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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

共 4812 条记录，以下是351-360 订阅

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Designing of Parameter of Exact and Inexact Compressor Based Low Power Multiplier 3

Designing of Parameter of Exact and Inexact Compressor Based...

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3rd international conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Nagar, Sheetal Saxena, Shanky Nayak, Seema Patel, Govind Singh Lovely Professional University Department of Electronics and Communication Engineering Jalandhar144411 India Lovely Professional University Department of Electronics and Electrical Engineering Jalandhar144411 India Iimt College of Engineering Department of Electronics and Communication Engineering Greater Noida India Sitcoe Department of Electronics and Communication Engineering Ichalkaranji MH Yadrav416121 India

ISBN: (纸本)9798350330724

Multiplication is the basic fundamental computational block of every computer system and has vast applications in today's era. To design a low power approximate low power multiplier we used compressor. Proposed approximate compressor based Multipliers have been designed for Error forbearing image analysis applications. In this paper, approximate compressor is proposed for error-tolerant image processing applications. An Inexact compressor based Multiplier introduced to get improved Results as compared with the Literature. Validation and Optimization of Designed Multipliers with the 4 to 2 compressor designs and ECM (Error Correcting Module) has been validated and optimized for Power, area and delay. A new Methodology and algorithm has been applied to compressor based 8 bit multiplier and for image application © 2023 ieee.

关键词： image processing

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The Virtual Air Canvas Using image processing

The Virtual Air Canvas Using Image Processing

引用

2023 international conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Pavithra, K. Geetha, A. Chinnaiyan, R. Alliance University Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350317060

One of the most interesting and challenging research focuses on pattern recognition and image processing has emerged in recent days is writing in the air. In many different applications, it can improve the interface between a machine and a human and offers a substantial contribution to the development of automated operations. In the field of computer vision, object tracking is considered a key challenge. The method of analyzing a video usually consists of three primary steps: recognizing the object, tracking its movement from frame to frame, and finally evaluating its behavior. Choosing an adequate object representation, selecting tracking features, identifying objects, and tracking them are the four problems taken into consideration for object tracking. Object tracking algorithms are widely used in many real-world applications, including autonomous surveillance, video indexing, and vehicle navigation. This work exploits this gap by developing a motion-to-text converter that may be used as software for wearable intelligent devices that allow writing in the air. The proposed work acts as a recorder of rare gestures. computer vision will be utilized to track the finger's path. With the generated text, messages, emails, and other kinds of correspondence can all be sent. It will enable effective communication for the deaf. Keywords - object, emoji's, image color, camera © 2023 ieee.

关键词： computer vision

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JPEG COMPLIANT COMPRESSION FOR DNN VISION 30

JPEG COMPLIANT COMPRESSION FOR DNN VISION

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30th ieee international conference on image processing (ICIP)

作者： Zheng, Kaixiang Salamah, Ahmed H. Ye, Linfeng Yang, En-Hui University of Waterloo Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9781728198354

Conventional image compression techniques are mostly developed for the human visual system. However, with the extensive use of deep neural networks (DNNs), more and more images will be consumed by DNN-based intelligent machines, which makes it crucial to develop image compression techniques customized for DNN vision while being JPEG compliant. In this paper, we first propose a new distortion measure, dubbed the sensitivity weighted error (SWE). Then, we develop OptS, a DNN-oriented compression algorithm with full JPEG compatibility, which designs optimal quantization tables for DNN models based on SWE. To test the performance of our algorithm, experiments of image classification are conducted on the imageNet dataset for two prevailing DNN models. Results demonstrate that our algorithm achieves better rate-accuracy (R-A) performance than the default JPEG. For some DNN model, the compression ratio of our algorithm can reach 8.3x(1), reducing the compression rate (bits per pixel, bpp) of the default JPEG by 57.4% with no accuracy loss. Our source code is available at https://***/zkxufo/***.

关键词： image compression deep learning JPEG quantization table distortion measure

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Interpretation of Neural Networks is Susceptible to Universal Adversarial Perturbations 48

Interpretation of Neural Networks is Susceptible to Universa...

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48th ieee international conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Oskouie, Haniyeh Ehsani Farnia, Farzan Sharif University of Technology Department of Computer Engineering Iran The Chinese University of Hong Kong Department of Computer Science & Engineering Hong Kong

ISBN: (纸本)9781728163277

Interpreting neural network classifiers using gradient-based saliency maps has been extensively studied in the deep learning literature. While the existing algorithms manage to achieve satisfactory performance in application to standard image recognition datasets, recent works demonstrate the vulnerability of widely-used gradient-based interpretation schemes to norm-bounded perturbations adversarially designed for every individual input sample. However, such adversarial perturbations are commonly designed using the knowledge of an input sample, and hence perform sub-optimally in application to an unknown or constantly changing data point. In this paper, we show the existence of a Universal Perturbation for Interpretation (UPI) for standard image datasets, which can alter a gradient-based feature map of neural networks over a significant fraction of test samples. To design such a UPI, we propose a gradient-based optimization method as well as a principal component analysis (PCA)-based approach to compute a UPI which can effectively alter a neural network's gradient-based interpretation on different samples. We support the proposed UPI approaches by presenting several numerical results of their successful applications to standard image datasets. © 2023 ieee.

关键词： image recognition

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Non Homogeneous Realistic Single image Dehazing

Non Homogeneous Realistic Single Image Dehazing

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23rd ieee/CVF Winter conference on applications of computer Vision (WACV)

作者： Vinay, P. Abhisheka, K. S. Shetty, Lithesh Kushal, T. M. Shylaja, S. S. PES Univ Bangalore Karnataka India

ISBN: (纸本)9798350320565

A hazy image is one where atmospheric effects degrade the contrast and visibility of the image. It is often caused by the dispersion of light into the moisture particles present, smoke etc. This results in lower performance in high level vision tasks such as object detection, free space detection, scene understanding, etc. Hence the images have to be dehazed before applying other high level algorithms. Dehazing is the process of reconstructing the original colour and contrast of the image if taken in normal conditions. image dehazing is a non-trivial task as it is hard to collect haze free ground truth images. Further, achieving dehazed images when variable haze is present is a significantly harder challenge. In this research, we propose the Non Homogeneous RESIDE dataset (NH-RESIDE) that contains images created synthetically using the principles of randomness and representativeness. Experimental results show that the model trained on our dataset produces visually more pleasing images with a much better dehazing effect on real world images. The model implemented in this paper also outperforms the state-of-the-art models by a huge margin on the NH-Haze dataset proposed by the NTIRE Non Homogeneous Dehazing Challenge at CVPR, achieving an average PSNR of 25.69 and an average SSIM of 0.80. It also achieves much better processing times when compared to other models, thereby facilitating real-time performance.

关键词： computer vision image color analysis conferences Moisture Object detection computer architecture Real-time systems

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An Improved Depth image Clustering Algorithm Based on Spatial Information 2

An Improved Depth Image Clustering Algorithm Based on Spatia...

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

作者： Yan, Li Su, Shan Hu, Xiao Zhou, Yuquan Chen, Changjun Wuhan University School of Geodesy and Geomatics Wuhan China Hubei Luojia Laboratory Wuhan China

ISBN: (纸本)9798350331417

Depth image spatial clustering is an important task in the fields of computer vision and machine learning, aiming to group pixels or point cloud data of depth images into clusters with similar features. This is crucial for tasks such as object recognition, scene segmentation, 3D reconstruction, and other applications. The K-means algorithm is a commonly used clustering method that divides data points into K clusters, ensuring that each data point belongs to the cluster whose center is closest to it. However, traditional K-means algorithms face several challenges in depth image spatial clustering, such as sensitivity to the choice of K and the influence of cluster center initialization on the results. In this study, we propose an improved depth image clustering method based on spatial information. The method includes depth image edge extraction, intra-edge region growing, unclassified point re-clustering, and connected region merging. Through experiments conducted on datasets from TUM, Bonn, and our collected data, our algorithm demonstrates superior performance in depth image clustering tasks, higher clustering accuracy, fewer unclassified points, and faster convergence speed. © 2023 ieee.

关键词： Depth image clustering K-Means RGB-D datasets Spatial information

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image Preheating: Enables Fast Startup of Large-Scale Parallel Containerized Jobs 6

Image Preheating: Enables Fast Startup of Large-Scale Parall...

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6th international conference on Software Engineering and computer Science, CSECS 2023

作者： Yeting Gaohongbo Tangbo Yangke Chengdu Aircraft Design & Research Institute Department of Information Technology Chengdu China

ISBN: (纸本)9798350306378

Containers and Kubernetes are becoming the normative way to deploy services in the data center. This rich virtualization ecosystem simplifies application development and management, resolves complex dependencies between applications and execution environments, and improves the efficiency of application deployment. Unfortunately, the startup and deployment delays of containerized jobs significantly limit the efficiency of job execution. In particular, the larger the container image, the longer the image download time, which seriously affects the service processing capability of the system. In this paper, we propose the image preheating technique to accelerate the startup of large-scale distributed parallel containerized jobs. According to the different working scenarios, image preheating is divided into layer preheating and image preheating. First, layer preheating is applied in the scenario where a single machine pulls a single image to start a single container, and we explore the parallel nature of multiple threads in pulling different layers of images and propose a more efficient heuristic algorithm to accelerate parallel pulling of images. Second, image preheating is applied in the scenario where the entire parallel job is deployed and started on the cluster. As soon as one task image of the job is downloaded, the preheating is completed and the entire job starts. In addition, we propose a very practical approach called manual image preheating to provide a better experience for users executing the job. Simulated and real experiments show that the method we designed can improve the download speed of images by 23.54% and the start of jobs in the cluster by 3.73%. © 2023 ieee.

关键词： Preheating

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Lightweight Approximate Multiplier with Improved Accuracy in FPGA for Error Resilient Application 36

Lightweight Approximate Multiplier with Improved Accuracy in...

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36th international conference on VLSI Design (VLSID) / 22nd international conference on Embedded Systems (ES)

作者： Padhy, Apurba Prasad Das, Bishnu Prasad Indian Inst Technol Roorkee Elect & Commun Engn Roorkee Uttar Pradesh India

ISBN: (纸本)9798350346787

Approximate computing finds its application in image processing, machine learning, data mining and multimedia data processing. The paper proposes a scalable and lightweight 4x4 approximate multiplier, which is designed to efficiently utilize the FPGA resources. We have divided the partial product matrix and selectively chosen the input variables for the effective usage of the LUTs and carry-chain. The bit error position due to the approximation is reduced by assigning the possible lower bits to '1' which reduces the maximum error magnitude from 16 to 4. The proposed 4x4 approximate multiplier achieves a reduction in area, power and latency of 26.7%, 57.6% and 60.1% respectively compared to the Xilinx multiplier IP. The proposed multiplier is used as a building block to implement 8x8 approximate multipliers for image sharpening applications. We achieve a high PSNR value of 69.4 dB using the 8x8 approximate multiplier.

关键词： approximate computing high performance image sharpening multiplier reduced area

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Improved ECA-DenseNet Framework for Brain MRI image Classification 31

Improved ECA-DenseNet Framework for Brain MRI Image Classifi...

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31st ieee conference on Signal processing and Communications applications (SIU)

作者： Aydin, Halise Nur Yildiz, Oktay Gazi Univ Bilgisayar Muhendisligi Bolumu Ankara Turkiye

ISBN: (纸本)9798350343557

Early diagnosis is very important in brain tumors. Although Magnetic Resonance (MRI) is widely used for brain tumor detection, it is difficult to detect the tumor manually. Therefore, computer-aided diagnosis systems have been frequently utilized in recent years. In this study, an Efficient Channel Attention-Dense Convolutional Network (ECA-DenseNet) framework is proposed to detect tumors in patients based on brain MRI images. While detecting the tumor, it is tried to determine which type of tumor is present in the patient. In the developed ECA-DenseNet structure, an ECA block has been added to the dense blocks. The ECA block aimed to discard unimportant information and thus reduce the computation time. The improved DenseNet model has been tested on an open-source dataset. The improved model is compared with DenseNet-121, DenseNet-169, DenseNet-201, and DenseNet-264. The experimental results show the improving model has better classification performance than the others. The accuracy of the proposed model was 95.07%.

关键词： DenseNet Efficient Channel Attention Classification SENet Brain MRId

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Developing a new modified Algorithms for Digital image Sampling and Quantization in Serial and Parallel image processing

Developing a new modified Algorithms for Digital Image Sampl...

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2023 ieee international conference on Paradigm Shift in Information Technologies with Innovative applications in Global Scenario, ICPSITIAGS 2023

作者： Jayashankari, J. Shalini, V. Pandey, Megha Swetha, S. Sharma, Mohit Kumar Khurana, Jatin Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology U P India Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Electrical Engineering Jaipur India University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350329773

The improvement of a new changed algorithms for virtual photograph sampling and quantization in serial and parallel image processing is proposed. The set of rules improves upon existing methods with the aid of combining picture sampling, quantization, and parallelization techniques with an efficient, in-region serial photograph processing framework. The ensuing algorithm has several advantages, which includes more accurate and steady photograph output, quicker convergence fees, and flexibility in process scheduling. The set of rules is nicely-suitable to parallel computing environments, together with clusters and multi-center structures. Via widespread experimentation and simulation, the proposed set of rules is located to be effective and to outperform present techniques. Moreover, the performance of the proposed set of rules changed into further confirmed thru a comparison to present methods, and the parameters of the version had been tuned in order to optimize overall performance. © 2023 ieee.

关键词： image enhancement

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