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PCaSM: Text-Guided Composed image Retrieval with Parallel Content and Style Modules

PCaSM: Text-Guided Composed Image Retrieval with Parallel Co...

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2023 ieee international conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Zhang, Jiayan Zhang, Jie Wu, Honghao Zhao, Zongwei Hu, Jinyu Li, Mingyong Chongqing Normal University Chongqing China

ISBN: (纸本)9798350313154

The query for text-guided image retrieval references two parts: the first part is the image, and the second part is the text describing the part of the image that needs to be modified. By composing the features of the text and the image, the goal is to retrieve a target image that complies with the demands of the modification text and resembles the reference image. modification text Currently, some image retrieval software fails to take into account the rich content and style information contained in images. To solve this problem, we propose a novel architecture designed by the image-text synthesis model, namely PCaSM, which consists of three modules: a content module, a style module, and a combination module. First, to ensure the stability of each module training, we normalize the reference image features using instance normalization and then implement content modification by using decomposed multi-modal non-local blocks. Next, the style module is implemented with the remaining stacks and skip links. Finally, the content and style modules are fed into the combination module outputs in parallel to fuse the local content features with the global style features. Therefore, the final combination features obtained are similar to the target image features. Adequate experiments show that PCaSM demonstrates good performance on the three datasets: FashionIQ, Fashion200K, and CIRR, compared with the latest proposed models such as CoSMo and RTIC. © 2023 ieee.

关键词： image retrieval

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Machine Learning KNN Classification An Approach on Detecting Abnormality in Brain Tissues MRI-Graphic User Interface 7

Machine Learning KNN Classification An Approach on Detecting...

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7th international conference on image and signal processing and their applications, ISPA 2022

作者： Boudjella, Aissa Boudjella, Manal Yasmine Bellebna, Bachir Bircham International University Dept. Electrical Engineering Miami United States University of Sciences and Technology Laboratory of Analysis and Application of Radiation Laar Oran Algeria Hospitalier Universitaire Service Neurochirurgie Etablissement Oran Algeria

ISBN: (纸本)9781665480420

In this study, we have investigated the k-nearest neighbors' classifier based on magnetic resonance image to perform the diagnostic of pathology on the brain tumor. We have developed a graphic user interface application to perform the diagnostic. The system is implemented and simulated in Anaconda, and its performance is tested on the real dataset that contains six (06) features and two (02) classes. Each class, an abnormal class (patients have brain tumor) and a normal class (healthy patients) consists of 2764 instances(images), and 500 instances (images), respectively. For quality analysis, the performance metrics are examined and evaluated. When k changes from 1 to 20, a comparison of the performance metric characteristics under fixed test sizes (10%~50%) at constant random state 66 is carried out to predict the class status label. The simulation results achieved (93.2±6.8)% train accuracy, (87.0±3.2)% test accuracy, (66.3±17.6)% precision score, (42.6±25.8)% recall, (47.7+21.5)% F1-score, and (95.1±3.1)% specificity. The performance can be improved by adjusting k and the test size in the range of [2~10], and [15%~25%], respectively. The proposed method shows the effectiveness of identifying a normal class and an abnormal class (brain tumor tissues) from the statistical proprieties of the brain MRI images. This technique is faster with reduced diagnostic time and effective decisions. It will serve as a guideline to evaluate the performance metrics to tells us how much better the proposed model is then making prediction. It also helps programmers to select the optimal model that generates the best performance scenario in order to design a better graphic user interface. © 2022 ieee.

关键词： Magnetic resonance imaging

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An image processing Application: Design of VLSI based Bilateral Filter using Distance Matrix Method 2

An Image Processing Application: Design of VLSI based Bilate...

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2nd ieee international conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication Engineering, ICATIECE 2022

作者： Saranya, C. Vijayananth, M.V. Mouleeshwaran, S. Kaviyarasu, D. K.S.Rangasamy College of Technology Department of ECE Tiruchengode India

ISBN: (纸本)9781665493963

The entire design structure is being impacted by advancements in electronic technology, which is posing a number of challenges for digital systems. In the fields of communications, image processing, and multimedia, VLSI-based image filter architectures are essential. Numerous applications based on VLSI architecture experience problems with significant size components that result in a filter design error during the floating-point arithmetic stages. The increased complexity of the filter design's component list is the issue encountered in the VLSI architecture. Noise removal often involves using the image filtering technique. There is a greater need for performance-based hardware sorting algorithms for low power embedded real-Time image filtering applications. Noise often taints an image while being transmitted from one location to another. The purpose of the denoising technique is to reduce noise while keeping any potential signal features that may be significant. Compare to the prior method, the usage of multipliers can be reduced based on the techniques of distance matrix and hardware resource sharing. The speed of the proposed VLSI architecture for the bilateral filter can be faster than the existing approach. © 2022 ieee.

关键词： Field programmable gate arrays (FPGA)

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Scene Simplification for Simulated Prosthetic Vision with Improved Scene Understanding

Scene Simplification for Simulated Prosthetic Vision with Im...

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2024 ieee international conference on Consumer Electronics, ICCE 2024

作者： Yang, Shijie Han, Dehao Wu, Jingbang Chen, Xiaoming Chung, Vera Beijing Technology and Business University Beijing China The University of Sydney Sydney Australia

ISBN: (纸本)9798350324136

Visual impairment or blindness affects millions of people worldwide, causing significant challenges in their daily activities and communication. These people are potentially a special group of users in future consumer electronics applications. Retinal prostheses have emerged as a promising solution for restoring vision in individuals with visual impairments. These prostheses can compute and display simplified representations of images to visually impaired individuals. Therefore, prostheses can be considered special consumer electronics devices. However, the quality of the generated images by these prostheses remains unsatisfactory. Moreover, advanced computer vision algorithms have been employed for image processing to enhance prosthetic vision's applicability. Recent studies have utilized object segmentation for scene simplification, but insufficient structural information compromises scene understanding in object-Absent scenes. To tackle this challenge, in our study, we have utilized an image fusion technique to integrate saliency information based on the semantic segmentation results of previous work. These fused images can improve the scene understanding of visually impaired individuals. To verify the effectiveness of our approach, a user study was conducted. The user study results, including the sensitivity coefficient and other relevant indicators, demonstrated notable improvements. We believe that this work will contribute to enhancing information accessibility for visually impaired individuals, thereby aiding their overall well-being. © 2024 ieee.

关键词： image fusion object segmentation Scene simplification simulated prosthetic vision vision augmentation

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An Enhanced Implementation Method for a Single Channel Portable EEG System on Brain Monitoring applications using Notch Filters

An Enhanced Implementation Method for a Single Channel Porta...

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2024 international conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Vijayashanthi, M. Abdul Naqi, Mohd Sathiya Suntharam, V. Yasmeen, M.D. Department of EEE CMR College of Engineering & Technology Telangana Hyderabad India Department of ECE CMR Technical Campus Telangana Hyderabad India CMR Engineering College Telangana Hyderabad India Department of ECE CMR Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350355338

The study offers a precise method for detecting brain waves that are known to be a significant sign of neurological issues. One of the world's most intricate machines is the human brain. Within the neurological and psychiatric fields, the electroencephalogram (EEG) is a highly sought-after signal since it is the sole way to obtain real-time data about the brain that has excellent temporal resolution. The application of brain waves to human-computer communication was made feasible by developments in computing and signal processing technologies. In order to create an EEG platform for Brain Computer Interface programs, this research combines programming assistance for the software interface with the hardware circuit design. Because human bodies often produce extremely weak EEG signals, it's important to eliminate waveform distortion, offset, and other related issues in addition to noise interference. Simultaneously, the variable amplifying and filter function are designed with the application of digital methods. Low pass and notch filters can be used for filtering, and instrumentation and gain stage amplifiers can be used for amplification. The one-channel EEG system is a dependable, affordable, and portable device. The brain waves are measured by the Electroencephalogram (EEG). The use of brain waves for human-computer communication was made possible by developments in equipment and signal processing technologies. The EEG system is employed to identify the brain waves' Alpha, Beta, Delta, and Theta frequencies. © 2024 ieee.

关键词： Notch filters

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Improved Radar signal Recognition by Combining ResNet with Transformer Learning

Improved Radar Signal Recognition by Combining ResNet with T...

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2024 international conference on Green Energy, Computing and Sustainable Technology, GECOST 2024

作者： Tang, Zeyu Shen, Hong Macao Polytechnic University Faculty of Applied Sciences Macao China School of Engineering and Technology Central Queensland University Australia

ISBN: (纸本)9798350357905

Modulation recognition of radar signals in time-frequency images (TFIs) under low signal-to-noise ratio (SNR) conditions has important applications such as electronic reconnaissance in electromagnetic environments. Existing work in applying deep learning for radar signal modulation recognition focuses on deploying convolutional neural networks (CNN) to extract signal's local features, but ignores global features and signal position features in the image, resulting in unsatisfactory recognition results under low SNR. This paper proposes an effective neural network based radar signal recognition model that is able to extract both local and global features of time-frequency radar signals more effectively by combining CNN-based ResNet and transformer learning to improve the recognition accuracy for radar signals under low SNR. Specifically, we apply Choi-Williams distribution (CWD) to transform the signal time domain waveform into a two-dimensional time-frequency image and display the intra-pulse characteristics of the signal in both time domain and frequency domain. We perform preprocessing including grayscale and binarization on the time-frequency images to better highlight the signal characteristics. Then, as the central part of this work, we integrate ResNet feature extraction with multi-head self-attention (MHSA) enabled transformer learning to build our ResNet-MHSA model, namely RM-Net, and use the SCR-loss evaluation module to train the model. Experimental results show that when the SNR is -10dB, the radar signal recognition rate of 12 different modulation types can reach 95.6% with our method, which is an increase of more than 6% than existing work. © 2024 ieee.

关键词： Convolutional neural networks

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One Shot Face image Style Transfer Method Based on GAN 2

One Shot Face Image Style Transfer Method Based on GAN

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

作者： He, Wenyin Wuhan University Department of Computer Science Hubei Wuhan430072 China

ISBN: (纸本)9798350331417

Style transformation on face images has traditionally been a popular research area in the field of computer vision, and its applications are quite extensive. Currently, the more mainstream schemes include Generative Adversarial Network (GAN)-based image generation as well as style transformation and Stable diffusion method. In 2019, the NVIDIA team proposed StyleGAN, which is a relatively mature scheme for generating real faces as well as face feature blending. The whole StyleGAN model is trained based on the Flickr-Faces-HQ Dataset (FFHQ) dataset, the This is a large dataset, so the model takes a long time to train. My aim is to form a One-shot stylized face image generator, which means that only one reference face and one stylized face need to be input, and a brand-new face with a mixture of features can be generated in a short training time. This is inspired by the existing research result JoJoGAN, which learns a style mapper from a single example of the style. JoJoGAN uses a GAN inversion procedure and StyleGAN's style-mixing property to produce a substantial paired dataset from a single example of the style. This paper will make improvements to JoJoGAN, including improving the encoder that utilizes the GAN Inversion method to generate latent codes for image features, and the random mixing of latent codes to produce a more refined paired dataset. © 2023 ieee.

关键词： deep learning GAN style transformation

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Classification and tracking of items on a moving conveyor belt using convolutional networks and image processing

Classification and tracking of items on a moving conveyor be...

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2024 international conference on Artificial Intelligence, Computer, Data Sciences, and applications, ACDSA 2024

作者： De Goes, Angelo Leite Medeiros Neto, Adriao Duarte Doria Andrade, Matheus Gomes Diniz De Oliveira, Thiago Theiry Federal University of Rio Grande Do Norte Electrical and Computer Engineering Postgraduate Program Natal Brazil Federal University of Rio Grande Do Norte Computer Engineering Natal Brazil

ISBN: (纸本)9798350394528

The prolonged waiting time at supermarket checkout lines poses a significant challenge to the shopping experience, impacting customer satisfaction and operational efficiency. This paper presents a prototype that addresses this issue by utilizing computer vision and deep learning. The model, incorporating convolutional neural networks such as YOLO v4 tiny and YOLO v5 small, along with tools like OpenCV and Roboflow for dataset management, achieves a remarkable 98% mean average precision for two-class detection. It efficiently detects, classifies, tracks, and counts items on a mobile supermarket conveyor belt. Additionally, we introduce a versatile framework designed for seamless integration into real-world applications. It comprises a customizable monitoring application and simulator that facilitates synthetic image data generation. Managing diverse items in a supermarket presents a major challenge for data gathering, labeling, and training. In that sense, the importance of customizable monitoring and simulation tools is highlighted, emphasizing their practical role. Our findings demonstrate the feasibility of maintaining a minimal 0% to 2.85% precision tradeoff while using half of the data as synthetic for two-class detection, indicating potential practicality in supermarkets with proper scaling. In summary, this study brings tangible benefits to both customers and retailers, offering a potential to streamline, speed up, and cut costs in the supermarket checkout process. © 2024 ieee.

关键词： Deep learning

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Regression Analysis in Electrical Engineering applications: A Machine Learning Approach

Regression Analysis in Electrical Engineering Applications: ...

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2023 international conference on signal processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2023

作者： Siva Suriya Narayanan, S. Yuvaraju, V. Thangavel, S. National Institute of Technology Puducherry Department of Electrical and Electronics Engineering Karaikal India

ISBN: (纸本)9798350312126

In the context of the Battery Management System (BMS) of an Electric Vehicle (EV), accurately predicting the terminal voltage of the battery is of utmost importance. However, this prediction model is dependent on the battery's chemistry and its overall lifespan. To address this issue, this work presents a generalized procedure for implementing a Machine Learning (ML) based prediction model. Specifically, we compare the performance of five distinct regression techniques, namely, decision tree, ensemble boost and bagg, support vector machine, and neural network, using a supervised ML approach. The performance of the different regression techniques is evaluated by means of the Root Mean Square Error (RMSE). The proposed method of using ML techniques to develop an accurate prediction model for a specific task, as discussed in this work, has the potential to be implemented in various other regression tasks of engineering applications. Therefore, the approach presented in this work can serve as a blueprint for developing accurate prediction models in other engineering applications, provided that the relevant data and training are available. © 2023 ieee.

关键词： Forecasting

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Elimination of Muscle Artifacts from the Single Channel EEG signal using Automatic Singular Spectrum Analysis 15

Elimination of Muscle Artifacts from the Single Channel EEG ...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Gajbhiye, Pranjali Kumar, Korupalli V. Rajesh Hemachandran, K. Woxsen University Hyderabad India

ISBN: (纸本)9798350370249

This paper introduces a novel approach for eliminating muscle artifacts from single-channel EEG signals by leveraging Automatic Singular Spectrum Analysis (ASSA) and statistical analysis. The method involves decomposing the contaminated EEG signals into reconstruction components (RCs) using ASSA, followed by an assessment of kurtosis measures for each RC to identify and isolate noisy components. Subsequently, the filtered EEG signal is reconstructed based on the linear combination of the remaining RCs. Through comprehensive evaluation using semi-synthetic and real EEG datasets sourced from public repositories like Biosource and Mendeley, the proposed approach demonstrates superior performance compared to existing methods. It achieves the lowest mean absolute error (MAE) in the power spectral density (PSD) domain while ensuring the preservation of essential clinical EEG features. By addressing a fundamental challenge in single-channel EEG processing, this method fills a crucial gap in biomedical signal analysis, particularly in ambulatory settings. Its simplicity and effectiveness make it a promising tool for real-world applications, challenging traditional paradigms in EEG signal denoising. © 2024 ieee.

关键词： ASSA brain-computer interface (BCI) electroencephalogram (EEG) filtering MAE muscle artifact PSD

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