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RGB image Guided Depth Hole-Filling Using Bidirectional Attention Mechanism 1

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17th International conference on Intelligent Information Hiding and Multimedia Signal processing (IIH-MSP) / 14th International conference on Frontiers of Information Technology, Applications and Tools (FITAT)

作者： Chen, Anran Zhao, Yao Lin, Chunyu Beijing Jiaotong Univ Inst Informat Sci Beijing Key Lab Adv Informat Sci & Network Beijing 100044 Peoples R China

ISBN: (数字)9789811910531

ISBN: (纸本)9789811910531;9789811910524

Depth maps obtained by commercial depth sensors are more likely to have missing values due to the occlusion effect, low-reflection objects, etc. Filling holes of depth maps is an important way to meet the demands of depth related computer vision tasks. In this paper, we propose an efficient end-to-end network that takes RGB image and mask of the hole to jointly guide the depth hole-filling. Previous algorithms indistinguishably treat valid pixels and holes, resulting in inaccurate depth values prediction and blurred boundaries. Nevertheless, the proposed algorithm uses the bidirectional attention mechanism which takes the surrounding valid values as the auxiliary information to focus on the process of depth hole-filling from edge to center. The proposed method achieves competitive performance on existing public datasets.

关键词： Bidirectional attention mechanism Depth map Hole-filling RGB guided

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Bidirectional Iterative Diffusion Encryption Algorithm Based on Improved Chaotic systems

Bidirectional Iterative Diffusion Encryption Algorithm Based...

引用

image processing, Computer Vision and Machine Learning (ICICML), International conference on

作者： Juan Wang Boyong Gao College of Information Engineering China Jiliang University Hangzhou China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Chaotic systems are widely used in cryptography due to their traversability, unpredictability and sensitivity to initial values. However, the traditional low-dimensional chaotic system has the disadvantages of small key space, short iteration period and easy to be constructed, which affects the security of encryption. Therefore, in order to solve this problem, this paper constructs a new one-dimensional chaotic mapping, which is experimentally shown to have wider chaotic range, better unpredictability and larger key space. On this basis, this paper proposes an encryption scheme that can encrypt different images of different sizes by iteratively diffusing the pixels in both directions through chaotic sequences, and the experimental results show the security and effectiveness of the algorithm.

关键词： Computer vision Sensitivity Correlation Chaotic mapping image processing Machine learning Color Iterative algorithms Encryption

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Robust Convolutional Neural Network for image Classification with Gaussian Noise 9

Robust Convolutional Neural Network for Image Classification...

引用

9th International conference on Fuzzy systems and Data Mining, FSDM 2023

作者： Surono, Sugiyarto Arofah, Dyiyah Kresna Eni Thobirin, Aris Department of Mathematic Fast Universitas Ahmad Dahlan Yogyakarta Indonesia

ISBN: (纸本)9781643684703

A Convolutional Neural Network (CNN) is one branch of Deep Learning widely used for image classification. CNN have complex architectures and capable of achieving high accuracy and producing good results. However, CNN have limitations, especially when dealing with noisy images. image noise can decrease classification performance and increase network training time. This research was tested the robustness of two CNN methods, namely VGG16 and ResNet50, in processing images with added Gaussian noise at various levels, without any preprocessing. The dataset used is the Rice image Dataset, which consists of images of 5 different types of rice. The data is divided into three parts: 70% for training, 20% for testing, and 10% for validation. The results of this study show that as the variability in generating Gaussian noise in the images increases, the loss function value consistently increases, and the accuracy decreases. However, the increase in the loss function value and the decrease in accuracy are not significantly different among the different levels of noise variability. © 2023 The authors and IOS Press.

关键词： image classification

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Data Optimization Method for Ship Infrared image Recognition

Data Optimization Method for Ship Infrared Image Recognition

引用

2023 International conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Zhang, Haoxiang Liu, Chao Ma, Jianguan Sun, Hui University of Electronic Science and Technology of China School of Mathematical Sciences Chengdu China Beijing China

ISBN: (纸本)9798350312492

This study aims to address two challenging problems that affect the accurate and reliable recognition of ship infrared (IR) images in various scenarios: the interference of radiation highlights from specular reflections of target background sunlight and the difficulty of distinguishing ship targets from background confusion under low light conditions. We propose a data optimisation method for ship IR deep learning recognition, which uses processing tools such as median noise reduction and local histogram equalisation image enhancement to improve the performance and stability of ship IR recognition under the above unfavourable conditions. And the effectiveness of the method is verified on high fidelity mid-wave infrared (MWIR) ship simulation data using deep learning image classification algorithms such as AlexNet, VggNet and EfficientNet. To further demonstrate the effectiveness of the method, research has also adopted model feature gradient visualization technique, which indirectly verifies that our method can effectively improve the predictive reliability of ship recognition models. Overall, the data optimization method proposed in this study provides a feasible solution to address the challenges in ship IR image recognition and can provide a valuable reference for military mission-related research. © 2023 IEEE.

关键词： Infrared radiation

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Two-Archive-Based Competitive Swarm Optimizer for Constrained Multiobjective Optimization 5

Two-Archive-Based Competitive Swarm Optimizer for Constraine...

引用

5th International conference on Data-Driven Optimization of Complex systems, DOCS 2023

作者： Li, Lianghao He, Cheng Lin, Jianqing Pan, Linqiang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Wuhan China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350393521

Constrained multiobjective optimization problems (CMOPs) are prevalent in various real-world applications, presenting a formidable challenge to existing evolutionary algorithms when faced with intricate constraints. When solving CMOPs, a crucial concern is achieving a balance between convergence, diversity, and feasibility. To address these challenges, this paper proposes a two-archive-based constrained multiobjective competitive swarm optimizer. The algorithm preserves two collaborative and complementary archives to improve population convergence and feasibility. By implementing a competitive particle swarm mechanism, offspring solutions are generated by drawing upon solutions from both archives, thus capitalizing on their synergistic effect and exceptional information. An adaptive parameter is also used to adjust the bias in choosing the winner in the paired competition during the evolution. The experimental results demonstrate the effectiveness of the proposed algorithm in handling constrained multiobjective optimization problems. © 2023 IEEE.

关键词： Constraint handling

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Automatic Segmentation of Rectal Tumor on Magnetic Resonance images via a Deep Discriminative Model Consisting of Asymmetric U-Net and Convolutional Conditional Random Field based Post-processing 19

Automatic Segmentation of Rectal Tumor on Magnetic Resonance...

引用

19th International conference on Natural Computation, Fuzzy systems and Knowledge Discovery, ICNC-FSKD 2023

作者： Li, Xing Chen, Hao Ge, Sisi Xi'an University of Posts and Telecommunications School of Computer Science Xi'an710121 China Xi'an University of Posts and Telecommunications School of Humanities and Foreign Languages Xi'an710121 China

ISBN: (纸本)9798350304398

The process of manual delineating is frequently time-consuming and can result in low consistency. Our goal was to design a deep discriminative model (DDM) to mitigate these issues of magnetic resonance imaging (MRI) for rectal tumor lesion boundary delineation, and we proposed an asymmetric U-Net rectal tumor MR images segmentation algorithm based on multi-scale dilated convolutional inputs and convolutional conditional random fields (ConvCRFs). A multiscale convolutional input module was designed as a preprocessing step to enrich the extraction and input of global contextual semantic information;then, an asymmetric U-Net network combined with ConvCRFs was used to discriminatively fine-tune the segmentation results so as to enhance segmentation accuracy of tumors. To verify the feasibility of the algorithm, the experiments were conducted on a private dataset provided by a medical institution, and the experimental Dice coefficient reached 0.876 on T2WI images, 0.851 on DWI images, respectively, indicating the existence of an important clinical guidance value for rectal tumor image segmentation algorithms. © 2023 IEEE.

关键词： Tumors

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image Registration for Virtual Drone Flight Simulation

Image Registration for Virtual Drone Flight Simulation

引用

2023 IEEE International conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Koniarski, Konrad Myśliński, Andrzej Systems Research Institute Polish Academy of Sciences ul. Newelska 6 Warsaw01-447 Poland

ISBN: (纸本)9798350327816

In the past decade, drones have expanded their applications, such as surveying, search and rescue, and last-mile delivery. Drone autonomy is a growing focus in these areas, requiring test environments for both drone operators and autonomous algorithms. This work addresses the creation of a realistic test environment for accurate position perception, motion planning, and drone control. Our approach uses RGB-D sensors and image registration techniques to construct a 3D map of the real environment, enabling drone navigation simulation. We adapt the Iterative Closest Point (ICP) algorithm, incorporating feature point filtering and local optical flow to expedite 3D point processing, particularly in static scenes. A challenge in the standard ICP algorithm is selecting suitable initial points for effective matching between consecutive point clouds. Our modified approach tackles this issue, optimizing the algorithm for 3D map construction. Since our application involves RGB-D image sequences with minimal variation between successive key frames, we use a scale-space pyramid and FAST techniques to select a limited number of key points, reducing the computational load. Moreover, by employing a local optical flow-based point-matching approach, we eliminate resource-intensive operations in each ICP optimization iteration. We demonstrate the practical utility of our method through a virtual 3D drone flight simulator. © 2023 IEEE.

关键词： drone operation simulation ICP registration structure from motion

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A Parallel and Asynchronous Blob Shape Descriptor Implemented in CMOS Technology 33

A Parallel and Asynchronous Blob Shape Descriptor Implemente...

引用

33rd IEEE International conference on Microelectronics, MIEL 2023

作者： Bogacki, P. Dlugosz, M. Talaska, T. Dlugosz, R. Aptiv Services Poland Kraków30-399 Poland Agh University of Science and Technology Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications Kraków30-059 Poland Poznan University of Technology Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan60-965 Poland Bydgoszcz University of Science and Technology Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz85-796 Poland

ISBN: (纸本)9798350347760

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in Advanced Driver Assistance systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock. © 2023 IEEE.

关键词： Pixels

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Fast Convergence and Low Complexity Algorithm for Multichannel Adaptive Filtering

Fast Convergence and Low Complexity Algorithm for Multichann...

引用

Telecommunications and Intelligent systems (ICTIS), International conference on

作者： Mohamed Zerouali Mohamed Djendi Signal Processing and Image Laboratory (LATSI) University of BLIDA 1 Blida Algeria

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

In this research, we propose a new multi-channel adaptive filtering method with low complexity and optimized convergence rate. The basic strategy behind the proposed algorithm is to generalize the decorrelation technique used in FNLMS algorithm to the multi-channel case by estimating the prediction parameters for each channel to decorrelate the multi-channel input signals. In simulation we confirm the high performances of the proposed algorithm with comparison of the two dimensions multi-channel Affine Projection (multi-channel APA) and the multi-channel Normalized Least Mean Square (multi-channel NLMS) algorithms.

关键词： Adaptive systems Echo cancellers Filtering Filtering algorithms Prediction algorithms Decorrelation Complexity theory Telecommunications Intelligent systems Convergence

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Glaucoma Stage Classification Using image Empirical Mode Decomposition (IEMD) and Deep Learning from Fundus images 4th

Glaucoma Stage Classification Using Image Empirical Mode Dec...

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4th EAI International conference on Cognitive Computing and Cyber Physical systems, IC4S 2023

作者： Shankar, D. Harsha, I. Sri Madhuri, P. Shyamala Naidu, J. N. S. S. Janardhana Madhuri, P. Krishna Cherukuvada, Srikanth Department of Computer Science and Engineering Vishnu Institute of Technology Kovvada India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9783031488870

Glaucoma is an ocular pathology characterized by the gradual deterioration of neural cells in the eye, which is attributed to elevated intra ocular pressure within the retina. Glaucoma takes the second spot in terms of its prevalence as a neurodegenerative eye disease, failure to diagnose glaucoma at an early stage can lead to complete blindness. This underlying issue requires a streamlined system that uses experienced medical experts, little equipment, and less time. To categories the stages of glaucoma, a Computer-Aided Diagnosis (CAD) method is used called image Empirical Mode Decomposition (IEMD). Segmentation-based algorithms utilizing features like cup-to-disc ratio (CDR) and textural characteristics close to the optic disc region can distinguish glaucoma. To capture pixel variations for this investigation, the pre-processed fundus images are separated and transformed into a diverse range of intrinsic mode function (IMFs). The study employs deep learning-based framework for finding the optic nerve head from coloured fundus photographs, to transform the fundus images into a delimited region of interest (ROI) and utilizes multiple deep networks like RFCN and RCNN classifiers identified glaucoma stages separately. The CAD system serves as an automated tool for retinal image processing and demonstrates superior performance over the RFCN classifier, achieving an impressive accuracy of 96% with the RCNN classifier in classifying glaucoma stages. © 2024, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Computer aided diagnosis

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