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A 21pJ/frame/pixel imager and 34pJ/frame/pixel image Processor for a Low-Vision Augmented-Reality Smart Contact Lens

A 21pJ/frame/pixel Imager and 34pJ/frame/pixel Image Process...

引用

ieee International Solid-State Circuits conference (ISSCC)

作者： Singh, Rituraj Bailey, Stevo Chang, Phillip Olyaei, Ashkan Hekmat, Mohammad Winoto, Renaldi Mojo Vis Saratoga CA 95070 USA

ISBN: (纸本)9781728195490

Over 253 million people worldwide suffer from permanent visual impairments (e.g., glaucoma, diabetic retinopathy, retinitis pigmentosa and macular degeneration) that cannot be rectified using refractive correction or surgery. Such patients rely on canes, monoculars, or service dogs for mobility, which while effective, do not provide rich information and are not hands-free. Recently, low-vision augmented-reality (AR) headsets have been developed to provide hands-free, real-time, enhanced-image overlays that highlight objects and obstacles. However, social stigma and form factor impede their adoption [1]. We introduce a complete, enhanced-image overlay solution for a socially transparent AR contact lens for low-vision patients that provides a rich set of controllable image overlays such as contrast enhancement, edge detection, and zoom. The lens consists of three main components: an outward-looking imager, an image processor, and a microLED display [2]. The lens captures the scene from the wearer's central vision, performs a user-specified image processing task (e.g., edge detection), and sends the result to the display, which, along with its miniature optics, projects images directly onto the retina, highlighting outlines of real-world objects. © 2021 ieee.

关键词： Augmented reality

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Pathological Section Staining Transferring with Tailored Metric-based Model Selection

Pathological Section Staining Transferring with Tailored Met...

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

作者： Yiming Ji Suyang Zhu Dong Zhang Shoushan Li School of Computer Science and Technology Soochow University Suzhou China Computing Science and Artificial Intelligence College Suzhou City University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

As the important pathological section staining, Immunohistochemistry (IHC) staining uses labeled antibodies to highlight specific antigens, providing clearer results for malignancy identification compared with Hematoxylin and Eosin (H&E) staining. However, obtaining IHC manually is labor-intensive and costly. In this study, we attempt to automatically generate IHC staining image by style transferring from H&E, which is easy to get. Due to the unalignment between single evaluation metric and real generation quality, we propose a tailored metric-based model selection (TMMS) method for pathological section staining transferring (PSST). Our method can fuse multiple traditional metrics and multiple relevant losses to measure the quality of generated IHC and select a best-performed model. Extensive experiments and analysis demonstrate the effectiveness of our method TMMS.

关键词： Pathology Antigens Translation Codes Fuses Signal processing visual effects Loss measurement Speech processing Software development management

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GPU-Accelerated Feature Extraction for Real-Time Vision AI and LLM Systems Efficiency: Autonomous image Segmentation, Unsupervised Clustering, and Smart Pattern Recognition for Scalable AI processing with 6.6× Faster Performance, 2.5× Higher Accuracy, and UX-Centric UI Boosting Human-in-the-Loop Productivity

GPU-Accelerated Feature Extraction for Real-Time Vision AI a...

引用

ieee/SEMI conference and Workshop on Advanced Semiconductor Manufacturing

作者： Kiarash Ahi Stewart Wu Satya Sriram Germain Fenger Siemens EDA USA Siemens EDA Belgium Siemens EDA India

ISBN: (数字)9798331531850

ISBN: (纸本)9798331531867

The high computational cost of digital image processing, requiring high-performance hardware and extensive resources, severely limits real-time applications. While advancements in algorithm design and GPU acceleration have significantly improved efficiency, modern AI-driven applications such as large language models (LLMs), Generative AI (GenAI), medical imaging, autonomous vehicle perception, photography, advanced nano-scale semiconductor metrology, satellite image analysis, high-precision manufacturing, robotics, and real-time anomaly detection, still demand further optimization to reduce computational overhead and improve *** this paper, we introduce GPU-Accelerated Feature Extraction to enhance runtime and efficiency in edge-based simulations. Our approach leverages AI-driven clustering, grouping images with similar visual and pattern characteristics to enable adaptive tuning on a small subset before generalizing across the full dataset. This method achieves a 3.78× reduction in ***, rather than processing an entire image, we recognize and extract a single representative pattern or region of interest (ROI) per image, removing redundant data and background noise. This refinement results in an additional 1.74× runtime improvement, culminating in an overall 6.6× speed boost, enhancing Scalable Real-Time AI processing. We also demonstrated that with a similar runtime, the accuracy achieved is 2.5× *** solution, integrated into Calibre SEMSuite™, supports multicloud and real-time deployment for enhanced scalability, usability, and performance, providing users with a powerful tool for fully automated, AI-driven image classification, making high-throughput image review feasible even at the scale required for cutting-edge *** performance gains, this approach introduces autonomous data cleaning, anomaly detection and defect identification mechanism, allowing failed patterns and defective images to be identified without h

关键词： Accuracy Runtime image edge detection Clustering algorithms Feature extraction Real-time systems Computational efficiency Artificial intelligence Anomaly detection Tuning

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Smart Tunnel Traffic Control System using IoT and Machine Learning

Smart Tunnel Traffic Control System using IoT and Machine Le...

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2025 International conference on visual Analytics and Data visualization, ICVADV 2025

作者： Subramani, V. Vishnu Kumar, S. Rishikanth, S.R. Poonkuzhali, Ramadoss K S Rangasamy College of Technology Department of Information Technology Tiruchengode India

ISBN: (纸本)9798331521394

Smart Tunnel Traffic Control System Using IoT and Machine Learning' is designed to offer safety and effective management of traffic in single-lane tunnels. The conventional tunnel system does not prohibit big vehicles from entering the tunnel through either end, which may lead to congestion and increase the likelihood of accidents. The designed system uses an ESP32-CAM module to sense the images of the vehicles in real time at any entry point of the tunnel. Using a CNN model, the system classifies incoming vehicles by size, and if a large vehicle is detected, it triggers a red 'STOP' signal and message at the opposite end. In this way, only one large vehicle occupies the tunnel at any time. The system further calculates the transit time in terms of the length of the tunnel and updates it dynamically to handle incoming vehicles. As soon as the tunnel becomes free, a green 'GO' signal allows the next vehicle to move forward. Keywords: loT, ESP32-CAM, CNN, traffic, tunnel safety, automation, image processing, classification, monitoring, accident prevention, congestion, ML. © 2025 ieee.

关键词： image analysis

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Boosting Light Field image Super-Resolution via Manually Shearing Disparity

Boosting Light Field Image Super-Resolution via Manually She...

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International conference on Signal processing Proceedings (ICSP)

作者： Jungang Yang Zhengyu Liang Qianyu Chen Yingqian Wang Wei An College of Electronic Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Light field imaging, with its ability to capture both the intensity and directionality of light rays, has gained significant attention in various fields including virtual reality and computational photography. Super-resolution (SR), the process of enhancing image resolution, is essential for improving the visual quality and detail of light field images. In this paper, we integrate data augmentation techniques into our approach to further enhance the generalization capability of existing light field image SR models. By manually adjusting the disparity range of existing light field image datasets, we augment the training data and introduce variations that help the models learn robust features and reduce overfitting. By doing so, we tame the models to capture larger contextual information among the sub-aperture images of light filed and improve their SR performance without the need for extra datasets. We demonstrate the effectiveness of our method through extensive experiments on benchmark datasets, showcasing existing light field SR networks to achieve superior reconstruction performance and angular consistency.

关键词： Training Solid modeling visualization Superresolution Training data Virtual reality Light fields Data models Spatial resolution Shearing

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Cheque Information processing and Validation using Deep Learning

Cheque Information Processing and Validation using Deep Lear...

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communications and Information Technologies (GCCIT), Global conference on

作者： Suganthi K Darshan N Shenoy School of Electronics Engineering Vellore Institute of Technology Chennai

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Understanding document images, such as invoices, is a difficult task. It necessitates reading the text as well as comprehending the document’s general structure. Conventional techniques first extract text using Optical Character Recognition (OCR), yet this method has drawbacks. In addition to being computationally costly, OCR can have trouble converting between various languages and document formats and introduce errors that can compound throughout the analysis *** is an OCR-free VDU (visual Document Understanding) model that challenges this paradigm. It does not even bother with the text extraction stage; it analyzes the document image straight. This creative strategy produces notable advancements. Donut performs at the cutting edge, which means it is quick and precise. This is a significant development in the realm of visual document understanding since it enables Donut to handle a greater variety of languages and document types. The model has been tested using BLEU scores and ROUGE scores over 3 parameters namely – payee name, amount in words and amount in figure. The model has also been tested over variety of sample sizes ranging from 50 to 1000.

关键词： Training Productivity visualization Target recognition Optical character recognition Transforms Streaming media Multilingual Data mining Testing

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An Unsupervised Multiple Joint Optimization Method for Underwater Manganese Nodule image Enhancement

An Unsupervised Multiple Joint Optimization Method for Under...

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Electrical, Automation and Computer Engineering (ICEACE), International conference on

作者： Wang Xin Yu Fubai School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China

The existing image enhancement methods for underwater manganese nodules have scene limitation and high algorithm complexity, resulting in low accuracy and processing efficiency of the effect. The article proposes an unsupervised multiple joint optimization algorithm based on the Retinex (UM-JOR). By introducing a fixed threshold differential function to optimize the global optimization algorithm, the problem of low accuracy and processing speed is solved. At the same time, a lightweight optimization network is designed based on Retinex, integrating three optimization units of brightness, color and noise for joint optimization. Finally, multiple joint search mechanism is designed to connect the network in series, and unsupervised optimization is carried out using multi-scale loss functions. The experimental results show that the effect of the proposed algorithm exceeds most existing algorithms in seven objective indicators, it has better visual performance and processing efficiency.

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Learning Features by Minimizing the Interframe Differences 19th

Learning Features by Minimizing the Interframe Differences

引用

19th Chinese conference on image and Graphics Technologies and Applications, IGTA 2024

作者： Zhao, Dong Zhang, Dan School of Computer Qinghai Normal University Xining810016 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810016 China

ISBN: (纸本)9789819799183

In this paper, we present a novel yet intuitive unsupervised feature learning approach, referred to as Minimizing Interframe Differences (MID). The idea is the following: as long as the unsupervised features successfully encode the essential information about the visual structures of the frames, the differences between the before and after frames can be minimally encoded. MID is implemented with a difference encoding module and a frame reconstruction module. The former tries to minimize the difference between frames, while the latter first maps the difference encoding to optical flow and then realizes frame reconstruction based on the learned optical flow. All the modules are jointly learned in an end-to-end way through the reconstruction loss. We verify the feasibility of MID in the image datasets by replacing the natural transformation of videos to artificially parameterized transformation for images. Experimental results show that MID is able to predict optical flow accurately based on the minimum difference coding and learn a powerful visual representation that greatly enhances other visual tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unsupervised learning

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Development of a VS ultrasound diagnostic system with image evaluation functions

Development of a VS ultrasound diagnostic system with image ...

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ieee Global conference on Consumer Electronics (GCCE)

作者： Jiayi Zhou Norihiro Koizumi Yu Nishiyama Ryosuke Tsumura Hiroyuki Tsukihara Naoki Matsumoto Department of Mechanical and Intelligent Systems The University of Electro-Communications Tokyo Japan Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan Health and Medical Research Institute National Institute of Advanced Industrial Science and Technology Tokyo Japan Graduate School of Engineering The University of Tokyo Tokyo Japan Department of Gastroenterology and Hepatology Nihon University Tokyo Japan

ISBN: (纸本)9781665492331

An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe position to obtain a cross-section of the target organ, which is constantly shifting due to patient respiratory motions. Therefore, we developed an ultrasound diagnostic robot that works in cooperation with a visual servo system based on deep learning that will help alleviate the burdens imposed on physicians. Two different image processing methods (BiSeNet V2) were used to detect the target kidney location, as well as to evaluate the appropriateness of the obtained ultrasound images (ResNet 50) in developed system.

关键词： Deep learning visualization Ultrasonic imaging image processing Medical services Probes Servomotors

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Investigating the Clusters Discovered By Pre-Trained AV-HuBERT

Investigating the Clusters Discovered By Pre-Trained AV-HuBE...

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

作者： Anja Virkkunen Marek Sarvaš Guangpu Huang Tamas Grosz Mikko Kurimo Department of Information and Communications Engineering Aalto University

Self-supervised models, such as HuBERT and its audio-visual version AV-HuBERT, have demonstrated excellent performance on various tasks. The main factor for their success is the pre-training procedure, which requires only raw data without human transcription. During the self-supervised pre-training phase, HuBERT is trained to discover latent clusters in the training data, but these clusters are discarded, and only the last hidden layer is used by the conventional finetuning step. We investigate what latent information the AV-HuBERT model managed to uncover via its clusters and can we use them directly for speech recognition. To achieve this, we consider the sequence of cluster ids as a ’language’ developed by the AV-HuBERT and attempt to translate it to English text via small LSTM-based models. These translation models enable us to investigate the relations between the clusters and the English alphabet, shedding light on groups of latent clusters specialized to recognise specific phonetic groups. Our results demonstrate that using the pre-trained system as a quantizer, we are able to compress the video to as low as 275 bit/sec while maintaining acceptable speech recognition accuracy. Furthermore, compared to the conventional finetuning step, our solution has considerably lower computational cost.

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