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DNAT: Multi-scale Transformer with Dilated Neighborhood Attention for image Classification 15

DNAT: Multi-scale Transformer with Dilated Neighborhood Atte...

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15th IEEE International conference on Wireless communications and Signal processing, WCSP 2023

作者： Jiang, Chenfeng Zhou, Quan Mo, Zhiyi Wang, Jing Fan, Yawen Wu, Xiaofu Zhang, Suofei Kang, Bin Nanjing University of Posts & Telecommunications National Engineering Research Center of Communications and Networking China Wuzhou University Guangxi Colleges and Universities Key Laboratory of Intelligent Industry Software Wuzhou China Nanjing University of Posts & Telecommunications Department of Internet of Things China

ISBN: (纸本)9798350324662

Recently, Transformer-based models have achieved remarkable progress for image classification. In order to reduce the computational costs of multi-head self-attention (MHSA) that is widely adopted in recently-proposed visual Transformers, some window-based Transformers employ local self-attention via shrinking number of input tokens. Nevertheless, these approaches to some extent are short to model long-range dependencies. To address this problem, this paper presents an effective and powerful Transformer backbone, called Dilated Neighborhood Attention Transformer (DNAT) that has a mutli-path architecture, for image classification. DNAT is able to explore long-range interactions through dilated neighborhood attention (DNA). When dilation rate is small, DNAT degenerates to local self-attention that is computationally efficient to capture local features. As the dilation rate increases, more and more long-range token interactions are explored step-by-step. Finally, by integrating weighted features derived from various dilation rates, DNAT seamlessly harvests local-to-global context, yet with acceptable growth of model size and computational budgets. The extensive experiments on imageNet-1K and CIFAR100 datasets demonstrate the effectiveness of DNAT for image classification. © 2023 IEEE.

关键词： Dilated Neighborhood Attention image Classification Self-attention visual Transformer

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Optimal Truncated MobileNet-Based image Binarization for Pose-Based visual Servoing of Autonomous Mobile Robot 28th

Optimal Truncated MobileNet-Based Image Binarization for Pos...

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28th International conference on Technologies and Applications of Artificial Intelligence, TAAI 2023

作者： Ho, Chian C. Lin, Cian-Duo Department of Electrical Engineering National Yunlin University of Science and Technology Yunlin Taiwan

ISBN: (纸本)9789819717132

Pose-based visual servoing (PBVS) can complement the frequent drift issue of light detection and ranging (LiDAR) coordinate of LiDAR-based simultaneous localization and mapping (SLAM), navigation, and servoing technology, especially when autonomous mobile robot (AMR) works on the automatic docking alignment missions for automatic pallet engaging or automatic battery charging whose alignment precision requirement is extremely stricter. But PBVS occasionally suffers from the poor detected image quality of ARTag landmark to cause the reading drift or error. This paper proposes a light-weight deep-learning image binarization method based on optimal truncated MobileNet model to preprocess the image quality of ARTag landmarks so that PBVS can evaluate the distance and pose between the ARTag landmark and the camera sensor more accurately, promptly, and steadily, for better feasibility of PBVS on the automatic docking alignment missions. Experimental results show, against conventional image-processing-based image binarization, conventional computer-vision-based image binarization, and conventional deep-learning-based image binarization, the proposed optimal truncated MobileNet-based image binarization not only raises the accuracy and reliability of ARTag’s reading, but also apparently improves the effectiveness and efficiency of PBVS’s operation, especially under environmental conditions of shadow occlusion, image blurring, low contrast, uneven illumination, or complex background. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： visual servoing

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The Effect of SAR Speckle Removal in SAR-Optical image Fusion 30

The Effect of SAR Speckle Removal in SAR-Optical Image Fusio...

引用

30th IEEE Signal processing and communications Applications conference (SIU)

作者： Gencay, Semih Ozcan, Caner Manisa Celal Bayar Univ Bilgisayar Teknol Manisa Turkey Karabuk Univ Yazilim Muhendisligi Karabuk Turkey

ISBN: (纸本)9781665450928

Due to the imaging mechanism of Synthetic Aperture Radar (SAR) and the noise in the images, visual identification of objects in the scene is not as easy as in optical images. SAR images have limited color information and cannot reflect the spectral information of objects. Optical images, on the other hand, have rich spectral information. SAR-Optical image fusion is an important area of study so that SAR data can be easily evaluated by anyone, but it is difficult to find a matching SAR and optical image of the same scene. In order to overcome this difficulty, Sentinel-1 and Sentinel-2 datasets have been published and image fusion studies have been carried out with various methods. However, it has been observed that the effect of SAR noise removal before merging on image fusion methods has not been investigated. In the studies conducted to investigate this effect, five different fusion algorithms used in the literature were tested with twenty different image groups using different noise reduction ratios. The success of the fusion results obtained was compared with five different metrics that are widely used in the literature. The images and metric results obtained as a result of the tests showed that the removal of speckle noise in the SAR data has a positive effect on the fusion results.

关键词： optical image SAR image image fusion remote sensing

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Fundus image Based DR Detection Using image processing

Fundus Image Based DR Detection Using Image Processing

引用

2023 International conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023

作者： Singh, Tarun Suresh, A. SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350334357

Almost any field of application that aims to address societal issues now relies heavily on image processing, thanks to recent breakthroughs in the field. Medical image processing is among the most widely appealing and vital disciplines of research in recent times, owing to its enormous potential as well as the vast volume of underlying information that needs to be recovered using systematic image processing techniques. However, this research work, has taken processing of retinal images as its prime issue of concern to predict and aid early detection of retinal disorders. This arises from the motivation that certain symptoms appearing in a particular part of the human body may indicate onset of certain diseases in which diabetes could be quoted as the best example. Retina of the eye is an essential component from an image processing aspect, as it reveals several disorders associated with it at an earlier stage, if properly processed and analysed. Some well-known retinal disorders include diabetic retinopathy, retinal tear and detachment, cataract, retinitis pigmentosa, age related macular disorders (AMD) etc. if these conditions are left undetected, they may result in increasing disorders affecting other parts of the human body. In view of the above issues and prospects of retinal image analysis, this thesis has taken retina as the prime subject of study for early detection of retinal disorders. © 2023 IEEE.

关键词： Eye protection

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Accelerating Traditional Object Detection Methods on Sophisticated Embedded Systems 4th

Accelerating Traditional Object Detection Methods on Sophist...

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4th International conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Sehairi, Kamal Bouhafs, Abdelkader Boucherit, Ibtissam Mama Bouwmans, Thierry Chouireb, Fatima Ecole Normale Super Laghouat Dept Phys Lab Appl Sci & Didact Laghouat Algeria Univ Amar Telidji Laghouat Dept Elect Telecommun Signals & Syst Lab Laghouat Algeria Univ La Rochelle Dept Comp Sci Lab Math Image & Applicat La Rochelle France

ISBN: (纸本)9783031821523;9783031821530

Identifying and locating objects in images and videos, including elements like traffic signs, vehicles, buildings, and people, constitutes a fundamental and demanding task in computer vision, known as object detection. Due to the higher computing complexity of this technique and the large amount of data carried by the video signal, it is nearly impossible for ordinary general-purpose processors GPPs or CPUs to run these techniques in real-time, especially for embedded systems applications. Therefore, special hardware that can acquire, control, or execute in parallel is required. These specialized hardware systems include Digital Signal Processors DSPs, Field Programmable Gate Arrays FPGAs, visual processing Units VPUs, Tensor processing Units TPUs, Neural processing Units NPUs or Graphics processing Units GPUs. This work presents the benefits of accelerating traditional object detection methods on a high-end embedded system, the Jetson Nano Developer Kit. This single computer board is equipped with the Tegra K1 System on Chip SoC, which is composed of a quad-core ARM A15 and 192 cores of Kepler-embedded GPU. Computing acceleration was ensured via the use of the CUDA OpenCV library for both the Histogram of Oriented Gradients HOG and the Haar Cascade Classifier. For VGA resolution, results reveal that the GPU implementation on this embedded system is 1.4x faster than the CPU for the HOG method and 2x for the Haar Cascade Classifier method.

关键词： Object detection Machine Learning Haar Cascade HOG Local feature descriptors GPU implementation OpenCV CUDA SoC Tegra X1 Jetson Nano

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Split-Conv: A Resource-efficient Compression Method for image Quality Assessment Models

Split-Conv: A Resource-efficient Compression Method for Imag...

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2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Zhang, Yiming Min, Xiongkuo Zhai, Guangtao Shanghai Jiao Tong University Institute of Image Communication and Network Engineering China

ISBN: (纸本)9798350359855

Blind image Quality Assessment (BIQA) models based on deep neural networks (DNNs) have achieved state-of-The-Art performance recently. However, the heavyweight architecture makes them hard to deploy on resource-constrained devices. Filter pruning is one of the most effective ways to compress the DNN model. However, most pruning methods need complete retraining after pruning which is too resource-consuming for IQA models with complex training processes. In this paper, we propose a resource-efficient structural pruning method for IQA models called Split-Conv, where the model only needs to be retrained on small IQA databases. Specifically, we split convolutional layers into sub-convolutional kernels and decorators, which can measure the importance of convolutional channels more precisely, thus reducing the requirement for retraining conditions. The experiments on several IQA models demonstrate the effectiveness of our IQA model pruning approach. For instance, DBCNN, StairIQA, and HyperIQA's performances can be kept at the baseline level when 50% parameters are pruned with the model being retrained on the authentically distort IQA database containing only 586 images, and the performances of StairIQA and HyperIQA on KonIQ-10k database are still acceptable after 80% of the parameters have been pruned. Besides, due to Split-Conv's capability to identify the important, useless, and harmful filters, the performances of some BIQA models can even be boosted after model compression with a proper pruning ratio. © 2023 IEEE.

关键词： Database systems

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Bird's-Eye-View-Based LiDAR Point Cloud Coding for Machines

Bird's-Eye-View-Based LiDAR Point Cloud Coding for Machines

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2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Gao, Xiang Zhu, Zhiwei Yu, Lu Zhejiang University Hangzhou China Zhejiang University College of Information Science and Electronic Engineering China Zhejiang Provincial Key Laboratory of Information Processing Hangzhou310027 China

ISBN: (纸本)9798350359855

Recently, there has been a growing interest in image/video coding tailored for intelligent analysis tasks, such as image/Video Coding for Machines (ICM/VCM). These approaches have shown remarkable results when compared to human perception-based coding methods. However, point cloud coding methods for machine intelligence have not been extensively studied. Inspired by current LiDAR point cloud intelligent analysis methods which convert point cloud into bird's-eye-view (BEV) perspective, we propose an end-To-end learnt point cloud coding framework for 3D machine intelligent tasks with BEV representation, named PC4M. Specifically, the PC4M system consists of a LiDAR encoder, a learnt BEV feature codec and a BEV region proposal network with a task-specific head. To achieve better rate-distortion performance for analysis tasks, we propose an efficient Res-NeXt fusion block with powerful multi-scale modeling ability to compress sparse BEV features, and design a long-distance adaptive attention module by using VanAtten block. Experimental results demonstrate that our method outperforms the state-of-The-Art MPEG standard Geometry-based Point Cloud Coding (G-PCC) on the object detection and BEV map segmentation by 83.12% and 85.32% of BD-rate gain on nuScenes, respectively. To the best of our knowledge, this is the first end-To-end learnt task-oriented point cloud codec. © 2023 IEEE.

关键词： Motion Picture Experts Group standards

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Color image Enhancement Algorithm on the Basis of Wavelet Transform and Retinex Theory 10th

Color Image Enhancement Algorithm on the Basis of Wavelet Tr...

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10th International conference on communications, Signal processing, and Systems

作者： Xia, Xinzhe Yang, Jie Li, Jinpeng Zhen, Jiaqi Heilongjiang Univ Coll Elect Engn Harbin 150080 Peoples R China

ISBN: (纸本)9789811903908;9789811903892

For enhance the contrast of low-illuminance images, reduce image noise, and maintain image edge detail information, a color image enhancement algorithm on the basis wavelet transform and Retinex theory is proposed in this paper. First, convert the low-illuminance color image to be enhanced from RGB space to HSV color space, perform wavelet transformation on the brightness component V, and separate several high-frequency and low-frequency children;then use improved single-scale Retinex algorithm to enhance the low-frequency offspring, reduce the influence of external light factors on the image and retain the edge texture of the image;the high-frequency children are subjected to blur enhancement processing to achieve image denoising and enhancement. The saturation component S is enhanced by segmented exponential transformation to make the image color more suitable for human visual habits;finally, the processed image is changed back to a color RGB image. The experimental results show that the edge texture of the image processed by the algorithm are maintained well, the contrast has been significantly improved, color distortion is avoided, and it has a good visual effect.

关键词： Low-light image image enhancement Wavelet transform Bilateral filtering Retinex algorithm Fuzzy enhancement

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Using Convolutional Neural Networks for image Recognition in IoT Applications

Using Convolutional Neural Networks for Image Recognition in...

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2025 International conference on Pervasive Computational Technologies, ICPCT 2025

作者： Rajababu, Durgam Veerraju, Sampenga Kumar, K. Suresh Gupta, Sonali Thurai Raaj, V.B. Joshi, Rahul Sr University School of Engineering Department of Eee Telangana Warangal India Dadi Institute of Engineering and Technology Department of Electronics and Communications Engineering Andhra Pradesh Anakapalli Visakhapatnam531002 India Panimalar Engineering College Mba Department Varadarajapuram Poonamallee Chennai600123 India J C Bose University of Science and Technology Ymca Faridabad India Madanapalle Institute of Technology & Science Electrical and Electronics Engineering Andhra Pradesh Madanapalle India Manav Rachna International Institute of Research and Studies Department of Journalism & Mass Communication SMeH Haryana India

ISBN: (纸本)9798331508685

Convolutional Neural Networks (CNNs) have grown into a powerful picture identification tool. They are an important part of making Internet of Things (IoT) apps. In this regard, CNNs make it possible to analyse visual data gathered from networked devices, like cameras and sensors, effectively and accurately. With an emphasis on its potential for real-time applications for example smart surveillance, healthcare monitoring, autonomous cars, and industrial automation, this study investigates the incorporation of CNN-based image identification into IoT contexts. We tackle important issues such as the requirement for low-latency processing, energy economy, and computing limitations on edge devices. In order to maximise CNN performance in the resource-constrained IoT ecosystem, strategies like model compression, edge computing, and distributed architectures are covered. The ability to interpret large volumes of visual data is improved by the integration of CNNs with IoT, providing creative solutions for automation and intelligent decision-making across numerous industries. © 2025 IEEE.

关键词： Convolution

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Towards Efficient Learned image Coding for Machines via Saliency-Driven Rate Allocation

Towards Efficient Learned Image Coding for Machines via Sali...

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2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Zhang, Zixiang Yu, Li Zhang, Qiang Mei, Junjun Guan, Tao Huazhong University of Science and Technology Wuhan China ZTE Corporation Nanjing China

ISBN: (纸本)9798350359855

Recently, efficient image coding for machines method is widely required under machine vision task-oriented coding scenarios. To achieve higher task accuracy under a certain bitrate constraint, existing solutions take advantage of cutting-edge learned image compression methods and cascade the compression and specific task model straightly to carry out the joint optimization, which lacks adaptability and flexibility to handling machine vision saliency and non-saliency regions, thus obtaining inferior performance. In this paper, we propose a learned image compression model with a saliency-driven rate allocation strategy for machine vision. Specifically, an auxiliary network is introduced to provide reference latent first which contains full image reconstruction required information. And then, a machine vision saliency detector is applied to generate the adjustment factor to achieve rate allocation in the latent domain. In addition, a spatial dynamic convolution module is designed to facilitate the compressor adaptively processing the different spatial areas. Finally, the multi-distortion constraint is utilized to achieve an end-To-end optimization. Experimental results show that the proposed method achieves-77.48% coding gains over VVC using Mask R-CNN as the back-end task network on the Cityscapes dataset. Moreover, the reconstructed image maintains considerable visual fidelity, making it possible both for human and machine consumption. © 2023 IEEE.

关键词： Computer vision

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