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Classification Of Human Facial Expressions For Emotion Recognition Using A distributed Computer System 5

Classification Of Human Facial Expressions For Emotion Recog...

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5th International conference on Computer Science and Engineering (UBMK)

作者： Korkmaz, Timucin Erol, Hamza Mersin Univ Grad Sch Nat & Appl Sci Dept Comp Engn Mersin Turkey Mersin Univ Engn Fac Dept Comp Engn Mersin Turkey

ISBN: (纸本)9781728175652

A computer system was developed to classify human facial expressions for emotion recognition using a distributed computer architecture within the scope of big data. Computers with normal standards and features were used in the distributed computer architecture. Necessary system software for the distributed computer architecture was established and mutual communication protocols were provided between the computers and databases in the computer network. Visual C# parallel programming was used as the software language on the distributed computer architecture. In the software prepared, face image files were processed and threads were created. The threads created later were processed in the processors of the computers. The threads were run on the processors in the distributed computer system, and facial expressions were classified for emotion recognition. In the distributed computer architecture: the number of image files, the volume of big data and the load of the threads to be processed arc taken into account;and databases and parallel programming were used and the classification of human face images was performed. Emotion analysis methods and facial expression recognition techniques were used in the classification process. The distributed computer system is in low cost and high processing speed. With the distributed computer system architecture, big data analytics has become convenient and feasible.

关键词： Big data distributed System Emotion analysis image classification parallel Programing Face expression recognition

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PickDet: A Detection Framework for Aerial-view Scene

PickDet: A Detection Framework for Aerial-view Scene

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IEEE Visual Communications and image processing (VCIP)

作者： Cheng Lyu Xiao Deng Shizun Wang Ming Wu Chuang Zhang Pattern Recognition and Intelligent Systems Lab. Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665475938

Detecting objects in the aerial-view scene is challenging for the objects usually have small scales relative to the image, making it hard to achieve high accuracy in full-image detection. Slice detection tries to overcome this by cutting the full image into slices before detecting them, but objects are sparsely distributed and usually clustered in local areas, a large number of background areas without objects can be ignored to improve detection efficiency. In this paper, we present PickDet, a framework for efficient and effective object detection in the aerial-view scene, which only chooses slices containing objects to conduct detection. The key components of PickDet include a lightweight convolutional network (PickNet), a screening strategy (SoftPick), and fine-tuned detectors. Given slices of aerial-view images, PickNet first outputs the probability of object existence. Then SoftPick conducts a double-threshold screening strategy to pick the slices which contain objects. Finally, all picked slices are fed into the detector in parallel and full-image detection is used as an auxiliary mean. Compared with previous methods, PickDet achieves higher accuracy and more efficiency in the aerial-view scene. We evaluate PickDet on Visdrone and Oiltank datasets, experiments show that PickDet can result in up to 28.0% AP improvement compared to full-image detection, and can result in up to 2.9% AP increase and up to 5 times inference speedup compared to slice detection.

关键词： Fault tolerance Visual communication image processing Fault tolerant systems Detectors Object detection

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image Analysis of Facial Blood Vessels for Anti-Spoofing of Printed image and 3D Mask Attacks

Image Analysis of Facial Blood Vessels for Anti-Spoofing of ...

引用

2022 International conference on image processing and Media Computing, ICIPMC 2022

作者： Li, Lei Yao, Zhihao Zhou, Shixi Ma, Yupeng Wu, Jun Xia, Zhaoqiang Shandong University of Finance and Economics School of Computer Science and Technology Jinan China Northwestern Polytechnical University School of Electronics and Information Xi'an China Hebei Normal University College of Computer and Cyber Security Shijiazhuang China Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei China

ISBN: (数字)9781665468725

ISBN: (纸本)9781665468725

In recent years, many face spoofing detection methods based on video frame or video sequence have been proposed to counteract the security threats of face recognition system. Although the video sequence based detection methods analyzing liveness signals have satisfactory performance, it is difficult to capture a long video in realistic applications;the video frame based methods have good real-time capabilities, but the detection performance is poor when dealing with high-resolution fake faces. Therefore, in this paper, we propose a novel video frame based face spoofing detection method exploiting facial blood flow differences, which achieves excellent detection performance and generalization capability. Specifically, compared with fake face, the subcutaneous tissue of live face is distributed with complex blood vessels. When the visible light penetrates face skin, its absorption in different regions is different due to the distribution of blood vessels. In order to extract the visible light absorption variations between different regions, the attentional face image is first fed into an 1D CNN to extract features for describing the absorption characteristics of each point;then, a GRU network is invoked to capture the absorption variations of the outputs of 1D CNN;finally, the Softmax function is used to classify whether the face image is a live face or a spoofing attack. Extensive experiments on three databases of 3DMAD, MSU and Replay-Attack demonstrate the effectiveness of our proposed method in distinguishing the printed images or 3D masks from the live ones and show that the detection performance outperforms other state-of-the-art methods. © 2022 IEEE.

关键词： Blood vessels

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Texture-Unet: A Texture-Aware Network for Bone Marrow Smear Whole-Slide image Region of Interest Segmentation

Texture-Unet: A Texture-Aware Network for Bone Marrow Smear ...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jian Chen Xing Wu Chengliang Wang Zailin Yang XueLian Wu Longrong Ran Yao Liu College of Computer Science Chongqing University Chongqing China Department of Hematology-Oncology Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment Chongqing University Cancer Hospital Chongqing China

Bone marrow smear cytology involves observing and analyzing the morphological features of bone marrow cells, and identifying regions of interest (ROI) where the cells are morphologically clear and evenly distributed is a crucial part of this process. However, existing deep learning methods for selecting ROI in whole-slide images (WSI) of bone marrow smears have overlooked the unique characteristics of the smears, particularly the texture information, resulting in inadequate performance. To overcome this issue, this paper proposes a texture-aware region of interest segmentation model (Texture-Unet) for WSI of bone marrow smears. To enhance the network’s ability to extract and perceive texture information, we specifically designed two modules, the Texture Extraction Module (TEM) and the Texture Deep Supervision Module (TDSM). We evaluated our method using a self-constructed dataset. The experimental results indicated a 4.64% improvement in terms of Intersection over Union (IoU), and a 3.04% improvement in terms of the Dice coefficient, compared to the baseline. The experimental results confirm that Texture-Unet performs well in terms of accuracy and efficiency for ROI segmentation in bone marrow smear WSI.

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Research on Computer processing Technology of Big Data Based on Cloud Environment

Research on Computer Processing Technology of Big Data Based...

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Industrial IoT, Big Data and Supply Chain (IIoTBDSC), International conference on

作者： Chenglong Du Guangdong University of Science and Technology Dongguan Guangdong China

With the development of The Times, the Internet has become an essential part of people’s *** order to meet the needs of the market, it is necessary to continuously optimize and innovate in technology and display various advantages in the era of big *** methods such as distributed computing or parallel computing are limited by various conditions and cannot meet specific computing ***, in order to solve this problem, people gradually begin to adopt the computer processing technology of big data based on the background of cloud environment, so as to break these limitations and carry out data calculation more quickly and *** the background of the era of big data, the choice of vigorously promoting the use of advanced computer information processing technology can continue to ensure the security of different types of data storage and application, so that the application value of data information can be presented in an all-round way.

关键词： Cloud computing Supply chains distributed databases Memory Information processing Big Data parallel processing

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Consensus-Based distributed Kernel One-class Support Vector Machine for Anomaly Detection

Consensus-Based Distributed Kernel One-class Support Vector ...

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International Joint conference on Neural Networks (IJCNN)

作者： Tianyao Wang Fan He Ruikai Yang Zhixing Ye Xiaolin Huang Department of Automation Ningbo Artificial Intelligence Institute Shanghai Jiao Tong University China Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

One-class support vector machine (OCSVM) is one of the most widely used methods for learning from imbalanced data and has been successfully applied to numerous tasks such as anomaly detection. However, the study on decentralized OCSVM is currently limited to linear cases. The main challenge is how to communicate the non-parametric and local-data-dependent decision functions between neighboring nodes. To tackle it, this paper proposes a projection consensus constraint to formulate a decentralized OCSVM, where local solutions are assumed to be the projection of the global optimum on local reproducing kernel Hilbert spaces. A fast non-parametric solving algorithm is then designed based on alternating direction method of multipliers. Experiments on real-world anomaly datasets indicate that our method outperforms the existing distributed OCSVM methods while reducing the communication cost.

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Boost the Adversarial Learning with Fourier Regulator: Bias-field Correction on MRI

Boost the Adversarial Learning with Fourier Regulator: Bias-...

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2025 conference on Medical Imaging

作者： Li, Tong Liu, Anran Kuegler, David Reuter, Martin Washington Univ St Louis Div Computat Data Sci St Louis MO 63130 USA Hong Kong Polytech Univ Dept Hlth Technol & Informat Hong Kong Peoples R China German Ctr Neurodegenerat Dis DZNE AI Med Imaging Bonn Germany Massachusetts Gen Hosp AA Martinos Ctr Biomed Imaging Boston MA USA Harvard Med Sch Dept Radiol Boston MA USA

ISBN: (纸本)9781510685901

In magnetic resonance imaging, signal intensity inhomogeneities due to intrinsic bias field pose a significant challenge for automated medical image analysis. Conventional methods to mitigate these effects, such as N4ITK, are time-consuming and unstable. The exploration of deep learning alternative approaches is still at an unknown stage. Previous studies have obtained preliminary results in GAN-based models, but we found the difficulty in aligning bias-corrected image domains with clean image domains during adversarial learning may affect the retention of normal organizational structures. Therefore, we propose a novel Fourier regulator structure that can be integrated into the general adversarial learning framework. It explicitly decouples different levels of semantic features based on the Fourier field and utilizes explicit feature learning to enhance intrinsic coherence and promote more organized domain alignment. By separating amplitude and phase features as well as splitting low and high-frequency information, our model preserves organizational details more efficiently and explicitly separates intensities across organizational boundaries. During the training process of adversarial learning, the generator generates the target domain while the regulator and discriminator are fixed;the regulator and discriminator are updated in parallel while the generator is fixed. Such a learning approach extends the original min-max optimization problem of adversarial learning to a multi-player mix-max optimization problem. The discriminator can quickly draw the generative domain closer to the target domain, while the regulator aligns the distance to the target domain in a more explicit feature-learning manner. Evaluated on the OASIS and BrainWeb datasets, our model outperforms traditional and deep learning methods to enhance homogeneity. It also shows consistent performance in other image reconstruction tasks, demonstrating its generalization capabilities.

关键词： Bias correction Magnetic resonance imaging Adversarial training image enhancement

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Deep Learning Framework for Brain Tumor Classification through Histopathological image

Deep Learning Framework for Brain Tumor Classification throu...

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2022 International conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Sandhiya, B. UshaKiruthika, S. Kanaga Suba Raja, S. Shiv Nadar University Department of CSE Chennai India Easwari Engineering College Chennai India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

Further most oncologist varies in their conclusions around the determination utilizing histopathological-pictures, so the manual diagnosis of brain tumour is troublesome, and it too needs elevated involvement. Building a show of brain tumour classification is a fundamental assignment. CAD empowers productive and exact conclusion of this sort of imaging. In addition, it makes a difference early determination of brain tumours. Profound learning has started to advance within the final decades, so convolutional neural systems/networks are utilized to classify brain tumours. We presented the programmed brain tumour classification strategies to assist specialists in examination and treatment, which can naturally classify the histopathological pictures as generous or dangerous. This study's hypothetical premise is creating a profound learning tumour classification framework. It employs a profound learning approach that is the proposed CNN model comprises of three stages. The primary stage is pre-processing. The moment stage is the highlight extraction organize that takes input as augmented preprocessed pictures. This stage has two divisions. The primary stage comprises of three parallel CNN branches with different filter sizes to extricate distinctive include levels. We blended the three parallel branches within the moment stage, which produced a highlight combination way that diminished the time complexity and upgraded the outlined methods, performance. The third stage classifies the melded highlights as generous or dangerous. The Cancer-Genome-Atlas Glioblastoma Multiform-DB (TCGA-GBM) is utilized &implemented and contains 19,507 files of brain tumour tissue for 237 patients. The data was parted into three sections: 60%, 20%, 20% for training, validation, and testing. A few tests were performed to attain the finest demonstrative precision of 91.37 %. The results are compared to the outcomes about of different methods within the writing. The presented preferences of our m

关键词： Statistical tests

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Focus on Inherent Attributes for Temporal Knowledge Graph Completion

Focus on Inherent Attributes for Temporal Knowledge Graph Co...

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International Joint conference on Neural Networks (IJCNN)

作者： Chen, Kai Li, Chenchen Li, Aiping Gao, Jingsheng Ma, Sixia Natl Univ Def Technol Coll Comp Changsha Peoples R China

ISBN: (纸本)9780738133669

In the last few years, the availability of temporal knowledge graphs has stimulated extensive research in temporal knowledge graph completion (TKGC) and temporal knowledge graph embedding (TKGE), where temporal information is added to static knowledge graphs that have been widely applied previously. However, most existing methods, such as current state-of-the-art DE-SimplE and TeRo, learn embeddings of temporalevolving attributes, overlooking the inherent attributes inside entities, where some essential and inherent features are included. In this paper, we introduce a novel method utilizing Inherent Attributes with a Graph Attention network (IAGAT) for TKGC. Our IAGAT extracts inherent attributes from sufficient features corresponding to various facts at different time stamps, to obtain the inherent embeddings. And we take advantages of previous rotation based methods to obtain the temporal-evolving embeddings. Through extensive experiments and sufficient comparisons, we demonstrate our model outperforms the current state-of-the-art models on link prediction task. Furthermore, we evaluate and prove the necessity of the inherent attributes in performance improvement, and study how our model functions in extracting inherent features.

关键词： Knowledge engineering distributed processing Neural networks Predictive models Feature extraction Task analysis Research and development

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Trustworthy distributed Deep Neural Network Training in an Edge Device Network

Trustworthy Distributed Deep Neural Network Training in an E...

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IEEE International conference on Big Data

作者： Sudipta Saha Shubha Haiying Shen Department of Computer Science University of Virginia USA

ISBN: (纸本)9781665480468

With the increased usage of edge devices having local computation capabilities, deep neural network (DNN) training in a network of edge devices becomes promising. Several recent works have proposed fully edge-based distributed training systems for situations when the communication to cloud is unstable or intermittent. However, such distributed systems become vulnerable when there are untrusted devices that launch data and model poisoning attacks during training, deteriorating the accuracy of the DNN model. To handle this challenge, we propose a Trustworthy distributed system for Machine learning training in an edge device network (TrustMe). TrustMe realizes both data and model parallelisms. It detects the untrusted devices producing illegitimate outputs. Next, it reassigns the training tasks of the untrusted devices to other trusted devices in such a way that the reassignment and the training that is restarted after the reassignment require minimal time. Our container-based emulation and real device experiments demonstrate that TrustMe achieves up to 12% higher accuracy and 45% less training time compared to existing methods in the presence of untrusted devices.

关键词： Training Deep learning image edge detection Neural networks Emulation distributed databases parallel processing

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