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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

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Robust Automated Fiber Tracking: A Hybrid Software Architecture for processing and Visualizing Diffusion Magnetic Resonance Imaging Data

Robust Automated Fiber Tracking: A Hybrid Software Architect...

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ieee international conference on Software Architecture Workshops (ICSAW)

作者： Sverre van Gompel Gerard Schouten Willem Huijbers Maarten Versluis Software Engineering Consultancy Infiniot B.V. Eindhoven The Netherlands School of Information and Communication Technology Fontys University of Applied Sciences Eindhoven The Netherlands Department of Digital Engagement Cognition & Behavior Philips Research Eindhoven The Netherlands Clinical Science Best Philips Healthcare Best The Netherlands

Robust Automated Fiber Tracking (RAFT) is a configurable software pipeline that automates extraction of fiber bundles from diffusion Magnetic Resonance Imaging (dMRI) data. RAFT uses a hybrid software architecture design and is developed keeping the SOLID (Scalable, Operationally agile, Lean, IT focused and Delivers results) principles of software development in mind. This design is based on (i) the pipeline pattern: a software design pattern that provides the ability to execute a sequence of operations, (ii) while taking elements from a microservices architecture: a method to build server applications using a set of small self-contained services. Thanks to this hybrid architecture design, RAFT is a modular and open system allowing for easy modifications and future extensions. The resulting prototype fully automates the extraction process with many configuration options, while keeping the installation simple and usage of the system intuitive.

关键词： Software architecture Magnetic resonance imaging Prototypes Microservice architectures computer architecture Streaming media Solids

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Polarization Multi-image Synthesis with Birefringent Metasurfaces

Polarization Multi-Image Synthesis with Birefringent Metasur...

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ieee international conference on Computational Photography (ICCP)

作者： Dean Hazineh Soon Wei Daniel Lim Qi Guo Federico Capasso Todd Zickler Department of Applied Physics Harvard University Boston MA Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette IN Department of Electrical and Computer Engineering Harvard University Boston MA

Optical metasurfaces composed of precisely engineered nanostructures have gained significant attention for their ability to manipulate light and implement distinct functionalities based on the properties of the incident field. Computational imaging systems have started harnessing this capability to produce sets of coded measurements that benefit certain tasks when paired with digital post-processing. Inspired by these works, we introduce a new system that uses a birefringent metasurface with a polarizer-mosaicked photosensor to capture four optically-coded measurements in a single exposure. We apply this system to the task of incoherent opto-electronic filtering, where digital spatial-filtering operations are replaced by simpler, per-pixel sums across the four polarization channels, independent of the spatial filter size. In contrast to previous work on incoherent opto-electronic filtering that can realize only one spatial filter, our approach can realize a continuous family of filters from a single capture, with filters being selected from the family by adjusting the post-capture digital summation weights. To find a metasurface that can realize a set of user-specified spatial filters, we introduce a form of gradient descent with a novel regularizer that encourages light efficiency and a high signal-to-noise ratio. We demonstrate several examples in simulation and with fabricated prototypes, including some with spatial filters that have prescribed variations with respect to depth and wavelength.

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Averting Frustration in Geriatric Patients through Identification, 2D Mapping and Navigation Using Machine Learning 4

Averting Frustration in Geriatric Patients through Identific...

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4th international conference on Cybernetics, Cognition and Machine Learning applications, ICCCMLA 2022

作者： Malhotra, Alekha Bhatia, Stuti Jain, Reetu Class of 2023 Cathedral and John Connon School 6 Purushottamdas Thakurdas Rd Marg Azad Maidan Fort Maharashtra Mumbai400001 India On My Own Technology Pvt. Ltd. 2A Pace House 7 Swastik Society Gulmohar Road Vile Parle West Mumbai400049 India

ISBN: (数字)9781665462464

ISBN: (纸本)9781665462464

Alzheimer's patients experience agitation and frequently display aggressive behaviors due to their inability to retain simple information, such as people's names, and often misplace items, among other causes. The incurable nature of this disease creates the need for assistive technology to help patients. This proposed methodology, MemoryMap, consists of a pair of glasses, a camera, hardware for audio output and input and a microprocessor. The algorithm uses image-processing and machine learning to perform object and facial recognition, map out the locations of both the object and patient and provides a navigation system to guide patients to any object they have misplaced. This prototype continually records the patient's view through the camera and performs real-time analysis on the video feed using OpenCV, allowing the identification of the object or face desired by the user through a NumPy model. Each object is recognised and entered into a database in the form of an excel sheet using XLWT and XLRT to maintain the location and time frame. Visual feedback of the navigation route along with audio feedback for recognition enables the patient to become more self-sufficient. This prototype is targeted toward patients in the Mild Stage of Alzheimer's and works towards relieving their irritation caused due to memory loss. © 2022 ieee.

关键词： Cameras

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MassNet: A Deep Learning Approach for Body Weight Extraction from A Single Pressure image

MassNet: A Deep Learning Approach for Body Weight Extraction...

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ieee international conference on Pervasive Computing and Communications (PerCom)

作者： Ziyu Wu Quan Wan Mingjie Zhao Yi Ke Yiran Fang Zhen Liang Fangting Xie Jingyuan Cheng School of Computer Science and Technology University of Science and Technology of China Hefei China

Body weight, as an essential physiological trait, is of considerable significance in many applications like body management, rehabilitation, and drug dosing for patient-specific treatments. Previous works on the body weight estimation task are mainly vision-based, using 2D/3D, depth, or infrared images, facing problems in illumination, occlusions, and especially privacy issues. The pressure mapping mattress is a non-invasive and privacy-preserving tool to obtain the pressure distribution image over the bed surface, which strongly correlates with the body weight of the lying person. To extract the body weight from this image, we propose a deep learning-based model, including a dual-branch network to extract the deep features and pose features respectively. A contrastive learning module is also combined to the deep-feature branch to help mine the mutual factors across different postures of every single subject. The two groups of features are then concatenated for the body weight regression task. To test the model's performance over different hardware and posture settings, we create a pressure image dataset of 10 subjects and 23 postures, using a self-made pressure-sensing bedsheet. This dataset, which is made public together with this paper, together with a public dataset, are used for the validation. The results show that our model outperforms the state-of-the-art algorithms over both 2 datasets. Our research constitutes an important step toward fully automatic weight estimation in both clinical and at-home practice. Our dataset is available for research purposes at: https://***/USTCWzy/MassEstimation.

关键词： Pervasive computing Drugs Privacy Estimation Lighting Feature extraction Physiology

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Efficient Technique for image Enhancement Using Generative Adversarial Network 3rd

Efficient Technique for Image Enhancement Using Generative A...

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3rd international conference on Machine Intelligence and Smart Systems, MISS 2023

作者： Jawdekar, Anand Dixit, Manish RGPV Bhopal India MITS Gwalior India

ISBN: (纸本)9783031691140

image processing is the recent trend in the computing, images play very crucial role in the field of engineering and technology. Many real-world applications used images as computation purpose. To find the high-resolution images still very challenging, due the capacity of the various acquisition devices, it is difficult to obtain the high-resolution images, still noise also present in the images. Various techniques for the image enhancement proposes by the various researchers to remove the noise present in the image. In this work generative adversarial network-based model is use here to improve image resolution. Adversarial network is the deep learning-based model which comprises various convolution layers, ReLU functions and normalization layers. GAN consists of discriminator and generator functions which is dedicated to perform different tasks. Single image super resolution-based GAN pretrained model used here to upscaling the images. This work upscale the different medical images with good PSNR values. All simulation performed in python environment with tensor flow and keras. The model training performs of colab with virtual GPU support. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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DELFlow: Dense Efficient Learning of Scene Flow for Large-Scale Point Clouds

DELFlow: Dense Efficient Learning of Scene Flow for Large-Sc...

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international conference on computer Vision (ICCV)

作者： Chensheng Peng Guangming Wang Xian Wan Lo Xinrui Wu Chenfeng Xu Masayoshi Tomizuka Wei Zhan Hesheng Wang Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiao Tong University Mechanical Systems Control Laboratory University of California Berkeley

Point clouds are naturally sparse, while image pixels are dense. The inconsistency limits feature fusion from both modalities for point-wise scene flow estimation. Previous methods rarely predict scene flow from the entire point clouds of the scene with one-time inference due to the memory inefficiency and heavy overhead from distance calculation and sorting involved in commonly used farthest point sampling, KNN, and ball query algorithms for local feature aggregation. To mitigate these issues in scene flow learning, we regularize raw points to a dense format by storing 3D coordinates in 2D grids. Unlike the sampling operation commonly used in existing works, the dense 2D representation 1) preserves most points in the given scene, 2) brings in a significant boost of efficiency, and 3) eliminates the density gap between points and pixels, allowing us to perform effective feature fusion. We also present a novel warping projection technique to alleviate the information loss problem resulting from the fact that multiple points could be mapped into one grid during projection when computing cost volume. Sufficient experiments demonstrate the efficiency and effectiveness of our method, outperforming the prior-arts on the FlyingThings3D and KITTI dataset. Our source codes will be released on https://***/IRMVLab/DELFlow.

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image Captioning - A Deep Learning Approach Using CNN and LSTM Network

Image Captioning - A Deep Learning Approach Using CNN and LS...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Preeti Voditel Aparna Gurjar Aakansha Pandey Akrati Jain Nandita Sharma Nisha Dubey Department of Computer Applications Shri Ramdeobaba College of Engineering and Management Nagpur India

An image caption generator is a system that uses artificial intelligence and computer vision to analyze an image and generate a written description or a caption. The caption gives a brief description which accurately aligns with the content of the image. The various elements within the image are recognized and interpreted using deep learning techniques. The process of using datasets to train the model to assign English language labels or descriptors to an image is known as image tagging. Tagging helps identify an image and its description for easier search and retrieval in the future. In our research, a new model is suggested that utilizes an encoder-decoder architecture to generate appropriate and grammatically correct captions for images. This model employs methods from both image analysis and natural language processing/generation to examine and characterize pictures. The goal is to generate accurate captions that precisely convey the content of the images. We utilize a particular deep learning architecture called VGG16 in this method. VGG16 is a Convolutional Neural Network (CNN) that has demonstrated exceptional performance in image recognition. The VGG16 architecture is used as the encoding layer to extract important features from the image. After the VGG16 model processes the image, the results are fed into an LSTM (Long Short-Term Memory), a type of recurrent neural network, which then predicts or generates a textual description of the image, one word at a time. For generating accurate captions, it is trained on a set of labelled images and their corresponding captions called the Flickr8k Captions dataset. This dataset is used to provide the model with the ground truth captions. After the training phase is completed, the model creates descriptions for a group of test images. These generated captions are then compared to the actual captions present in the test dataset. The comparison is done using a metric called the BLEU score, which is a measure of the accu

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Edge-Cloud Collaboration Architecture for Efficient Web-Based Cognitive Services

Edge-Cloud Collaboration Architecture for Efficient Web-Base...

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international conference on Big Data and Smart Computing (BIGCOMP)

作者： Zhaoyan Wang In-Young Ko School of Computing Korea Advanced Institute of Science and Technology Daejeon Republic of Korea

Many web services are often latency-sensitive, have high network capabilities, and have computing resource limitations, as demonstrated by deep neural network (DNN) model-based web applications. In this work, a DNN model-based web application providing visual cognitive services is implemented and explored to address latency-sensitive problems, taking into account the broad practicability and importance of web object recognition tasks in intelligent applications and modern systems. We propose a collaborative architecture of enduser, edge server, and cloud server, employing a binary offloading strategy to reduce the upload rate of images while ensuring good detection performance, thereby reducing the response time. Detection scenario-oriented data augmentation is carried out on the dataset to improve detection accuracy. Finally, we compare the performance of the proposed approach with traditional object recognition services running entirely on the cloud. The experimental results show that the detection scenariooriented strategy significantly improves the detection accuracy of fine-trained YOLOv5 models on the PASCAL VOC 2012 dataset. Compared with the traditional cloud-based architecture, the proposed edge-cloud collaboration architecture can detect more objects and reduce response time efficiently.

关键词： Visualization Web services image edge detection Computational modeling Collaboration computer architecture Time factors

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Revolutionizing Plant Disease Management Through image processing Technology

Revolutionizing Plant Disease Management Through Image Proce...

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Computational Intelligence and Sustainable Engineering Solutions (CISES), international conference on

作者： Gaurav Kumar Ankit Kumar Manoj Singhal Kamred Udham Singh Lalan Kumar Teekam Singh Department of Computer Engineering & Applications GLA University Mathura UP India Department of Information Technology GL Bajaj Institute of Technology and Management India School of Computing Graphic Era Hill University Dehradun India Department of Master of Computer Application GL Bajaj Institute of Technology and Management India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

Agricultural productivity plays a vital role in the economy, and disease detection in plants is crucial for the agriculture sector as plant diseases are widespread. Failure to take appropriate care can lead to serious consequences for plant quality, quantity, or productivity. For instance, a disease called little leaf disease has been found in pine trees in the United States, which can severely affect plant health. Automated methods for detecting plant diseases are useful as they reduce the need for manual inspection in large crop fields, and they can identify disease symptoms as soon as they appear on plant leaves. This paper presents an algorithm for image segmentation for the automatic identification and classification of plant leaf diseases. It also discusses various disease classification methods that can be used for plant leaf disease detection. Genetic algorithms are used for image segmentation, which is a crucial aspect of disease detection in plant leaves. The paper focuses on identifying the Powdery Mildew disease in organic grape products, which can cause significant damage if not detected in time. The accurate identification and classification of diseases from leaf images are crucial for preventing significant losses in output and agricultural inputs. The proposed process can help improve the accuracy and efficiency of disease detection in plants and lead to better agricultural productivity.

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A Fast Adversarial Sample Detection Approach for Industrial Internet-of-Things applications

A Fast Adversarial Sample Detection Approach for Industrial ...

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international Workshop on Quality of Service

作者： Shigeng Zhang Yudong Li Shuxin Chen Xuan Li Jian Zhang School of Computer Science and Engineering Central South University College of Computer Science and Electronic Engineering Hunan University The Science and Technology on Parallel and Distributed Processing Laboratory (PDL) China

Adversarial attacks reveal the inherent vulnerability of deep neural networks, which face serious security issues for their security. Among them, the attack against the Deep Neural Network (DNN) application used in the Industrial Internet of Things (IIoT) is a key area in adversarial attacks. Adversarial examples generated by attackers by adding human-undetectable interference to legitimate examples may cause models to make wrong decision results, resulting in serious accidents. Many detection technologies have been proposed to mitigate the harm of adversarial examples to neural networks, among which the methods based on the difference of feature attribution between normal examples and adversarial examples show state-of-the-art detection performance, but they suffer from detection efficiency. In this work, we focus on improving the detection efficiency of the feature-attribution-based detection methods. We observe that there is still a significant difference in the feature attribution distribution of a normal image and an adversarial image even only some pixels in the image are processed, which can be verified by utilizing the Kolmogorov-Smirnov test. Based on this observation, we first adopt a variety of strategies to sample partial pixels in an image and then utilize the selected pixels to train a feature-attribution-based detector for detecting adversarial examples. Extensive experiments conducted on four datasets (MNIST, CIFAR-10, SVHN, CIFAR-100) against various attacks proved that the detection efficiency of the accelerated detection method is improved (for example, the average execution time was increased by 8.7 times on CIFAR-10) while the detection performance maintains state-of-the-art.

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