检索结果-内蒙古大学图书馆

Automatic organ localizations on 3D CT images by using majority-voting of multiple 2D detections based on local binary patterns and Haar-like features

Automatic organ localizations on 3D CT images by using major...

引用

Conference on Medical Imaging - Computer-Aided Diagnosis

作者： Zhou, Xiangrong Yamaguchi, Shoutarou Zhou, Xinin Chen, Huayue Hara, Takeshi Yokoyama, Ryujiro Kanematsu, Masayuki Fujita, Hiroshi Gifu Univ Grad Sch Med Div Regenerat & Adv Med Sci Dept Intelligent Image Informat Gifu 5011194 Japan

ISBN: (纸本)9780819494443

This paper describes an approach to accomplish the fast and automatic localization of the different inner organ regions on 3D CT scans. The proposed approach combines object detections and the majority voting technique to achieve the robust and quick organ localization. The basic idea of proposed method is to detect a number of 2D partial appearances of a 3D target region on CT images from multiple body directions, on multiple image scales, by using multiple feature spaces, and vote all the 2D detecting results back to the 3D image space to statistically decide one 3D bounding rectangle of the target organ. Ensemble learning was used to train the multiple 2D detectors based on template matching on local binary patterns and Haar-like feature spaces. A collaborative voting was used to decide the corner coordinates of the 3D bounding rectangle of the target organ region based on the coordinate histograms from detection results in three body directions. Since the architecture of the proposed method (multiple independent detections connected to a majority voting) naturally fits the parallel computing paradigm and multi-core CPU hardware, the proposed algorithm was easy to achieve a high computational efficiently for the organ localizations on a whole body CT scan by using general-purpose computers. We applied this approach to localization of 12 kinds of major organ regions independently on 1,300 torso CT scans. In our experiments, we randomly selected 300 CT scans (with human indicated organ and tissue locations) for training, and then, applied the proposed approach with the training results to localize each of the target regions on the other 1,000 CT scans for the performance testing. The experimental results showed the possibility of the proposed approach to automatically locate different kinds of organs on the whole body CT scans.

关键词： Torso CT images Organ localization Object detection Majority voting Haar-like feature local binary patterns

来源：评论

学校读者我要写书评

暂无评论

Illumination Invariant Face Recognition with Whitening Filter and local binary patterns

Illumination Invariant Face Recognition with Whitening Filte...

引用

6th International Symposium on Computational Intelligence and Design (ISCID)

作者： Li, Shun Long, Fei Cheng, Xuefeng Xiamen Univ Software Sch Xiamen Peoples R China

ISBN: (纸本)9780769550794

In this paper we present an approach for the illumination invariant face recognition, which combines 2D spatial whitening filter with the LBP descriptor. Firstly, we preprocess each face image by applying whitening filter in the logarithm domain. Then LBP features are extracted based on the filtered images. After representing image with a LBP operator, the image will be divided into non-overlapped blocks and histograms are used to represent each block. Finally the concatenated histograms are used for recognition. The experimental results on the Yale B and Extended Yale B prove the outperformance of the proposed method compared to other existing methods.

关键词： Face Recognition Whitening Filter local binary patterns

来源：评论

学校读者我要写书评

暂无评论

A new speaker verification spoofing countermeasure based on local binary patterns

A new speaker verification spoofing countermeasure based on ...

引用

14th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2013)

作者： Alegre, Federico Vipperla, Ravichander Amehraye, Asmaa Evans, Nicholas EURECOM Multimedia Commun Dept Sophia Antipolis France Nuance Commun Cambridge England

ISBN: (纸本)9781629934433

This paper presents a new countermeasure for the protection of automatic speaker verification systems from spoofed, converted voice signals. The new countermeasure is based on the analysis of a sequence of acoustic feature vectors using local binary patterns (LBPs). Compared to existing approaches the new countermeasure is less reliant on prior knowledge and affords robust protection from not only voice conversion, for which it is optimised, but also spoofing attacks from speech synthesis and artificial signals, all of which otherwise provoke significant increases in false acceptance. The work highlights the difficulty in detecting converted voice and also discusses the need for formal evaluations to develop new countermeasures which are less reliant on prior knowledge and thus more reflective of practical use cases.

关键词： speaker verification biometrics imposture countermeasures local binary patterns

来源：评论

学校读者我要写书评

暂无评论

Human Activity Recognition Based On Silhouette Analysis Using local binary patterns

Human Activity Recognition Based On Silhouette Analysis Usin...

引用

10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Su, Han Zou, Jiayun Wang, Wenjie Sichuan Normal Univ Key Lab Virtual Real & Visual Comp Sichuan Chengdu 610066 Peoples R China Sichuan Normal Univ Coll Comp Sci Chengdu 610066 Peoples R China

ISBN: (纸本)9781467352536

In this paper, we present a novel method for activity analysis in outdoor environment. Difficulties, such as low resolution, shadows, long distances, and segmentation problems, usually exist in outdoor environment. To deal with these difficulties, the activity width sequence image which maintains 3D features using 2D representation is exploited to represent the silhouette structure information of each frame and dynamic properties of activity. The activity width vectors are converted to the gray value successively according to the order in activity sequence and the gray image is formed in spatio-temporal space. We regard the activity width sequence image as the texture and choose local binary patterns which is a powerful texture extraction operator to analyze the spatio-temporal pattern. The method is simple and effective. We test our method based on the outdoor database and the results are encouraging.

关键词： Human Activity Recognition local binary patterns Silhouette Width

来源：评论

学校读者我要写书评

暂无评论

FACE DETECTION IN MOBILE PHONES USING CO-OCCURRENCE OF ADJACENT local binary patterns

FACE DETECTION IN MOBILE PHONES USING CO-OCCURRENCE OF ADJAC...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wang, Jeaff Lee, Dohyoung Plataniotis, K. N. Univ Toronto Multimedia Lab Edward S Rogers Dept Elect & Comp Engn Toronto ON Canada

ISBN: (纸本)9781479903566

This paper investigates a novel combination of Co-occurrence of adjacent local binary patterns histogram and local binary patterns feature extraction methods for face detection in mobile phone applications. In particular, Co-occurrence of adjacent local binary patterns histogram feature extraction provides exceptionally high discriminative power in face/non-face classification and hence is used to ensure the high accuracy of the proposed face detector. local binary patterns feature extraction has low computation complexity and is thus used to reduce the overall processing speed. In the conducted face detection experiments, the proposed face detector yields comparable or better performance as well as faster computation speed than the existing best methods.

关键词： Frontal face detection Feature extraction local binary patterns Mobile Phone

来源：评论

学校读者我要写书评

暂无评论

DISCRIMINATIVELY WEIGHTED MULTI-SCALE local binary patterns: APPLICATIONS IN PROSTATE CANCER DIAGNOSIS ON T2W MRI

DISCRIMINATIVELY WEIGHTED MULTI-SCALE LOCAL BINARY PATTERNS:...

引用

IEEE 10th International Symposium on Biomedical Imaging - From Nano to Macro (ISBI)

作者： Wang, Haibo Viswanath, Satish Madabuhshi, Anant Case Western Reserve Univ Dept Biomed Engn Cleveland OH 44106 USA

ISBN: (纸本)9781467364553

In this paper, we present discriminatively weighted local binary patterns (DWLBP), a new similarity metric to match Multi-scale LBP (MsLBP) in Hamming space. While MsLBP is widely used in image processing on account of its extremely fast bitwise operations on modern CPU, identifying a good metric that measures the dissimilarity of MsLBP remains an open problem. The Hamming score is typically computed at each individual scale and the scores across scales are summed up. This approach however often results in underestimating salient patterns. In this paper we seek to learn a vector obtained by optimally weighing the contribution of each individual scale when performing MsLBP based matching. Inspired by supervised learning, our methodology aims to learn the multi-scale, weight vector by minimizing the Hamming scores between positive class samples and jointly maximizing the scores between positive and negative class samples. This objective function leads to a convex formulation with equality and inequality constraints, which can then be solved via the interior-point optimization method. In this paper we evaluate the efficacy of the DWLBP scheme in detecting prostate cancer from T2w MRI and demonstrate that the approach statistically significantly outperforms MsLBP.

关键词： Prostate Cancer MRI Image Processing local binary patterns multi-scale

来源：评论

学校读者我要写书评

暂无评论

Enhancing medical image classification via federated learning and pre-trained model

引用

EGYPTIAN INFORMATICS JOURNAL 2024年 27卷

作者： Srinivasu, Parvathaneni Naga Lakshmi, G. Jaya Narahari, Sujatha Canavoy Shafi, Jana Choi, Jaeyoung Ijaz, Muhammad Fazal Amrita Vishwa Vidyapeetham Amrita Sch Comp Amaravati 522503 Andhra Pradesh India VR Siddhartha Engn Coll Dept Informat Technol Vijayawada 520007 India Sreenidhi Inst Sci & Technol Dept Elect & Commun Engn Hyderabad 501301 India Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Alddawasir Dept Comp Engn & Informat Wadi Alddawasir 11991 Saudi Arabia Gachon Univ Sch Comp Seongnam Si 13120 South Korea Melbourne Inst Technol Sch IT & Engn Melbourne 3000 Australia

The precise classification of medical images is crucial in various healthcare applications, especially in fields like disease diagnosis and treatment planning. In recent times, machine-intelligent models are desired to work in remote settings. However, the potential privacy concerns that arise from sharing confidential patient information to train traditional centralized machine learning models cannot be ignored. Federated learning (FL) offers a promising method for collaborative training on distributed data held by various entities, ensuring the privacy of patient information. This study evaluated the efficiency of the pre-trained models in the FL environment for medical image classification. The Convolutional Neural Network (CNN) model with Gray-Level Co-occurrence Matrix (GLCM) and local binary patterns (LBP), along with the EfficientNet model, are being used as the local models. The trainable parameters from the local models are fed as input for building the global model. Pretrained models trained on extensive datasets, possess valuable characteristics that can be utilized by FL models trained on proprietary datasets. Implementing this method can improve the efficacy and precision of FL models while also ensuring data confidentiality. The proposed model is evaluated using two distinct medical imaging datasets: Magnetic Resonance Image(MRI) and Computed Tomography (CT) scan images. The research highlights the advantages of utilizing pre-trained models in federated learning for medical image classification (MIC). The model's performance is assessed across several assessment criteria, demonstrating the model exhibited a satisfactory accuracy rate of 97.4% and 98.8% for MRI and CT scan images, respectively. The model is evaluated concerning to Diagnostic Odds Ratio (DOR), where the proposed global model has exhibited 1164.54 for the MRI images and 6825.17 for CT scan images, and the values have outperformed the pretrained model.

关键词： Federated learning Convolutional Neural Network Gray-Level Co-occurrence Matrix local binary patterns Efficient Net Medical Imaging

来源：评论

学校读者我要写书评

暂无评论

FAST FACIAL EXPRESSION RECOGNITION BASED ON local binary patterns

FAST FACIAL EXPRESSION RECOGNITION BASED ON LOCAL BINARY PAT...

引用

26th IEEE Annual Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Verma, Rohit Dabbagh, Mohamed-Yahia Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781479900336;9781479900312

Automatic facial expression analysis is an interesting and challenging problem which impacts important applications in many areas such as human-computer interaction and data-driven animation. Deriving effective facial representative features from face images is a vital step towards successful expression recognition. In this paper, we evaluate facial representation based on statistical local features called local binary patterns (LBP) for facial expression recognition. Simulation results illustrate that LBP features are effective and efficient for facial expression recognition. A real-time implementation of the proposed approach is also demonstrated which can recognize expressions accurately at the rate of 4.8 frames per second.

关键词： Facial expression recognition local binary patterns Support vector machine AdaBoost

来源：评论

学校读者我要写书评

暂无评论

An Ensemble of Hyperdimensional Classifiers: Hardware-Friendly Short-Latency Seizure Detection With Automatic iEEG Electrode Selection

引用

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2021年第4期25卷 935-946页

作者： Burrello, Alessio Benatti, Simone Schindler, Kaspar Benini, Luca Rahimi, Abbas Univ Bologna Dept Elect Elect & Informat Engn I-40136 Bologna Italy Univ Bern Bern Univ Hosp Inselspital Sleep Wake Epilepsy CtrDept Neurol CH-3010 Bern Switzerland Swiss Fed Inst Technol Dept Informat Technol & Elect Engn CH-8092 Zurich Switzerland IBM Res Zurich CH-8803 Zurich Switzerland

We propose a new algorithm for detecting epileptic seizures. Our algorithm first extracts three features, namely mean amplitude, line length, and local binary patterns that are fed to an ensemble of classifiers using hyperdimensional (HD) computing. These features are embedded into prototype vectors representing ictal (during seizures) and interictal (between seizures) brain states are constructed. These vectors can be computed at different spatial scales ranging from a single electrode up to many electrodes. This flexibility allows our algorithm to identify the electrodes that discriminate best between ictal and interictal brain states. We assess our algorithm on the SWEC-ETHZ iEEG dataset that includes 99 short-time iEEG seizures recorded with 36 to 100 electrodes from 16 drug-resistant epilepsy patients. Using $k$-fold cross-validation and all electrodes, our algorithm surpasses state-of-the-art algorithms yielding significantly shorter latency (8.81 s vs. 11.57 s) in seizure onset detection, and higher specificity (97.31% vs. 94.84%) and accuracy (96.85% vs. 95.42%). We can further reduce the latency of our algorithm to 3.74 s by allowing a slightly higher percentage of false alarms (2% specificity loss). Using only the top 10% of the electrodes ranked by our algorithm, we still maintain superior latency, sensitivity, and specificity compared to the other algorithms with all the electrodes. We finally demonstrate the suitability of our algorithm to deployment on low-cost embedded hardware platforms, thanks to its robustness to noise/artifacts affecting the signal, its low computational complexity, and the small memory-footprint on a RISC-V microcontroller.

关键词： Electrode selection ensemble of classifiers hyperdimensional computing ieeg line length local binary patterns low-latency seizure detection symbolic dynamics

来源：评论

学校读者我要写书评

暂无评论

Detection of Breast Cancer Through Mammogram Using Wavelet-Based LBP Features and IWD Feature Selection Technique

引用

SN Computer Science 2022年第2期3卷 175页

作者： Kalita, Dhruba Jyoti Singh, Vibhav Prakash Kumar, Vinay Department of Computer Science and Engineering Gaya College of Engineering Bihar Gaya India Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Prayagraj India Department of Computer Science and Engineering National Institute of Technology Jamshedpur India

Breast cancer is as one of the common reasons of deaths in women. To detect this cancer in early stage, a computer-aided diagnosis (CAD) system can be designed using X-ray mammograms, which further can assist the doctors and radiologists in their clinical decisions. This paper work proposes a CAD system which is designed by extracting the local binary patterns (LBP) in wavelet domain from mammograms and integrating intelligent water drop (IWD) algorithm to extract most important set of features from extracted features set. With this selected set of features, we then train a support vector machine (SVM) to classify new mammograms. The accuracy, precision and recall rates for our proposed CAD system are found to be 97.9%, 95.499% and 97.3%, respectively. Further, it has been found that the proposed model outperforms many of the existing CAD systems. We have also compared IWD with other Meta-heuristic features selection techniques such as ant colony optimization (ACO), particle swarm optimization (PSO), simulated annealing (SA) and genetic algorithm (GA) and found that it outperforms the others. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022.

关键词： Computer-aided diagnosis Discrete wavelet transform local binary patterns Mammography Meta-heuristic optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：