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TechRxiv 2021年

作者： Jarraya, Islem BenSaid, Fatma Ouarda, Wael Pal, Umapada Alimi, Adel M. BP 1173 Sfax3038 Tunisia Digital Research Center of Sfax B.P. 275 Sakiet Ezzit Sfax3021 Tunisia Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T. Road Kolkata700 108 India Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg South Africa

This paper focuses on the face detection problem of three popular animal categories that need control such as horses, cats and dogs. To be precise, a new Convolutional Neural Network for Animal Face Detection (CNNAFD) is actually investigated using processed filters based on gradient features and applied with a new way. A new convolutional layer is proposed through a sparse feature selection method known as Automated Negotiation-based Online Feature Selection (ANOFS). CNNAFD ends by stacked fully connected layers which represent a strong classifier. The fusion of CNNAFD and MobileNetV2 constructs the new network CNNAFD-MobileNetV2 which improves the classification results and gives better detection decisions. Our work also introduces a new Tunisian Horse Detection Database (THDD). The proposed detector with the new CNNAFDMobileNetV2 network achieved an average precision equal to 99.78%, 99% and 98.28% for cats, dogs and horses respectively. © 2021, CC BY.

关键词： Animals

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Weakly Supervised Geodesic Segmentation of Egyptian Mummy CT Scans

Weakly Supervised Geodesic Segmentation of Egyptian Mummy CT...

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International Conference on pattern recognition

作者： Avik Hati Matteo Bustreo Diego Sona Vittorio Murino Alessio Del Bue Pattern Analysis and Computer Vision (PAVIS) Istituto Italiano di Tecnologia Genova Italy University of Genova Italy Neuroinformatics Laboratory Fondazione Bruno Kessler Trento Italy Ireland Research Center Huawei Technologies Co. Ltd. Dublin Ireland

In this paper, we tackle the task of automatically analyzing 3D volumetric scans obtained from computed tomography (CT) devices. In particular, we address a particular task for which data is very limited: the segmentation of ancient Egyptian mummies CT scans. We aim at digitally unwrapping the mummy and identify different segments such as body, bandages and jewelry. The problem is complex because of the lack of annotated data for the different semantic regions to segment, thus discouraging the use of strongly supervised approaches. We, therefore, propose a weakly supervised and efficient interactive segmentation method to solve this challenging problem. After segmenting the wrapped mummy from its exterior region using histogram analysis and template matching, we first design a voxel distance measure to find an approximate solution for the body and bandage segments. Here, we use geodesic distances since voxel features as well as spatial relationship among voxels is incorporated in this measure. Next, we refine the solution using a GrabCut based segmentation together with a tracking method on the slices of the scan that assigns labels to different regions in the volume, using limited supervision in the form of scribbles drawn by the user. The efficiency of the proposed method is demonstrated using visualizations and validated through quantitative measures and qualitative unwrapping of the mummy.

关键词： Three-dimensional displays Computed tomography Semantics Pipelines Metals Transforms pattern recognition

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Soft Computing Applications and Intelligent Systems 1

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丛书名： Communications in computer and Information Science

1000年

作者： Shahrul Azman Noah Azizi Abdullah Haslina Arshad Zulaiha Ali Othman Zalinda Othman Azuraliza Abu Bakar Shahnorbanun Sahran Nazlia Omar

ISBN: (数字)9783642405679

ISBN: (纸本)9783642405662

This book constitutes the refereed proceedings of the International Second International Multi-Conference on Artificial Intelligence Technology, M-CAIT 2013, held in Shah Alam, in August 2013. The 25 revised full papers presented were carefully reviewed and selected from 110 submissions. M-CAIT 2013 hosted four special tracks in a single event: Intelligence Computation on pattern Analysis and Robotics (ICPAIR 2013), Data Mining and Optimization (DMO 2013), Semantic Technology and Information Retrieval (STAIR 2013) and Industrial Computing & Applied Informatics (IComp 2013). The papers address issues of state-of-the-art research, development, implementation and applications within the four focus areas in CAIT: pattern recognition, data mining and optimization, knowledge technology and industrial computing.

关键词： Artificial Intelligence computer Imaging, vision, pattern recognition and Graphics Data Mining and Knowledge Discovery Information Storage and Retrieval

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Retinal blood flow speed quantification at the capillary level using temporal autocorrelation fitting OCTA

arXiv

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arXiv 2023年

作者： Hwang, Yunchan Won, Jungeun Yaghy, Antonio Takahashi, Hiroyuki Girgis, Jessica M. Lam, Kenneth Chen, Siyu Moult, Eric M. Ploner, Stefan B. Maier, Andreas Waheed, Nadia K. Fujimoto, James G. Department of Electrical Engineering and Computer Science Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States New England Eye Center Tufts Medical Center BostonMA02116 United States Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

Optical coherence tomography angiography (OCTA) can visualize vasculature structures, but provides limited information about the blood flow speeds. Here, we present a second generation variable interscan time analysis (VISTA) OCTA, which evaluates a quantitative surrogate marker for blood flow speed in vasculature. At the capillary level, spatially compiled OCTA and a simple temporal autocorrelation model, ρ(τ) = exp(-ατ), were used to evaluate a temporal autocorrelation decay constant, α, as the blood flow speed marker. A 600 kHz A-scan rate swept-source provides short interscan time OCTA and fine A-scan spacing acquisition, while maintaining multi mm2field of views for human retinal imaging. We demonstrate the cardiac pulsatility and repeatability of α measured with VISTA. We show different α for different retinal capillary plexuses in healthy eyes and present representative VISTA OCTA of eyes with diabetic retinopathy. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical tomography

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Hybrid Multi-Task Learning for End-To-End Multimodal Emotion recognition

Hybrid Multi-Task Learning for End-To-End Multimodal Emotion...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Junjie Chen Yongwei Li Ziping Zhao Xuefei Liu Zhengqi Wen Jianhua Tao College of Computer and Information Engineering Tianjin Normal University Tianjin China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Department of Automation Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

Multimodal emotion recognition plays a pivotal role in the advancement of natural human-computer interaction systems. Recent studies have attempted to apply multi-task learning to emotion recognition. However, the multi-task shared feature extractor of traditional methods needs to integrate the feature representations of different tasks, which may lead to the feature extractor failing to focus on the learning of emotion representations. To address this problem, we propose a hybrid multi-task learning framework for end-to-end multimodal emotion recognition, in which the primary task is emotion classification, and the auxiliary tasks are emotion regression and gender classification. This framework consists of two networks specialized in gender and emotion recognition, where the latter transfers knowledge from the former through our proposed Deep Aggregation LSTM (DA-LSTM). The DA-LSTM could more precisely capture emotional information in discourse by aggregating emotion and gender feature extractors. Experimental results on a commonly used dataset IEMOCAP demonstrate the effectiveness of our proposed method.

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CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofing

arXiv

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arXiv 2019年

作者： Zhang, Shifeng Liu, Ajian Wan, Jun Liang, Yanyan Guo, Guogong Escalera, Sergio Escalante, Hugo Jair Li, Stan Z. National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Macau University of Science and Technology Macau China Institute of Deep Learning Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application Universitat de Barcelona Computer Vision Center Barcelona Catalonia Instituto Nacional de Astrofsica Ptica y Electrnica Puebla72840 Mexico

Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face antispoofing benchmarks have limited number of subjects (≤170) and modalities (≤2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIASURF, which is the largest publicly available dataset for face antispoofing in terms of both subjects and modalities. Specifically, it consists of 1;000 subjects with 21;000 videos and each sample has 3 modalities (i.e., RGB, Depth and IR). We also provide comprehensive evaluation metrics, diverse evaluation protocols, training/validation/testing subsets and a measurement tool, developing a new benchmark for face anti-spoofing. Moreover, we present a novel multi-modal multi-scale fusion method as a strong baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modality across different scales. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at http://***/***/chalearnfacespoofingattackdete/. Copyright © 2019, The Authors. All rights reserved.

关键词： Face recognition

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Attentive Part-aware Networks for Partial Person Re- identification

Attentive Part-aware Networks for Partial Person Re- identif...

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International Conference on pattern recognition

作者： Lijuan Huo Chunfeng Song Zhengyi Liu Zhaoxiang Zhang Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences (CASIA) Artificial Intelligence ResearchChinese Academy of Sciences Jiaozhou Qingdao China School of Computer Science and Technology Anhui University Hefei China University of Chinese Academy of Sciences (UCAS)

Partial person re-identification (re-ID) refers to re-identify a person through occluded images. It suffers from two major challenges, i.e., insufficient training data and incomplete probe image. In this paper, we introduce a part-aware learning method for partial person re-identification. On the one hand, we adopt data augmentation operation to enrich the training data and improve the robustness of the model. On the other hand, we intuitively find that the partial person images usually have fixed percentages of parts, therefore, in partial person re-ID task, the probe image could be cropped from the pictures and divided into several different partial types following fixed ratios. Based on the cropped images, we propose the Cropping Type Consistency (CTC) loss to classify the cropping types of partial images. Moreover, in order to help the network better fit the generated and cropped data, we incorporate the Block Attention Mechanism (BAM) into the framework for attentive learning. To enhance the retrieval performance in the inference stage, we implement cropping on gallery images according to the predicted types of probe partial images. Through calculating feature distances between the partial image and the cropped holistic gallery images, the model can recognize the right person from the gallery. To validate the effectiveness of our approach, we conduct extensive experiments on the partial re- ID benchmarks and achieve state-of-the-art performance.

关键词： Training Learning systems Image recognition Semantics Training data Robustness Data models

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LBAA: A novel load balancing mechanism in cloud environments using ant colony optimization and artificial bee colony algorithms

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International Journal of Communication Systems 2023年第9期36卷 e5481-e5481页

作者： Mohammadian, Vahid Navimipour, Nima Jafari Hosseinzadeh, Mehdi Darwesh, Aso Department of Computer Engineering Qeshm Branch Islamic Azad University Qeshm Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Pattern Recognition and Machine Learning Lab Gachon University Seongnam Korea Republic of Information Technology Departments University of Human Development Sulaimaniyah Iraq

Recently, cloud computing has been recognized as an effective paradigm for offering an on-demand platform, software services, and an efficient infrastructure to cloud clients. Due to the exponential growth of cloud tasks and the rapidly increasing number of cloud users, scheduling and balancing these tasks among involved heterogeneous virtual machines becomes an Non-deterministic Polynomial hard (NP-hard) optimization problem considering significant constraints, such as high rate of resource usage, low scheduling time, and low implementation cost. Therefore, various meta-heuristic algorithms have been widely used to tackle the issue. The current paper proposes a novel load balancing mechanism using the ant colony optimization and artificial bee colony algorithms, called LBAA, which aims to balance the load division among systems in data centers. The simulation outcomes confirm that our algorithm outperforms previous works regarding response time, imbalance degree, makespan, and resource utilization up to 25%, 15%, 12%, and 10%, respectively. © 2023 John Wiley & Sons Ltd.

关键词： Ant colony optimization

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Medical Imaging and Augmented Reality 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Guang-Zhong Yang TianZi Jiang Dinggang Shen Lixu Gu Jie Yang

ISBN: (数字)9783540372219

ISBN: (纸本)9783540372202

The Third International Workshop on Medical Imaging and Augmented Reality, MIAR 2006, was held in Shanghai, China at the Regal International East Asia Hotel during August 17-18, 2006. The goal of MIAR 2006 was to bring together researchers in medical image computing and intervention to present the state-of-the-art devel- ments in this ever-growing research area. The meeting consisted of a single track of oral/poster presentations, with each session led by an invited lecture from our dist- guished international faculty members. For MIAR 2006, we received 87 full subm- sions, which were subsequently reviewed by up to 5 reviewers, resulting in the acc- tance of 45 full papers included in this volume. For this workshop, we also included four papers from the invited speakers covering shape modeling, fMRI analysis, and study of cerebral connectivity and plasticity. Running such a workshop requires dedication, and we appreciate the commitment of the MIAR 2006 Programme Committee and reviewers who worked to a tight de- line in putting together this workshop. We would also like to thank members of the local Organizing Committee, who have been working so hard behind the scenes to make MIAR 2006 a great success. It was our great pleasure to welcome this year’s MIAR attendees to Shanghai, which is the largest base of Chinese industrial technology, an important seaport and China's largest commercial and financial center.

关键词： Image Processing and computer vision Health Informatics Imaging / Radiology Artificial Intelligence computer Graphics pattern recognition

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MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation

arXiv

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arXiv 2021年

作者： Srivastava, Abhishek Jha, Debesh Chanda, Sukalpa Pal, Umapada Johansen, Håvard D. Johansen, Dag Riegler, Michael A. Ali, Sharib Halvorsen, Pål Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India SimulaMet Oslo Norway UiT The Arctic University of Norway Tromsø Norway Østfold University College Halden Norway Indian Statistical Institute Kolkata India The Department of Engineering Science University of Oxford Oxford NIHR Biomedical Research Centre Oxford United Kingdom Oslo Metropolitan University Oslo Norway

Methods based on convolutional neural networks have improved the performance of biomedical image segmentation However, most of these methods cannot efficiently segment objects of variable sizes and train on small and biased datasets, which are common for biomedical use cases While methods exist that incorporate multi-scale fusion approaches to address the challenges arising with variable sizes, they usually use complex models that are more suitable for general semantic segmentation problems In this paper, we propose a novel architecture called Multi-Scale Residual Fusion Network (MSRF-Net), which is specially designed for medical image segmentation The proposed MSRF-Net is able to exchange multi-scale features of varying receptive fields using a Dual-Scale Dense Fusion (DSDF) block Our DSDF block can exchange information rigorously across two different resolution scales, and our MSRF sub-network uses multiple DSDF blocks in sequence to perform multi-scale fusion This allows the preservation of resolution, improved information flow and propagation of both high-and low-level features to obtain accurate segmentation maps The proposed MSRF-Net allows to capture object variabilities and provides improved results on different biomedical datasets Extensive experiments on MSRF-Net demonstrate that the proposed method outperforms the cutting-edge medical image segmentation methods on four publicly available datasets We achieve the Dice Coefficient (DSC) of 0.9217, 0.9420, and 0.9224, 0.8824 on Kvasir-SEG, CVC-ClinicDB, 2018 Data Science Bowl dataset, and ISIC-2018 skin lesion segmentation challenge dataset respectively We further conducted generalizability tests and achieved DSC of 0.7921 and 0.7575 on CVC-ClinicDB and Kvasir-SEG, respectively. © 2021, CC BY.

关键词： Image enhancement

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