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Content-Aware image Smoothing Based on Fuzzy Clustering 19th

Content-Aware Image Smoothing Based on Fuzzy Clustering

引用

19th International conference on Information processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2022

作者： Antunes-Santos, Felipe Lopez-Molina, Carlos Mir-Fuentes, Arnau Mendioroz, Maite De Baets, Bernard Department of Estadistica Informatica y Matematicas Universidad Pública de Navarra Pamplona31006 Spain KERMIT Department of Data Analysis and Mathematical Modelling Ghent University Ghent9000 Belgium NavarraBiomed Hospital Universitario de Navarra Pamplona31008 Spain

ISBN: (纸本)9783031089732

Literature contains a large variety of content-aware smoothing methods. As opposed to classical smoothing methods, content-aware ones intend to regularize the image while avoiding the loss of relevant visual information. In this work, we propose a novel approach to content-aware image smoothing based on fuzzy clustering, specifically the Spatial Fuzzy c-Means (SFCM) algorithm. We develop the proposal and put it to the test in the context of automatic analysis of immunohistochemistry imagery for neural tissue analysis. © 2022, Springer Nature Switzerland AG.

关键词： Fuzzy clustering

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RETINOSCAN: Retinal image-Based Diabetic Retinopathy Detection

RETINOSCAN: Retinal Image-Based Diabetic Retinopathy Detecti...

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Integrated Circuits and Communication Systems (ICICACS), ieee International conference on

作者： Nanditha Krishna Ashrita Dikshit V A Kavana G R Nishita S Rakshit Preran B Department of Medical Electronics Engineering Center for Medical Electronics and Computational Intelligence Dayananda Sagar College of Engineering Bengaluru India Department of Medical Electronics Engineering Dayananda Sagar College of Engineering Bengaluru India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Diabetic Retinopathy (DR) is a leading cause of vision impairment worldwide, necessitating early detection and accurate diagnosis. This study presents a reliable system that leverages advanced image processing and deep learning techniques to identify and classify DR stages. MATLAB is utilized for fundus image preprocessing, integrating a pre-trained Inception-v3 model for feature extraction and stage-wise classification. A web-based graphical user interface enhances clinical applicability. Deployment through MATLAB Compiler and Docker ensures real-world accessibility. The model, trained and validated on a comprehensive retinal image dataset, demonstrates high reliability and accuracy. This system has the potential to improve patient outcomes and establish new standards in automated retinal disease diagnostics.

关键词： Deep learning Diabetic retinopathy Accuracy visual impairment Retina Mathematical models MATLAB Integrated circuit modeling Graphical user interfaces Diseases

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Customized K-Means Clustering Based Color image Segmentation Measuring PRI

Customized K-Means Clustering Based Color Image Segmentation...

引用

International conference on Electronics, communications and Information Technology (ICECIT)

作者： Islam, Md Zahidul Nahar, Shamsun Islam, Sm Shariful Islam, Saria Mukherjee, Arnab Ershad, Lasker Khulna Univ Math Discipline Sci Engn & Technol Sch Khulna 9208 Bangladesh

ISBN: (纸本)9781665423632

image segmentation has been considered as one of the most important tool for image processing in many application of computer vision, remote sensing, medical science and so on. The principle target of image segmentation is the way toward partitioning an image into various fragments that cover the whole image. However, it is very challenging task to segment or partitioning the images exactly. This paper proposes a customized k-means clustering based image segmentation method. This customized form of this technique implies k-means clustering algorithm, de-noising factor associated with velocity field of each pixel and edge distinguishing using Canny edge detector. For the evaluation, we conduct the experiment on the Berkeley segmentation dataset (BSDS300). Probabilistic rand index (PRI) is utilized for the similarity measure of segmented images and their predetermined ground truths. The experimental results reveal that the proposed method is highly capable of partitioning the images and in some cases, the outcomes of this method is better than the state-of-the-art methods. .

关键词： Color image image segmentation K-means clustering PRI

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HETEROGENEOUS TWO-STREAM NETWORK WITH HIERARCHICAL FEATURE PREFUSION FOR MULTISPECTRAL PAN-SHARPENING

HETEROGENEOUS TWO-STREAM NETWORK WITH HIERARCHICAL FEATURE P...

引用

ieee International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Wang, Dong Bai, Yunpeng Bai, Bendu Wu, Chanyue Li, Ying Northwestern Polytech Univ Sch Comp Sci Shaanxi Prov Key Lab Speech & Image Informat Proc Natl Engn Lab Integrated AeroSp Ground Ocean Big Xian Peoples R China Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian Peoples R China

ISBN: (纸本)9781728176055

Multispectral (MS) pan-sharpening aims at producing a high spatial resolution (HR) MS image by fusing a single-band HR panchromatic (PAN) image and a corresponding MS image with low spatial resolution. In this paper, we propose a heterogeneous two-stream network (HTSNet) with hierarchical feature prefusion for MS pan-sharpening. The HTSNet employs a heterogeneous group of spatial and spectral streams for spatial and spectral information extraction, respectively. The spatial stream utilizes a 2D CNN for spatial information extraction from the PAN images, and the spectral stream obtains spectral feature cubes from the MS images by a 3D CNN. At the same time, a prefusion module is introduced to prefuse the spatial details with spectral information and transfer information between different streams, which can enhance later processing. In the experiment, the Gaofen-2 satellite dataset is utilized to compare the proposed method with the state-of-the-art MS pan-sharpening methods. Experimental results demonstrate the superiority of our HTSNet in terms of visual effect and quantitative qualities.

关键词： Pan-sharpening heterogeneous network two-stream network hierarchical feature prefusion

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MCTS-Based Robotic Exploration for Scene Graph Generation 6th

MCTS-Based Robotic Exploration for Scene Graph Generation

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6th International conference on Cognitive Systems and Signal processing, ICCSIP 2021

作者： Zhou, Fangbo Liu, Huaping Li, Xinghang Zhao, Huailin School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9789811692468

Many researchers have used scene graphs to improve performance in some tasks in recent years, such as complete image matching, image generation, and visual questions answers. However, the current scene graph generation mainly constructs a single picture or a scene graph of a part of the scene. To construct a scene graph of the entire room, the agent needs to explore the entire room autonomously. We propose a scene graph generation method based on Monte Carlo Tree Search (MCTS). In the agent’s exploration of the room, the agent uses MCTS to select a reliable location among the locations that the agent can reach in the next step. After multiple searches, the agent can find most or all objects in the room. Experiments show that our method helps the agent finds more objects in a fixed step size than the agent’s random exploration of the room. The agent can create a complete scene graph because it sees most of the objects in the room. © 2022, Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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RPG-Diff: Precise Adversarial Defense Based on Regional Positioning Guidance

RPG-Diff: Precise Adversarial Defense Based on Regional Posi...

引用

ieee International conference on Trust, Security and Privacy in Computing and communications (TrustCom)

作者： Haotian Wang Jing Liu College of Computer Science Inner Mongolia University Hohhot China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Deep learning models have achieved unprecedented success in various visual tasks. However, they are highly susceptible to adversarial perturbations that are imperceptible to humans. The presence of this risk significantly hinders the deployment of deep learning models in real-world scenarios, especially in safety-critical contexts. Input reconstruction has emerged as an effective defense mechanism against adversarial examples, capable of countering both known and unknown adversarial attacks. Nonetheless, methods based on input reconstruction often suffer from slow training speeds and unsatisfactory reconstruction quality, limiting their practical *** address these issues, we propose a region localization-guided input reconstruction defense method, named RPG-Diff. This approach utilizes image segmentation algorithms to locate key regions, which then guide the reconstruction process of the diffusion model. By leveraging information from these key regions, the diffusion model can more effectively eliminate adversarial perturbations while maintaining consistency between the reconstructed output and the original input. This significantly reduces the impact of adversarial perturbations on model accuracy. Experimental results demonstrate that, compared to existing methods, our approach exhibits superior defensive performance against various adversarial attacks and surpasses other preprocessing-based defense methods in terms of robust accuracy and average generation time.

关键词： Location awareness Deep learning Training image segmentation visualization Accuracy Perturbation methods Diffusion models Security image reconstruction

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Design of Distributed Ultra-High-Definition Resolution Display System

Design of Distributed Ultra-High-Definition Resolution Displ...

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International conference on Computer Science and Network Technology (ICCSNT)

作者： Qiaojie Huang Peiqiang Liu Qiye Fu Yunjian Xu School of Intelligent Engineering Guangdong Agriculture Industry Business Polytechnic Guangzhou China Computing Network Research and Operation Department GuangDong Branch China United Network Communications Corporation Limited Guangzhou China

A practical and scalable ultra-high-definition resolution display system for multi-screen display wall is constructed based on multiple distributed low-definition display nodes, and the collaborative processing mechanism between distributed display nodes is the key to the performance of the display system. In order to improve the performance of image synchronization between distributed nodes, a node collaboration mechanism based on ieee1588 time synchronization protocol and phase-locked loop is proposed. A synchronization processing system applied to ultra-high-definition resolution displays is designed, which not only shortens the image processing time but also improves the image synchronization processing performance. The system synchronization test results show that the maximum display frame difference between splicing display units is 0.47 frame and the average system delay is 98.67ms. The provided system can effectively shorten system data processing time, solve synchronization problems of ultra-high-definition resolution displays, meet requirements of large screen display images, and improve users' experience effect.

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Computer Vision and Voice Assisted image Captioning Framework for visually Impaired Individuals using Deep Learning Approach

Computer Vision and Voice Assisted Image Captioning Framewor...

引用

Global conference for Advancement in Technology (GCAT)

作者： K M Safiya R Pandian Department of Computer Science and Engineering Sathyabama Institute of Science and Technology (Deemed to be University) Chennai India Department of Electronics and Communication Engg Sathyabama Institute of Science and Technology (Deemed to be University) Chennai India

Blind or visually challenged have considerable obstacles when obtaining visual material, restricting their capacity to derive meaningful information from pictures. This research proposing a novel framework combining computer vision with voice-based image captioning using deep learning techniques. The proposed system employs VGGNet-16 model for photo processing and long short-term memory networks (LSTMs) for natural language processing. The models used in this research were trained on Flickr8k, Flickr30k, and a bespoke dataset. Subsequently, these models were deployed on a Raspberry Pi 4B single-board computer that was equipped with a graphics processing unit. A two-fold approach is used, whereby the first phase entails exposing the input image to preprocessing using a pre-established VGGNet-16 model. This methodology enables the retrieval of relevant visual attributes, therefore capturing the intrinsic semantic content of the picture. The aforementioned attributes are fed into a language model based on LSTM to generate explanatory captions. To enhance communication with visually impaired people, the generated captions are converted into audible speech using text-to-speech synthesis technology. The effectiveness of the proposed architecture is evaluated via extensive experiments using benchmark datasets and real-time images obtained with a NoIR camera unit. The generated captions are evaluated using quantitative assessment criteria, namely BLEU and ROUGE scores. The developed VGGNet16 model has exceptional performance in terms of accuracy (95.620%), precision (96.928%), recall (87.879%), and F1 score (92.182%). The results suggest that using computer vision in conjunction with voice-based image captioning offers a promising solution for addressing the challenges faced by those with visual impairments in perceiving and comprehending visual content.

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Oct image Blind Despeckling Based on Gradient Guided Filter with Speckle Statistical Prior

Oct Image Blind Despeckling Based on Gradient Guided Filter ...

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

作者： Sanqian Li Muxing Xiong Bing Yang Xiaoqing Zhang Risa Higashita Jiang Liu Department of Computer Science and Engineering Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Tomey Corporation Japan

Optical coherence tomography (OCT) imaging technique has been widely used for ocular disease diagnosis. However, speckles occur in OCT images due to the property of coherent imaging, inevitably affecting the visual quality and clinical analysis. To alleviate this problem, we propose a novel gradient-guided speckle image filtering method (GGSF) with structure enhancement for directly removing speckles in OCT images. Specifically, the multiplicative characteristic of speckle noise is incorporated into the guided filtering processing for modeling raw OCT images. To avoid getting trapped in image distortions, we further employ gradient regularization to integrate the structure prior information into the guided speckle image filtering procedure. Additionally, we introduce the statistical property of speckle noise obeying a gamma distribution into the least square method solver for the resulting non-convex GGSF model. Experimental results on the AS-OCT dataset demonstrate the effectiveness of GGSF for OCT image despeckling compared with competitive methods. Furthermore, we validate the benefits of GGSF for subsequent clinical analysis with the CM-OCT dataset.

关键词： Optical filters visualization Signal processing algorithms Speckle Optical imaging image filtering Clinical diagnosis

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SAE-wAMD: A Self-Attention Enhanced convolution neural network for fine-grained classification of wet Age-Related Macular Degeneration using OCT images

SAE-wAMD: A Self-Attention Enhanced convolution neural netwo...

引用

International conference on image processing, Computer Vision and Machine Learning (ICICML)

作者： Haihong, E. Ding, Junpeng Yuan, Lifei Beijing Univ Posts & Telecommun Beijing Peoples R China Hebei Eye Hosp Xingtai Hebei Peoples R China

ISBN: (纸本)9781665464680

Wet Age-related Macular Degeneration (wet AMD) which is the leading cause of irreversible visual loss for elder people can be subdivided into Neovascular AMD and Polypoid Choroidal Vasculopathy (PCV). Their clinical manifestations are similar, but the treatment schemes are different. To diagnose these two subtypes, Optical Coherence Tomography (OCT) is the crucial technique relied upon by ophthalmologists. Recently, the convolutional neural network (CNN) has been widely explored in the automatic diagnosis of fundus diseases including AMD. Their excellent performance proved that the CNN-based deep learning model has great potential in the fine-grained classification task of wet AMD. However, the improvement on existing methods is limited due to the tiny lesions area and the high similarity of OCT images. To overcome these challenges, in this paper, we propose a self-attention enhanced CNN framework for fine-grained classification of wet AMD (SAE-wAMD) that contains the SAE-VGG16 network and a self-supervised pre-training strategy. Specifically, to capture global features and determine the location of lesions area, we designed the SAE-VGG16 model which integrates self-attention layers and convolution layers in a VGG model architecture. Furthermore, we introduced the method of contrastive learning to use self-supervised pre-training to help the SAE-VGG16 model precisely distinguish every OCT image instance and further promote its performance in the classification task. Experimental results on our OCT dataset which contains more than 4900 OCT images including neovascular AMD, PCV and normal instances show that our SAE-wAMD model outperforms state-of-the-art methods, and verify the effectiveness of our model.

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