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Contact classification for human-robot interaction with densely connected convolutional neural network and convolutional block attention module

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signal image AND VIDEO processing 2024年第5期18卷 4363-4374页

作者： Aydin, Ahmet Avaroglu, Erdinc Gaziantep Islam Sci & Technol Univ Vocat Sch Tech Sci TR-27070 Gaziantep Turkiye Mersin Univ Dept Comp Engn TR-33100 Mersin Turkiye

Human-robot interaction (HRI) is a challenging topic to perform various tasks in many repetitive and dangerous tasks. However, humans not only share a workspace with robots, they also use them as helpful assistants. In this study, a solution is sought for the problem of recognizing human hand touch, which is equipped with large-area tactile sensors on the surface of a robot, which comes into contact with these sensors. The main purpose of the study is to distinguish between the contacts of human-robot interaction. Thanks to this separation, it is possible to trigger robot movements, activate them, or make the physical interaction of living-inanimate beings safe. Dense connected Convolutional Block Attention Module based network is proposed for classification of human hand touch. A number of experimental studies are carried out to verify the classification efficiency of the proposed Convolutional neural Network (CNN) model. The obtained results are extensively compared with the commonly used pre-trained CNN models. From the results of this study, it is seen that the proposed CNN model provides more satisfactory results than other methods with an accuracy rate of 98.20%.

关键词： Human-robot interaction Tactile sensing Contact classification Deep learning

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Spatial Adaptive Filter Network With Scale-Sharing Convolution for image Demoireing

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IEEE signal processing LETTERS 2024年 31卷 2495-2499页

作者： Xu, Yong Wei, Zhiyu Xu, Ruotao Zhou, Zihan Yu, Zhuliang South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Guangdong Prov Key Lab Multimodal Big Data Intelli Guangzhou 519085 Peoples R China PaZhou Lab Guangzhou Guangzhou 510335 Peoples R China Inst Super Robot Huangpu Guangzhou 510555 Peoples R China South China Agr Univ Sch Coll Math & Informat Guangzhou Peoples R China South China Univ Technol Shien Ming Wu Sch Intelligent Engn Guangzhou 511442 Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Peoples R China

Removing moire patterns is a challenging task as it is a spatially varying degradation that varies in shape, color and scale. Existing image restoration models often rely on static convolutional neural networks (CNNs)-based architectures, and hence potentially suboptimal for addressing the diverse manifestations of moire patterns across different images and spatial positions. To this end, we propose a spatially adaptive neural network for image demoireing. This network introduces a dual-branch filter prediction module engineered to predict pixel-wise adaptive filters that can process moire patterns of varying orientations and color-shift issues. To further tackle the challenge presented by scale variability, a scale-sharing convolution module is proposed, utilizing pixel-wise adaptive filters with multiple dilations to handle moire patterns of different sizes but similar shapes effectively. Upon extensive evaluations of three benchmark datasets, our model consistently outperforms existing methods, yielding a PSNR improvement of over 0.37dB across all evaluated datasets and providing additional benefits in terms of model size.

关键词： Convolution Feature extraction Adaptive filters image color analysis Shape Tensors Semantics Moir & eacute pattern removal dynamic filtering spatial-varying processing

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NL-CS Net: Deep Learning with Non-local Prior for image Compressive Sensing

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CIRCUITS SYSTEMS AND signal processing 2024年第8期43卷 5191-5210页

作者： Bian, Shuai Qi, Shouliang Li, Chen Yao, Yudong Teng, Yueyang Northeastern Univ Coll Med & Biol Informat Engn Shenyang 110169 Peoples R China Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA Minist Educ Key Lab Intelligent Comp Med Shenyang 110169 Peoples R China

Deep learning has been applied to compressive sensing (CS) of images successfully in recent years. However, existing network-based methods are often trained as the black box, in which the lack of prior knowledge is often the bottleneck for further performance improvement. To overcome this drawback, this paper proposes a novel CS method using non-local prior which combines the interpretability of the traditional optimization methods with the speed of network-based methods, called NL-CS Net. We unroll each phase from iteration of the augmented Lagrangian method solving non-local and sparse regularized optimization problem by a network. NL-CS Net is composed of the up-sampling module and the recovery module. In the up-sampling module, we use learnable up-sampling matrix instead of a predefined one. In the recovery module, patch-wise non-local network is employed to capture long-range feature correspondences. Important parameters involved (e.g. sampling matrix, nonlinear transforms, shrinkage thresholds, step size, etc.) are learned end-to-end, rather than hand-crafted. Furthermore, to facilitate practical implementation, orthogonal and binary constraints on the sampling matrix are simultaneously adopted. Extensive experiments on natural images and magnetic resonance imaging demonstrate that the proposed method outperforms the state-of-the-art methods while maintaining great interpretability and speed.

关键词： Compressive sensing image reconstruction neural network Non-local prior

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A synergistic CNN-transformer network with pooling attention fusion for hyperspectral image classification

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DIGITAL signal processing 2025年 160卷

作者： Chen, Peng He, Wenxuan Qian, Feng Shi, Guangyao Yan, Jingwen Shantou Univ Coll Engn Shantou 515063 Guangdong Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Hubei Peoples R China Chinese Acad Sci Changchun Inst Opt & Fine Mech & Phys Changchun 130033 Jilin Peoples R China Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing 400065 Peoples R China Guangzhou Inst Sci & Technol Sch Intelligent Mfg & Elect Engn Guangzhou 510000 Guangdong Peoples R China

In the hyperspectral image (HSI) classification task, each pixel is categorized into a specific land-cover category or material. Convolutional neural networks (CNNs) and transformers have been widely used to extract local and non-local features in HSI classification. Recent works have utilized a multi-scale vision transformer (ViT) to enhance spectral feature capture and yield promising results. However, most existing methods still face challenges in the effective joint use of spatial-spectral information and in preserving information across layers during the propagation process. To address these issues, we propose a synergistic CNN-Transformer network with pooling attention fusion for HSI classification, which collaboratively utilizes CNNs and ViT to process spatial and spectral features separately. Specifically, we propose a Twin-Branch Feature Extraction (TBFE) module, which employs 3D and 2D convolution in parallel to comprehensively extract spectral and spatial features from HSI. A hybrid pooling attention (HPA) module is designed to aggregate spatial attention. Moreover, a cascade transformer encoder is employed for global spectral feature extraction, and a simple yet efficient cross-layer feature fusion (CFF) module is designed to reduce the loss of crucial information in the previous network layers. Extensive experiments are conducted on several representative datasets to demonstrate the superior performance of our proposed method compared to the state-of-the-art works.

关键词： Hyperspectral image classification Remote sensing Convolutional neural network Transformer Cross-dimension interaction

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TA2P: TASK-AWARE ADAPTIVE PRUNING METHOD FOR image CLASSIFICATION ON EDGE DEVICES 49

TA2P: TASK-AWARE ADAPTIVE PRUNING METHOD FOR IMAGE CLASSIFIC...

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49th IEEE International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Wang, Yanting Li, Feng Zhang, Han Northwestern Polytech Univ Sch Software Xian Peoples R China Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen Peoples R China China Unicom Res Inst Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Convolutional neural networks (CNNs) have been extensively used in image classification. However, it is still challenging to directly deploy them on edge devices because of high computation requirements. To solve this problem, many pruning schemes for neural networks have been proposed. However, these methods ignore the fact that most edge devices only need to handle some instead of the whole classes. If the classification of several classes is defined as one task, then tasklevel information could be utilized to further lighten CNNs. To this end, we propose such a task-aware adaptive model pruning method in this paper. Specifically, we first employ control gates to learn weights of each convolutional channel of each class, and then merge class-level control gates through linear combinations to obtain the pruned model for an adaptive task on edge devices. Experiments demonstrate that our proposed method could significantly reduce the number of parameters up to 33.3% and 13.5% on CIFAR-10 and CIFAR-100, while achieving almost the same accuracy compared with baseline methods.

关键词： Model compression channel pruning edge computing task aware

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Advocating Pixel-Level Authentication of Camera-Captured images

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IEEE ACCESS 2024年 12卷 45839-45846页

作者： Punnappurath, Abhijith Zhao, Luxi Abdelhamed, Abdelrahman Brown, Michael S. Samsung AI Ctr Toronto Toronto ON M5G 1L7 Canada Google Res Toronto ON M5H 2G4 Canada

The authenticity of digital images posted online and shared on social media is often questioned due to the ability of photo-editing software to alter image content and generative AI methods that can produce visually compelling deepfakes. Only images directly produced by cameras are deemed unaltered and beyond suspicion, as they have not undergone any modifications. However, there is a recent trend among camera manufacturers to integrate AI-based modules into the dedicated onboard hardware, specifically the image signal processor (ISP), responsible for processing the captured sensor image into the final saved image for users. Many of these AI modules utilize perceptual or generative losses during training, which can "hallucinate" image content. While this hallucinated content often manifests as small details and textures, there are instances where these regions unintentionally impact the interpretation of the entire image. This paper aims to bring attention to this issue and advocate for in-camera strategies to validate the authenticity of camera-captured images at a pixel level. We propose the creation of an "authenticity" mask that could be stored as additional metadata with each image. This information can be extracted and overlaid on the image to easily identify the hallucinated regions. Considering the widespread implications of image authenticity (e.g., in courtroom evidence, news broadcasts, and other media forms), we anticipate that authentication metadata will become a standard practice for any ISP utilizing AI.

关键词： Authenticity AI camera metadata neural ISP generative models adversarial loss perceptual loss digital forensics

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SA-BiSeNet: Swap attention bilateral segmentation network for real-time inland waterways segmentation

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IET image processing 2023年第1期17卷 166-177页

作者： Zhang, W. B. Wu, C. Y. Bao, Z. S. Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China

The technology for autonomous navigation on inland waterways is worth investigating, and navigable water surface segmentation is a key part of this technology. Semantic segmentation methods based on deep learning are able to distinguish between water surface areas and non-water surface areas. However, existing semantic segmentation methods cannot meet the requirements of the water surface segmentation task in terms of both segmentation precision and real-time performance. In this study, a Swap Attention Bilateral Segmentation Network (SA-BiSeNet) is proposed to improve segmentation performance while ensuring model inference speed by better fusing the two features of the dual-branch down-sampling network using the attention mechanism. Specifically, an innovative Swap Attention Module is designed to model the dependency between the features of the spatial detail branch and the features of the semantic branches, thus expanding the receptive fields of the spatial detail and semantic branches to each other's global contexts. This design can effectively fuse features and thus enhance feature representation. Experiments were conducted on the inland waterway dataset USVInland to verify the performance of SA-BiSeNet in terms of segmentation precision and inference speed, and SA-BiSeNet achieved 93.65% Mean IoU and maintained the same level of fps as the baseline.

关键词： image segmentation feature extraction semantic branches water surface segmentation task inland waterway dataset USVInland real-time inland waterways segmentation neural nets inference speed water surface areas nonwater surface areas segmentation performance SA-BiSeNet Swap attention bilateral segmentation network Optical, image and video signal processing Computer vision and image processing techniques innovative Swap Attention Module real-time performance object detection deep learning (artificial intelligence) navigable water surface segmentation semantic segmentation methods Swap Attention Bilateral Segmentation Network segmentation precision geophysical image processing

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A comparative analysis of image harmonization techniques in mitigating differences in CT acquisition and reconstruction

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PHYSICS IN MEDICINE AND BIOLOGY 2025年第5期70卷 055015-055015页

作者： Yadav, Anil Welland, Spencer Hoffman, John M. Kim, Grace Brown, Matthew S. Prosper, Ashley E. Aberle, Denise R. McNitt-Gray, Michael F. Hsu, William Univ Calif Los Angeles Samueli Sch Engn Dept Bioengn Los Angeles CA 90095 USA UCLA Dept Radiol Sci Med & Imaging Informat Grp David Geffen Sch Med Los Angeles CA 90095 USA UCLA Ctr Comp Vis & Imaging Biomarkers Dept Radiol Sci David Geffen Sch Med Los Angeles CA 90095 USA

Objective. The study aims to systematically characterize the effect of CT parameter variations on images and lung radiomic and deep features, and to evaluate the ability of different image harmonization methods to mitigate the observed variations. Approach. A retrospective in-house sinogram dataset of 100 low-dose chest CT scans was reconstructed by varying radiation dose (100%, 25%, 10%) and reconstruction kernels (smooth, medium, sharp). A set of image processing, convolutional neural network (CNNs), and generative adversarial network-based (GANs) methods were trained to harmonize all image conditions to a reference condition (100% dose, medium kernel). Harmonized scans were evaluated for image similarity using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS), and for the reproducibility of radiomic and deep features using concordance correlation coefficient (CCC). Main Results. CNNs consistently yielded higher image similarity metrics amongst others;for Sharp/10%, which exhibited the poorest visual similarity, PSNR increased from a mean +/- CI of 17.763 +/- 0.492 to 31.925 +/- 0.571, SSIM from 0.219 +/- 0.009 to 0.754 +/- 0.017, and LPIPS decreased from 0.490 +/- 0.005 to 0.275 +/- 0.016. Texture-based radiomic features exhibited a greater degree of variability across conditions, i.e. a CCC of 0.500 +/- 0.332, compared to intensity-based features (0.972 +/- 0.045). GANs achieved the highest CCC (0.969 +/- 0.009 for radiomic and 0.841 +/- 0.070 for deep features) amongst others. CNNs are suitable if downstream applications necessitate visual interpretation of images, whereas GANs are better alternatives for generating reproducible quantitative image features needed for machine learning applications. Significance. Understanding the efficacy of harmonization in addressing multi-parameter variability is crucial for optimizing diagnostic accuracy and a critical step toward building gen

关键词： Computed Tomography image Reconstruction Harmonization Convolutional neural Network Generative Adversarial Network

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Learning Deep Scene Curve for Fast and Robust Underwater image Enhancement

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IEEE signal processing LETTERS 2024年 31卷 6-10页

作者： Xue, Xinwei Ma, Tianjiao Han, Yidong Ma, Long Liu, Risheng Dalian Univ Technol Sch Software Technol Dalian 116024 Peoples R China

Learning-based approaches inspired by the scattering model for enhancing underwater imagery have gained prominence. Nevertheless, these methods often suffer from time-consuming attributable to their sizable model dimensions. Moreover, they face challenges in adapting unknown scenes, primarily because the scattering model's original design was intended for atmospheric rather than marine condition. To address these obstacles, we begin by investigating the inherent differences in imaging characteristics between atmospheric and marine conditions based on statistical distributions. Building on these observations, we introduce an efficient and effective algorithm called Deep Scene Curve, abbreviated as DSC. This method comprises two essential steps: scene-irrelevant zero-mean adjustment and scene-oriented hyperparameter estimation. The first step transforms scene features into a unified zero-mean space, thereby reducing interference from scene-specific attributes. In the second step, we employ a lightweight neural network to estimate scene-oriented hyperparameters for a defined pixel-level curve based on underwater observations. This approach enables us to generate a deep curve that excels in both adaptability and efficiency, as substantiated by extensive experiments. Notably, our method achieves a significant 56% improvement in average inference time while reducing FLOPs by 92% compared to existing techniques. Furthermore, our extensive experiments in low-light image enhancement tasks highlight the potential advantages of DSC.

关键词： image color analysis Training Adaptation models image enhancement Computational modeling Atmospheric modeling Estimation Deep learning underwater image enhancement curve estimation

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Learning Point Spread Function Invertibility Assessment for image Deconvolution 32

Learning Point Spread Function Invertibility Assessment for ...

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32nd European signal processing Conference (EUSIPCO)

作者： Gualdron-Hurtado, Romario Jacome, Roman Urrea, Sergio Arguello, Henry Gonzalez, Luis Univ Ind Santander Dept Comp Sci Bucaramanga Colombia Univ Ind Santander Dept Elect Engn Bucaramanga Colombia

ISBN: (纸本)9789464593617;9798331519773

Deep-learning (DL)-based image deconvolution (ID) has exhibited remarkable recovery performance, surpassing traditional linear methods. However, unlike traditional ID approaches that rely on analytical properties of the point spread function (PSF) to achieve high recovery performance-such as specific spectrum properties or small conditional numbers in the convolution matrix-DL techniques lack quantifiable metrics for evaluating PSF suitability for DL-assisted recovery. Aiming to enhance deconvolution quality, we propose a metric that employs a non-linear approach to learn the invertibility of an arbitrary PSF using a neural network by mapping it to a unit impulse. A lower discrepancy between the mapped PSF and a unit impulse indicates a higher likelihood of successful inversion by a DL network. Our findings reveal that this metric correlates with high recovery performance in DL and traditional methods, thereby serving as an effective regularizer in deconvolution tasks. This approach reduces the computational complexity over conventional condition number assessments and is a differentiable process. These useful properties allow its application in designing diffractive optical elements through end-to-end (E2E) optimization, achieving invertible PSFs, and outperforming the E2E baseline framework.

关键词： image deconvolution computational imaging diffractive optical element design

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