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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

共 2642 条记录，以下是2321-2330 订阅

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A Pseudoinverse Incremental Algorithm for Fast Training Deep neural networks with Application to Spectra Pattern Recognition

A Pseudoinverse Incremental Algorithm for Fast Training Deep...

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International Joint conference on neural networks

作者： Ke Wang Ping Guo Qian Yin A-Li Luo Xin Xin School of Computer Science and Technology Beijing Institute of Technology Image Processing and Pattern Recognition Laboratory Beijing Normal University Key Laboratory of Optical Astronomy National Astronomical Observatories Chinese Academy of Sciences

ISBN: (纸本)9781509006212

Deep learning scheme has received significant attention during these years, particularly as a way of building hierarchical representations from unlabeled data for a variety of signal and information processing tasks. However, deep neural networks suffer from slow learning speed since most used training algorithms are based on variations of the gradient descent algorithms which require iterative optimization and thus are time-consuming. In addition, a series of control parameters need to be specified empirically which lacks of the theoretical guidance, and current learning algorithms for deep networks are not very suitable to incremental learning scenario. To address these issues, we propose a fast learning scheme in this paper. The basic idea of our approach is to pre-train basic units such as auto-encoders of the deep architecture in an analytical way without any iterative optimization procedure. This scheme is also extended to an incremental learning version. The experimental result shows the superiority of our approach over the state-of-the-art gradient descent based algorithms. To demonstrate the impact of our algorithm on complicated real world applications, we give an example of its performance in astronomical spectra pattern recognition.

关键词： astronomical spectra Spectra Pattern recognition learning (artificial intelligence) Recognition (Psychology) neural network algorithms control parameter Optimization Procedure descent Fasting Functional training Road

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Individual processing speed analysis for traffic sign detection and recognition

Individual processing speed analysis for traffic sign detect...

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IEEE International conference on Smart Instrumentation, Measurement and applications (ICSIMA)

作者： Nursabilillah Mohd Ali Nur Maisarah Mohd Sobran Syahar Azalia Ab. Shukur Mariam Md Ghazaly Ahmad Fayeez Tuani Ibrahim Department of Mechatronic Engineering University Teknikal Malaysia Melaka Malaysia Department of Mechatronic Engineering Faculty of Electrical Engineering University Teknikal Malaysia Melaka Malaysia

Of late, traffic sign detection and recognition are becoming very prevalent topic as it enhances drivers towards safety and alert them with precaution information. This study reports about processing time of the individual color detection and recognition of the partial occlusion traffic sign that have been previously implemented using HSV and RGB color ratio and ANN and PCA method respectively for detection and recognition. The data set for detection and classification process has been successfully created in various places in Malaysia that involved with degradation and out of planes rotated of the signs. There are three standard types of colored images have been used in the study namely Red, Blue and Yellow signs. In this study, we analyze the system processing speed of individual color detection and classification respectively using red, green and blue (RGB) and hue, saturation and value (HSV) color segmentation techniques, supervised feed forward artificial neural network (ANN) and principal component analysis (PCA). The experimental result shown that processing time of individual color detection during daytime and at night using HSV method is slightly faster than RGB technique. On the other hand, supervised feed forward neural network has reached almost 1s in recognizing traffic sign images rather than PCA with only 0.0238s.

关键词： image color analysis Vehicles Principal component analysis artificial neural networks Roads Accidents Hardware

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High Speed, Approximate Arithmetic Based Convolutional neural Network Accelerator

High Speed, Approximate Arithmetic Based Convolutional Neura...

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International SoC Design conference, ISOCC

作者： Mohammed E. Elbtity Hyun-Wook Son Dong-Yeong Lee HyungWon Kim Nanoelectronics Integrated Systems Center Nile University Giza Egypt Mixed-Signal Integrated Systems Center Chunguk National University Cheongju South Korea

ISBN: (数字)9781728183312

ISBN: (纸本)9781728183329

Convolutional neural networks (CNNs) for artificial Intelligence (AI) algorithms have been widely used in many applications especially for image recognition. However, the growth in CNN-based image recognition applications raised challenge in executing millions of Multiply and Accumulate (MAC) operations in the state-of-the-art CNNs. Therefore, GPUs, FPGAs, and ASICs are the feasible solutions for balancing processing speed and power consumption. In this paper, we propose an efficient hardware architecture for CNN that provides high speed, low power, and small area targeting ASIC implementation of CNN accelerator. To realize low cost inference accelerator. we introduce approximate arithmetic operators for MAC operators, which comprise the key datapath components of CNNs. The proposed accelerator architecture exploits parallel memory access, and N-way high speed and approximate MAC units in the convolutional layer as well as the fully connected layers. Since CNNs are tolerant to small error due to the nature of convolutional filters, the approximate arithmetic operations incur little or no noticeable loss in the accuracy of the CNN, which we demonstrate in our test results. For the approximate MAC unit, we use Dynamic Range Unbiased Multiplier (DRUM) approximate multiplier and Approximate Adder with OR operations on LSBs (AOL) which can substantially reduce the chip area and power consumption. The configuration of the approximate MAC units within each layer affects the overall accuracy of the CNN. We implemented various configurations of approximate MAC on an FPGA, and evaluated the accuracy using an extended MNIST dataset. Our implementation and evaluation with selected approximate MACs demonstrate that the proposed CNN Accelerator reduces the area of CNN by 15% at the cost of a small accuracy loss of only 0.982% compared to the reference CNN.

关键词： Power demand image recognition neural networks Hardware Convolutional neural networks artificial intelligence Field programmable gate arrays

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Cross-modal Deep Learning applications: Audio-Visual Retrieval 25th

Cross-modal Deep Learning Applications: Audio-Visual Retriev...

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25th International conference on Pattern Recognition Workshops, ICPR 2020

作者： Jin, Cong Zhang, Tian Liu, Shouxun Tie, Yun Lv, Xin Li, Jianguang Yan, Wencai Yan, Ming Xu, Qian Guan, Yicong Yang, Zhenggougou School of Information and Communication Engineering Key Laboratory of Convergent Media and Intelligent Technology Ministry of Education Communication University of China Beijing100024 China School of Information and Engineering Zhengzhou University Zhengzhou450001 China Communication University of China Beijing100024 China School of Animation and Digital Arts Communication University of China Beijing100024 China Broadcasting and Anchoring School Communication University of China Beijing100024 China

ISBN: (纸本)9783030687793

Recently, deep neural networks have exhibited as a powerful architecture to well capture the nonlinear distribution of high-dimensional multimedia data such as image, video, text and audio, so naturally does for multi-modal data. How to make full use of multimedia data? This leads to an important research direction: cross-modal learning. In this paper, we introduce a method based on the content of audio and video data modalities implemented with a novel two-branch neural network is to learn the joint embeddings from a shared subspace for computing the similarity between the two modalities. In particular, the contribution of proposed method is mainly manifested in the three aspects: i) Using feature selection model for choosing top-k audio and visual feature representation;ii) A novel combination of training loss function concerning inter-modal similarity and intra-modal invariance is used;iii) Due to the lack of video-music paired dataset, we construct dataset of video-music pairs from YouTube 8M and MER31K datasets. The experiments have proved that our proposed model has a better performance compared with other methods. © 2021, Springer Nature Switzerland AG.

关键词： Music

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Approaches of artificial intelligence in biomedical image processing: A leading tool between computer vision & biological vision

Approaches of artificial intelligence in biomedical image pr...

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(Spring) International conference on Advances in Computing, Communication, & Automation (ICACCA)

作者： Shruti Shukla Ashish Lakhmani Ambuj Kumar Agarwal Department of Biomedical Engineering-B. Tech graduated (BME) Babu Banarsi Das-Northern India Engineering College Lucknow Uttar Pradesh Department of Computing Science Engineering-B. Tech Graduate (CSE) Babu Banarsi Das-National Institute Of Technology & Management Lucknow Uttar Pradesh College of Computing Science and Information Technology Teerthankar Mahaveer University Moradabad India

artificial Intelligence or AI is a subfield of computer science, which can be defined as the intelligence exhibited by a machine or a software having a remarkable impact on the field of biology and medicine. Imaging, on the other hand has become an essential component of many fields in medicine, biomedical applications, biotechnology and laboratory research by which images are processed and analysed. Putting together AI and imaging, the tools and techniques of artificial intelligence are useful for solving many biomedical problems and using a computer based equipped hardware software application for understanding images, researchers and clinicians can enhance their ability to study, diagnose, monitor, understand and treat medical disorders. Therefore the main idea behind this research paper is to focus on understanding the artificial intelligence, its concepts and various models available for the segmentation(or classification) of medical images, its applications, advantages and disadvantages and results and more.

关键词： image segmentation artificial intelligence Medical diagnostic imaging artificial neural networks Computational modeling Feature extraction

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Lagrangian support vector regression based image watermarking in wavelet domain

Lagrangian support vector regression based image watermarkin...

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International conference on Signal processing and Integrated networks (SPIN)

作者： Rajesh Mehta Virendra P. Vishwakarma Navin Rajpal Department of Computer science & Engineering Amity school of Engineering & Technology New Delhi India University School of Information & Communication Technology Guru Gobind Singh Indraprastha University New Delhi India

ISBN: (纸本)9781479959921

To enhance the imperceptibility and robustness against image processing operations, the advantage of artificial neural network (ANN) and machine learning algorithms such as support vector regression (SVR), extreme learning machine (ELM) etc. are employed into watermarking applications. In this paper, Lagrangian support vector regression (LSVR) based blind image watermarking scheme in wavelet domain is proposed. The good learning capability, high generalization property against noisy datasets and less computational cost of LSVR compared to traditional SVR and ANN based algorithms makes the proposed scheme more imperceptible and robustness. Firstly, four sub images of host image are obtained using sub sampling. Each sub image is decomposed using discrete wavelet transform (DWT) to obtain the low frequency subband. Low frequency coefficients of each sub image are used to form the dataset act as input to LSVR. The output obtained by trained LSVR is used to embed the binary watermark. The security of the watermark is enhanced by applying Arnold transformation. Experimental results show the imperceptibility and robustness of the proposed scheme against several image processing attacks. The visual quality of watermarked image is quantified by the peak-signal-to noise ratio (PSNR) and the similarity between the original and extracted watermark is evaluated using bit error rate (BER). Performance of the proposed scheme is verified by comparing with the state-of-art techniques.

关键词： Watermarking Boats Bit error rate Discrete wavelet transforms Robustness PSNR

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Survey of Literature on Machine Intelligence and Deep learning for Smart Grid applications

Survey of Literature on Machine Intelligence and Deep learni...

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International conference on Power Systems (ICPS)

作者： Vaibhav Garg Bajrang Agarwal Research and Development Genus Power Infrastructures Limited Jaipur India

ISBN: (数字)9781728141039

ISBN: (纸本)9781728141046

Deep neural networks (DNNs) are currently the state of the art for many modern artificial intelligence (AI) applications. You have surely been touched by one of its influences. Automatic tagging of contacts in Facebook, personal digital assistants (Siri/Cortana/Alexa/ Google Assistant), or automatic creation of albums by Google Photos; all use variants of a DNN. Since the breakthrough application of DNNs to image recognition, Natural Language processing, Machine Translation, Spam & Fraud detection and Speech Recognition, the number of applications that use DNNs has *** Grid is fundamentally defined as a system that has bi-directional information flow in addition to bi-directional flow of Energy. The Energy flow is bidirectional as there is a mechanism through which distributed generation and distributed storage can be integrated with the grid. The Information flow is bidirectional as the information flows from the devices to the central server and control signals flow from the server to the smart *** paper seeks to explore the modern Deep Leaning (DL) and Deep neural networks (DNN) landscape, and point to some of the possible directions in the space of Smart metering and Smart Grid.

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Implementation of online optical character recognition system using embedded DSP platform

Implementation of online optical character recognition syste...

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International conference on Advanced Electronic Systems (ICAES)

作者： Nirmal Savio Paul J Suriya Prakash Student Trainee CSIR-CEERI Chennai Centre CSIR Madras Complex Chennai India Central Electronics Engineering Research Institute CSIR Pilani Rajasthan IN

This paper describes the implementation of an Optical Character Recognition (OCR) system using embedded DSP hardware and software platforms. The system utilizes an area-scan camera and the HALCON image processing library running upon the Strampe VisionBox hardware platform programmed through the Code Composer Studio IDE. This prototype system can be modified for several potential applications such as sorting of packed food based on expiry date.

关键词： Optical character recognition software Cameras Character recognition artificial neural networks Hardware Digital signal processing Optical imaging

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Classification and tracking of items on a moving conveyor belt using convolutional networks and image processing

Classification and tracking of items on a moving conveyor be...

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artificial Intelligence, Computer, Data Sciences and applications (ACDSA), International conference on

作者： Angelo Leite Medeiros De Góes Adrião Duarte Dória Neto Matheus Gomes Diniz Andrade Thiago Theiry De Oliveira Electrical and Computer Engineering Postgraduate Program Federal University of Rio Grande do Norte Natal Brazil Computer Engineering Federal University of Rio Grande do Norte Natal Brazil

The prolonged waiting time at supermarket checkout lines poses a significant challenge to the shopping experience, impacting customer satisfaction and operational efficiency. This paper presents a prototype that addresses this issue by utilizing computer vision and deep learning. The model, incorporating convolutional neural networks such as YOLO v4 tiny and YOLO v5 small, along with tools like OpenCV and Roboflow for dataset management, achieves a remarkable 98% mean average precision for two-class detection. It efficiently detects, classifies, tracks, and counts items on a mobile supermarket conveyor belt. Additionally, we introduce a versatile framework designed for seamless integration into real-world applications. It comprises a customizable monitoring application and simulator that facilitates synthetic image data generation. Managing diverse items in a supermarket presents a major challenge for data gathering, labeling, and training. In that sense, the importance of customizable monitoring and simulation tools is highlighted, emphasizing their practical role. Our findings demonstrate the feasibility of maintaining a minimal 0% to 2.85% precision tradeoff while using half of the data as synthetic for two-class detection, indicating potential practicality in supermarkets with proper scaling. In summary, this study brings tangible benefits to both customers and retailers, offering a potential to streamline, speed up, and cut costs in the supermarket checkout process.

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A Proposed Heterogeneous Convolutional neural Network for Embedded Automotive Vision Algorithms

A Proposed Heterogeneous Convolutional Neural Network for Em...

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IEEE International conference on Electro Information Technology

作者： Iyad Mansour Robert Hoffman Osamah Rawashdeh Bodo Seifert Dura Automotive Systems Auburn Hills MI Electrical and Computer Engineering Dept. Oakland University Rochester MI

The computer vision industry is relying more and more on artificial intelligence to process image data generated by cameras, LIDAR and RADAR applications. In the past years, neural networks (NN) have been specifically successful to process image data and to perform classification tasks that now exceed human classification capabilities. Traditional CNN based algorithms require significant use of central processing unit (CPU) cycles and/or graphics processing units (GPU). The majority of these models are built to operate on laptops or server like solutions that are not limited by the amount of CPU/GPU available for processing. In the transportation industry it is necessary to operate in an environment that is limited in processing power, current consumption, and thermal output and for that reason server-based AI solutions are not always practical in a land, sea or aerial vehicles. This publication will overview automotive computer vision (CV) perception algorithms for a variety of CNN models and offer a novel solution for heterogeneous CNN, H-CNN, which is optimized for automotive grade embedded System on Chips SoC's that limit the computational, current consumption and thermal output of the system.

关键词： H-CNN LIDAR RADAR

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