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检索条件"任意字段=IEEE International Conference on Signal Image Technology and Internet Based Systems"

共 20492 条记录，以下是4491-4500 订阅

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A Key to Effective Multi-task Learning: Separate Query Selection for Task-Synergized Handling and Node Utilization

A Key to Effective Multi-task Learning: Separate Query Selec...

引用

international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Shan-Ya Yang Hao-Chung Cheng Chien-Yao Wang Jia-Ching Wang Chun-Yi Lee National Taiwan University National Central University Academia Sinica Taiwan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the realm of computer vision, effectively handling multi-tasks simultaneously presents a challenge that necessitates innovative solutions. To better address multiple vision problems, we introduce SeTano, an integrated Graph Neural Network (GNN)-based framework. This framework comprises a Dynamic Edge-Sensing GNN (DES-GNN) backbone, which can dynamically adjust edges to extract more pivotal features, and a downstream design which includes a node reduction and a separate query selection strategy. To validate our approach, we perform multi-task experiments on the imageNet and MS COCO datasets. The results indicate that the integrated design of SeTano leads to enhanced performance in various vision multi-tasks.

关键词： Computer vision image edge detection signal processing Integrated design Multitasking Feature extraction Graph neural networks Acoustics Speech processing

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Local Feature Enhanced Adversarial Network for the Blind image Quality Assessment

Local Feature Enhanced Adversarial Network for the Blind Ima...

引用

international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Xiaomei Shi Min Zhang Shouhai Xia Ruxue Zhang Jun Feng School of Mathematics Northwest University China School of Information Science and Technology Northwest University China

As a hot research topic in the field of computer vision, blind image quality assessment (BIQA) can provide high-quality images for end-users and promote the development of other fields of computer vision. Although the existing BIQA based on convolution neural networks has made significant progress in synthetic distortion evaluation, it still cannot be well extended to authentic distortion and algorithm-related distortion. Therefore, this paper proposes a BIQA adversarial network with local feature enhancement to deal with this challenge. First, the ResNeSt50 network with local feature enhancement is used to extract the features of images, which effectively combines the overall semantic information and the local features of the images. Then, the mapping of distorted images to their quality scores is learned by the adversarial network. Extensive experiments demonstrate that the proposed method performs best on three categories of distorted scenario databases (nine databases) compared with state-of-the-art BIQA methods.

关键词： image quality Computer vision Visualization Acoustic distortion Databases Semantics signal processing algorithms

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Bias Identification with RankPix Saliency

Bias Identification with RankPix Saliency

引用

international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Salamata Konate Léo Lebrat Rodrigo Santa Cruz Clinton Fookes Andrew Bradley Olivier Salvado CSIRO Data61 Brisbane Australia Queensland University of Technology Brisbane Australia

Saliency methods are critical tools that allow the estimation of the most important features of an input image that contribute to the network’s prediction. These tools are pivotal in high-stakes applications such as medical diagnosis or autonomous driving. Additionally, these tools can help identify models’ biasedness, such as a strong prior on object placement, easily distinguishable background features, or frequent object co-occurrence. We introduce RankPix, a novel saliency method for visual bias identification in image classification tasks. RankPix is a derivative-free approach that allows the identification of a minimum subset of pixels/features at a given network layer that changes the output of a classifier. Surprisingly, this approaches provides equivalent performance to gradient-based approaches on the standard pointing game benchmark. More interestingly, RankPix outperforms traditional approaches for systematic bias identification.

关键词： Training Visualization Systematics Games Benchmark testing signal processing Reliability

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Segment Anything Model (SAM) for Medical image Segmentation: A Preliminary Review

Segment Anything Model (SAM) for Medical Image Segmentation:...

引用

ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： Leying Zhang Xiaokang Deng Yu Lu College of Big Data and Internet Shenzhen Technology University Shenzhen China

Medical image segmentation is a critical component in a variety of clinical applications, facilitating accurate diagnosis and treatment planning. The Segment Anything Model (SAM), a deep learning architecture, has emerged as a promising solution to the challenges inherent in medical image segmentation. SAM’s superior zero-shot capability allows it to generalize effectively, even in the absence of task-specific segmentation samples. This unique characteristic broadens its application potential across various medical image modalities. This paper provides an in-depth review of SAM, focusing on its application in medical image segmentation. The review discusses the advantages of deep learning image segmentation over traditional methods, emphasizing the superior accuracy, efficiency, and automation that deep learning models offer. The paper also highlights the applications of SAM across various medical imaging modalities, demonstrating its versatility and adaptability. A taxonomy of SAM approaches in medical image segmentation is presented, categorizing them based on modality, dimension, organ, dataset, prompt, and performance. Despite the promising results of SAM, challenges remain in the field of medical image segmentation. The paper identifies these challenges and suggests potential directions for future research. In conclusion, this review aims to provide a comprehensive understanding of SAM and its potential to revolutionize medical image analysis and contribute to advancements in healthcare.

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Research on Canny Edge Feature Detection technology of Color image based on Vector Properties 15

Research on Canny Edge Feature Detection Technology of Color...

引用

15th ieee international conference on Electronic Measurement and Instruments, ICEMI 2021

作者： Kong, Weidi Zhang, Haitao Zhao, Wei Tang, Kai College of Defense Engineering PLA Army Engineering University Nanjing China

ISBN: (纸本)9781665444910

Canny edge detection based on optimization algorithm has the advantages of high accuracy and high signal-to-noise ratio. However, because the Canny algorithm was based on gray image, some edge information will be lost during the process of converting color image to gray image. Aiming at this problem, the edge detection method of color image based on vector properties was proposed in this paper. According to the properties of vectors with direction and length, the image gradient calculation of Canny algorithm was improved from the angle and length of vector, in order to realize edge feature detection of color image directly. Compared with the results detected by the conventional Canny algorithm, this method can get clearer and more accurate edges. When combined with specific color markers in specific application scenarios, this method can obtain ideal detection results that only contains the edge of the marker. © 2021 ieee.

关键词： Vectors

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Reliable Drowsy Driver Detection Using Machine Vision and Deep Learning with Rich Facial Feature Analysis

Reliable Drowsy Driver Detection Using Machine Vision and De...

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Delhi Section conference (DELCON), ieee

作者： Bader Al-Haran Fares Mohammad Aaron Rasheed Rababaah Mohammad Ashkanani Bader Al-Otaibi Department of Computer Science American University of Kuwait Salmyia Kuwait Department of Computer Science American University of Kuwait Salmyia Kuwait

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

Drowsy driving is one of the major causes of car accidents leading to a huge number of injuries and deaths every year. This paper presents a comprehensive study about developing an AI-based system to detect drowsiness consisting of three sub-systems which are eye's state detection, yawn detection, and head tilde detection. The first approach is using CNN and an augmented image dataset to detect the eye's state and yawning where an accuracy of around 91% was achieved for eye detection and around 75% for yawn detection. The other technique used was using face mesh which extracts facial landmarks and identifies 468 points in the face. MAR and EAR can be calculated using these landmarks. In addition, the head tilde angle can also be calculated using these landmarks. Finally, all the sub-systems will be integrated into one whole system with a GUI for easieruse by users and multi-threaded to run in parallel and avoid single point of failure.

关键词： Accuracy Multithreading Machine vision signal processing Magnetic heads internet Reliability Faces Graphical user interfaces Vehicles

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Machine Learning based Early Debris Detection Using Automotive Low Level Radar Data

Machine Learning Based Early Debris Detection Using Automoti...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Kanishka Tyagi Shan Zhang Yihang Zhang John Kirkwood Sanling Song Narbik Manukian Aptiv Advance Research Center Agoura Hills California USA

Road safety for automated vehicles requires accurate and early detection of stationary objects in the vehicle’s path. Radar can use doppler to effectively identify stationary objects and make these identifications at long range and in severe weather and poor light conditions. In this paper, we propose a radar-based stationary object detection system that combines signal processing techniques with machine learning technology to detect stationary in-path objects from the low level spectra of front looking radars. The proposed system consists of novel signal and image processing methods to extract key features from the raw data, which are fed into a long short-term memory (LSTM) to determine the probability of a stationary object in-lane at each range. Experiments with collected data in controlled and uncontrolled scenarios demonstrate the effectiveness of our approach.

关键词： Radar detection Radar Machine learning Feature extraction Radar signal processing Road safety Object recognition

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Novel Photodetectors and image Sensors based on SOI Substrate 14

Novel Photodetectors and Image Sensors based on SOI Substrat...

引用

14th ieee international conference on ASIC, ASICON 2021

作者： Zuo, Jiaxing Wan, Jing Fudan University School of Information Science and Technology Shanghai200433 China

ISBN: (纸本)9781665411493

Recently, our group has been developing novel semiconductor photodetectors and image sensors based on silicon-on-insulator (SOI) substrate. The interface coupled photodetector (ICPD) utilizes the interface coupling effect in SOI and has achieved high responsivity. It further demonstrates tunable response spectrum when combining SOI and MoS2. based on dual photogating effect, the wavelength photodetector has been demonstrated with the output signal only sensitive to the wavelength instead of intensity of the light. The deep depletion effect in SOI has been discovered and utilized for photodetection application with tunable responsivity and sensing range. In the end, this effect is further used to demonstrate photoelectron in-situ sensing device (PISD) for one-transistor active-pixel sensor (1T-APS). © 2021 ieee.

关键词： Photodetectors

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3D Adaptive SMCM Filtering of CT images 11

3D Adaptive SMCM Filtering of CT Images

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11th ieee international conference on Intelligent Data Acquisition and Advanced Computing systems - technology and Applications (IDAACS)

作者： Draganov, Ivo Mironov, Rumen Tech Univ Sofia 8 Kliment Ohridski Blvd Sofia 1756 Bulgaria

ISBN: (纸本)9781665426053

In this paper a new implementation of the Saint-Marc-Chen-Medioni (SMCM) filter is proposed in a 3D variant. It uses adaptive selection of the threshold k, found from a 2D projection of the input image. The algorithm has been tested over Computed Tomography (CT) images with Additive White Gaussian Noise (AWGN) at variance levels 0.01 and 0.001. It leads to better quality of filtered images in terms of Peak signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), compared to 3D Gaussian and 3D Averaging filters. The proposed 3D SMCM filter is considered applicable for filtering of other types of 3D images, such as Magnetic Resonance images (MRI).

关键词： AWGN SMCM filter Gaussian filter CT image 3D

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A Novel Deep Learning Approach for Heliostat Dirt Segmentation and Severity Assessment

A Novel Deep Learning Approach for Heliostat Dirt Segmentati...

引用

ieee international conference on signal Processing, Communications and Computing (ICSPCC)

作者： Wei Zhao Zhongtao Pan Tao Sheng Junfeng Zhang Yiming Xue Luoxiao Yang Department of New Energy Project Powerchina Northwest Engineering Corporation Limited Xi'an China Qinghai Branch Powerchina Renewable energy Corporation Limited Qinghai China Department of Automation and Information Engineering Xi'an University of Technology Xi'an China

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

Photothermal power generation is a promising technique for converting solar radiation into electricity with high efficiency and stability. However, the performance and maintenance of photothermal power plants depend on the cleanliness and reflectivity of the heliostats. This paper introduces an innovative approach to addressing the challenges of dirt detection and segmentation on heliostats. Leveraging the capabilities of deep learning, we propose the Multi-Scale Heliostat Dirt Segmentation and Classification (MSHDSC) framework, integrating a novel multi-scale feature fusion module (MSFFM) with an enhanced DeepLabV3+ network. This framework effectively segments small dirt areas on heliostat images, facilitating precise cleaning strategies. A unique aspect of our work is the introduction of an unsupervised clustering algorithm post-segmentation, which categorizes dirt based on color and texture, assigning a severity score to each category. This categorization assists in determining the cleaning complexity and prioritizing maintenance efforts. Experimental results show that our method outperforms several state-of-the-art image segmentation models in terms of accuracy and efficiency and provides useful information for targeted and prioritized cleaning of heliostats by robots or drones.

关键词： Deep learning image segmentation Accuracy signal processing algorithms Clustering algorithms Cleaning Stability analysis Maintenance Power generation Surface treatment

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