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Application on the Loop of Multimodal image Fusion: Trends on Deep-Learning Based Approaches

Application on the Loop of Multimodal Image Fusion: Trends o...

引用

pattern recognition Systems (ICPRS), international conference on

作者： Gisel Bastidas-Guacho Patricio Moreno-Vallejo Boris Vintimilla Angel D. Sappa ESPOL Polytechnic University Ecuador ESPOCH Polytechnic University Ecuador Computer Vision Center Spain

Multimodal image fusion allows the combination of information from different modalities, which is useful for tasks such as object detection, edge detection, and tracking, to name a few. Using the fused representation for applications results in better task performance. there are several image fusion approaches, which have been summarized in surveys. However, the existing surveys focus on image fusion approaches where the application on the loop of multimodal image fusion is not considered. On the contrary, this study summarizes deep learning-based multimodal image fusion for computer vision (e.g., object detection) and image processing applications (e.g., semantic segmentation), that is, approaches where the application module leverages the multimodal fusion process to enhance the final result. Firstly, we introduce image fusion and the existing general frameworks for image fusion tasks such as multifocus, multiexposure and multimodal. then, we describe the multimodal image fusion approaches. Next, we review the state-of-the-art deep learning multimodal image fusion approaches for vision applications. Finally, we conclude our survey with the trends of task-driven multimodal image fusion.

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Explaining Relation Classification Models with Semantic Extents 1

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4th international conference on Deep Learning theory and Applications (DeLTA)

作者： Kloeser, Lars Buesgen, Andre Kohl, Philipp Kraft, Bodo Zuendorf, Albert Aachen Univ Appl Sci D-52066 Aachen Germany Univ Kassel D-34109 Kassel Germany

ISBN: (数字)9783031390593

ISBN: (纸本)9783031390586;9783031390593

In recent years, the development of large pretrained language models, such as BERT and GPT, significantly improved information extraction systems on various tasks, including relation classification. State-of-the-art systems are highly accurate on scientific benchmarks. A lack of explainability is currently a complicating factor in many real-world applications. Comprehensible systems are necessary to prevent biased, counterintuitive, or harmful decisions. We introduce semantic extents, a concept to analyze decision patterns for the relation classification task. Semantic extents are the most influential parts of texts concerning classification decisions. Our definition allows similar procedures to determine semantic extents for humans and models. We provide an annotation tool and a software framework to determine semantic extents for humans and models conveniently and reproducibly. Comparing both reveals that models tend to learn shortcut patterns from data. these patterns are hard to detect with current interpretability methods, such as input reductions. Our approach can help detect and eliminate spurious decision patterns during model development. Semantic extents can increase the reliability and security of natural language processing systems. Semantic extents are an essential step in enabling applications in critical areas like healthcare or finance. Moreover, our work opens new research directions for developing methods to explain deep learning models.

关键词： Relation classification Natural language processing Natural language understanding Explainable artificial intelligence Trustworthy artificial intelligence Information extraction

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Brain tumor image segmentation method based on M-Unet network 4

Brain tumor image segmentation method based on M-Unet networ...

引用

4th international conference on pattern recognition and Artificial Intelligence, PRAI 2021

作者： Ru, Qingjun Chen, Guangzhu Tang, Zaizuo College of Computer Science and Cyber Security Chengdu University of Technology Industrial Internet Center Chengdu China

ISBN: (纸本)9781665413220

Brain tumor image segmentation is a challenging problem in medical image processing. the purpose is to obtain tumor location, size, structure and other information through intelligent processing of medical image data. Accurate brain tumor segmentation algorithm plays an important role in the early diagnosis and treatment planning of tumors. therefore, this paper proposes a brain tumor segmentation algorithm based on M-Unet network(Multi-scale Unet). this method adds multi-scale convolution modules to the feature fusion part of the original Unet network, so that the network can better extract the multiscale features of the image. At the same time, the cosine annealing learning rate attenuation strategy is used in the training process, so that the network can jump out of the local optimal solution to obtain the best network weight. the results show that the M - Unet network achieves high segment results in MRI image dataset, and the Jaccard coefficient and Dice coefficient of the algorithm reach 0.783 and 0.873 respectively;Compared with the original Unet network, the Jaccard coefficient and Dice coefficient are increased by 2.5% and 4.3% respectively, which shows that M-Unet network can effectively improve the segmentation performance of brain tumor image. © 2021 IEEE.

关键词： Magnetic resonance imaging

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Illumination patterns and Hue control using Hand gesture recognition system

Illumination Patterns and Hue control using Hand gesture rec...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： A Saravanan H S Niranjana Murthy Ananth R S Lakshan S Patil Ashwin A Aditi H Department of Electronics and Instrumentation Engg. M S Ramaiah Institute of Technology Bengaluru India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

In today’s world, switches and IR remotes are frequently utilized to operate electrical appliances and gadgets. Now it is time to set up a new control system to replace the old one. the primary objective of this new technology is to control devices through finger movements and gestures. Home automation refers to the automatic control and operation of household appliances. the proposed work includes a quick algorithm for detecting a collection of hand movements from images. For this, an algorithm-based software tool is used. It provides real-time image processing by capturing an image using a camera. the obtained image of the hand is processed by the software and compared to previously loaded gestures. If it matches, data is passed to the microcontroller circuitry, which sends signals to the devices in the same way that the remote control does. this approach may also be the most suitable for those who are physically challenged or unable to reach switches. the common hardware modules include a camera, a PIC microcontroller, a fan, a light, and a power supply. this hardware module communicates with simulation software via the USB (Universal Serial Bus) to the serial converter bus. Keywords—Digital image processing, computer vision, OpenCV, Hand gestures, Microcontroller.

关键词： Hands computer vision Microcontrollers image processing Software algorithms Universal Serial Bus Cameras Hardware Software tools Remote control

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Facial Features based recognition System using LBPH and AdaBoost

Facial Features based Recognition System using LBPH and AdaB...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： Mayank Srivastava Kalash Khatiyan Pratibha Dixit Department of Computer Engineering & Applications GLA University Mathura UP India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Face recognition stands as a pivotal domain within computer vision, boasting myriad applications in security, identification, and biometrics. this research study proposes a face recognition system using image processing techniques with the combination of two algorithms, Local Binary pattern Histogram (LBPH) and AdaBoost. the LBPH algorithm was used to extract features from the images and create a feature vector for each face. the AdaBoost algorithm was then used to train a classifier on the feature vectors and classify new faces. After testing on the Face recognition Database, the results reveal that the fusion of LBPH and AdaBoost algorithms achieves an impressive accuracy rate of 96.15% which is better as compared to other similar algorithms. the proposed algorithm is implemented in Python to recognize faces in a dataset of images.

关键词： Histograms Accuracy image recognition Law enforcement Face recognition Feature extraction Vectors Real-time systems Classification algorithms Testing

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A Hybrid Tree Point Cloud Compression Method for Complex City Scenes 4

A Hybrid Tree Point Cloud Compression Method for Complex Cit...

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4th international conference on Natural Language processing, ICNLP 2022

作者： Da, Ai Yurong, Yang Xiaoyang, Zhang Ying, Liu Xi'an University of Posts and Telecommunications School of Communication and Information Engineering Xi'an710121 China Xi'an University of Posts and Telecommunications Center for Image and Information Processing Xi'an710121 China

ISBN: (数字)9781665495448

ISBN: (纸本)9781665495448

Point cloud compression has become an important research issue of computer vision. A lossy point cloud compression method with hybrid tree is proposed to cope with the problems of feature loss and high bit-rates of complex 3D surfaces in existing techniques with tree structure. In complex city scenes, due to surface differences between various regions of the LiDAR point cloud, a partitioning module based on Euclidean clustering is added obtain the different partitions of point cloud, and then the number of partitions is reduced by Gaussian curvature and mean curvature. In the hybrid tree compression module, the minimum partition bounding box is employed to select the tree structure and hyper-parameters, and obtain the optimal bit rates allocation scheme. Experimental results reveal that the proposed method can effectively reduce the coding redundancy of point cloud and have better rate distortion performance while ensuring compression quality in complex city scenes. © 2022 IEEE.

关键词： Optical radar

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MSTCNet: Parallel Multi-Scale Network for Medical image Segmentation 23

MSTCNet: Parallel Multi-Scale Network for Medical Image Segm...

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2023 4th international conference on Control, Robotics and Intelligent System, CCRIS 2023

作者： Xiao, Hongzhao Tang, Jie Song, Hongjian Hu, Juncheng South China University of Technology Guangdong Guangzhou China Guilin Medical University Guangxi Guilin China

ISBN: (纸本)9798400708190

Transformer-like architectures, which are the model of choice in the field of natural language processing, have recently been adapted to computer vision (CV) fields and demonstrated remarkable effectiveness on various CV tasks. However, current transformer-based methods require large-scale datasets, which are usually unavailable in medical image analysis, thus resulting in adverse achievement. To this end, we propose a novel segmentation model, named MSTCNet, which constructs a parallel multi-scale transformer (MST) encoder in U-Net. In MST, we devise multi-scale patch partition and multi-scale mix attention to perform multi-scale long-range dependencies modeling. the U-Net encoder paralleled with MST alleviates the burden of large-scale datasets and extract local features supplementarily. We also propose Feature Fusion Head to narrow the gap between convolutional features and transformer features. Sufficient experiments demonstrate that our MSTCNet outperforms state-of-the-art methods on GlaS and ISIC18 datasets and is more suitable for medical image segmentation with small-scale datasets. © 2023 ACM.

关键词： Signal encoding

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ISP4ML: the Role of image Signal processing in Efficient Deep Learning vision Systems 25

ISP4ML: The Role of Image Signal Processing in Efficient Dee...

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25th international conference on pattern recognition (ICPR)

作者： Hansen, Patrick Vilkin, Alexey Khrustalev, Yury Imber, James Talagala, Dumidu Hanwell, David Mattina, Matthew Whatmough, Paul N. Arm Res San Jose CA 95134 USA Twitter San Francisco CA USA Imaginat Technol Kings Langley Herts England

ISBN: (纸本)9781728188089

Convolutional neural networks (CNNs) are now widely deployed in a variety of computer vision (CV) systems. these systems typically include an image signal processor (ISP), even though the ISP is traditionally designed to produce images that look appealing to humans. In CV systems, it is not clear what the role of the ISP is, or if it is even required at all for accurate prediction. In this work, we investigate the efficacy of the ISP in CNN classification tasks and outline the system-level trade-offs between prediction accuracy and computational cost. To do so, we build software models of a configurable ISP and an imaging sensor to train CNNs on imageNet with a range of different ISP settings and functionality. Results on imageNet show that an ISP improves accuracy by 4.6%-12.2% on MobileNets. Results from ResNets demonstrate these trends also generalize to deeper networks. An ablation study of the various processing stages in a typical ISP reveals that the tone mapper is the most significant stage when operating on high dynamic range (HDR) images, by providing 5.8% average accuracy improvement alone. We also show that the ISP increases the generalization of CNNs across two different image sensors by a significant 17.5%. Overall, the ISP benefits system efficiency because the memory and computational costs of the ISP is minimal compared to the cost of using a larger CNN to achieve the same accuracy.

关键词： Convolutional neural networks

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Visual Question Answering Model Based on CAM and GCN 5

Visual Question Answering Model Based on CAM and GCN

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5th international conference on Artificial Intelligence and pattern recognition, AIPR 2022

作者： Wen, Ping Li, Ma Zhen, Zhang Ze, Wang Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing China

ISBN: (纸本)9781450396899

Visual Question Answering (VQA) is a challenging problem that needs to combine concepts from computer vision and natural language processing. In recent years, researchers have proposed many methods for this typical multimodal problem. Most existing methods use a two-stream strategy, i.e., compute image and question features separately and fuse them using various techniques, rarely relying on higher-level image representations, to capture semantic and spatial relationships. Based on the above problems, a visual question answering model (CAM-GCN) based on Cooperative Attention Mechanism (CAM) and Graph Convolutional Network (GCN) is proposed. First, the graph learning module and the concept of graph convolution are combined to learn the problem-specific graph representation of the input image and capture the interactive image representation of the specific problem. image region dependence, and finally, continue to optimize the fused features through feature enhancement. the test results on the VQA v2 dataset show that the CAM-GCN model achieves better classification results than the current representative models. © 2022 ACM.

关键词： Semantics

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Convolutional Neural Network based Leaf Disease Detection 4

Convolutional Neural Network based Leaf Disease Detection

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4th international conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Jancy, P. Leela Sudarson, S. Manthira Perakshi Iwin thanakumar Joseph, S. Sudha, M. Swara Raga Priya, K. Sri Department of Information Technology Sri Sai Ram Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur District Andhra Pradesh India

ISBN: (纸本)9781665401180

India is known as an agricultural country where about 41.49% of the labor force depends on agri culture. A leaf disorder in plants is in an infinitesimal life form, and thus it cannot be ascertained by typical human eyes. Leaves have special scale to differentiate the disorders because the image data of the leaves change when the leaf identifies the disease. the image processing methods can also applicable in farming zone. the experimentation work presents a categorization of leaf disorders such as Bacterial Blight, Cercosporin, Alternaria Alternate leaf patch, and soon. In this paper, RGB color space transforms into Hue Saturation Intensity tone. In division step, k-means congregation is used to choose the deformity area, and it uproots the characteristics by using Grey Level Co-occurrence Matrix and Local binary pattern. Proceeding to the feature's extraction, the average decontaminate is used forgetting still feature outcome. Classification is carried out by algorithms like VGG19 and NAS Net and the performances of the models are analyzed. © 2022 IEEE

关键词： Convolutional neural networks

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