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Analytical and Neural processing of Multispectral Images for Identification of Water Pollution 7

Analytical and Neural Processing of Multispectral Images for...

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7th International conference on information, Control, and communication Technologies, ICCT 2023

作者： Diane, Sekou Vytovtov, Konstantin Barabanova, Elizaveta V.A. Trapeznikov Institute of Control Sciences Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9798350340945

The article is devoted to the development of algorithms for the analysis of water pollution based on visual information obtained using a multispectral camera mounted on the UAV. The structure of the algorithmic complex for the analysis of multispectral aerial images is proposed. In the developed approach, each of the analyzed images undergoes a preprocessing procedure that aligns and combines its spectral channels into a single multidimensional raster. The developed analytical algorithm allows processing and convolution of multispectral image channels using three mathematical operators - value filtering, contrast change and brightness change. At the same time, the choice of parameters for identifying pollution on the surface of water bodies is based on a preliminary stage, associated with maximizing the contrast ratio for the reference area. At the same time the proposed neural pollution analysis algorithm is based on the application of the sliding window method in combination with the convolutional architecture of the neural network classifier for image fragments located on a rectangular grid. Experimental studies have shown that the neural network algorithm wins in accuracy, and the analytical approach is easier to interpret from the point of view of an expert. © 2023 IEEE.

关键词： Water pollution

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Design and Evaluation of Generative Adversarial Network for Automated Image Colorization System 5

Design and Evaluation of Generative Adversarial Network for ...

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5th International conference on Smart Electronics and communication, ICOSEC 2024

作者： Vidyalakshmi, R. Gayathri, N. Aarthi, M.S. Kathirvel, N. Bhuvaneshvari, K. Sivaramkrishnan, M. Karpagam College of Engineering Department of Electrical and Electronics Engineering Coimbatore India Sri Eshwar College of Engineering Department of Computer Science Engineering Coimbatore India P.A. College of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Information Technology Chennai India

ISBN: (纸本)9798331504403

For the preservation of historical materials and the enhancement of visual representation, image colorization is crucial. Image colorization adds color to black-and-white or grayscale images, revitalizing old photographs and papers. It affords us a more immersive and relatable experience of history. Additionally, colorization imparts realism and liveliness to visual content. It facilitates audience engagement, emotional expression, and the development of visually captivating content that elicits a response and imparts a memory. The process of image colorization utilizing Deep Learning (DL) models is investigated in this study. The pre-processing techniques utilized to ensure data integrity and consistency are sourced from Kaggle. The image colorization challenge is built using DL models, notably Generative Adversarial Networks (GANs) and Convolutional Autoencoders (CAEs). These algorithms learn to colorize grayscale images through the use of training data. Peak Signal-to-Noise Ratio (PSNR), Learned Perceptual Image Patch Similarity (LPIPS), Structural Similarity Index (SSIM), and Fréchet Inception Distance (FID) are some of the metrics utilized to assess the models. The evaluation results indicate that the GAN model exhibits superior performance in image colorization compared to the CAE model. This is supported by higher PSNR and SSIM values, as well as lower values for LPIPS and FID. © 2024 IEEE.

关键词： Generative adversarial networks

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A Supervised Quantification of the Color Names Characterizing the visual Component Color in the ABCD Dermatological Criteria for a Further Melanoma Inspection 8

A Supervised Quantification of the Color Names Characterizin...

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8th International conference on information and communication Technologies for Ageing Well and e-Health (ICT4AWE)

作者： Daghrir, Jinen Tlig, Lotfi Bouchouicha, Moez Litaiem, Noureddine Zeglaoui, Faten Sayadi, Mounir Univ Sousse ISITCom Hammam Sousse 4011 Tunisia Univ Tunis ENSIT Lab SIME Tunis Tunisia Univ Toulon & Var Aix Marseille Univ CNRS LIS Toulon France Univ Tunis El Manar Charles Nicolle Hosp Fac Med Tunis Dept Dermatol Tunis Tunisia

ISBN: (纸本)9789897585661

Digital imaging is widely used for creating automated systems for medical purposes such as the diagnosis of certain kinds of diseases. One typical use of these computer vision diagnosis systems in dermatology is the inspection of melanoma skin cancer, which is one of the most fatal skin cancer. For the early detection of melanoma, a lot of systems have been proposed. Most of them use some visual features through image processing methods, such as color processing and border and texture inspection. Color variation is a good clue to differentiate melanoma and benign lesions. Thus, it is important to process skin lesion images to extract the various colors. The paper presents a new method that extracts the different color names from a skin lesion in a supervised way based on observed skin condition types. These features can ensure accurate melanoma detection with other types of features. To demonstrate the effectiveness of our suggested representation, we construct a prediction system for inspecting the malignancy of skin lesions. The experimental results show a consistent improvement in the prediction performance against other color representations.

关键词： Melanoma Inspection Medical Imaging Color Name Extraction Machine Learning Computer Vision Image processing

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Using Deep Learning to Build a Chatbot Supporting the Promotion of Speciality Dishes in Mekong Delta 1st

Using Deep Learning to Build a Chatbot Supporting the Promot...

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1st International conference on Intelligent Systems and Data Science, ISDS 2023

作者： Tran, Quoc-Khang Pham, Nguyen-Khang College of Information and Communication Technology Can Tho University Cantho92000 Viet Nam

ISBN: (纸本)9789819976652

Based on the images of dishes in the Mekong Delta along with questions about the dishes such as: What is the name of this dish? Where is it famous? What are the main ingredients? How is it made? An application chatbot will be built to promote the speciality dishes of the Mekong Delta. This report outlines a method for training a visual Question Answering (VQA) model for classification tasks using Transformer-based models, such as ViT for image data, BERT/PhoBERT for text data, or ViLT for simultaneous processing of image and text data. After that, a visual Encoder-Decoder model for the task of generating sentences will be built using the VQA model as a visual Encoder and a GPT-2 as a Decoder. The experimental dataset, which includes 7,694 photos of dishes from the Mekong Delta, is a subset of the datasets 30VNFoods and VinaFood21. The accuracy metric was used to evaluate the VQA models, and the results were relatively good. For Model 1: ViT and BERT, the accuracy scores for English and Vietnamese are 94% and 95%, respectively, while the accuracy score for Model 2: ViLT is over 92% on English-only. According to the ROUGE evaluation method, Model 3’s answer sentence generation model, on English only, which used ViLT along with GPT-2, yielded results of 49.92, 39.26, and 47.53 for the ROUGE-1, ROUGE-2, and ROUGE-L, respectively. Finally, the trained models were applied to build a chatbot. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decoding

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SeeClear: Semantic Distillation Enhances Pixel Condensation for Video Super-Resolution 38

SeeClear: Semantic Distillation Enhances Pixel Condensation ...

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38th conference on Neural information processing Systems, NeurIPS 2024

作者： Tang, Qi Zhao, Yao Liu, Meiqin Yao, Chao Institute of Information Science Beijing Jiaotong University China Visual Intelligence + X International Cooperation Joint Laboratory of MOE Beijing Jiaotong University China School of Computer and Communication Engineering University of Science and Technology Beijing China

Diffusion-based Video Super-Resolution (VSR) is renowned for generating perceptually realistic videos, yet it grapples with maintaining detail consistency across frames due to stochastic fluctuations. The traditional approach of pixel-level alignment is ineffective for diffusion-processed frames because of iterative disruptions. To overcome this, we introduce SeeClear-a novel VSR framework leveraging conditional video generation, orchestrated by instance-centric and channel-wise semantic controls. This framework integrates a Semantic Distiller and a Pixel Condenser, which synergize to extract and upscale semantic details from low-resolution frames. The Instance-Centric Alignment Module (InCAM) utilizes video-clip-wise tokens to dynamically relate pixels within and across frames, enhancing coherency. Additionally, the Channel-wise Texture Aggregation Memory (CaTeGory) infuses extrinsic knowledge, capitalizing on long-standing semantic textures. Our method also innovates the blurring diffusion process with the ResShift mechanism, finely balancing between sharpness and diffusion effects. Comprehensive experiments confirm our framework's advantage over state-of-the-art diffusion-based VSR techniques. The code is available: https://***/Tang1705/SeeClear-NeurIPS24. © 2024 Neural information processing systems foundation. All rights reserved.

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A Reversible Data Hiding Algorithm Based on Image Region Division 3

A Reversible Data Hiding Algorithm Based on Image Region Div...

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3rd International conference on Computer Graphics, Image, and Virtualization, ICCGIV 2023

作者： Chi, Lingyi Feng, Liuping Zhou, Yangquan Wang, Guokai Beijing Key Laboratory of Signal and Information Processing for High-end Printing Equipments Beijing Institute of Graphic Communication Beijing China

ISBN: (纸本)9781510671720

Many reversible data hiding algorithms only scan images in a fixed sequence, leading to low embedding capacity and poor stego-image quality. This paper proposes an adaptive algorithm that divides images into smooth and texture regions based on complexity, and uses different embedding algorithms for each region. Three predictors are used to generate asymmetric histograms and increase embedding capacity through two rounds of embedding in smooth regions. Pixel value ordering (PVO) is used for embedding fewer data in texture regions to maintain image quality. Experimental results show that the algorithm has high embedding capacity and low visual distortion. © 2023 SPIE.

关键词： Image quality

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Region Adaptive Self-Attention for an Accurate Facial Emotion Recognition

Region Adaptive Self-Attention for an Accurate Facial Emotio...

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14th Annual Summit and conference of the Asia-Pacific-Signal-and-information-processing-Association (APSIPA ASC)

作者： Lee, Seongmin Lee, Jeonghaeng Kim, Minsik Lee, Sanghoon Yonsei Univ Seoul South Korea

ISBN: (数字)9786165904773

ISBN: (纸本)9786165904773

Recognizing facial emotion is important in human communication, especially non-verbal communication. Despite the recent advancements in deep learning, facial emotion recognition has not achieved high performance compared to other classification tasks. Motivated by the mechanism of human visual perception, in which humans recognize the facial emotion by combining the informative facial regions (i.e., eyebrows, eyes, nose, and mouth) with different weights, we propose a novel facial emotion recognition network. To effectively train the informative facial regions, we introduce adaptive patch extraction and region adaptive self-attention schemes. The adaptive patch extraction initially decides the informative facial region based on the human facial perception. Then, based on the decided informative facial regions, attention weights between regions are estimated from the region adaptive self-attention scheme. Finally, by combining the features of facial regions with attention weights, the proposed network accurately recognizes facial emotion. The experimental results show that the proposed network effectively focuses on the informative region of the human face. Furthermore, through the comparison of facial emotion recognition accuracy, it is verified that the proposed network remarkably outperforms the state-of-the-art methods.

关键词： Emotion recognition Adaptive systems Face recognition Nose Mouth information processing Feature extraction

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Adaptive grayscale method based on weighted guided filtering

Adaptive grayscale method based on weighted guided filtering

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2022 International conference on Signal processing and communication Technology, SPCT 2022

作者： Bin, Xie Yan, Xu Guanchao, Wang Shumin, Yang Yanweia, Li College of Information Engineering Jiangxi University of Science and Technology Ganzhou341000 China

ISBN: (纸本)9781510664333

The essence of grayscale is to convert a three-dimensional color image into a one-dimensional grayscale image, and information loss is inevitable, so it is necessary to preserve the contrast, detail and structure of the original color image as much as possible. In this paper, we propose a new contrast retention grayscale method based on weighted guided filtering. Firstly, considering the different sensitivity of the human visual system to the three color channels of R, G, and B and the different information contained in the images of each channel, a new contrast feature coefficient is defined to better retain the contrast characteristics of the original color image. Secondly, in order to make the grayscale image have a better sense of hierarchy, the saturation feature holding term is designed. Finally, the discrete search strategy is used to optimize the proposed new model, and on this basis, weighted guided filtering is used to enhance the edge details of grayscale images. A large number of experimental results on the three datasets show that the grayscale effect of the proposed method is better than that of the traditional grayscale method. © 2023 SPIE.

关键词： Image enhancement

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High-Fidelity Free-View Talking Head Synthesis for Low-Bandwidth Video conference

High-Fidelity Free-View Talking Head Synthesis for Low-Bandw...

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2023 IEEE International conference on visual communications and Image processing, VCIP 2023

作者： Zhang, Zhiyu Tang, Anni Zhu, Chen Lu, Guo Xie, Rong Song, Li Shanghai Jiao Tong University Institute of Image Communication and Network Engineering Shanghai China

ISBN: (纸本)9798350359855

As video conferencing becomes an indispensable part of human's daliy life, how to achieve a high-fidelity calling experience under low bandwidth has been a popular and challenging issue. Deep generative models have great potential in low-bandwidth facial video compression due to the excellent generation capability based on abridged information. Nevertheless, exsiting deep generation-based compression methods tend to handle motion information in pure 2D or pseudo 3D space, causing facial distortion when large head poses are encountered. In this paper, we propose a 3D-aware high-fidelity facial video conferencing system based on a parameterized NeRF-based face model. Through the compression of the parameterized face model and the transmisstion of extracted facial parameters, we implement high-fidelity talking head synthesis for video conferencing at an ultra-low bitrate. Additionally, the 3D perception capability of the system allows for viewpoint control over the head, achieving higher interactivity and practicability. Extensive experiments verify the effectiveness of the proposed 3D-aware high-fidelity free-view facial video conferencing system. © 2023 IEEE.

关键词： Video conferencing

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Enhanced Multi-Class Skin Cancer Classification Using Attention-Based Convolutional Neural Network with HOG Feature Extraction and ROI-Centred Pre-processing

Enhanced Multi-Class Skin Cancer Classification Using Attent...

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2024 International conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Hariarasi, A. Banumathi, J. Faculty of Information and Communication Engineering University College of Engineering Anna University Nagercoil629001 India Department of Information Technology University College of Engineering Anna University Nagercoil629001 India

ISBN: (纸本)9798331510299

Skin cancer, a common increasingly prevalent disease, encompasses eight distinct diagnostic categories, posing significant challenges for dermatologists due to the highly similar visual characteristics of these categories. Traditional diagnostic accuracy for multiclass skin cancer detection ranges between 70% and 99%, often falling short in multi-class scenarios compared to binary classification performance. To address this, an Attention-Based Convolutional Neural Network (CNN) designed this research for multiclass skin cancer classification. The proposed work begins with a robust pre-processing stage involving Min-Max scaling for normalization and Region of Interest (ROI) cropping, which effectively reduces computational complexity. RGB images are transformed into single-channel grayscale, and ROI cropping emphasizes relevant regions, aiding in the model's localization capability. Following feature extraction, Histogram of Oriented Gradients (HOG) is used to capture important material details, improving the quality of input features. The classification stage utilizes the Attention based CNN to focus on diagnostically relevant areas, thereby improving classification accuracy across multiple skin cancer types. The proposed method demonstrates a significant improvement in diagnostic performance, offering a promising solution for efficient and accurate cancer classification. The accuracy of the proposed method is achieved as 96% and the specificity is of 0.92. © 2024 IEEE.

关键词： Convolutional neural networks

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