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检索条件"任意字段=Conference on Statistical and Stochastic Methods for Image Processing"

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stochastic BINARY-TERNARY QUANTIZATION FOR COMMUNICATION EFFICIENT FEDERATED COMPUTATION 29

STOCHASTIC BINARY-TERNARY QUANTIZATION FOR COMMUNICATION EFF...

引用

IEEE International conference on image processing (ICIP)

作者： Goutham, Rangu Afrabandpey, Homayun Cricri, Francesco Zhang, Honglei Aksu, Emre Hannuksela, Miska Tavakoli, Hamed R. Nokia Technol Espoo Finland

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

A stochastic binary-ternary (SBT) quantization approach is introduced for communication efficient federated computation;form of collaborative computing where locally trained models are exchanged between institutes. Communication of deep neural network models could be highly inefficient due to their large size. This motivates model compression in which quantization is an important step. Two well-known quantization algorithms are binary and ternary quantization. The first leads into good compression, sacrificing accuracy. The second provides good accuracy with less compression. To better benefit from trade-off between accuracy and compression, we propose an algorithm to stochastically switch between binary and ternary quantization. By combining with uniform quantization, we further extend the proposed algorithm to a hierarchical method which results in even better compression without sacrificing the accuracy. We tested the proposed algorithm using Neural network Compression Test Model (NCTM) provided by MPEG community. Our results demonstrate that the hierarchical variant of the proposed algorithm outperforms other quantization algorithms in term of compression, while maintaining the accuracy competitive to that provided by other methods.

关键词： Federated learning neural network compression quantization

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Performance Analysis of image Denoising Techniques in Skin Cancer Detection 4th

Performance Analysis of Image Denoising Techniques in Skin C...

引用

4th International conference on Data, Engineering, and Applications, IDEA 2022

作者： Patil, Sheetal Nana Patil, Hitendra D. India

ISBN: (纸本)9789819724505

Because visual information is increasingly being sent in digital image format, identifying noisy data becomes a prevalent difficulty in many research and application sectors. To reduce noise while maintaining image information, many noise reduction techniques have been developed in the modern period. Researchers always struggle with the challenge of noise removal from original image. This study proposed a pre-processing method to improve image quality in terms of performance parameters, making the subsequent image processing work in the diagnosis of any type of skin cancer easier. The major focus of this work is on image denoising or filtering, which improves image quality for subsequent image processing processes in the diagnosis of normal vs. malignant skin. Objectives: To calculate various denoising performance parameters like peak signal to noise ratio (SNR), mean square error (MSE), normalized root mean square error (NRMSE), and others. methods: In this research work, image denoising technique was performed by using different types of noises and filters. statistical Analysis: In this research work, we found observational outcomes after noise addition to skin images in terms of pixel value, darkness, brightness related to each pixel. Gaussian and median filter gives MSE between 0 and 17%, NRMSE ranges between 0.025 and 0.425, and PSNR is obtained from Min 15 dB to Max 40 dB. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Median filters

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DIFFUVST: Narrating Fictional Scenes with Global-History-Guided Denoising Models

DIFFUVST: Narrating Fictional Scenes with Global-History-Gui...

引用

conference on Empirical methods in Natural Language processing (EMNLP)

作者： Wu, Shengguang Yuan, Mei Su, Qi Peking Univ Beijing Peoples R China

ISBN: (纸本)9798891760615

Recent advances in image and video creation, especially AI-based image synthesis, have led to the production of numerous visual scenes that exhibit a high level of abstractness and diversity. Consequently, Visual Storytelling (VST), a task that involves generating meaningful and coherent narratives from a collection of images, has become even more challenging and is increasingly desired beyond real-world imagery. While existing VST techniques, which typically use autoregressive decoders, have made significant progress, they suffer from low inference speed and are not well-suited for synthetic scenes. To this end, we propose a novel diffusion-based system DIFFUVST, which models the generation of a series of visual descriptions as a single conditional denoising process. The stochastic and non-autoregressive nature of DIFFUVST at inference time allows it to generate highly diverse narratives more efficiently. In addition, DIFFUVST features a unique design with bi-directional text history guidance and multimodal adapter modules, which effectively improve inter-sentence coherence and image-to-text fidelity. Extensive experiments on the story generation task covering four fictional visual-story datasets demonstrate the superiority of DIFFUVST over traditional autoregressive models in terms of both text quality and inference speed.

关键词： stochastic systems

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Automating Dataset Production Using Generative Text and image Models 30

Automating Dataset Production Using Generative Text and Imag...

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Joint 30th International conference on Computational Linguistics and 14th International conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Thierauf, Christopher Abrams, Mitchell Scheutz, Matthias Tufts University United States

ISBN: (纸本)9782493814104

Practical and ethical dataset collection remains a challenge blocking many empirical methods in natural language processing, resulting in a lack of benchmarks or data on which to test hypotheses. We propose a solution to some of these areas by presenting a pipeline to reduce the research burden of producing image and text datasets when datasets may not exist. Our approach, with accompanying software tools, involves (1) generating text with LLMs;(2) creating accompanying image vignettes with text-to-image transformers;and (3) low-cost human validation. Based on existing literature that has struggled with quantitative evaluation (due to difficulty of data collection), we present the creation of 3 relevant datasets, and conduct a user study that demonstrates this approach is able to aid researchers in obtaining previously-challenging datasets. We provide sample data generated with this technique, the source code used to produce it, and discuss applicability and limitations. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： statistical tests

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statistical prior knowledge for robust medical image segmentation by level set 31

Statistical prior knowledge for robust medical image segment...

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31st European Signal processing conference, EUSIPCO 2023

作者： Hmida, Dorsaf Mezghich, Mohamed Amine Sakly, Ines Mhiri, Slim Ghorbel, Fouzi La Manouba University La Manouba2010 Tunisia

ISBN: (纸本)9789464593600

Region-based active contours give interesting results when applied for objects detection with poor contrasting edges and noise, but for medical image segmentation, where objects of interest are often present with inhomogeneity, these models fail. In [16], we presented an original approach that integrate prior knowledge on the target shape into active contours with free registration based on a complete and stable set of invariant descriptors. In this work, we intend to present a novel method that incorporate statistical prior knowledge into level set model for robust medical image segmentation resulting from machine learning algorithms. In fact, for medical imaging, in many situations like tumors segmentation, prior knowledge on shape is insufficient or unavailable because it vary from a patient to another. The adopted approach is demonstrated with two unsupervised learning methods. The first one use the Fuzzy C-Means (FCM) and the second one is based on the Hidden Markov Random Field Expectation-Maximization algorithm (HMRF-EM). Both of the two are used to classify the pixels of the target image into two classes: the object and the background to construct our reference shape to be used as prior knowledge. A weighting schema is then considered to guide the active contour model under both forces: the traditional and the proposed one. We validate the robustness and the accuracy of our approach using ground truths given from experts segmentation and standard data bases. Experiments and comparative studies with several recent works are highlighted and show that the integrated method achieves good performance results. © 2023 European Signal processing conference, EUSIPCO. All rights reserved.

关键词： Object detection

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Mitigating the Correlation Problem in Multi-Layer stochastic Circuits 8

Mitigating the Correlation Problem in Multi-Layer Stochastic...

引用

8th IEEE International conference on Rebooting Computing, ICRC 2023

作者： Hoffend, Owen Hayes, John P. University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI48109 United States

ISBN: (纸本)9798350382044

stochastic computing is a low-cost non-standard computer architecture that processes pseudo-random bitstreams. Its effectiveness, and that of other probabilistic methods, requires maintaining desired levels of correlation among interacting input bitstreams, for example, SCC = 0 or SCC = +1, where SCC is the stochastic cross-correlation metric. Correlation errors are systematic (bias-causing) errors that cannot be corrected by increasing bitstream length. A typical stochastic design C1 only controls correlation at its primary input lines. This is a fairly straightforward task, however it limits the scope of SC to 'single layer,' usually combinational, designs. In situations where a second processing layer C2 follows C1, the output correlation of C1 must satisfy the input correlation needs of C2. This can be done by inserting a (sequential) correlation control layer S12 between C1 and C2, which incurs high area and delay overhead. S12 transforms intralayer bitstreams Z with unknown or undesired SCC values into numerically equivalent ones Z∗ with desired correlation. The fundamental problem of designing C1 to produce Z∗ directly, thereby dispensing with S12, which apparently has not been considered before, is addressed in this paper. We focus on two-layer designs C1C2 requiring SCC = +1 between layers, and present a method called COMAX for (re)designing C1 so that it outputs bitstreams with correlation that is as close as possible to +1. We demonstrate on a representative image processing application that, compared to alternative correlation control techniques, COMAX reduces area by about 50% without reducing output image quality. © 2023 IEEE.

关键词： stochastic systems

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Bayesian Learning via Q-Exponential Process 37

Bayesian Learning via Q-Exponential Process

引用

37th conference on Neural Information processing Systems, NeurIPS 2023

作者： Li, Shuyi O'Connor, Michael Lan, Shiwei School of Mathematical & Statistical Sciences Arizona State University TempeAZ85287 United States

ISBN: (纸本)9781713899921

Regularization is one of the most fundamental topics in optimization, statistics and machine learning. To get sparsity in estimating a parameter u ∈ Rd, an q penalty term, uq, is usually added to the objective function. What is the probabilistic distribution corresponding to such q penalty? What is the correct stochastic process corresponding to uq when we model functions u ∈ Lq? This is important for statistically modeling high-dimensional objects such as images, with penalty to preserve certain properties, e.g. edges in the image. In this work, we generalize the q-exponential distribution (with density proportional to) exp (- 1/2 |u|q) to a stochastic process named q-exponential (Q-EP) process that corresponds to the Lq regularization of functions. The key step is to specify consistent multivariate q-exponential distributions by choosing from a large family of elliptic contour distributions. The work is closely related to Besov process which is usually defined in terms of series. Q-EP can be regarded as a definition of Besov process with explicit probabilistic formulation, direct control on the correlation strength, and tractable prediction formula. From the Bayesian perspective, QEP provides a flexible prior on functions with sharper penalty (q © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Random processes

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Application of Computer Vision for Efficient Maintenance of Cleanliness and Order in Commercial Areas: An Invention Approach for Automated Management in Commercial Zones 8

Application of Computer Vision for Efficient Maintenance of ...

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8th International conference on Communication and Information Systems, ICCIS 2024

作者： Llacta, Jesus David Villon Simeon, Wiler Yosef Hurtado Rosales, Jorge Martin Vega Universidad Continental Facultad de Ingeniería Huancayo Peru Universidad Continental Faculty of Engineering Huancayo Peru

ISBN: (纸本)9798331531171

This study explores the application of computer vision technology to improve the efficiency of maintaining cleanliness and order in high-traffic commercial areas. Given the growing importance of automation in managing commercial spaces, this paper proposes a technological model that utilizes strategically placed cameras and image processing algorithms to detect disorganized or dirty tables, sending real-time alerts to cleaning staff via a mobile application. The goal is to optimize resource use, reduce response times, and enhance customer experience. To validate the model, an experiment was conducted with a sample of 30 customers, measuring their satisfaction levels before and after the system's implementation. The results, analyzed through statistical tests, show a significant improvement in customer satisfaction. Additionally, the study discusses its limitations and proposes future research directions, including an evaluation of the costs associated with the automated system compared to traditional methods. © 2024 IEEE.

关键词： automation cleaning management commercial areas Computer vision customer experience detection algorithms image processing operational efficiency

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Multi-label image Classification model based on Multi-scale Semantic Attention and Graph Attention Network 3

Multi-label Image Classification model based on Multi-scale ...

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3rd International conference on Signal image processing and Communication, ICSIPC 2023

作者： Jiang, Lu Ye, JiHua Xiao, ShunJie Zong, Yi Jiang, AiWen School of Computer Information Engineering Jiangxi Normal University Nanchang330022 China

ISBN: (纸本)9781510670945

Current research on multi-label image classification mainly focuses on exploring the correlation between labels to improve the classification accuracy of multi-label images. However, in the existing methods, the label correlation is calculated based on the statistical information of the data. This label correlation is global and depends on the data set, and is not suitable for all samples. In the process of extracting image features, the The characteristic information of small objects is easily lost, resulting in low classification accuracy of small objects. For this reason, this paper innovatively proposes a multi-label image classification model based on multi-scale semantic attention and graph attention network. vector, followed by feature fusion to enhance the feature information of small objects, and then use the self-attention mechanism in the graph attention module to adaptively mine the correlation between categories in the image, and propose an attention regularization loss. The mAP of the model on the two public datasets of VOC 2007 and MS-COCO 2014 reached 95.5% and 83.4%, respectively, and most of the indicators are better than the existing state-of-the-art methods. © 2023 SPIE.

关键词： image classification

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REVISITING NATURAL SCENE statistical MODELING USING DEEP FEATURES FOR OPINION-UNAWARE image QUALITY ASSESSMENT 29

REVISITING NATURAL SCENE STATISTICAL MODELING USING DEEP FEA...

引用

IEEE International conference on image processing (ICIP)

作者： Mahmoudpour, Saeed Schelkens, Peter Vrije Univ Brussel VUB Imec Dept Elect & Informat ETRO Kapeldreef 75 B-3001 Leuven Belgium

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Opinion-unaware no-reference (OU-NR) methods for image quality assessment (IQA) are of great interest since they can predict visual quality independent of a reference image and knowledge of human quality opinions. Models of image naturalness trained on a corpus of pristine images have shown potential for developing OU-NR methods. However, the extracted features may not match the preferences of the human visual system (HVS). This paper aims to utilize the features of convolutional neural networks to achieve a richer representation of the naturalness space. In addition, the IQA processing steps from training to quality measurement are revisited and the naturalness model is improved by incorporating HVS-inspired criteria. Experimental results show the higher performance and generalizability of the naturalness model - constructed using HVS-aligned deep features - under different distortion types and image contents. The source code of the quality index is available at https://***/saeedmp/dni.

关键词： image quality Natural scene statistics Multivariate Gaussian Deep neural network Visual cortex

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