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Proceedings of the 2021 IEEE International conference on signal and image processing applications, ICSIPA 2021

Proceedings of the 2021 IEEE International Conference on Sig...

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7th IEEE International conference on signal and image processing applications, ICSIPA 2021

ISBN: (纸本)9781665435925

The proceedings contain 46 papers. The topics discussed include: development of a deep learning model to classify x-ray of Covid-19, normal and pneumonia-affected patients;deep learning radio frequency signal classification with hybrid images;sparse checkerboard corner detection from global perspective;fusion of 2.5D face recognition through extreme learning machine via manifold flattening;training convolutional neural networks to detect waste in train carriages;an insight into the rise time of exponential smoothing for speech enhancement methods;spoken Malay profanity classification using convolutional neural network;automatic diagnosis and prediction of cognitive decline associated with Alzheimer’s dementia through spontaneous speech;monaural speech enhancement using deep neural network with cross-speech dataset;and a comparative study of in-domain vs cross-domain learning for porn cartoon classification.

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EBAnet: Improved Deep Learning Model for the Detection of Epidermolysis Bullusa Acquisita

EBAnet: Improved Deep Learning Model for the Detection of Ep...

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International conference on Communications, signal processing, and their applications (ICCSPA)

作者： Mohammad Tariqul Islam Mais Alkhateeb Saleh Musleh Nady El Hajj Tanvir Alam Southern Connecticut State University Connecticut USA College of Science and Engineering Hamad Bin Khalifa University Doha Qatar College of Health and Life Sciences Hamad Bin Khalifa University Doha Qatar

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

Epidermolysis bullosa acquisita (EBA) represents a big challenge as a rare skin disorder, with no established markers for early detection for patients. Moreover, as a rare disease, it is extremely difficult to acquire good number of patient sample to diagnose accurately with high confidence. EBA has many biomarkers very similar to other bullosa diseases and needs specific clinical expertise to detect it using immunofluorescence microscopy. In this study, we introduce a deep learningbased method, EBAnet, that leveraged Convolutional Neural Network (CNN) based model for the detection of EBA based on Direct immunofluorescence (DIF) microscopy image. The proposed EfficientNet-based model achieved 97.3% sensitivity, 96.1% precision, and 96.7% accuracy in distinguishing EBA from other class and outperformed the existing model for the same purpose. GradCAM based class activation map also highlighted the important region of the DIF images that was focused by the proposed model leveraging the explainability of the model. We believe, EBAnet will add value in the early and accurate detection of EBA, addressing a critical need in clinical practice.

关键词： Deep learning Accuracy Sensitivity Biological system modeling signal processing Biomarkers Skin Immunofluorescence Convolutional neural networks Diseases

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Physical Domain Adversarial Attacks Against Source Printer image Attribution

Physical Domain Adversarial Attacks Against Source Printer I...

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Asia-Pacific signal and Information processing Association Annual Summit and conference (APSIPA)

作者： Nischay Purnekar Benedetta Tondi Mauro Barni University of Siena Italy

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

Deep learning networks are vulnerable to adversarial examples, subtle, human-imperceptible modifications that can deceive them. While most research has focused on digital adversarial attacks, in several applications it is necessary that the adversarial examples operate in the physical domain. Physical domain adversarial examples are usually crafted by requiring that the artefacts introduced by the attack survive the digital to analog and analog to digital transformations involved by such attacks. In this paper, we introduce an approach to generate adversarial examples against a source printer attribution system aiming at deciding which printer was used to print a given image document. With respect to conventional physical domain attacks, attacking a source printer attribution system poses additional challenges since the subtle features the attribution network relies on are introduced again during the Print and Scan (P&S) process that follows the attack, thus possibly nullifying the attack. We address this challenge by applying Expectation Over Transformation including within the pool of transformations a simulation of the P&S process relying on two GAN models trained for this aim. Experimental results demonstrate that our approach yields a significant increase of the attack success rates, surpassing those of baseline models.

关键词： Deep learning Digital transformation Asia Information processing Generative adversarial networks Printers

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Col-OLHTR: A Novel Framework for Multimodal Online Handwritten Text Recognition

Col-OLHTR: A Novel Framework for Multimodal Online Handwritt...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Chenyu Liu Jinshui Hu Baocai Yin Jia Pan Bing Yin Jun Du Qingfeng Liu University of Science and Technology of China Hefei China iFLYTEK Research Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Online Handwritten Text Recognition (OLHTR) has gained considerable attention for its diverse range of applications. Current approaches usually treat OLHTR as a sequence recognition task, employing either a single trajectory or image encoder, or multi-stream encoders, combined with a CTC or attention-based recognition decoder. However, these approaches face several drawbacks: 1) single encoders typically focus on either local trajectories or visual regions, lacking the ability to dynamically capture relevant global features in challenging cases; 2) multi-stream encoders, while more comprehensive, suffer from complex structures and increased inference costs. To tackle this, we propose a Collaborative learning-based OLHTR framework, called Col-OLHTR, that learns multimodal features during training while maintaining a single-stream inference process. Col-OLHTR consists of a trajectory encoder, a Point-to-Spatial Alignment (P2SA) module, and an attention-based decoder. The P2SA module is designed to learn image-level spatial features through trajectory-encoded features and 2D rotary position embeddings. During training, an additional image-stream encoder-decoder is collaboratively trained to provide supervision for P2SA features. At inference, the extra streams are discarded, and only the P2SA module is used and merged before the decoder, simplifying the process while preserving high performance. Extensive experimental results on several OLHTR benchmarks demonstrate the state-of-the-art (SOTA) performance, proving the effectiveness and robustness of our design.

关键词： Training Handwriting recognition Visualization image recognition Text recognition Speech recognition Trajectory Decoding Speech processing Streams

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Towards image denoising in the latent space of learning-based compression 44

Towards image denoising in the latent space of learning-base...

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conference applications of Digital image processing xLIV

作者： Testolina, Michela Upenik, Evgeniy Ebrahimi, Touradj Ecole Polytech Fed Lausanne EPFL Multimedia Signal Proc Grp MMSPG CH-1015 Lausanne Switzerland

ISBN: (数字)9781510645233

ISBN: (纸本)9781510645233;9781510645226

In recent years, learning-based image compression has demonstrated similar or superior performance when compared to conventional approaches in terms of compression efficiency and visual quality. Typically, learning-based image compression takes advantage of autoencoders, which are architectures consisting of two main parts: a multi-layer neural network encoder and its dual decoder. The encoder maps the input image represented in the pixel domain to a compact representation, also known as latent space. Consequently, the decoder reconstructs the original image in the pixel domain from its latent representation, as accurately as possible. Traditionally, image processing algorithms, and in particular image denoising, are applied to images in the pixel domain before compression, and eventually in some cases as a post-processing stage after decompression. In this context, the combination of denoising operations with the autoencoder might reduce the computational cost while achieving similar performance in accuracy. In this paper, the idea of combining the image denoising task with compression is examined. In particular, the integration of denoising convolutional layers in the decoder of a learning-based compression network is investigated. Results show that, while the rate-distortion performance of the method is slightly reduced, a gain in the computational complexity can be achieved.

关键词： image denoising learning-based compression compressed domain denoising latent space image processing

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Towards a secure and trustworthy imaging with non-fungible tokens 44

Towards a secure and trustworthy imaging with non-fungible t...

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conference applications of Digital image processing xLIV

作者： Martinod, Nicolas J. Homayounfar, Kambiz Lazzarotto, Davi Nachtigall Upenik, Evgeniy Ebrahimi, Touradj Ecole Polytech Fed Lausanne EPFL Multimedia Signal Proc Grp MMSPG Lausanne Switzerland RayShaper SA Crans Montana Switzerland

ISBN: (数字)9781510645233

ISBN: (纸本)9781510645233;9781510645226

Non-fungible tokens (NFTs) are used to define the ownership of digital assets. More recently, there has been a surge of platforms to auction digital art as well as other digital assets in form of image, video, and audio contents of all sorts of purposes ranging from collectibles to means of investment. Although NFTs have the potential of revolutionizing the foundations of ownership and possession of digital assets, they also face various challenges in terms of trust, security as well as legal implications and even social acceptance. This paper identifies the main challenges in NFTs and discusses potential directions in order to remedy to their current shortcomings, with emphasis on digital assets in form of images.

关键词： Standards development Legal Computer security Digital imaging

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Conceptual importance of Imaging and signal processing in the Medical Science

Conceptual importance of Imaging and Signal Processing in th...

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Healthcare Innovations, Software and Engineering Technologies (HISET), International conference on

作者： Pratik Yadav Ashok Lokhande B Lakshmi Prasanna Ganesh Thorat Department of E&TC Engineering Jaywant College of Engineering and Polytechnic Karad India Jaywant College of Management Karad India Department of C Department of Electronics and Communication Engineering Institute of Aeronautical Engineering Hyderabad India Department of Medicine Krishna Vishwa Vidyapeeth Deemed To Be University Karad India

ISBN: (数字)9798350360707

ISBN: (纸本)9798350360714

Information technology has impacted every part of human life, and in the last few years, the fields of healthcare and medical science have felt the effects of this innovation. Biomedical images, which are defined as representations of the body that aid in understanding the nature of the biological systems, are an essential component of medical science. applications of biomedical engineering include the creation of pharmaceutical medications, regenerative tissue growth, therapeutic biology, common imaging devices like MRIs and EEGs, and diagnostics and therapeutics medical device ranging from clinical equipment’s to micro-implant. x-rays, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and ultrasound are the most often utilized imaging modalities in medical applications. Improving the information illustrates interpretability is the only goal of medical image processing. Medical image analysis falls into the following primary categories: localization, detection, classification, segmentation, enhancement, and registration. Integrating imaging data with non-imaging data is the computational approach. Expert systems like neural networks and fuzzy logic are linked with image processing techniques including enhancement, segmentation, and restoration morphological systems to meet this problem. Future diagnosis should be made possible for doctors by the interoperability of essential algorithm integration and support for standard approaches.

关键词： Fuzzy logic image segmentation Magnetic resonance imaging Neural networks Medical services image restoration Expert systems Medical diagnostic imaging Standards Interoperability

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Depth-resolved color separation using diffuse reflectance polarization-based images 9

Depth-resolved color separation using diffuse reflectance po...

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conference on Light in Nature Ix

作者： Hanlon, Katharine L. Shah, Kara N. Grichnik, James M. Univ S Florida Dept Dermatol & Cutaneous Surg 13220 USF Laurel Dr Tampa FL 33612 USA Medpace 5375 Medpace Way Cincinnati OH 45227 USA

ISBN: (纸本)9781510654136;9781510654129

Biological structures located at various depths in tissue, such as blood and melanin, are challenging to perceive from digital images acquired with incoherent light sources. Use of cross-polarized light produces a low-coherence signal with latent embedded data. We demonstrate the ability to modulate visible depth of high-resolution digital image data acquired with commercially available equipment. Superficial and deep structures are visualized on a polarization-gated continuum. Conspicuity of chromophores contained in hemoglobin or melanin are increased independently through a color luminance/temperature processing approach. Qualitative visual analysis of structures distributed in skin tissue is achieved by targeting the region where red and green photosite values have equal spectral responsivity, at approximately 580nm, corresponding to a sharp spike in hemoglobin reflectance. This region contains both sufficient quantum efficiency (QE) from red and green color filter channels as well as overlapping melanin and hemoglobin signals from within skin tissue. Leveraging this overlapping tissue signal and sensor QE in the orange (580nm) region, luminance is increased or decreased according to the structure of interest. Shifting the illuminant light's properties from high to low frequencies using Kelvin temperature, polarized light is gated, allowing observation of skin structures at distinct depths. For example, superficial skin layers are separately visualized from deeper vessels or pigment. The novel viewing process may be considered independent component analysis of diffuse reflectance polarization-based optical images, or as low-coherent backscatter imaging of spatial encoded skin features on a spectral continuum. The technique has implications for an array of dermatological and tissue imaging applications.

关键词： Polarized light low-coherence skin imaging polarization gating kelvin temperature signal processing spatiotemporal polarimetric

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CYCLE GENERATIVE ADVERSARIAL NETWORK: TOWARDS A LOW-COST VEGETATION INDEx ESTIMATION

CYCLE GENERATIVE ADVERSARIAL NETWORK: TOWARDS A LOW-COST VEG...

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IEEE International conference on image processing (ICIP)

作者： Suarez, Patricia L. Sappa, Angel D. Vintimilla, Boris x. Escuela Super Politecn Litoral ESPOL CIDIS Fac Ingn Elect & Comp Campus Gustavo Galindo Guayaquil Ecuador Comp Vis Ctr Edifici OCampus UAB Barcelona 08193 Spain

ISBN: (纸本)9781665441155

This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI). The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands;in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index's image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach.

关键词： CyclicGAN NDVI near infrared spectra instance normalization

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UV-Mamba: A DCN-Enhanced State Space Model for Urban Village Boundary Identification in High-Resolution Remote Sensing images

UV-Mamba: A DCN-Enhanced State Space Model for Urban Village...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Lulin Li Ben Chen xuechao Zou Junliang xing Pin Tao School of Computer Technology and Applications Qinghai University Xining China School of Computer Science and Technology Beijing Jiaotong University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Due to the diverse geographical environments, intricate landscapes, and high-density settlements, the automatic identification of urban village boundaries using remote sensing images remains a highly challenging task. This paper proposes a novel and efficient neural network model called UV-Mamba for accurate boundary detection in high-resolution remote sensing images. UV-Mamba mitigates the memory loss problem in lengthy sequence modeling, which arises in state space models (SSM) with increasing image size, by incorporating deformable convolutions (DCN). Its architecture utilizes an encoder-decoder framework and includes an encoder with four deformable state space augmentation (DSSA) blocks for efficient multi-level semantic extraction and a decoder to integrate the extracted semantic information. We conducted experiments on two large datasets showing that UV-Mamba achieves state-of-the-art performance. Specifically, our model achieves 73.3% and 78.1% IoU on the Beijing and xi’an datasets, respectively, representing improvements of 1.2% and 3.4% IoU over the previous best model while also being 6× faster in inference speed and 40× smaller in parameter count. Source code and pre-trained models are available at https://***/Devin-Egber/UV-Mamba.

关键词： Deformable models Location awareness image segmentation Source coding Semantics Neural networks Data mining Sustainable development Speech processing Remote sensing

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