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2nd IEEE International Congress of Biomedical Engineering and Bioengineering (CI-IB and BI)

作者： Erazo-Navas, Alejandra Quiroz, Isabella Ramos-Guerra, Ana S. Sanchez-Renteria, Santiago Univ Los Andes Dept Biomed Engn Bogota Colombia

ISBN: (纸本)9781665408554

Retinopathies affect over 2200 million people and may lead to severe vision loss and blindness. The classification of retinopathies from Optical Coherence Tomography (OCT) images has been broadly studied given the relevance of its early detection under non-invasive modalities. The following work presents the development of a multiclass classification algorithm based on Histograms of Oriented Gradients (HOG), local binary patterns (LBP) and a Random Forest Classifier (RF) for the detection of Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV), and Drusen. Function parameters for HOG, LBP and RF were optimized iteratively. As a result, the proposed classification algorithm presented a Precision, Recall and F-Score of 0.48, 0.49 and 0.48. Although the combined use of shape and texture descriptors improved the method's performance, several limitations of the algorithm, such as the non-flattening of the retinal curvature in the OCT images, were identified. Future work should focus in overcoming these limitations.

关键词： Classification retinopathy optical coherence tomography histogram of oriented gradients local binary patterns

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Multi-Scale LBP Texture Feature Learning Network for Remote Sensing Interpretation of Land Desertification

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REMOTE SENSING 2022年第14期14卷 3486页

作者： Wang, Wuli Jiang, Yumeng Wang, Ge Guo, Fangming Li, Zhongwei Liu, Baodi Cina Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China China Univ Petr East China Coll Control Sci & Engn Qingdao 266580 Peoples R China

Land desertification is a major challenge to global sustainable development. Therefore, the timely and accurate monitoring of the land desertification status can provide scientific decision support for desertification control. The existing automatic interpretation methods are affected by factors such as "same spectrum different matter", "different spectrum same object", staggered distribution of desertification areas, and wide ranges of ground objects. We propose an automatic interpretation method for the remote sensing of land desertification that incorporates multi-scale local binary pattern (MSLBP) and spectral features based on the above issues. First, a multi-scale convolutional LBP feature extraction network is designed to obtain the spatial texture features of remote sensing images and fuse them with spectral features to enhance the feature representation capability of the model. Then, considering the continuity of the distribution of the same kind of ground objects in local space, we designed an adaptive median filtering method to process the probability map of the extreme learning machine (ELM) classifier output to improve the classification accuracy. Four typical datasets were developed using GF-1 multispectral imagery with the Horqin Left Wing Rear Banner as the study area. Experimental results on four datasets show that the proposed method solves the problem of ill classification and omission in classifying the remote sensing images of desertification, effectively suppresses the effects of "homospectrum" and "heterospectrum", and significantly improves the accuracy of the remote sensing interpretation of land desertification.

关键词： desertification land cover classification extreme learning machine local binary patterns Horqin Left Wing Rear Banner

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Texture image Classification based on improved local Quinary patterns

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第14期78卷 18995-19018页

作者： Armi, Laleh Fekri-Ershad, Shervan Islamic Azad Univ Najafabad Branch Fac Comp Engn Najafabad Iran Islamic Azad Univ Najafabad Branch Big Data Res Ctr Najafabad Iran

Texture image classification is an active research topic in computer vision that play an important role in many applications such as visual inspection systems, object tracking, medical image analysis, image segmentation, etc. So far, there are many descriptors for texture image analysis such as local binary patterns (LBP). LBP is a nonparametric operator, which describes the local spatial structure and the local contrast of an image. local quinary patterns (LQP) is one of the improved versions of LBP in terms of classification accuracy. Statistic input parameters and don't providing significant binary patterns are some disadvantages of LQP. In this paper a new version of LBP is proposed, which is known as improved local quinary patterns (ILQP). In this paper, a new definition is proposed to divide local quinary codes to four binary patterns. Each extracted binary patterns represent a subset of local features. Also, a new algorithm is proposed here to provide dynamic thresholds in dividing process of LQP. The proposed approach is evaluated using Outex, and Brodatz data sets. Our approach has been compared with some state-of-the-art methods. It is experimentally demonstrated that the proposed approach achieves the highest accuracy in comparison with most of the state-of-the-art texture classification approaches. Low computational complexity, rotation invariant, low impulse-noise sensitivity and high usability are advantages of the proposed texture analysis descriptor.

关键词： Texture image classification local Quinary patterns local binary patterns Feature extraction

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Efficient Face Verification Under Makeup Changes Using Few Salient Regions 18

Efficient Face Verification Under Makeup Changes Using Few S...

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18th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA)

作者： Ferchichi, Khouloud Ghazouani, Haythem Barhoumi, Walid Univ Tunis El Manar Inst Super Informat Manar Res Team Intelligent Syst Imaging & Artificial Vi LR16ES06 Lab Rech Informat Modelisat & Traitement 2 Rue Abou Rayhane Bayrouni Ariana 2080 Tunisia Univ Carthage Ecole Natl dIngenieurs Carthage 45 Rue Entrepreneurs Tunis 2035 Tunisia

ISBN: (纸本)9781665409698

Face recognition has attracted the attention of many researchers during the last years due to its many applications in various fields. However, this task faces several challenges related to many changes that can affect the human face. In particular, make-up faces represent a major challenge for facial verification. To deal with this issue, we propose in this work an efficient salient patch-based method for verifying faces under makeup variation. Firstly, we use Mutli-Task Cascaded Convolutional Neural Networks (MTCNN) in order to jointly detect and align the face. Then, we have adapted the O-NET network in order to robustly detect five landmarks by training it on makeup faces. The Histogram of Oriented Gradients (HOG) descriptor and the local binary patterns (LBP) are then used to represent the face by concatenating their histogram features in few salient regions around the detected landmarks. Finally, we estimate the similarity measure between the extracted features in order to compare the two faces while determining whether they are for the same person or not. The performance of the proposed method has been validated on the challenging YMU (YouTube Makeup dataset ) and MIFS (Makeup Induced Face Spoofing) datasets, and the obtained results proved the superiority of the proposed method against relevant multi-patch-based methods from the state of the art.

关键词： Face Verification Mutli-task Cascaded Convolutional Networks Histogram of Oriented Gradients local binary patterns Salient Regions

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Detecting smoky vehicles from traffic surveillance videos based on dynamic features

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APPLIED INTELLIGENCE 2020年第4期50卷 1057-1072页

作者： Tao, Huanjie Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China

Existing smoky vehicle detection methods are vulnerable to false alarms because of the continuous interferences from common passed vehicles and the complex characteristics of smoke. This paper presents a video smoky vehicle detection method based on dynamic features. Three groups of features, including Multi-Sequence Integral Projection (MS-IP), Center-Symmetric local binary patterns on Three Orthogonal Planes (CSLBP-TOP) and Histograms of Oriented Optical Flow (HOOF), are proposed or employed to characterize dynamic features of successive Region of Interest (ROIs). More specifically, the MS-IP characterizes the diffusion and distribution information based on multiple-sequence analysis and integral projection. The CSLBP-TOP characterizes the spatiotemporal texture information by (1) combining the strengths of Shift-Invariant Feature Transform (SIFT) and LBP and (2) extending the spatial features to three-dimensional (3D) space based on three orthogonal planes (TOP). The HOOF characterizes the motion information by inducing a very characteristic optical flow profile to distinguish smoky objects and non-smoky objects in successive ROIs based on the fact that the smoke is ejected from vehicle exhaust port and then gradually spreads around. The above three groups of features are complementary, and we fuse them to increase algorithm robustness. Experiment results show that our method achieves better performances than existing methods.

关键词： Smoky vehicle detection Integral projection local binary patterns Optical flow Dynamic features

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Facial Feature Extraction Using a Symmetric Inline Matrix-LBP Variant for Emotion Recognition

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SENSORS 2022年第22期22卷 8635-8635页

作者： Babu, Eaby Kollonoor Mistry, Kamlesh Anwar, Muhammad Naveed Zhang, Li Northumbria Univ Fac Engn & Environm Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England Univ London Dept Comp Sci Surrey TW20 0EX England

With a large number of local binary patterns (LBP) variants being currently used today, the significant and importance of visual descriptors in computer vision applications are prominent. This paper presents a novel visual descriptor, i.e., SIM-LBP. It employs a new matrix technique called the Symmetric Inline Matrix generator method, which acts as a new variant of LBP. The key feature that separates our variant from existing counterparts is that our variant is very efficient in extracting facial expression features like eyes, eye brows, nose and mouth in a wide range of lighting conditions. For testing our model, we applied SIM-LBP on the JAFFE dataset to convert all the images to its corresponding SIM-LBP transformed variant. These transformed images are then used to train a Convolution Neural Network (CNN) based deep learning model for facial expressions recognition (FER). Several performance evaluation metrics, i.e., recognition accuracy rate, precision, recall, and Fl-score, were used to test mode efficiency in comparison with those using the traditional LBP descriptor and other LBP variants. Our model outperformed in all four matrices with the proposed SIM-LBP transformation on the input images against those of baseline methods. In comparison analysis with the other state-of-the-art methods, it shows the usefulness of the proposed SIM-LBP model. Our proposed SIM-LBP variant transformation can also be applied on facial images to identify a person's mental states and predict mood variations.

关键词： local binary patterns adaptive image transformation coded visual descriptors image encoding facial expression recognition

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MD-LBP: An Efficient Computational Model for Protein Subcellular localization from HeLa Cell Lines Using SVM

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CURRENT BIOINFORMATICS 2020年第3期15卷 204-211页

作者： Tahir, Muhammad Idris, Adnan Saudi Elect Univ Coll Comp & Informat POB 53307 Al Munawarrah Saudi Arabia Univ Poonch Dept Comp Sci & IT Rawalakot Pakistan

Background: The knowledge of subcellular location of proteins is essential to the comprehension of numerous protein functions. Objective: Accurate as well as computationally efficient and reliable automated analysis of protein localization imagery greatly depend on the calculation of features from these images. Methods: In the current work, a novel method termed as MD-LBP is proposed for feature extraction from fluorescence microscopy protein images. For a given neighborhood, the value of central pixel is computed as the difference of global and local means of the input image that is further used as threshold to generate a binary pattern for that neighborhood. Results: The performance of our method is assessed for 2D HeLa dataset using 5-fold cross-validation protocol. The performance of MD-LBP method with RBF-SVM as base classifier, is superior to that of standard LBP algorithm, Threshold Adjacency Statistics, and Haralick texture features. Conclusion: Development of specialized systems for different kinds of medical imagery will certainly pave the path for effective drug discovery in pharmaceutical industry. Furthermore, biological and bioinformatics based procedures can be simplified to facilitate pharmaceutical industry for drug designing.

关键词： Protein images subcellular localization local binary patterns support vector machine classification threshold

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A novel dual-channel brain tumor detection system for MR images using dynamic and static features with conventional machine learning techniques

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WAVES IN RANDOM AND COMPLEX MEDIA 2022年第3期35卷

作者： Zahoor, Mirza Mumtaz Qureshi, Shahzad Ahmad Khan, Asifullah ul Rehman, Aziz Rafique, Muhammad Pakistan Inst Engn & Appl Sci PIEAS Dept Comp & Informat Sci Islamabad Pakistan PIEAS Agri & Biophoton Div Natl Inst Lasers & Optron Coll Islamabad Pakistan Univ Azad Jammu & Kashmir Dept Phys Muzaffarabad Pakistan

Brain tumor detection, at the early stages of its development for the timely cure of the patient, is a challenging task due to its complex nonlinear nature. We propose a dual-channel brain tumor detection (DC-BTD) framework for magnetic resonance imaging scans with optimum false negatives, based on the idea of using D-channel for extremely discriminant dynamic features and S-channel for static features using data normalization, augmentation, and different machine learning (ML) classifiers, namely, support vector machine, k-nearest neighbor, naive Bayes, and XGBoost. The D-channel features are extracted using a proposed Fine-Tuned Convolutional Neural Network (FT-CNN), while the S-channel features are extracted using the histogram of oriented gradients (HOG)- and local binary patterns (LBP)-based operators. The dual-channel data form the hybrid feature space (HFS) gives improved performance using two types of features. Computer experiments have been conducted on publically available brain tumors dataset obtained from Nanfang Hospital, Guangzhou, and Tianjin Medical University General Hospital, China. The finding of the current study shows that the proposed framework for brain-tumor prediction outperforms other contemporary existing methods with the highest generalization performance as 98.70% (accuracy) and 98.56% (F-score) on the same dataset that can lead to better brain tumor detection at an early stage.

关键词： Brain tumor deep convolutional neural networks histogram of oriented gradients local binary patterns machine learning

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Depth data and fusion of feature descriptors for static gesture recognition

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IET IMAGE PROCESSING 2020年第5期14卷 909-920页

作者： Sharma, Prachi Anand, Radhey Shyam IIT Roorkee Dept Elect Engn Roorkee Uttarakhand India

In this study, the authors propose a novel methodology for static gesture recognition in a complex background using only depth map from Microsoft's Kinect camera. Four different types of features are extracted and analysed on two public static gesture datasets. The features extracted from the segmented hand are geometrical, local binary patterns, number of fingers (Num) raised in a gesture and distance of hand palm centre from the fingertips and the valley between the fingers. The hand region is first segmented from the image using depth data followed by the forearm removal. Four multi-class support vector machine (SVM) kernels are also compared and used for recognition of gestures with extracted feature vector as an input. The experimental results achieved recognition accuracy of 99 and $95.7\%$95.7% on two public complex static gesture datasets using Gaussian SVM kernel function as a classifier. The proposed approach is found to be comparable and even outperforms some of the state-of-the-art techniques in terms of high recognition accuracies, even after using a single cue for hand segmentation and extraction of features in the complex background which results in non-dependency on too many cues and much hardware.

关键词： support vector machines feature extraction image segmentation gesture recognition image motion analysis complex background depth data feature descriptors static gesture recognition depth map Microsoft's Kinect camera public static gesture datasets segmented hand local binary patterns hand palm centre hand region multiclass support vector machine kernels extracted feature vector recognition accuracy public complex static gesture datasets Gaussian SVM kernel function high recognition accuracies hand segmentation

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Optimal Placement and Intelligent Smoke Detection Algorithm for Wildfire-Monitoring Cameras

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IEEE ACCESS 2020年 8卷 72326-72339页

作者： Shi, Jie Wang, Wei Gao, Yuanqi Yu, Nanpeng Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92501 USA

Smoke produced by wildfires is usually visible much earlier than flames. Hence, early detection of wildfire smoke is essential to prevent severe property losses and heavy casualties from catastrophic wildfires. Camera networks are being built and expanded to achieve timely wildfire smoke detection. To achieve the best camera coverage and detection accuracy with limited budget, an intelligent video smoke detection algorithm and an optimal wildfire camera placement strategy are in a critical need. In this paper, we propose an efficient video smoke detection framework designed for embedded applications on local cameras. It consists of two modules. In the first module, the original video frames are processed by local binary patterns and a dense optical flow estimator. In the second module, the produced features are then fed into a lightweight deep convolutional neural network, which serves as a binary classifier to detect the presence of smoke. We also formulate the wildfire camera placement problem as a binary integer programming problem to minimize the overall fire risk of a given area. Case studies on real-world videos are carried out to validate the accuracy as well as the computational and memory efficiency of the proposed smoke detection framework. We also validate our proposed camera placement strategy by simulating the deployment of wildfire cameras across a test region in Southern California.

关键词： Cameras Feature extraction Image color analysis Detection algorithms Computer vision Neural networks Machine learning Smoke detection camera placement deep neural network local binary patterns optical flow

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