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Research on visual Cognitive Model Modeling based on Design Psychology

Research on Visual Cognitive Model Modeling based on Design ...

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2022 International conference on Culture-Oriented Science and Technology, CoST 2022

作者： Hua, Tianyu Wang, Shuang Liu, Jingyu Jiang, Jian State Key Laboratory of Media Convergence and Communication Communication University of China China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism China China China Digital Culture Group Co. Ltd China

ISBN: (数字)9781665462488

ISBN: (纸本)9781665462488

This article is based on the theoretical achievements of design psychology, takes graphic poster design as the research object, and combines experimental psychology methods with information processing technology to construct a visual cognitive model that can serve art design. This article firstly starts from the laws of formal beauty in design psychology, and screens the formal beauty features of posters. Secondly, a subjective evaluation experiment for the formal beauty characteristics of the poster materials was conducted, and correlation analysis and factor analysis on the processed experimental data were completed. Finally, this article uses a variety of machine learning algorithms to construct a visual cognitive prediction model. This article summarizes seven low-level and four high-level feature descriptors of the beauty of poster form, and constructs a prediction model from low-level features to high-level features. In addition, this article also quantifies the 'Balance' feature which has the highest word frequency in the low-level features, realizing the calculation of the balance degree of the poster image. © 2022 IEEE.

关键词： Machine learning

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VisionPal: visual Assistant System for the visually Impaired People

VisionPal: Visual Assistant System for the Visually Impaired...

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2023 International conference on Innovative Computing, Intelligent communication and Smart Electrical Systems, ICSES 2023

作者： Caldera, Sasanka Madushika, Vishwani Herath, Sasini Alwis, Sahan Thelijjagoda, Samantha Krishara, Jenny Sri Lanka Institute of Information Technology Department of Information Technology Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Sri Lanka

ISBN: (纸本)9798350319200

visual impairment affects millions of individuals worldwide, hindering their ability to interact with society. This research introduces VisionPal, a comprehensive smartphone-based application for the visually Impaired. The system employs advanced image processing and deep learning techniques to address the challenges visually impaired individuals face in outdoor environments. The core of VisionPal is a smartphone application that integrates features such as obstacle detection, vacant seat identification, road sign recognition, and pedestrian crossing assistance. The background and literature survey delve into related works that are focused on this study. Leveraging YOLOv8, InceptionV3, Tesseract, and MobileNetV2 models, the methodology incorporates machine learning models to detect obstacles, vacant seats, crowd levels, road signs, and color lights. Researchers developed a mobile application allowing users to interact via vocal commands. Testing reveals high accuracy, and the findings depict that the YOLOv8 model precisely identifies vacant seats and crowd levels, achieving an overall accuracy of 98.74%, and obstacle identification depicts an overall accuracy of 94.38%. Further, the InceptionV3 model exhibits 97% accuracy in recognizing road signs and colors. The MobileNetV2 model achieves 98.5% accuracy in identifying traffic lights and pedestrian crossings. User feedback emphasizes the user-friendliness and effectiveness of the application in enhancing outdoor experiences for visually impaired individuals. The significance of this work is its ability to empower visually impaired individuals, enhancing their autonomy, safety, and quality of life. VisionPal addresses real-world challenges by offering a reliable and innovative solution for the visually impaired to navigate public places. The integration of state-of-the-art technologies makes VisionPal a promising tool for promoting inclusion and enabling visually impaired individuals to engage more fully in society.

关键词： independence real-time assistance smartphone application technological solutions VisionPal visual impairment

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ARIVA: Artificial Intelligence Enabled Voice Assistance System using Natural Language processing 8

ARIVA: Artificial Intelligence Enabled Voice Assistance Syst...

引用

8th International conference on communication and Electronics Systems, ICCES 2023

作者： Barnwal, Varsha Kumari Shaw, Anjali Sarkar, Kanchan Chakraborty, Sheuli Mukhopadhyay, Anup Kumar Asansol Engineering College Department of Information Technology West Bengal Asansol India

ISBN: (纸本)9798350396638

In this modern era, the regular life of people have become more competent and are interlinked with technology. There are many voice assistants like Google and Siri. etc., getting used in the present day. Here, the proposed Artificial Intelligence Enabled Voice Assistance System (ARIVA) using Natural Language processing facilitates domains such as drug prescribers, to-do lists, note-writing, calculators, and searching tools. The ARIVA model takes input as a voice signal and yields output in numerous ways, like voice and a visual display. This virtual assistant aims to provide users with instant, accomplished results. The proposed ARIVA system accepts the command via the microphone of the executing system and converts human voice into computer-understandable language, and then generates the required solutions for the said queries. The speech recognition and processing are carried out using Natural Language processing (NLP) algorithm that aids computer systems in interacting through natural human speech in different forms. ARIVA can connect with the world wide web to procure the results of the individual's query. The ARIVA aids the user with everyday actions such as general verbal interaction, exploring Google for query processing, video searching, searching for videos, playing songs, predicting future weather reports, analyzing synonyms, searching for health and hygiene, and assisting the user by scheduling tasks and events. In terms of performance, ARIVA, when compared to IntelliAssistant, it is seen that ARIVA consumes lesser time and exhibits lesser memory requirement. © 2023 IEEE.

关键词： Artificial intelligence

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Image and Video Captioning Using Deep Learning and Natural Language processing 8

Image and Video Captioning Using Deep Learning and Natural L...

引用

8th IEEE International conference on Computing, communication, Control and Automation, ICCUBEA 2024

作者： Naidu, Manoj Kulkarni, Athrva Kadam, Sahil Joshi, Siddhesh Sable, Nilesh P. Yenkikar, Anuradha Vishwakarma Institute of Information Technology Department of Cse - Artificial Intelligence Pune India

ISBN: (纸本)9798350391770

Deep learning models have been a huge success in image recognition which hence can be used for the purpose of text generation. In the field of imaging science, captioning images and videos is regarded as an intellectually difficult job. visual Geometry Group (VGG);is a standard deep Convolutional Neural Network (CNN) architecture with multiple layers, specifically focusing on the integration of CNN for image feature extraction. Exploring this underlying method, the use of another model is essential for caption generation. Here the Recurrent Neural Network (RNN) comes in use for caption generation from the extracted features. Models named Long Short-Term Memory (LSTM) based on RNN and Bidirectional encoder representation transformer (BERT) based on Transformers have been prominent in ensuring accurate results. The Flicker8k dataset is used which provides a variety of information useful for model training. By testing validation data along with evaluation metrics, we analyze the effectiveness of different models to create consistent and descriptive headlines. Extending our inquiry to encompass title generation using transformer models, while also exploring learning techniques for real-time title generation and delivery using the Open-CV library available in Python to get the output from the camera and display it on screen. The result shows that the LSTM is the best model for captioning, with an accuracy of 65.07% at the epochs of 300 and the BERT model has an accuracy of 31% at the epochs of 2. The findings of this study not only contribute to advancing subtitle enhancement methodologies but also broaden the potential applications of deep learning techniques in this domain. © 2024 IEEE.

关键词： Convolutional neural networks

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A Novel visual communication Scenario Based on Image processing Algorithms 2

A Novel Visual Communication Scenario Based on Image Process...

引用

2nd International conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Yang, Jinhua Hou, Li Xijing University Shaanxi Xi'an710123 China

ISBN: (纸本)9798331505745

In order to comply with the trend of intelligent visual communication, this study proposed an innovative visual communication scenario based on image processing algorithms. The framework aims to optimize traditional key technologies such as the image generation, editing, style transfer and image compression. First, as the foundation of visual communication, this study proposes a generative adversarial network model based on text semantic information for image generation and editing. The model achieves stable image generation and efficient editing from a theoretical level through paired training of text and image pairs. Secondly, for image style transfer, this study designed an improved VGG19 convolutional neural network. At the same time, the adaptive instance normalization technology was combined to optimize the effect of style transfer. Finally, in terms of image compression, the study proposed an improved generative adversarial network (REVISED-GAN) model. This model can dynamically adjust the compression error based on structured information to improve image compression efficiency. Through comparative tests, the proposed image style transfer and image compression algorithms have shown excellent performance in terms of structural similarity, image quality and compression ratio. © 2025 IEEE.

关键词： visual communication

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Microbial Image Data Enhancement Framework with Deep Learning Algorithm 5

Microbial Image Data Enhancement Framework with Deep Learnin...

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5th International conference on Electronics and Sustainable communication Systems, ICESC 2024

作者： Chen, Lin Yang, Shuhan Qujing Medical College Basic Medical College Yunnan Qujing655000 China

ISBN: (纸本)9798350379945

This study presents a comprehensive framework for enhancing microbial image data using a deep learning algorithm to improve the quality and analysis of microscopic images. The framework includes a deep learning-based image de-noising algorithm, an image anti-blurring algorithm, and an edge enhancement algorithm. The image de-noising process utilizes an iterative learning mapping function to progressively approximate actual noise distribution, resulting in clearer images with reduced noise. The image anti-blurring algorithm employs GAN-based methods to adjust and correct blur information caused by sample movements or lens jitter. The edge enhancement technique uses the Sobel operator to extract and enhance edge features, thereby improving the visual clarity and detail of microbial images. To evaluate the performance of the framework, a diverse database of 1,000,000 microbial images was constructed using the Google image search engine. The effectiveness of the approach was evaluated using three key parameters: Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Model Classification Accuracy (MCA). Experimental results show significant improvements in all metrics, with the proposed methods achieving an average PSNR of 29.0 dB, an average SSIM of 0.923, and an average MCA of 85%. These results highlight the potential of the proposed framework to improve image quality and accuracy in microbial image analysis. © 2024 IEEE.

关键词： Data processing Deep learning algorithm Image enhancement Microbial image

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Empowering Women: A Creative Approach to Integrated Safety with Machine Learning Algorithms

Empowering Women: A Creative Approach to Integrated Safety w...

引用

2024 International conference on Signal processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Kiran, Ajmeera Sundaram, Ajith Varghese, Issac K Dhanasekaran, S. Ayyasamy, Ramesh Kumar Krisnan, Saraswathy MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Amrita School of Business Kochi Amrita Vishwa Vidyapeetham Amrita Nagar Edappally Kerala Ernakulam682041 India T A Pai Management Institute Manipal Academy of Higher Education Manipal India Kalasalingam Academy of Research and Education Deemed to be University Department of Information Technology Tamilnadu Srivilliputtur India Faculty of Information and Communication Technology Kampar Malaysia Kampar Kampar Malaysia

ISBN: (纸本)9798331540685

An extensive platform created to provide women with a sense of security and empowerment in their everyday lives is the Integrated Women's Safety Application. Users can use apps with assurance that their data is safe thanks to secure login functionality. The software offers users messaging warnings alerting them to potential hazards in their neighbourhood, as well as a panic button for emergency assistance in case of an emergency. In addition to offering directions, Route Navigation also indicates the locations of neighbouring police stations for an extra layer of security. A smooth and effective method for requesting assistance and recording hazardous circumstances is created by enabling users to report issues straight from the app. In addition, the heatmap visualization tool helps users make educated judgments about their travels by providing a visual depiction of safety ratings in various locations. The Integrated Women's Safety Application attempts to equip women with the tools to navigate the world confidently and safely by combining these essential aspects into a single platform. © 2024 IEEE.

关键词： Hazards

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Automated Classification of Snoring and Non-Snoring Sound Signals Based on Singular Superlet Transform 10

Automated Classification of Snoring and Non-Snoring Sound Si...

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10th International conference on Signal processing and communication, ICSC 2025

作者： Pachori, Dhanhanjay Raj, Kamal Jain, Tapan Kumar Indian Institute of Information Technology Department of Electronics and Communication Engineering Nagpur India

ISBN: (纸本)9798331511890

This paper proposes a new method for automated classification of snoring and non-snoring sound signals. The snoring and non-snoring sound signals have been represented using time-frequency representations (TFRs) obtained from singular superlet transform (SST). The obtained TFRs have been given as an input to various machine learning based classifiers, like convolutional neural network (CNN), visual geometry group 16-layer network (VGG-16), EfficientNet-B0, and hybrid VGG-16 models for the classification of snoring and non-snoring sound signals. From the experimental study, it has been observed that the hybrid VGG-16 with support vector machine (SVM) model has provided best performance among all the studied models with an accuracy, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 96.00%, 95.60%, 96.40%, 0.9602, 95.63%, and 0.9200 respectively. The good performance measures in automated classification of snoring and non-snoring sound signals show the effectiveness of the proposed method. The proposed method finds applications in the area of sleep disorder diagnosis and it is suitable for real-time implementation. © 2025 IEEE.

关键词： Support vector machines

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Voice-based Image Captioning using Florence-2

Voice-based Image Captioning using Florence-2

引用

International conference on Signal processing, communication, Power and Embedded System (SCOPES)

作者： Devanshi Mittal Prashant Ahlawat Tushar Nag Om Brahma Shrimaroj Computer Science and Engineering Chandigarh University Mohali India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

As visual information becomes the predominant tool for passing information in the society, people with visual impairment have a difficult time in comprehending images. This paper aims to meet this challenge by proposing a machine learning enabled method of producing descriptive text captions for images in a bid to provide a link to comprehend the visual content. This solution will involve processing and translating visual information into meaningful textual accounts in order to offer the visually impaired a way of gaining better understand and a more effective way of participating in the visual environment. The first strategy used a conventional image recognition technique, while the second strategy improved the method to improve captioning accuracy. This process was done not with the help of the old dataset but with the help of a new one in order to get improved results. Future work will concentrate on enhancing the system for special application of real-time captioning so that it can be easily used in real life.

关键词： visualization Image recognition Translation visual impairment Signal processing Predictive models Multitasking Real-time systems Object recognition Videos

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Audio-visual Speech Enhancement with Deep Multi-modality Fusion 5

Audio-Visual Speech Enhancement with Deep Multi-modality Fus...

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5th International conference on information communication and Signal processing, ICICSP 2022

作者： Yu, Bin Zhang, Zhan Zhao, Ding Wang, Yuehai Polytechnic Institute Zhejiang University Hangzhou China College of Information and Electronic Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9781665485890

In daily interactions, human speech perception is inherently a multi-modality process. Audio-visual speech enhancement (AV-SE) aims to aid speech enhancement with the help of visual information. However, the fusion strategy of most AV-SE approaches is too simple, resulting in the dominance of audio modality. The visual modality is usually ignored, especially when the signal-to-noise ratio (SNR) is medium or high. This paper proposes an encoder-decoder-based convolutional neural network of AV-SE with deep multi-modality fusion. The deep multi-modality fusion uses temporal attention to align multi-modality features selectively and preserves the temporal correlation by linear interpolation. The novel fusion strategy can take full advantage of video features, leading to a balanced multi-modality representation. To further improve the performance of AV-SE, mixed deep feature loss is introduced. Two neural networks are applied to model the characteristics of speech and noise signals, respectively. The experiment conducted on NTCD-TIMIT demonstrates the effectiveness of our proposed model. Compared to audio-only baseline and simple fusion approaches, our model achieves better performance in objective metrics under all SNR conditions. © 2022 IEEE.

关键词： Speech enhancement

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