which found better than traditional ones in quantitative evolution. To dig for more potential information, a learnable super-resolution (SR) is embedded into the deep CNN. The feature is fused using Gabor Wavelet Transform (GWT) and Local Binary Pattern (LBP) with a pre-trained deep CNN model. Moreover, we propose a Bayes rule-based

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Montazer, Gholam Ali, and Davar Giveki. "Scene Classification Using Multi-Resolution WAHOLB Features and Neural Network Classifier." Neural Processing Letters 46, no. 2 (2017): 681–704. https://doi.org/10.1007/s11063-017-9614-6.

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This article approaches scene classification problem by proposing an enhanced bag of features (BoF) model and a modified radial basis function neural network (RBFNN) classifier.&lrm; The proposed BoF model integrates the image features extracted by histogram of oriented gradients, local binary pattern and wavelet coefficients.&lrm; The extracted features are obtained in a hierarchical multi-resolution manner.&lrm; The proposed approach is able to capture multi-level (the pixel-, patch-, and image-level) features.&lrm; The histograms of features constructed by BoF model are then used for traini

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Wang, Hao-Nan, Li-Xin Zheng, Shu-Wan Pan, Tan Yan, and Qiu-Ling Su. "Image Recognition of Pediatric Pneumonia Based on Fusion of Texture Features and Depth Features." Computational and Mathematical Methods in Medicine 2022 (August 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/1973508.

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Pneumonia is one of the diseases that seriously endangers human health, and it is also the leading cause of death of children under the age of five in China. The most commonly used imaging examination method for radiologists is mainly based on chest X-ray images. Still, imaging errors often result during imaging examinations due to objective factors such as visual fatigue and lack of experience. Therefore, this paper proposes a feature fusion model, FC-VGG, based on the fusion of texture features (local binary pattern LBP and directional gradient histogram HOG) and depth features. The model im

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Ranganarayana, Katakam, and Gurrala Venkateswara Rao. "Modified Ant Colony Optimization for Human Recognition in Videos of Low Resolution." Revue d'Intelligence Artificielle 36, no. 5 (2022): 731–36. http://dx.doi.org/10.18280/ria.360510.

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Privacy protections for people filmed in public settings is a prerequisite to widespread camera use. For this reason, low-resolution videos are used from which specific people can be reliably obscured. Since the human region in low-resolution videos comprises of so few pixels and so little information, human detection is more challenging there than it is in high-resolution videos. With the current state of affairs, one of the most important challenges is tracking a target from lower resolution movies. Identification or monitoring of persons in low-resolution movies has become a common issue in

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Donga, Harsha Vardhan, Jaya Sai Aditya Nandan Karlapati, Harsha Sri Sumanth Desineedi, Prakasam Periasamy, and Sureshkumar TR. "Effective Framework for Pulmonary Nodule Classification from CT Images Using the Modified Gradient Boosting Method." Applied Sciences 12, no. 16 (2022): 8264. http://dx.doi.org/10.3390/app12168264.

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Lung carcinoma, which is commonly known as lung cancer, is one of the most common cancers throughout the world. Mostly, it is not diagnosed until it has spread, and it is very difficult to treat. Hence, early diagnosis of benign and malignant pulmonary nodules can help in the risk assessment of lung cancer for patients, and with proper treatment can save their lives. In this study, a framework for the classification of pulmonary nodules from Computerized Tomography (CT) images using the machine learning-based modified gradient boosting method is proposed. Initially, the obtained CT scan images

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Yang, Xiuzhu, Xinyue Zhang, Yi Ding, and Lin Zhang. "Indoor Activity and Vital Sign Monitoring for Moving People with Multiple Radar Data Fusion." Remote Sensing 13, no. 18 (2021): 3791. http://dx.doi.org/10.3390/rs13183791.

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The monitoring of human activity and vital signs plays a significant role in remote health-care. Radar provides a non-contact monitoring approach without privacy and illumination concerns. However, multiple people in a narrow indoor environment bring dense multipaths for activity monitoring, and the received vital sign signals are heavily distorted with body movements. This paper proposes a framework based on Frequency Modulated Continuous Wave (FMCW) and Impulse Radio Ultra-Wideband (IR-UWB) radars to address these challenges, designing intelligent spatial-temporal information fusion for acti

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Liu, Yiying, and Young Chun Ko. "Image Processing Method Based on Chaotic Encryption and Wavelet Transform for Planar Design." Advances in Mathematical Physics 2021 (October 31, 2021): 1–12. http://dx.doi.org/10.1155/2021/7511245.

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This paper provides an in-depth study and analysis of image processing for graphic design through chaotic encryption combined with a wavelet transform algorithm. Firstly, the traditional Mallat algorithm is optimized; since the mean value of the transform coefficients generated after the wavelet transform of the image is used as the initial value of the chaotic system to iterate, when the image is modified, then the mean value of the wavelet coefficients will also change, and the final iteration comes out as two different sequences using the property that the chaotic system is extremely sensit

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Jadhav, Ambaji S., Pushpa B. Patil, and Sunil Biradar. "Computer-aided diabetic retinopathy diagnostic model using optimal thresholding merged with neural network." International Journal of Intelligent Computing and Cybernetics 13, no. 3 (2020): 283–310. http://dx.doi.org/10.1108/ijicc-11-2019-0119.

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PurposeDiabetic retinopathy (DR) is a central root of blindness all over the world. Though DR is tough to diagnose in starting stages, and the detection procedure might be time-consuming even for qualified experts. Nowadays, intelligent disease detection techniques are extremely acceptable for progress analysis and recognition of various diseases. Therefore, a computer-aided diagnosis scheme based on intelligent learning approaches is intended to propose for diagnosing DR effectively using a benchmark dataset.Design/methodology/approachThe proposed DR diagnostic procedure involves four main st

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Kaplan, Kaplan, Yılmaz Kaya, Melih Kuncan, and H. Metin Ertunç. "Brain tumor classification using modified local binary patterns (LBP) feature extraction methods." Medical Hypotheses 139 (June 2020): 109696. http://dx.doi.org/10.1016/j.mehy.2020.109696.

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Rosu, Varkeyachan Padayatty. "EMD-CNN Based Classifier to Detect Schizophrenia from EEG Signals." Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) 5, no. 1 (2024): 1–7. https://doi.org/10.54105/ijainn.C1063.05011224.

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<strong>Abstract:</strong> The United Nations has designated schizophrenia (SZ) as a serious mental illness that affects 20 million people globally. Hallucinations, delusions, and incredibly chaotic thought and behavior are some of the symptoms. SZ has an impact on a person in all facets of his life and makes it challenging to go on. Traditionally, a skilled psychiatrist uses thorough and incisive patient interviews to make the diagnosis of SZ. This procedure takes a long time and couldlead to mistakes. Therefore, the purpose of our effort is to assist physicians in making diagnoses effectivel

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Badi Mame, Antoine, and Jules-Raymond Tapamo. "Parameter optimization of histogram-based local descriptors for facial expression recognition." PeerJ Computer Science 9 (June 28, 2023): e1388. http://dx.doi.org/10.7717/peerj-cs.1388.

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An important task in automatic facial expression recognition (FER) is to describe facial image features effectively and efficiently. Facial expression descriptors must be robust to variable scales, illumination changes, face view, and noise. This article studies the application of spatially modified local descriptors to extract robust features for facial expressions recognition. The experiments are carried out in two phases: firstly, we motivate the need for face registration by comparing the extraction of features from registered and non-registered faces, and secondly, four local descriptors

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Al-Hameed, Wafaa Mohammed Saeed Hamzah, and Marwan B. Mohammed. "Evaluating face recognition with different texture descriptions and convolution neural network." Indonesian Journal of Electrical Engineering and Computer Science 30, no. 1 (2023): 332. http://dx.doi.org/10.11591/ijeecs.v30.i1.pp332-340.

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Extracting the remarkable attributes of the image objects is an issue of ongoing research special in the face recognition problem. This paper presents two directions. The first is a comparison between the local binary patterns (LBP) and its modified center symmetric LBP drawn from localized facial expressions and due to the efficiency, K-nearest neighbor (KNN) and the support vector machine (SVM) techniques play significant roles in this research used to implement the proposed system efficiently. The second direction proposes an efficient architecture by depending on deep learning convolution

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Wafaa, Mohammed Saeed Hamzah Al-Hameed, and B. Mohammed Marwan. "Evaluating face recognition with different texture descriptions and convolution neural network." Evaluating face recognition with different texture descriptions and convolution neural network 30, no. 1 (2023): 332–40. https://doi.org/10.11591/ijeecs.v30.i1.pp332-340.

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Extracting the remarkable attributes of the image objects is an issue of ongoing research special in the face recognition problem. This paper presents two directions. The first is a comparison between the local binary patterns (LBP) and its modified center symmetric LBP drawn from localized facial expressions and due to the efficiency, K-nearest neighbor (KNN) and the support vector machine (SVM) techniques play significant roles in this research used to implement the proposed system efficiently. The second direction proposes an efficient architecture by depending on deep learning convolution

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Khalil, Samar Samir, Sherin M. Youssef, and Sherine Nagy Saleh. "iCaps-Dfake: An Integrated Capsule-Based Model for Deepfake Image and Video Detection." Future Internet 13, no. 4 (2021): 93. http://dx.doi.org/10.3390/fi13040093.

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Fake media is spreading like wildfire all over the internet as a result of the great advancement in deepfake creation tools and the huge interest researchers and corporations are showing to explore its limits. Now anyone can create manipulated unethical media forensics, defame, humiliate others or even scam them out of their money with a click of a button. In this research a new deepfake detection approach, iCaps-Dfake, is proposed that competes with state-of-the-art techniques of deepfake video detection and addresses their low generalization problem. Two feature extraction methods are combin

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Rezaei, Zahra, Ali Selamat, Arash Taki, et al. "Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features." Applied Sciences 8, no. 9 (2018): 1632. http://dx.doi.org/10.3390/app8091632.

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Atherosclerotic plaque rupture is the most common mechanism responsible for a majority of sudden coronary deaths. The precursor lesion of plaque rupture is thought to be a thin cap fibroatheroma (TCFA), or “vulnerable plaque”. Virtual Histology-Intravascular Ultrasound (VH-IVUS) images are clinically available for visualising colour-coded coronary artery tissue. However, it has limitations in terms of providing clinically relevant information for identifying vulnerable plaque. The aim of this research is to improve the identification of TCFA using VH-IVUS images. To more accurately segment VH-

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Kumar, Manish, Rahul Gupta, Kota Solomon Raju, Dinesh Kumar, and Dinesh Kumar. "Modified Local Binary Pattern Algorithm for Feature Dimensionality Reduction." Recent Patents on Computer Science 12 (July 30, 2019). http://dx.doi.org/10.2174/2213275912666190730160705.

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A face recognition algorithm with feature dimensionality reduction is proposed. The proposed algorithm is based on a variant of Local Binary Pattern (LBP) for face detection and recognition. The features of each block of face image is extracted and then global feature of face is constructed from super histogram. For recognition, traditional methods are used. The query image is compared with the dataset (ORL Dataset, LFW Dataset and Yale Dataset) in similarity index and the minimum distance. The maximum similarity is used to define as the class of query image. The reduction in number of feature

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Abraham, Varghese, Balakrishnan Kannan, R. Varghese Reji, and S. Paul Joseph. "Content Based Image Retrieval of Brain MR Images across Different Classes." August 26, 2013. https://doi.org/10.5281/zenodo.1086643.

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Magnetic Resonance Imaging play a vital role in the decision-diagnosis process of brain MR images. For an accurate diagnosis of brain related problems, the experts mostly compares both T1 and T2 weighted images as the information presented in these two images are complementary. In this paper, rotational and translational invariant form of Local binary Pattern (LBP) with additional gray scale information is used to retrieve similar slices of T1 weighted images from T2 weighted images or vice versa. The incorporation of additional gray scale information on LBP can extract more local texture info

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"Face Spoofing Detection using Enhanced Local Binary Pattern." International Journal of Engineering and Advanced Technology 9, no. 2 (2019): 3365–71. http://dx.doi.org/10.35940/ijeat.b3834.129219.

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Among various biometric systems, over the past few years identifying the face patterns has become the centre of attraction, owing to this, a substantial improvement has been made in this area. However, the security of such systems may be a crucial issue since it is proved in many studies that face identification systems are susceptible to various attacks, out of which spoofing attacks are one of them. Spoofing is defined as the capability of making fool of a system that is biometric for finding out the unauthorised customers as an actual one by the various ways of representing version of synth

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Al-Kaltakchi, Musab T. S., Manhal Ahmad Saleh Al-Hussein, and Raid Rafi Omar Al-Nima. "Identifying three-dimensional palmprints with Modified Four-Patch Local Binary Pattern (MFPLBP)." International Journal of Electronics and Telecommunications, January 7, 2025, 555–59. https://doi.org/10.24425/ijet.2025.153604.

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Palmprint biometrics is the best method of identifying an individual with a unique palmprint for every person. The present paper formulates a new methodology towards the identification of 3D palmprints using the Modified Four- Patch Local Binary Pattern (MFPLBP). It improves upon the conventional Four-Patch Local Binary Pattern (FPLBP) by integrating the adaptive weight with the improved texture extraction. Both approaches are created to support the intricate surface information of 3D palmprints. The MFPLBP can exactly capture local variations and is noise and illumination invariant. There are

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Chavda, Sagar M., and Mahesh M. Goyani. "Hybrid approach to Content-Based Image Retrieval using modified multi-scale LBP and color features." Computer Science 23, no. 1 (2022). http://dx.doi.org/10.7494/csci.2022.23.1.3821.

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The objective of the Content-Based Image Retrieval (CBIR) system is to retrieve the visually identical images from the database efficiently and effectively. It is a broad research realm with the availability of numerous applications. Performance dependency of CBIR focuses on the extraction, reduction, and selection of the features along with the practice of classification technique. In this work, we have proposed the hybrid approach of two different feature descriptors namely, Global Color Histogram and Multi-Scale Local Binary Pattern (MS-LBP). Furthermore, PCA is used for dimension reduction

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"A Multi Model Framework for Grading of Human Emotion Using CNN and Computer Vision." International Journal of Computer Vision and Image Processing 12, no. 1 (2022): 0. http://dx.doi.org/10.4018/ijcvip.2022010102.

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Emotion analysis is an area which is been widely used in the forensic crime detection domain, a mentoring device for depressed students, psychologically affected patient treatment. The current system helps only in identifying the emotions but not in identifying the level of emotions like whether the individual is truly happy/sad or pretending to be happy /sad. In this proposed work a novel methodology has been introduced. We have rebuilt the Traditional Local Binary Pattern (LBP) feature operator to image the expression and combine the abstract characteristics of facial expression learned from

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S, Karthick. "Modified Convolutional Neural Networks for Efficient and Precise Brain Tumor Diagnosis in Clinical Application Use." International Journal of Advanced Trends in Engineering and Management III, no. 10 (2024). https://doi.org/10.59544/jaga3465/ijatemv03i10p4.

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Brain tumor is the growth of abnormal brain cells, some of which have the potential to develop into cancer. For medical professionals, it is quite difficult to diagnose brain tumors early. They assess the Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scans for finding the brain tumor. Searching through the vast number of MRI images by hand to find a brain tumor is now extremely time consuming and inaccurate. Using basic imaging techniques to identify aberrant brain areas is challenging. This work uses image processing techniques to automatically detect and classify brain tumors

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V Patil, Bhushan, and Pravin S. Patil. "A Composite Meta Model for the Identification of Cotton Pathologies Utilizing an IoT-Enabled Framework and Stacked Generalization Learning Methodology." International Research Journal of Multidisciplinary Technovation, November 20, 2024, 128–44. http://dx.doi.org/10.54392/irjmt2469.

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This research proposes a novel framework for predicting cotton plant diseases using IoT, deep learning, and meta-heuristic optimization techniques. High-definition images of cotton leaves are captured in the field, processed through IoT, and enhanced using a Probabilistic Hybrid Wiener Filter. The Modified Dilated U-Net segments pathological regions, while features are extracted using Improved Local Binary Pattern (LBP), Gray Level Co-Occurrence Matrix (GLCM), and Gray Level Run Length Matrix (GLRLM). Feature dimensionality is reduced by the Binary Guided Whale-Dipper Throated Optimizer. The c

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Tyagi, Anshuman, Pawan Singh, and Harsh Dev. "PRR-FCM-Based Segmentation Model for Human Activity Recognition: A Hybrid Deep Learning Strategy." International Journal of Image and Graphics, February 5, 2025. https://doi.org/10.1142/s0219467827500276.

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Recognizing human activity is a challenging task in many applications like visual surveillance, Human–computer interaction, etc. Numerous machine learning (ML) and deep learning (DL) approaches are used to emulate the human behavior of everyday life, as they provide better recognition by learning complex features. This paper proposes an innovative human activity recognition framework with the following steps. Initially, the given video is converted into a set of frames and then applies median filtering to remove the noise for efficient recognition. Subsequently, the pixel-relatedness retrieval

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Pei, Mo-chao, Hong-ru Li, and He Yu. "Degradation state identification for hydraulic pumps using modified hierarchical decomposition and image processing." Measurement and Control, December 18, 2021, 002029402110648. http://dx.doi.org/10.1177/00202940211064803.

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Monitoring the degradation state of hydraulic pumps is of great significance to the safe and stable operation of equipment. As an important step, feature extraction has always been challenging. The non-stationary and nonlinear characteristics of vibration signals are likely to weaken the performance of traditional features. The two-dimensional image representation of vibration signals can provide more information for feature extraction, but it is challenging to obtain sufficient information based on small-size images. To solve these problems, a method for feature extraction based on modified h

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Al Sameera, B. N., and Vilas H. Gaidhane. "An efficient no‐reference image quality analysis based on statistical perceptual features." IET Image Processing, December 16, 2024. https://doi.org/10.1049/ipr2.13302.

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AbstractIt is well known that image quality needs to be measured with human perception in many computer vision applications. However, these approaches are expensive and require more time for image quality analysis. Therefore, this paper proposes a robust and computationally efficient objective‐mathematical model based on statistical perceptual features. The structural and textural features are computed using the modified regularized heaviside local binary pattern (RH‐LBP) approach and the concept of entropy. The higher‐order probability coefficients of images are considered to extract features

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Gunasekaran, Someshwaran, and Sarada Vivekasaran. "Disease Prognosis of Fetal Heart’s Four-Chamber and Blood Vessels in Ultrasound Images Using CNN Incorporated VGG 16 and Enhanced DRNN." International Arab Journal of Information Technology 21, no. 6 (2024). http://dx.doi.org/10.34028/iajit/21/6/13.

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Fetal Heart Disease (FHD) based Structural Heart Disorders (SHD) occur when certain features of the heart develop abnormally. These flaws may cause blood flow to circulate in the erroneous spot, slow down, or be utterly blocked. Heart Defects (HD) caused via FHD or disorders that primarily impact embryonic heart conditions are alternatively referred to as Congenital Heart Defects (CHD). Multiple prior investigation algorithms such as Multi-Resolution Convolutional Neural Network (MRCNN), Deep Convolutional Neural Network (DCNN), Faster-RCNN (FRCNN) and DANomaly Wgan-GP and Convolutional Neural

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Bayazitov, M. R., A. V. Liashenko, D. M. Bayazitov, T. V. Stoeva, and T. L. Godlevska. "Machine learning with different digital images classification in laparoscopic surgery." March 31, 2022. https://doi.org/10.12775/JEHS.2022.12.03.025.

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Bayazitov M. R., Liashenko A. V., Bayazitov D. M., Stoeva T. V., Godlevska T. L. Machine learning with different digital images classification in laparoscopic surgery. Journal of Education, Health and Sport. 2022;12(3):295-304. eISSN 2391-8306. DOI http://dx.doi.org/10.12775/JEHS.2022.12.03.025 https://apcz.umk.pl/JEHS/article/view/JEHS.2022.12.03.025 https://zenodo.org/record/7058246         The journal has had 40 points in Ministry of Education and Science of Poland parametric evaluation. Annex to the announcement of the Minister of Education and Science of December 1, 20

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