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image compression and enlargement algorithms

International Journal of Circuits, systems and signal processing 2021年 15卷 836-846页

作者： Burova, I.G. Dem’yanovich, Yu.K. Terekhov, A.N. Altynova, A.Yu. Satanovskiy, A.D. Babushkin, A.A. St. Petersburg State University 7/9 Universitetskaya nab St.Petersburg199034 Russia

In some cases, there are problems associated with the compression and enlargement of images. The use of splines is quite effective in some cases. In this paper, a new image compression algorithm is presented. The features of increasing the size of an image when using local polynomial or non-polynomial splines are considered. The main method for enlarging an image is based on the use of splines of the second and third order of approximation. Polynomial and trigonometric splines are considered. To speed up the process of enlarging the image, we used the parallelization techniques. © 2021, North Atlantic University Union NAUN. All rights reserved.

关键词： image compression

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Enhancing Video Protection: A Reversible Digital Watermarking approach using Swin features and Arnold Transform enabled Multi-Aggregation Model

Enhancing Video Protection: A Reversible Digital Watermarkin...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： Mali Satish Dilip Agilandeeswari L School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Nowadays, the widespread internet technology brings out rapid advancement in the use of videos for sharing huge amounts of secret data. Conversely, the privacy of transmitted digital content is jeopardized by digital image processing techniques that are capable of producing flawless image or video duplication. Moreover, providing security to digital records in the form of videos is a highly challenging task, which requires a fusion-based watermarking technique to protect the data from illegitimate access and fraudulent usage. In this investigation, a tiny Swin and Arnold Transform-enabled Multi-aggregation model was introduced to protect video based on digital watermarking characteristics at a very low computational cost. Here, the Swin-T features are extracted from the input video, over which the Arnold transform and multi-aggregation model is developed in two phases. The best embedding location is identified by using multi aggregation model initially at the embedding phase and the inverse Arnold transform for secret image (VIT Logo) embedding in a binary embedding location to obtain the embedded video. The secret image is extracted from the attacked embedded video using the Arnold transform-multi-aggregation model in the extraction phase. The developed data-hiding technique is also evaluated to compute the robustness using the evaluation parameters. The simulation results reveal that the developed scheme achieved Peak-signal-to-Noise Ratio (PSNR) and Similarity Index Measure (SSIM) values of $64.57 \mathrm{~dB}$ and 0.9993 respectively.

关键词： Privacy Computational modeling simulation Watermarking Transforms Feature extraction Transformers

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Task-based assessment of binned and list-mode SPECT systems

Task-based assessment of binned and list-mode SPECT systems

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Conference on Medical Imaging - image Perception, Observer performance, and Technology Assessment

作者： Rahman, Md Ashequr Jha, Abhinav K. Washington Univ Dept Biomed Engn St Louis MO 63110 USA Washington Univ Mallinckrodt Inst Radiol St Louis MO 63110 USA

ISBN: (纸本)9781510640283

In SPECT, list-mode (LM) format allows storing data at higher precision compared to binned data. There is significant interest in investigating whether this higher precision translates to improved performance on clinical tasks. Towards this goal, in this study, we quantitatively investigated whether processing data in LM format, and in particular, the energy attribute of the detected photon, provides improved performance on the task of absolute quantification of region-of-interest (ROI) uptake in comparison to processing the data in binned format. We conducted this evaluation study using a DaTscan brain SPECT acquisition protocol, conducted in the context of imaging patients with Parkinson's disease. This study was conducted with a synthetic phantom. A signal-known exactly/background-known-statistically (SKE/BKS) setup was considered. An ordered-subset expectation-maximization algorithm was used to reconstruct images from data acquired in LM format, including the scatter-window data, and including the energy attribute of each LM event. Using a realistic 2-D SPECT system simulation, quantification tasks were performed on the reconstructed images. The results demonstrated improved quantification performance when LM data was used compared to binning the attributes in all the conducted evaluation studies. Overall, we observed that LM data, including the energy attribute, yielded improved performance on absolute quantification tasks compared to binned data.

关键词： List-mode SPECT binning reconstruction quantification objective assessment of image quality

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Comparative Study of Transformer Robustness for Multiple Particle Tracking Without Clutter

Comparative Study of Transformer Robustness for Multiple Par...

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European signal processing Conference (EUSIPCO)

作者： Piyush Mishra Philippe Roudot Aix Marseille Univ CNRS Centrale Med I2M Institut Fresnel Turing Centre for Living Systems Marseille France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

The tracking of multiple particles in image se-quences is challenged by the stochastic nature of displacements, particles detection errors, and the combinatorial explosion of all possible trajectories. As such, extensive work has focused on the modeling of noisy trajectories to try and predict the most likely trajectory-to-measurements associations. Recently, transformers have been shown to significantly accelerate the evaluation of probabilistic models for the system dynamics and detection clutter generated from false positives. However, little work has focused on clutter-free scenarios with multiple particles moving erratically, where the challenge resides not in the model complexity, but in the combinatorial burden of considering all possible trajectory-to-measurements associations. This is a common occurrence in fluorescence microscopy at low framerate. This paper offers a proof-of-concept study of the benefit of the transformer architecture in such scenarios through the simulation of two-particle-systems undergoing Brownian motion. Specifically, we designed a transformer for this estimation task and compared it with the Multiple Hypothesis Tracker (MHT), the optimal estimator when all trajectory-to-measurement associ-ations can be computed. We first show increased robustness of the transformer against erratic displacements over long sequences, with significantly lower computational complexity than MHT. Then, we show that while the transformer requires very little training to significantly outperform MHT on long sequences, it cannot match the theoretically optimal performances of MHT on short sequences even with extensive training. Hence, our work motivates the broader application of transformers in high-SNR sequences and opens the way to the development of frugal methods thanks to the combination of both statistical and neural network frameworks for particle tracking.

关键词： Training Tracking System dynamics Stochastic processes signal processing Transformers Robustness Trajectory Particle tracking Clutter

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Effect of situational and instrumental distortions on the classification of brain MR images

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BIOMEDICAL signal processing AND CONTROL 2023年第Part2期79卷 000-000页

作者： Devi, Swagatika Bakshi, Sambit Sahoo, Manmath Narayan Natl Inst Technol Rourkela Dept Comp Sci & Engn Rourkela 769008 Orissa India

Magnetic Resonance (MR) images of the brain play key role in exploiting pathological changes and non-invasive investigation of many neuro-degenerative diseases. Computer Aided Diagnosis (CAD) systems assist radiologists in interpreting MR images and classifying them into "normal"and "abnormal"categories. However, reduced strength of the used magnet in the machine or involuntary motions of the patients may lead to degraded MR images, which can negatively affect the performance of CAD system compromising the classification accuracy. This work aims at modeling these types of situations via out-of-focus blur, motion blur, effect of variation in resolution, and a combination of these on brain MR images for validating the impact of image quality on classification performance. To validate this, this article mathematically models the blurs (both individually and simultaneously) by varying the strength of image quality covariates and afterwards Deep Convolutional Neural Networks (DCNN) are employed to train and classify the distorted brain MR images. Besides, a single DCNN is experimented with a good mix of image quality and characteristics to test the reliability of the model for real-life scenario. The CNN models are validated through comprehensive evaluation on both original and degraded versions of brain MR images from two benchmark datasets DS-75 and DS-160 collected by Harvard Medical School as well as a self-collected dataset NITR-DHH. This study reveals that the models are able to classify distorted MR images and hence can be used for assisting the clinicians.

关键词： Computer aided diagnosis Convolutional neural network Data augmentation Out-of-focus blur Low resolution images Motion blur MR image classification

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performance of Linear Coding and Transmission in Low-Latency Computer Vision Offloading

Performance of Linear Coding and Transmission in Low-Latency...

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IEEE Conference on Wireless Communications and Networking

作者： Jakub Žadník Anthony Trioux Michel Kieffer Markku Mäkitalo François-Xavier Coudoux Patrick Corlay Pekka Jääskeläinen Tampere University Finland UMR 8520-IEMN UPHF CNRS Univ. LIlle. YNCREA Centrale Lille France Laboratoire des Signaux et Systèmes Univ. Paris-Saclay CNRS CentraleSupélec France

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

image communication increasingly involves machine-to-machine delivery. For example, images acquired by an autonomous drone can be compressed and sent to an edge server over a wireless network for resource-intensive processing. Traditional compression techniques involving transform, quantization, and entropy coding reach high compression efficiency, but channel conditions worse than expected may lead to a sharp decrease in the decoded image quality. As an alternative, Linear Coding and Transmission (LCT) systems have been proposed to avoid this digital cliff problem: The reconstructed image quality decreases gradually as channel conditions degrade. This paper presents a comprehensive evaluation of computer vision tasks with input images processed and transmitted using LCT. It also analyses the benefits of network retraining, accounting for impairments due to LCT and noisy channel. Considering object detection and semantic segmentation over images transmitted and received by LCT systems, we show that the task accuracy degrades smoothly when the channel quality decreases, avoiding the cliff effect. Retraining with noisy images processed by LCT restores detection mAP degradation from 23.8% to 4.4% and segmentation mIoU degradation from 43.2% to 8.1 % when the channel signal-to-noise ratio is 10 dB.

关键词： image quality image coding Accuracy Computational modeling Semantic segmentation Transforms Object detection

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Supporting Deep Learning Based Autonomous Driving Applications on Resource-Constrained Devices

Supporting Deep Learning Based Autonomous Driving Applicatio...

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Mobility, Operations, Services and Technologies (MOST), IEEE International Conference on

作者： Ishparsh Uprety Griffen Agnello Xinghui Zhao School of Engineering and Computer Science Washington State University Vancouver Vancouver WA USA

ISBN: (数字)9798350307733

ISBN: (纸本)9798350307740

With recent advances in computing and sensing technologies, autonomous driving has gained increasing interest and become a promising platform to support the next generation intelligent transportation systems. A critical requirement for autonomous driving systems is to be able to utilize AI and machine learning techniques to make reliable decisions on edge devices in a timely manner. Deploying reliable machine learning models on edge devices in a real-time environment is a challenging task. Real-time applications such as traffic surveillance or traffic sign detection require consistently low latency in order for the device to keep up with its environment. Edge devices are able to compute machine learning tasks without needing to offload computation to a cloud server, however they often have a tight limit on the amount of available resources. Quantization is a model optimization technique used to increase inference speed at the cost of accuracy. In this paper, we carried out a comprehensive study on utilizing quantization to enable efficient and effective decision making for autonomous driving applications. Various quantization methods are analyzed, with a focus on the trade-offs between the learning performance and computational performance. Furthermore, we identify pre-processing techniques and types of data input that may improve the model’s accuracy. The proposed approach will enhance the decision making process in resource-constrained environment, and provide more flexibility for deploying the autonomous driving applications.

关键词： Deep learning performance evaluation Quantization (signal) Accuracy image edge detection Computational modeling Decision making

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Improving Minimum Variance Beamforming with Sub-Aperture processing for Photoacoustic Imaging 43

Improving Minimum Variance Beamforming with Sub-Aperture Pro...

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43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Al Mukaddim, Rashid Ahmed, Rifat Varghese, Tomy Univ Wisconsin UW Sch Med & Publ Hlth SMPH Dept Med Phys Madison WI 53706 USA Duke Univ Dept Biomed Engn Durham NC 27706 USA

ISBN: (纸本)9781728111797

Minimum variance (MV) beamforming improves resolution and reduces sidelobes when compared to delay-and-sum (DAS) beamforming for photoacoustic imaging (PAI). However, some level of sidelobe signal and incoherent clutter persist degrading MV PAI quality. Here, an adaptive beamforming algorithm (PSAP(MV)) combining MV formulation and sub-aperture processing is proposed. In PSAP(MV), the received channel data are split into two complementary nonoverlapping sub-apertures and beamformed using MV. A weighting matrix based on similarity between sub-aperture beamformed images was derived and multiplied with the full aperture MV image resulting in suppression of sidelobe and incoherent clutter in the PA image. Numerical simulation experiments with point targets, diffuse inclusions and microvasculature networks are used to validate PSAP(MV). Quantitative evaluation was done in terms of main-lobe-to-side-lobe ratio, full width at half maximum (FWHM), contrast ratio (CR) and generalized contrast-to-noise ratio (gCNR). PSAP(MV) demonstrated improved beamforming performance both qualitatively and quantitatively. PSAP(MV) had higher resolution (FWHM =0.19 mm) than MV (0.21 mm) and DAS (0.22mm) in point target simulations, better target detectability (gCNR =0.99) than MV (0.89) and DAS (0.84) for diffuse inclusions and improved contrast (CR in microvasculature simulation, DAS = 15.38, MV = 22.42, PSAP(MV) = 51.74 dB).

关键词： In vivo image resolution Array signal processing Photoacoustic imaging Biological system modeling Phantoms Numerical simulation

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Clinical Breast Imaging and Deep Learning Model for image Malignancy Prediction Using Resource-Constrained Embedded Devices

Clinical Breast Imaging and Deep Learning Model for Image Ma...

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Artificial Intelligence and signal processing (AISP), INDIA, International Conference on

作者： Sumedha R. Chokhandre Pranjali Jadhav Nilesh Gole Amit N. Thakare Akansha R Bondade Priyanka Singh Dept. of Information Technology YCCE Nagpur AI & DS Department Vishwakarma Institute of Information Technology Pune Department of CSE Vidharbha Institute of Technology Nagpur Department of CSE Cummins College of Engineering for Women Nagpur Department of CSE G.H. Raisoni University Saikheda School of Computer Science VIT-AP University Andhra Pradesh India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

Computer assisted diagnosis(CAD) of diseases provides more accurate and precise diagnostic reports towards better information regarding the medical condition of patients. A clinician can minimize the error by applying his experience acquired by practice, cognitive intuition or scientific research backed by laboratory reports and computer assisted medical image analysis. The findings by the experts based on the analysis of such data are crucial as the suggested treatment is dependent on evaluation at this stage. Machine learning techniques while applied in the medical field performs decision making by mimicking the steps performed by a medical expert in diagnosing the disease, but using algorithms rather intuitive. It brings out accurate medical data through analysis of images performed by computing devices that can reveal valuable information regarding the disease prognosis. Computer aided disease diagnosis with state-of-the-art machine learning and deep learning offers seamless assistance in medical care with near human accuracy. Technology integrated medical support systems combined with sophisticated algorithms can reduce the number of false positive incidents as well as false negative cases. Convolutional neural network (CNN) models can be trained using handcrafted features to derive conclusive inferences for binary class as well as multi-class classification. Artificial Intelligence (AI) supported techniques in disease diagnosis provide assistance to medical experts in decision making by virtue of cloud based data analytics tools for storage and computing.

关键词： Deep learning performance evaluation Accuracy Machine learning algorithms Computational modeling Decision making Predictive models Medical diagnosis Medical diagnostic imaging Diseases

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A Real-World Testbed for V2X in Autonomous Vehicles: From simulation to Actual Road Testing

A Real-World Testbed for V2X in Autonomous Vehicles: From Si...

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IEEE International Conference on Fog and Edge Computing (ICFEC)

作者： Ammar Elmoghazy Khalid Elgazzar Sanaa Alwidian Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

ISBN: (数字)9798350361353

ISBN: (纸本)9798350361360

This paper proposes an edge computing-enhanced testbed for Vehicle-to-Everything (V2X) communication, using a scaled autonomous vehicle model. Addressing the limitations of traditional vehicle testing and simulations, our approach utilizes an F1Tenth scale vehicle to replicate real-world traffic scenarios, with a focus on non-line-of-sight and pedestrian-occluded challenges. The testbed incorporates a Road Side Unit (RSU) for edge-based data processing, allowing rapid data acquisition and transmission. Our controlled experiments assess the vehicle's response to V2X communication in complex environments specifically in NLOS scenarios such as occluded pedestrians, highlighting the potential of edge computing to augment autonomous vehicle perception and decision-making. performance evaluation shows that the vehicle can effectively handle emergencies, achieving an end-to-end response time of less than 146 ms on an edge device. Compared to other popular autonomy frameworks, which rely on higher computational units unsuitable for deployment on edge devices. This study advances V2X technology in edge computing contexts for autonomous vehicles and paves the way for future research in robust, safe, and reliable autonomous systems.

关键词： performance evaluation Pedestrians Computational modeling image edge detection Sensors Time factors Vehicle-to-everything Autonomous vehicles Testing Edge computing

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