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Smart Saliency Detection for Prosthetic Vision

Smart Saliency Detection for Prosthetic Vision

引用

Workshop held at the 12th International conference on Model and Data Engineering, MEDI 2023

作者： Khalifa, Nermin Al-Atabany, Walid Selim, Sahar School of Information Technology and Computer Science Nile University Giza12677 Egypt Medical Imaging and Image Processing Research Group Center for Informatics Science Nile University Giza12677 Egypt

ISBN: (纸本)9783031557286

People with visual impairments often have difficulty locating misplaced objects. This can be a major barrier to their independence and quality of life. Retinal prostheses can restore some vision to people with severe vision loss. We introduce a novel real-time system for locating any misplaced objects for people with visual impairments using retinal prostheses. The system combines One For All (OFA) for visual Grounding and Google Speech Recognition to identify the object to be located. It then uses an image processing technique called grabCut to extract the object from the background to emphasize the located object and incorporate it with the background frame to send it to the retinal implant for stimulation. The system was evaluated on a set of images containing misplaced objects and was able to locate the objects in real-time successfully. We believe that this system could be used to improve the independence and quality of life of people with visual impairments. The system is still in the early stages of development, but we are working to improve its performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： prosthetic vision simulated prosthetic vision vision augmentation vision language models visual grounding

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Weighted Robinson Compass Gradient and Charbonnier Penalty Function as a Loss Function 7

Weighted Robinson Compass Gradient and Charbonnier Penalty F...

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7th IEEE International conference on Recent Advances and Innovations in Engineering, ICRAIE 2022

作者： Rachna, U. Dhruv Shindhe, S. Omkar, S.N. PES University Department of Electronics and Communications Bangalore India Indian Institute of Science ARTPARK Bangalore India Indian Institute of Science Department of Aerospace Bangalore India

ISBN: (纸本)9781665489102

Loss function quantifies how well a machine learning model is performing, it computes the difference between the ground truth and the reconstructed outcomes. In image processing tasks such as image dehazing, image deblurring and image denoising the most popular loss functions used are Mean Squared error(MSE) and perceptual loss. MSE is less computationally expensive but fails to recreate precise boundaries whereas perceptual loss is more computationally expensive as they use feature vectors from a pre-trained model like VGG-net. In smaller on the edge systems like autonomous navigation systems, one of the most critical tasks is relying on visual cues, the take in the video feed from the environment and make decision on it after a few prepossessing steps to eliminate things like physical barriers, blurr, and smoke that clutter a robot's environment are one of the several factors that affect autonomous navigation. We need systems that aren't computationally expensive but also have a good reconstruction. For this we propose a loss function that incorporates gradients along with MSE for faster and better reconstruction. © 2022 IEEE.

关键词： Smoke

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Deep Dictionary Learning with an Intra-Class Constraint

Deep Dictionary Learning with an Intra-Class Constraint

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2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Yuan, Xia Gou, Jianping Yu, Baosheng Yu, Jiali Yi, Zhang School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China School of Computer Science The University of Sydney DarlingtonNSW Australia School of Mathematical Sciences University of Electronic Science and Technology of China China School of Computer Science Sichuan University Chengdu China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

In recent years, deep dictionary learning (DDL)has attracted a great amount of attention due to its effectiveness for represen-tation learning and visual recognition. However, most existing methods focus on unsupervised deep dictionary learning, failing to further explore the category information. To make full use of the category information of different samples, we pro-pose a novel deep dictionary learning model with an intra-class constraint (DDLIC) for visual classification. Specif-ically, we design the intra-class compactness constraint on the intermediate representation at different levels to encour-age the intra-class representations to be closer to each other, and eventually the learned representation becomes more dis-criminative. Unlike the traditional DDL methods, during the classification stage, our DDLIC performs a layer-wise greedy optimization in a similar way to the training stage. Experi-mental results on four image datasets show that our method is superior to the state-of-the-art methods. © 2022 IEEE.

关键词： image classification

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Quality-Oriented Feature Regression for Robust image Similarity Metric

Quality-Oriented Feature Regression for Robust Image Similar...

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2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Guo, Jingyu Bao, Qiqi Zhu, Rui Yang, Wenming Liao, Qingmin Tsinghua University Shenzhen International Graduate School Department of Electronic Engineering Beijing China City University of London London United Kingdom

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Full-reference image quality assessment aims to predict the perceptual quality of a distorted image based on its similarity to the pristine reference. In this paper, we propose a robust image similarity metric by fully exploring the representation power of deep learning-based features. A convolutional neu-ral network (CNN) is adopted to extract deep features from multiple scales. We show that such CNN features that con-tain multi -scale visual information are comprehensive and ro-bust enough for quality assessment. We further propose a quality-oriented feature regression (QOFR) module based on the multi-layer perceptron architecture. The QOFR module can efficiently integrate hierarchy CNN features and generate the final quality score. Extensive experiments on the bench-mark datasets demonstrate that our method achieves state-of-the-art performance with outstanding robustness and general-ization ability. © 2022 IEEE.

关键词： image quality

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Improving Spherical image Resampling Through Viewport-Adaptivity

Improving Spherical Image Resampling Through Viewport-Adapti...

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IEEE International conference on image processing

作者： Andy Regensky Viktoria Heimann Ruoyu Zhang André Kaup Multimedia Communications and Signal Processing Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

The conversion between different spherical image and video projection formats requires highly accurate resampling techniques in order to minimize the inevitable loss of information. Suitable resampling algorithms such as nearest neighbor, linear or cubic resampling are readily available. However, no generally applicable resampling technique exploits the special properties of spherical images so far. Thus, we propose a novel viewport-adaptive resampling (VAR) technique that takes the spherical characteristics of the underlying resampling problem into account. VAR can be applied to any mesh-to-mesh capable resampling algorithm and shows significant gains across all tested techniques. In combination with frequency-selective resampling, VAR outperforms conventional cubic resampling by more than 2 dB in terms of WS-PSNR. A visual inspection and the evaluation of further metrics such as PSNR and SSIM support the positive results.

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IRAD: Input-Reference Joint Driven Reconstruction for Unified Anomaly Detection

IRAD: Input-Reference Joint Driven Reconstruction for Unifie...

引用

IEEE visual communications and image processing (VCIP)

作者： Zixin Chen Xincheng Yao Yan Luo Zhenyu Liu Baozhu Zhang Chongyang Zhang School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Ningbo Haitang Information Technology Co. Ltd Ningbo China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Unified (multi-class and cross-class) anomaly detection (AD) is a growing area of interest in real-world applications. However, the popular reconstruction-based AD approach usually faces two significant challenges: the "identical shortcut" issue (copying the input as output) and the lack of class adaptability (the AD model cannot be directly applied to new classes). To address these challenges, we propose a novel unified AD method, named IRAD (Input-Reference Joint Driven). Our core insight is to effectively incorporate both input and references into the reconstruction process. Our IRAD consists of three components: 1) A Suspicious Anomaly Substituting module that replaces the potential abnormal regions of input with anomaly-free reference patches to prevent abnormal information leakage, effectively addressing the "identical shortcut". 2) An Input-Reference Fusing module that merges reference embeddings with input, which urges the subsequent Decoder to effectively utilize the normal reference patterns to reconstruct anomaly-free samples, making our model more class-adaptive. 3) A Rich Feature Preserving Decoder that efficiently preserves low-level details, mitigating low-level information degradation during reverse construction from high to low level. In multi-class AD, IRAD achieves better results on Mvtec-AD, BTAD, and VisA. In cross-class AD, IRAD also outperforms the baesline methods on Mvtec-AD and VisA.

关键词： Degradation Adaptation models visual communication Benchmark testing Information leakage Decoding image reconstruction Anomaly detection Faces

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A Recent Advancements in Multi-Modal Medical image Fusion Techniques using SWT, NSST, NSCT, and CNN

A Recent Advancements in Multi-Modal Medical Image Fusion Te...

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Cognitive Computing in Engineering, communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International conference on

作者： Shailesh Bhosekar Prabhishek Singh Deepak Garg School of Computer Science and Artificial Intelligence SR University Warangal Telangana India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

image fusion is a method used in image processing to provide a more complete representation by amalgamating features and data from many images. Multimodal medical image fusion involves the integration of medical images from many imaging modalities, including computed tomography (CT) scans, positron emission tomography (PET), and magnetic resonance imaging (MRI), into one single dataset. This integration enhances the visualisation of anatomical structures and clinical situations, hence improving diagnostic accuracy by leveraging the strengths of each medium. This study employs MRI, CT, and PET scans as experimental modalities. This review aims to compare various multi modal medical image approach based on Stationary Wavelet Transform (SWT), Non-Subsampled Shearlet Transform (NSST), Convolutional Neural Network (CNN) and NonSubsampled Contourlet Transform (NSCT). This study examines the latest conventional and non-conventional research conducted within these disciplines. It further evaluates these approaches according to diverse image quality metrics and many quantitative assessments. According to this comparison, CNN-based fusion demonstrates superior results, as the overall visual and parametric quality of the fusion outcomes surpasses that of the other approaches evaluated.

关键词： image quality Wavelet transforms visualization Reviews Magnetic resonance imaging Computed tomography Convolutional neural networks Positron emission tomography Medical diagnostic imaging image fusion

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Intelligent Reflection Elimination Imaging Device based on Polarizer

Intelligent Reflection Elimination Imaging Device based on P...

引用

IEEE visual communications and image processing (VCIP)

作者： Xinru Chen Hang Liu Menghan Hu Lejing Zhang Yunmian Li East China Normal University Ruian Dawan Polaroid Material Technology CO. LTD.

ISBN: (纸本)9781665475938

Glass reflection is a problem when taking photos through glass windows or showcases. As the visual quality of captured image can be enhanced by removing reflection, we develop an intelligent reflection elimination imaging device based on polarizer to minimize reflection effect on the images. The system mainly consists of a polarizing module, an image analysis module and a reflection removal module. The users can hold the device and capture images with minimum reflection whether in the day or night. The demo video is available at: https://***/10.6084/***.19687830.v1.

关键词： visualization image analysis visual communication Imaging Glass Reflection Windows

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Smart Waste Collecting Robot Integration With IoT and Machine Learning 2

Smart Waste Collecting Robot Integration With IoT and Machin...

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2nd International conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Srinivasan, S. Jambulingam, Umamagewaran Purushothaman, K.E. Jegan Amarnath, J. Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chengalpattu India College of Computer Science King Khalid University Saudi Arabia Department of Computer Science and Engineering Kammavari Sangham Institute of Technology Karnataka Bangalore India

ISBN: (纸本)9798350305418

Non-biodegradable waste collecting from a wide area is a time-consuming process. The study aims to create an intelligent robot that can independently collect waste utilizing innovative technology. The study uses the Internet of Things (IoT) to connect and manage the various robot parts. A Raspberry Pi organizes many functions as the system's central processing unit. Real-time visual data, essential for waste detection and identification, is captured by a camera module. Ultrasonic sensors identify obstacles and measure distances so the robot can navigate its environment. The sensor helps the robot avoid collisions and navigate the predetermined space effectively. The robot's robotic arm, which enables it to pick up and collect waste materials physically, is one of its primary features. The Raspberry Pi controls the arm, allowing for accurate and synchronized motions for waste collection. They use machine learning methods to facilitate waste identification and categorization, especially a CNN model. To reliably identify various forms of waste, the CNN model is trained using a collection of waste images. These technologies may be combined to enable the automated waste-collected robot to function independently while effectively locating and collecting rubbish. By seamlessly integrating IoT, machine learning, and robots, this solution helps to save human labor and enhance waste management procedures. © 2023 IEEE.

关键词： Convolutional Neural Network (CNN) Environmental sustainability image processing Internet of Things (IoT) Waste Collecting Robot

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Exploring Hierarchical Changes in Functional Brain Network Hubs Through Brain-Activity Prediction with Convolutional Neural Networks

Exploring Hierarchical Changes in Functional Brain Network H...

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IEEE International conference on Systems, Man and Cybernetics

作者： Haruka Kawasaki Satoshi Nishida Ichiro Kobayashi Advanced Sciences GS of Humanities and Sciences Ochanomizu University Tokyo Japan Center for Information and Neural Networks (CiNet) Advanced ICT Research Institute National Institute of Information and Communications Technology (NICT) Osaka Japan

This study aims to clarify how functional network hubs change during hierarchical visual processing in the human brain through the estimation of brain states from features extracted using a convolutional neural network (CNN), a hierarchical model of image processing. We used representational similarity analysis for brain states predicted through encoding models based on feature representations at each layer of the CNN, and applied the PageRank algorithm to matrices converted from the generated representational dissimilarity matrices to capture the hub characteristics of brain region-related systems. This succeeded in capturing changes in the hubness of interregional brain coordination during hierarchical information processing in the human cerebral cortex in visual processing. Specifically, we found that the hubness of the occipital visual cortex increased in the early phase of visual processing, and that the hubness of the prefrontal cortex and temporal lobe increased in the late phase of visual processing. From the above, we found that our proposed method allows us to capture hierarchical changes in the hubness of interregional coordination.

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