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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

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Chambolle's Projection-based Total Variation Deepnet Layer for image Denoising 19

Chambolle's Projection-based Total Variation Deepnet Layer f...

引用

19th ieee international conference on Intelligent computer Communication and processing conference, ICCP 2023

作者： Grosu, George Florin Terebes, Romulus Technical University of Cluj-Napoca Communications Department Cluj-Napoca Romania

ISBN: (纸本)9798350370355

This research paper presents the implementation and evaluation of a Total Variation (TV) layer within a deep learning framework for image denoising tasks. The TV layer is based on Chambolle's projection method and aims to enhance the performance of a baseline denoising network. The study investigates the effectiveness of incorporating a single TV denoising layer, which adds only six parameters to an arbitrary baseline autoencoder. The resulting models are evaluated across three datasets with different Gaussian noise. By comparing an arbitrary network architecture with an identical counterpart model that incorporates the TV denoising layer, the results indicate significant improvements in performance. The magnitude of these performance gains diminishes as the variance of noise increases. Notably, for lower Gaussian noise variance such as for a standard deviation of 25, the gain in terms of the mean peak signal-to-noise ratio (PSNR) metric is estimated to be approximately +0.89 dB, a discernible improvement that is visually perceptible. Although the resulting model has no significant increase in terms of model size, it does present significant increases in complexity. © 2023 ieee.

关键词： image denoising

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Segmentation of skin layers in ultrasound images using a crowdsourcing and deep learning-based system 20

Segmentation of skin layers in ultrasound images using a cro...

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20th ACS/ieee international conference on computer Systems and applications (AICCSA)

作者： Moran, Maira Vaz Oliveira, Larissa Aparecida Faria, Marcelo Bastos, Luciana Giraldi, Gilson Canella, Clarissa Conci, Aura Univ Fed Fluminense Niteroi Brazil Univ Estado Rio de Janeiro Rio De Janeiro Brazil Lab Nacl Comp Cient Petropolis Brazil

ISBN: (纸本)9798350319439

Ultrasonography (US) has demonstrated many advantages in the detection, characterization, and monitoring of different diseases. Through high frequency probes, it is possible to visualize and characterize the anatomical layers, such as tissues, which can constitute a very helpful supporting tool in several procedures, e.g., surgeries. However, the visual identification of tissues in this type of image is still a challenge for some professionals. In this work, we evaluate deep learning (DL) segmentation algorithms for the tissue segmentation task in US. Moreover, we briefly assess whether their performance can be improved by including a crowdsourcing step. In order to perform the segmentation task, different segmentation models are trained, as posteriorly, the crowdsourcing step is included. The proposed approach is composed of the following steps: 1- automatic segmentation using a deep learning algorithm;2 - crowd evaluation and correction of the results. In order to perform step 1, different segmentation models are trained. The second step includes a visual interface where users can: a) validate the quality of the automatic segmentation;or b) correct the segmentation whether the DL result is inconsistent. All users are scored, denoting the quality of their annotations, considering the manual annotations provided by an expert for a small group of images. In order to evaluate the method and compare it to a DL algorithms alone, a total of 100 US images were used. Our experiments show that the inclusion of crowdsourcing significantly improved the performance of the tissue segmentation task compared to using the DL models alone. The performance of our method demonstrated the feasibility of applying this type of solution for the considered problem of segmenting tissues in US facial images. Moreover, the results suggest that this tool can be employed as an auxiliary tool in oral procedures.

关键词： image processing Ultrasound Segmentation Collective Intelligence Crowdsourcing

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Maturity Level Detection of Banana Fruit by image processing Technique 8

Maturity Level Detection of Banana Fruit by Image Processing...

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8th international conference on computers and Devices for Communication, CODEC 2023

作者： Maity, Indranil Kashyap, Manas Sarkar, Moubani Kumar, Rohan West Bengal Kolkata India Kolkata UEM Department of Electronics and Communication Engineering West Bengal Kolkata India

ISBN: (纸本)9798350317176

The assessment of fruit ripeness is a critical task in various agricultural and food processing applications. Traditional methods of ripeness evaluation, which relied on human judgment, were subjective and labor-intensive. In this paper, a comprehensive approach is presented for fruit ripeness detection of banana using image processing technique. An algorithm was developed in MATLAB for finding different image components of banana sample and fundamental parameters like the number of yellow and white pixels. © 2023 ieee.

关键词： image processing

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Construction of image Recognition Model Based on Second-Order Local Feature Aggregation 3

Construction of Image Recognition Model Based on Second-Orde...

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3rd ieee international conference on Power, Electronics and computer applications, ICPECA 2023

作者： Min, Gang Shu, Yajie Zhang, Qian Zhao, Huaju Shu, Zhong Jingmen Mobile Media Co. Ltd Jingmen Daily Hubei Jingmen China Jingchu University of Technology School of Electronic Information Engineering Hubei Jingmen China

ISBN: (纸本)9781665472784

This paper analyzes the mathematical model constructed by the Net VLAD method, and proposes a second-order function based on the Net VLAD method to solve the problem that the obtained image feature encoding is a first-order function of the local information of the image. image feature encoding learning dictionary. Use the Net VLAD classifier as the fully connected layer of the CNN model, design the reconstruction residual weight, and redesign the image feature extraction module for the control of the reconstruction residual value range;when the image feature encoding in the Net VLAD learning dictionary is described by a first-order vector, there is a problem that it is difficult to derive and calculate the minimum value of image features. In the image dimensionality reduction transformation kernel, the first-order linear function is promoted to a second-order nonlinear function, and the image dimensionality reduction transformation kernel is used as a breakthrough, and the second-order nonlinear transformation kernel is used. The activation function has the characteristic of being derivable, and the local features of the image samples are subjected to dimensionality reduction processing to achieve more accurate dimensionality reduction calculations for image features. In the experiment, the accuracy of image feature classification, the recognition rate of image samples, and the training and testing efficiency of image samples are used as verification data. By using the CNN deep convolutional neural network model, the Net VLAD learning dictionary and the improved learning dictionary proposed in this paper, the Multiple image sample sets are compared for training and testing, which verifies that the improved learning dictionary based on Net VLAD proposed in this paper has obvious advantages. © 2023 ieee.

关键词： image enhancement

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Research on Elderly Services Based on Matlab image processing and SPSSPRO 6

Research on Elderly Services Based on Matlab Image Processin...

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2023 ieee 6th international conference on Information Systems and computer Aided Education, ICISCAE 2023

作者： Yang, Yijin North University of China Shanxi Taiyuan China

ISBN: (纸本)9798350313444

At present, the trend of aging in China is getting more and more serious, and it is of great significance to predict the number of beds demanded by the elderly. In this paper, we firstly investigated and analyzed, we chose the number of the elderly population, the "elderly model 9037", the constant level of Chinese household consumption and the degree of self-reliance of the elderly as the variables of the model of the demand for elderly service beds. By reviewing the number of elderly population and the number of labor employment in China from 2009-2018 using polynomial fitting to reasonably predict the values of the two variables for each year from 2019-2025, substituting them into the model of demand for senior care service beds to derive the values of the demand for senior care service beds. Secondly, the model of balance of payments and revenues is established to consider the costs and revenues, and the amount of revenues and expenditures is calculated according to the different types of elderly people. The potential revenue in different years is calculated to quantify the size of the market opportunity. Meanwhile, to further address the current situation of the prominent contradiction between supply and demand of elderly facilities in Jiancaoping District, Taiyuan City, this paper utilizes Matlab image processing technology and SPSSPRO software to carry out K-mean clustering algorithm, and designs a reasonable planning scheme for the elderly facilities in the district. © 2023 ieee.

关键词： 9037 Pension Model K-mean Clustering Algorithm Matlab image processing Polynomial Fitting SPSSPRO

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Enabling ISPless Low-Power computer Vision 23

Enabling ISPless Low-Power Computer Vision

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23rd ieee/CVF Winter conference on applications of computer Vision (WACV)

作者： Datta, Gourav Liu, Zeyu Yin, Zihan Sun, Linyu Jaiswal, Akhilesh R. Beerel, Peter A. Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9781665493468

Current computer vision (CV) systems use an image signal processing (ISP) unit to convert the high resolution raw images captured by image sensors to visually pleasing RGB images. Typically, CV models are trained on these RGB images and have yielded state-of-the-art (SOTA) performance on a wide range of complex vision tasks, such as object detection. In addition, in order to deploy these models on resource-constrained low-power devices, recent works have proposed in-sensor and in-pixel computing approaches that try to partly/fully bypass the ISP and yield significant bandwidth reduction between the image sensor and the CV processing unit by downsampling the activation maps in the initial convolutional neural network (CNN) layers. However, direct inference on the raw images degrades the test accuracy due to the difference in covariance of the raw images captured by the image sensors compared to the ISPprocessed images used for training. Moreover, it is difficult to train deep CV models on raw images, because most (if not all) large-scale open-source datasets consist of RGB images. To mitigate this concern, we propose to invert the ISP pipeline, which can convert the RGB images of any dataset to its raw counterparts, and enable model training on raw images. We release the raw version of the COCO dataset, a large-scale benchmark for generic high-level vision tasks. For ISP-less CV systems, training on these raw images result in a similar to 7.1% increase in test accuracy on the visual wake works (VWW) dataset compared to relying on training with traditional ISP-processed RGB datasets. To further improve the accuracy of ISP-less CV models and to increase the energy and bandwidth benefits obtained by in-sensor/in-pixel computing, we propose an energy-efficient form of analog in-pixel demosaicing that may be coupled with in-pixel CNN computations. When evaluated on raw images captured by real sensors from the PASCALRAWdataset, our approach results in a 8.1% increase in mA

关键词： Training image sensors computer vision Visualization Computational modeling Bandwidth Sensors

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Staged Progressive Single image Generative Adversarial Network 3

Staged Progressive Single Image Generative Adversarial Netwo...

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3rd international conference on Electronic Information Engineering and computer Communication, EIECC 2023

作者： Cai, Qiang Xiong, Shaohua Wang, Chen Li, Ziyao School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing China

ISBN: (纸本)9798350359961

This paper focuses on exploring unconditional generative model training methods for single image learning and proposes a new staged progressive single image generative adversarial network (SPSI-GAN). By conducting staged multiscale training on a single image, we gradually learn the feature block information of different areas within the image and pay more systematic attention to the overall structure of the image, the basic outline of the scene, and the details of the content. Experiments verified the application effect of this method in multiple image tasks and compared it with other mainstream methods. This method shows excellence in generated image quality and feature distribution and could improve training efficiency and performance metrics while maintaining high quality generated images. © 2023 ieee.

关键词： Generative Adversarial Network image processing Single-image learning

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Fibonet: A Light-weight and Efficient Neural Network for image Segmentation 30

Fibonet: A Light-weight and Efficient Neural Network for Ima...

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30th ieee international conference on image processing (ICIP)

作者： Wu, Ruohao Xiao, Xi Hu, Guangwu Zhao, Hanqing Zhang, Han Peng, Yongqing

ISBN: (纸本)9781728198354

In recent years, accurate models for image segmentation have become larger and more complex. However, it is hard to apply them into embedded devices which usually have limited space for data, energy and computing. Meanwhile, since many embedded devices do not support modifications of computing units, simple models with such requirements cannot be adapted to these devices. To achieve high accuracy for image segmentation in embedded devices, we propose a light-weight and efficient neural network, named Fibonet. Fibonet is constructed by cascading two Fiboblocks, with the Fibonacci structure which adjusts the combination of basic computing units through skip connections and feature reusing so that it is less computationally intensive and dataset-demanding. The experiments demonstrate that Fibonet can be embedded to the mobile terminal for real-time segmentation and can effectively balance accuracy and computing resources. Compared with Resnet18, Fibonet achieves 10.8% higher accuracy performance (mIoU: 0.533 vs. 0.481) with 84.4% fewer parameters (0.441M vs. 2.82M) using similar model width and depth.

关键词： Fibonacci Structure Computing Resources image Segmentation

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Privacy-Preserving, Low-Storage, and High-Quality image processing in IoT and Clouds

Privacy-Preserving, Low-Storage, and High-Quality Image Proc...

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2023 international conference on Intelligent Communication and computer Engineering, ICICCE 2023

作者： Zhao, Shushan Chiang, Wes Central Connecticut State University Department of Computer Electronics & Graphics Technology New BritainCT United States International Business & Strategy Brock University Department of Marketing St. CatharinesON Canada

ISBN: (纸本)9798350395617

In Internet of Things (IoT) and clouds, while many image processing tasks are outsourced to third party cloud computing platforms, image processing in encrypted domain is needed in many services for data confidentiality and privacy-preserving purposes. There are many solutions for image scaling, cropping and color correction in this context. However, most of the solutions are not compatible with each other and there are few solutions supporting all these operations. In this paper, we propose a scheme that supports image scaling, cropping, and color correction in encrypted domain with minimum storage overhead. An optional improvement solution is added for a conditional quality issue in certain circumstances. Analysis shows storage requirement is drastically reduced while performance otherwise remains unchanged. © 2023 ieee.

关键词： Internet of things

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Panoramic image depth estimation based on deformable convolution and attention mechanism 2

Panoramic image depth estimation based on deformable convolu...

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2nd international conference on image processing, computer Vision and Machine Learning, ICICML 2023

作者： Shao, Fangfang Xu, Chao Han, Cheng Wang, Shiwen Changchun University of Science and Technology School of Computer and Science Changchun China

ISBN: (纸本)9798350331417

Currently, mainstream panoramic depth estimation methods primarily focus on correcting distortion effects. For Convolutional Neural Networks (CNN) based on standard convolution, it is difficult to fully perceive the complexity of the panoramic structure due to the fixation of its receptive fields. In this research, we employ deformable convolutions as a replacement for traditional standard convolutions, allowing sample points to adapt more flexibly to changes in object shapes, thereby extending the effective receptive field. Furthermore, this study incorporates the Bottleneck Attention Module (BAM) attention model in the feature fusion module to enhance focus on key *** a series of experiments, the effectiveness of deformable convolution and BAM is verified. © 2023 ieee.

关键词： BAM deformable convolutions depth estimation

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