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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XIII"

共 14919 条记录，以下是51-60 订阅

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Improving skin color accuracy in DLP-based 3D reconstruction 13

Improving skin color accuracy in DLP-based 3D reconstruction

引用

conference on Dimensional Optical Metrology and Inspection for Practical Applications xiii

作者： Barrios, Erik Pineda, Jesus Romero, Lenny A. Millan, Maria S. Marrugo, Andres G. Univ Tecnol Bolivar Fac Ingn Cartagena Colombia Univ Nacl Abierta & Distancia Esc Ciencias Basicas Tecnol & Ingn Corozal Colombia Univ Tecnol Bolivar Fac Ciencias Basicas Cartagena Colombia Univ Politecn Cataluna Dept Opt & Optometria Terrassa Spain

ISBN: (纸本)9781510673953;9781510673946

Color accuracy is crucial in several domains such as biomedical imaging, cosmetics, and multimedia. Digital Light processing (DLP) with LEDs has increasingly become a popular lighting source in 3D scanning systems. Although DLP provides advantages in 3D reconstruction, it poses challenges in maintaining color accuracy. Our research focused on using hybrid lighting to improve the color accuracy of DLP-based 3D sensing systems. We developed an empirical dataset featuring skin tones captured under multiple lighting environments, including variations in indoor ambient lighting. Through qualitative and quantitative evaluations of color differences, we conclude that including auxiliary lighting with DLP is beneficial for color accuracy, particularly in biomedical imaging and other applications in which color accuracy is essential.

关键词： Color accuracy Digital Light processing (DLP) light sources image color processing

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Impact of In-Pixel processing Circuit Non-idealities on Multi-object Tracking Accuracy for Autonomous Driving 67

Impact of In-Pixel Processing Circuit Non-idealities on Mult...

引用

67th IEEE International Midwest Symposium on Circuits and systems (MWSCAS)

作者： Sharda, Janak Wu, Qiucheng Chang, Shiyu Yu, Shimeng Georgia Inst Technol Elect & Comp Engn Atlanta GA 30332 USA Univ Calif Santa Barbara Comp Sci Santa Barbara CA 93106 USA

ISBN: (纸本)9798350387186;9798350387179

Deep learning algorithms are robust to a small amount of noise in the input image. Traditionally, image signal processors (ISP) are used with the CMOS image sensor (CIS) to enhance image quality which consume additional energy and latency. Here, we evaluate an ISP-less CIS architecture in the presence of noise and other in-pixel circuit non-idealities for the autonomous driving application. By integrating the in-pixel processing circuits to CIS, we filter out the redundant frames and only pass the critical bit information downstream to the backend processor. Such in-pixel processing does not allow ISP operations to be applied to the captured raw image. To reflect these limitations, we model and apply circuit non-idealities to the regenerated artificial raw images as an input to the QDTrack network for multi-object tracking. We evaluate the accuracy loss on the BDD100K dataset and examine its sensitivity on each of the image processing steps. We observe an overall accuracy drop of less than 1.2% in Identification F1-score (IDF1) and 2.1% in Multi-Object Tracking Accuracy (MOTA), suggesting that an ISP-less in-pixel processing circuit is feasible to reject 40% redundant frames directly on CIS.

关键词： Autonomous driving CIS demosaicing ISP multi-object tracking

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Real-Time Data processing for Embedded Communicating systems: a Hardware/Software Co-Design Approach 31

Real-Time Data Processing for Embedded Communicating Systems...

引用

31st conference on Electronics Circuits and systems

作者： Saussereau, Jonathan Jego, Christophe Leroux, Camille Begueret, Jean-Baptiste Univ Bordeaux Bordeaux INP CNRS UMR 5218Lab IMS Bordeaux France

ISBN: (纸本)9798350377217;9798350377200

Embedded systems typically require the transmission of significant amounts of data to small-scale CPUs for applications such as radar signal processing, image processing, and embedded AI. Ensuring data integrity during transmission is typically managed using Cyclic Redundancy Check (CRC) algorithms. However, achieving real-time CRC calculation and data storage poses challenges, often necessitating large FIFO memories and multiple clock domains. These additional resources involve a greater hardware complexity. This paper presents an approach aimed at synchronizing the CPU frequency with data transmission. This enables having a single clock domain and a reduction of power consumption. Using hardware/software co-design, it is possible to achieve real-time data storage and CRC calculation without data loss and with a low power consumption.

关键词： Real-Time Hardware Software Co-Design CRC Embedded systems CPU RISC-V Low-Power SPI

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FPGA Implementation of Various Division algorithms for image processing Applications - A Comparative Analysis

FPGA Implementation of Various Division Algorithms for Image...

引用

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

作者： Jayanthi, B. Kumar, L.S. Someshwaran, A. Sandya, M. Bharadwaj, Nandhini National Institute of Technology Puducherry Department of ECE Karaikal India

ISBN: (纸本)9798331540685

Division is one of the most commonly sort after algorithm for performing image processing operations such as normalization, filtering, enhancement, deconvolution etc. Hence, the design of efficient division algorithm is highly essential in order to obtain a better image processing algorithm The first and foremost step in the implementation of any algorithm in hardware is to identify the number systems used to represent the inputs and outputs. The inputs, outputs and complexity of the algorithm varies based on the number system used. This paper proposes a recursive subtraction based fixed-point division algorithm. This work also provides a comparative analysis on various aspects such as area, power, delay and throughput, on comparing the proposed recursive subtraction based fixed-point division algorithm with the existing floating-point division, restoring integer division and non-restoring integer division algorithms and operator based fixed-point division algorithm. © 2024 IEEE.

关键词： image enhancement

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Medical image Segmentation Techniques: Advances and Challenges 6

Medical Image Segmentation Techniques: Advances and Challeng...

引用

6th IEEE International conference on image processing, Applications and systems, IPAS 2025

作者： Boulehmi, Hela Filali, Rania Linda DGET ISET de Bizerte Bizerte Tunisia

ISBN: (纸本)9798331506520

Medical image segmentation plays a pivotal role in computer-aided diagnosis by facilitating the extraction of essential features necessary for disease detection and treatment strategies. The continuous progress in image processing technologies has led to the development of numerous segmentation methods, encompassing traditional algorithms, machine learning (ML)-driven approaches, and cutting-edge deep learning (DL) techniques. This study undertakes a comparative evaluation of these methods, focusing on their efficiency, accuracy, and suitability across different medical imaging modalities. It also delves into prominent segmentation techniques like thresholding, region-based methods, edge detection, graph cuts, active contour models, and convolutional neural networks (CNNs). Additionally, the paper explores ongoing challenges and prospective advancements aimed at enhancing segmentation efficacy in medical imaging. © 2025 IEEE.

关键词： Convolutional neural networks

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Scheduling algorithms on Heterogeneous Architectures for Efficient Vision systems 47

Scheduling Algorithms on Heterogeneous Architectures for Eff...

引用

conference on Applications of Digital image processing XLVII

作者： Ali, Teymoor Paul, George Nicol, Robert Bhowmik, Deepayan Newcastle Univ Sch Comp Newcastle Upon Tyne Tyne & Wear England STMicroelectronics 1 Tanfield Edinburgh Midlothian Scotland

ISBN: (纸本)9781510679344;9781510679351

Deep Neural Network (DNN) algorithms have become ubiquitous within the vision domain, encompassing various tasks, including object detection, segmentation, and classification. However, executing complex DNNs in real-time systems demands improved energy efficiency, runtime, and accuracy. Traditional embedded imaging designs, typically implemented on homogeneous architectures, face hardware limitations, prompting the need for heterogeneous computing architectures. These architectures combine CPUs, GPUs, FPGAs, and other accelerators, enabling applications to use the most efficient architecture for a given algorithm. The challenge lies in scheduling and partitioning algorithms across accelerators with different computing paradigms and tool-sets. This requires balancing computational power, memory bandwidth, and communication overhead. Effective scheduling involves considering task dependencies, resource availability, and synchronisation. Current deep learning libraries often target single architectures and lack mechanisms to intelligently partition sub-operations across the most suitable processors. This paper introduces a scheduler for heterogeneous vision systems that finely partitions and maps sub-operations of convolutional neural networks and image processing algorithms. Leveraging state-of-the-art compiler frameworks such as PyTorch, TVM, and ONNX, the proposed scheduler optimally distributes tasks across heterogeneous components. Experimental results show that the heterogeneous platform achieves on average 1.12x & 1.08x improvements in kernel runtime and energy consumption, compared to the best-performing discrete hardware counterparts, GPU and FPGA. The study demonstrates that partitioning algorithms based on their runtime and energy properties and optimally scheduling them improves energy and runtime efficiency compared to homogeneous components executing the complete algorithm.

关键词： Heterogeneous Computing Computer Vision FPGA-GPU Heterogeneous Scheduler PyTorch TVM ONNX

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An Investigation on Classical Methods and Unsupervised Deep Learning algorithms for image Segmentation in High Variance images for Industrial Application 35

An Investigation on Classical Methods and Unsupervised Deep ...

引用

35th Irish systems and Signals conference, ISSC 2024

作者： Henry, Nathan Reiter, Tamas

ISBN: (纸本)9798350352986

This paper investigates classical image processing techniques and unsupervised deep learning algorithms for segmenting images with high variance for an under researched industrial problem, focusing on beam burns generated by Focused Ion Beam (FIB) technology. Classical methods (CM) include preprocessing techniques such as edge detection, while Deep Learning (DL) employs Convolutional Neural Networks (CNNs) for unsupervised image segmentation. Experimental results demonstrate the superior accuracy of DL, but with increased computational overhead, in contrast to the efficiency and controllability of CM. Combining both approaches offers a potential solution for optimizing accuracy and efficiency in image segmentation. © 2024 IEEE.

关键词： image segmentation

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Implementation of a Comparative Study of Convolutional Neural Network Architectures for image Blind Noise Elimination 4th

Implementation of a Comparative Study of Convolutional Neura...

引用

4th International conference on Intelligent systems and Pattern Recognition-ISPR

作者： Anzal, Oumaima Guessous, Najib Ouakrim, Youssef Univ Sidi Mohamed Ben Abdellah Dept Math & Comp Sci M2PA Lab ENS Fes 30030 Morocco

ISBN: (纸本)9783031821523;9783031821530

The field of image manipulation is dynamic, exploiting a range of algorithms to analyze, manipulate and enhance digital images. Our study focuses on a crucial application of image processing, which is the elimination of blind Gaussian noise in order to improve image quality and facilitate image analysis by preserving essential details. In this research, we explore the use of different convolutional neural network (CNN) architectures to tackle the problem of blind Gaussian noise, applying different noise levels, ranging from low to high. We present an in-depth comparative analysis of the three main CNN architectures: DnCNN, DRNet and RIDNet, highlighting the quantitative and qualitative experimental results of these different approaches. These methods have demonstrated remarkable performance in image processing tasks, particularly denoising, using various techniques built into CNNs, such as batch normalization and residual learning. Our results show that these techniques bring significant improvements to all three CNN approaches, as evidenced by the remarkable performance observed in the experimental results. These findings underline the robustness of CNN architectures in the face of complex noise scenarios, such as the blind noise scenario addressed in our study.

关键词： Deep Learning image processing image Denoising Convolutinal Neural Networks (CNNs) Blind Noise DnCNN DRNet RIDNET PSNR SSIM

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Classification of Red Watermelon Varieties Using Canny Edge Detection and CNN

Classification of Red Watermelon Varieties Using Canny Edge ...

引用

IEEE International conference on Automatic Control and Intelligent systems (I2CACIS)

作者： Martinez, Milca Rejaiah B. Dayrit, Kian Matthew D. Yumang, Analyn N. Mapua Univ Sch Elect Elect & Comp Engn Manila Philippines

ISBN: (纸本)9798350372113;9798350372106

Watermelon is a commonly cultivated fruit worldwide, especially in Southeast Asia. As one of the top exports in Asia, the commercialization of watermelon has its market. Its consumer appeal makes it one of the most sought-after fruits globally. With the watermelon fruit consisting of different varieties, consumers usually have difficulty in classifying watermelons due to their similar external appearances, especially when labeled under the same name. This study is conducted to implement a system that detects and classifies three red watermelon varieties, Red Export, Orchid Sweet, and Dixie Queen, with image processing algorithms. The researchers utilized Canny Edge Detection for the dataset's preprocessing phase and Convolutional Neural Network (CNN) for its classification. The Raspberry Pi is also applied to this study. Moreover, the researchers created and collected their datasets for testing and validation data. The model used in this study has acquired 84.71% overall accuracy.

关键词： Watermelon Classification image Classification Canny Edge Detection CNN image processing Raspberry Pi

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The application of deep learning algorithms in power system load forecasting 6

The application of deep learning algorithms in power system ...

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6th International conference on Wireless Communications and Smart Grid, ICWCSG 2024

作者： Wang, Shilin Cui, Wei Yang, Yang Meng, Xiangliang State Grid Information & Telecommunication Group Co. Ltd. No. 10 Xuanwumen Outer Street Xicheng District 95555-0345 Beijing100029 China

ISBN: (纸本)9781510682962

This article explores the application of deep learning (DL) algorithms in power system load forecasting. With the continuous advancement of the construction of new power systems, traditional load forecasting models designed based on time series analysis are no longer in line with the actual needs of the current power system. To improve the accuracy of load forecasting, this paper attempts to transform time series data into images for analysis, and then use deep learning methods widely used in the field of image processing for power load forecasting. Convolutional neural network (CNN), as a commonly used image processing algorithm, has been applied in time series data processing. However, data is still processed as a sequence matrix, and the advantages of CNN algorithm in processing image matrices have not been fully utilized. Therefore, this article proposes a CNN algorithm based on sequence to image conversion (STI-CNN). Firstly, the load sequence is converted into a load image. Then, a dual branch deep network model is used to accurately cluster the input data. Finally, the STI-CNN model is used for load prediction. Matlab simulation experiments show that the proposed STI-CNN model has excellent performance in different prediction metrics and has higher prediction accuracy. © 2024 SPIE.

关键词： Data modeling Data conversion image processing systems modeling Convolutional neural networks Evolutionary algorithms Statistical modeling Education and training Support vector machines

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