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

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Boolean matrix compressed sensing

Boolean matrix compressed sensing

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

作者： Qiang Liu Mahdi Soleymani Hessam Mahdavifar National Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Halıcıoğlu Data Science Institute University of California San Diego La Jolla CA USA Department of Electrical and Computer Engineering Northeastern University Boston MA USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In real-world datasets, leveraging the low-rank and sparsity properties enables developing efficient algorithms across a diverse array of data-related tasks, including compression, compressed sensing, matrix completion, etc. Notably, these two properties often coexist in certain real-world datasets, especially in Boolean datasets and quantized real-valued datasets. To harness the advantages of low-rank and sparsity simultaneously, we adopt a technique inspired by compressed sensing and Boolean matrix completion. Our approach entails compressing a low-rank sparse Boolean matrix by performing inner product operations with a randomly generated Boolean matrix. We then propose a decoding algorithms based on message-passing techniques to recover the original matrix. Our experiments demonstrate superior recovery performance of our proposed algorithms compared to Boolean matrix completion, with equal measurement requirements.

关键词： image coding Signal processing algorithms Signal processing Acoustics Sensors Decoding Sparse matrices Speech processing Compressed sensing

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image Enhancement Algorithm based on Local Contrast for Convolutional Neural Network-Based Infrared Target Recognition: ∗Note: Subtitles are not captured in Xplore and should not be used 24

Image Enhancement Algorithm based on Local Contrast for Conv...

引用

24th IEEE International conference on High Performance Computing and Communications, 8th IEEE International conference on Data Science and systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Nian, BingKun Ma, LiYuan Zhang, Yan Zhang, Yi Shi, HuaJun The 32nd Institute China Electricity Science and Technology China College of Science National University of Defense Technology China College of Electronic Science National University of Defense Technology China

ISBN: (纸本)9798350319934

In the task of infrared weak and small target recognition, in order to improve the image quality and solve the problem of poor learning ability of convolutional neural network (CNN) due to the imbalance of positive and negative samples, this study proposes a three-stage image enhancement algorithm named adaptive filter for quality enhancement based on local contrast (AFQELC). Firstly, AFQELC analyzes the statistical characteristics of the image and constructs a local contrast adaptive filter (LCAF) to enhance the detailed information of the image, which promotes the deep learning model to learn low-level semantic information. Secondly, principal component analysis (PCA) fusion combines information from the original image and the image enhanced by LCAF to reduce noise. Finally, the gradient component of the original image is extracted to further revise the processing result, which promotes the deep learning model to learn advanced semantic information. The proposed non-data-driven algorithm has a clear and interpretable process, which is superior to other traditional and neural network based image enhancement algorithms. The experiment results show that the quality of images enhanced by AFQELC is improved significantly. In addition, AFQELC can improve the recognition and positioning accuracy of the CNN-based algorithm for infrared target recognition by alleviating the imbalance of positive and negative samples. © 2022 IEEE.

关键词： image enhancement

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Deep Learning-Based Improved Automatic Building Extraction from Open-Source High Resolution Unmanned Aerial Vehicle (UAV) imagery 1

引用

2nd International conference on Unmanned Aerial systems in Geomatics (UASG)

作者： Maniyar, Chintan B. Kumar, Minakshi Indian Inst Remote Sensing Photogrammetry & Remote Sensing Dept Dehra Dun 248001 Uttarakhand India

ISBN: (数字)9783031193095

ISBN: (纸本)9783031193118;9783031193095;9783031193088

Automatically extracting buildings from remotely sensed imagery has always been a challenging task, given the spectral homogeneity of buildings with non-building features as well as the complex structural diversity within the image. Traditional machine learning (ML) based methods deeply rely on a huge number of samples and are best suited for medium-resolution images. Unmanned aerial vehicle (UAV) imagery offers the distinct advantage of very high spatial resolution, which is helpful in improving building extraction by characterizing patterns and structures. However, with increased finer details, the number of images also increases many folds in a UAV dataset, which require robust processing algorithms. Deep learning algorithms, specifically Fully Convolutional Networks (FCNs) have greatly improved the results of building extraction from such high resolution remotely sensed imagery, as compared to traditional methods. This study proposes a deep learning-based segmentation approach to extract buildings by transferring the learning of a deep Residual Network (ResNet) to the segmentation-based FCN U-Net. This combined dense architecture of ResNet and U-Net (Res-U-Net) is trained and tested for building extraction on the open source Inria Aerial image Labelling (IAIL) dataset. This dataset contains 360 orthorectified images with a tile size of 1500 m(2) each, at 30 cm spatial resolution with red, green and blue bands;while covering total area of 805 km(2) in select US and Austrian cities. Quantitative assessments show that the proposed methodology outperforms the current deep learning-based building extraction methods. When compared with a singular U-Net model for building extraction for the IAIL dataset, the proposed Res-U-Net model improves the overall accuracy from 92.85% to 96.5%, the mean F1-score from 0.83 to 0.88 and the mean IoU metric from 0.71 to 0.80. Results show that such a combination of two deep learning architectures greatly improves the building extract

关键词： Transfer learning Fully convolutional networks image segmentation Building extraction

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Fundus image Classification for Glaucoma using U-Net Architecture and Logistic Regression

Fundus Image Classification for Glaucoma using U-Net Archite...

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International conference on the Paradigm Shifts in Communication, Embedded systems, Machine Learning and Signal processing (PCEMS)

作者： Vanshika Bajaj Deepali M. Kotambkar Shelke Dept. of Information Technology Shri Ramdeobaba College of Engineering and Management Nagpur India

The leading cause of visual impairment after cataract, is glaucoma and the only way to combat it is to detect it early. It is imperative to develop a system that can work effectively without a lot of equipment, qualified medical personnel, and takes less time in order to address this fundamental issue. A Computer-Aided Diagnosis (CAD) system, which employs different algorithms for medical image processing and analysis, can assist in achieving this. One of the ways to diagnose glaucoma is to calculate Optic Cup to Optic Disc ratio (CDR) and this can be done with the help of CAD algorithms. In medical image processing the primary focus is on image segmentationand its classification in order to obtain a result. In this paper, the exploration the best-known CNN model, U-Net for image segmentation of Optic Disc and Optic Cup from a fundus image and Logistic Regression, a classification model to determine a relationship between these two terms rather than previously used CDR formulas.

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Multi-Direction Strategy with Honey Badger Algorithm and Convolutional Neural Network for Industrial Robot Visual Servo image processing

Multi-Direction Strategy with Honey Badger Algorithm and Con...

引用

Intelligent algorithms for Computational Intelligence systems (IACIS), International conference on

作者： Hao Chen Deppartment of Engineering & Mechanics Jilin Communications Polytechnic Changchun China

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

The Industrial robot visual servo image processing requires highly autonomous and intelligence robotic manipulators, with goal of performing manipulation tasks independently without human interventions. However, limit efficiency for large scale, sensitive to noise in input data which affect classification accuracy. This paper, proposed Multi-Direction Strategy with Honey Badger Algorithm (MHBA) and Convolutional Neural Network (CNN) for classification is effectively explore the hyper parameter space of CNN technique is help achieve classification accuracy. The MHBA ability to adapt and explore multiple directions in the parameter space makes CNN maximum efficient to variation in input data. The MHBA model is applied to large scale CNN model is efficiently narrowing down the search space and balances exploration and exploitation allowing for search space. The Pre-processing using linear transformation such as translation or rotation help in adjusting the image dimension while maintaining the essential content. The proposed MHBA-CNN technique for industrial robot is achieving better outcomes such as Mean Absolute Error (MAE) of 2.03, Mean Absolute Scaled Error (MASE) of 3.45, R 2 score of 2.45 on raw dataset. The existing technique such as Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) are evaluated of proposed method.

关键词： Adaptation models Visualization Recurrent neural networks Accuracy Industrial robots Space exploration Classification algorithms Convolutional neural networks Servomotors Long short term memory

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Interactive Sign Language Learning System using Deep Learning

Interactive Sign Language Learning System using Deep Learnin...

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Self Sustainable Artificial Intelligence systems (ICSSAS), International conference on

作者： S Ravikumar P. Dimple Praharsha J. Sri Harsha Priya V. Naga Rohit Sai Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Chennai Tamil Nadu

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

The “Interactive Sign Language Learning System” is a sophisticated application designed to facilitate the learning process of sign language learners. This comprehensive system encompasses several key features, including sign language alphabet and word recognition, text-to-action conversion for learners, multi-language support, and integrated voice output functionality. The system utilizes advanced algorithms for sign language recognition, employing techniques such as image processing and machine learning to accurately interpret hand gestures and movements. For sign language alphabet and word recognition, a combination of computer vision algorithms, possibly including convolutional neural networks (CNNs) or recurrent neural networks (RNNs), may be employed to analyze input images or video streams and classify them into corresponding sign language symbols or words. Text-to-action conversion involves mapping textual input to corresponding sign language actions or gestures, possibly using natural language processing (NLP) algorithms to understand the semantics of the text and generate appropriate sign language representations. The system’s accuracy, measured in terms of correctly recognized sign language symbols or words, would depend on the effectiveness of the algorithms employed and the quality of the training data. With rigorous development and training, the system aims to achieve high accuracy levels, in sign language recognition and text-to-action conversion tasks.

关键词： Sign language Accuracy Machine learning algorithms image recognition Symbols Training data Speech recognition Natural language processing Classification algorithms Convolutional neural networks

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Mechanisms For Using image Properties And Neural Networks In Identification Of Micro-Objects 16

Mechanisms For Using Image Properties And Neural Networks In...

引用

16th IEEE International conference on Application of Information and Communication Technologies, AICT 2022

作者： Jumanov, Isroil I. Safarov, Rustam A. Djumanov, Olimjon I. Samarkand State University Applied Mathimatics and Informatics Samarkand Uzbekistan

ISBN: (纸本)9781665451628

The problem of visualization, recognition, classification of images of micro-objects, in particular, pollen grains, unicellular organisms, fingerprints based on the definition of their variety, belonging to a class, the use of information of geometric shapes, morphology, dynamic, specific characteristics, unique features of neural networks has been investigated, in control systems of industrial and technological complexes, environmental monitoring, ecology, and medical diagnoses. Methods, learning algorithms, component computational schemes of neural networks have been developed, which provide the best quality of image identification in conditions of a priori insufficiency, uncertainty of parameters, and low accuracy of data processing. Mathematical expressions are obtained for estimating identification errors associated with information distortions at the measurement, input, and transmission stages due to nonstationarity, the inadequacy of approximation, interpolation, and extrapolation of the image contour. A software package for the recognition and classification of pollen grains has been built and implemented, which includes algorithms for a three-layer, loosely coupled neural network, Hopfield's network, bidirectional associative memory, Kohonen. Results are obtained for correct, incorrect recognition, and rejected pollen samples based on with-teacher and unsupervised learning algorithms, which are synthesized with cubic, biquadratic, and interpolation spline functions. © 2022 IEEE.

关键词： Interpolation

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Smart Elevator Control and Management

Smart Elevator Control and Management

引用

Data Science and Business systems (ICDSBS), International conference on

作者： Bhanu Venkat Galla Nishita Satya Gatti Karthick S S. Selva Kumara Samy Department of Computational Intelligence SRMIST Kattankulathur Chennai India

ISBN: (数字)9798331585600

ISBN: (纸本)9798331585617

The Smart elevator system that adapts elevator stops according to actual occupancy by people in real-time manner. In our proposed system, computer vision and object detection techniques are incorporated in determining whether people are standing at a floor prior to a stop. An image is taken by the system using a camera that takes a view of the elevator lobby herein recognition of persons is done using machine learning algorithms to make requisite decisions of the elevator's halt. If there are people outside the elevator, a door will open to admit them otherwise the elevator simply skips the floor making movement faster and reduce the travel time. The recommendation decision draws on heuristic image recognition computations like YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector). By using a camera to monitor the outside area, the system makes a binary decision to pull the emergency brake and open the door or to proceed with the next desired floor. This project sets a foundation for future improvements in smart elevator systems, leveraging IoT integration, advanced machine learning models, and cloudbased real-time data processing to further enhance the intelligence and responsiveness of the system.

关键词： YOLO Computer vision image recognition Machine learning algorithms Detectors Cameras Elevators Real-time systems Floors Monitoring

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Wavelet Analysis of Cardiograms in EDF Format

Wavelet Analysis of Cardiograms in EDF Format

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Digital Signal processing and its Applications (DSPA), International conference on

作者： Sinan V. Kurbanov Denis A. Andrikov Department of Mechanics and Control Processes Peoples' Friendship University of Russia (PFUR) Moscow Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The correlation of the wavelet shape and the characteristic ECG section has been confirmed, and wavelets for the identification of P-, R-, and T waves have been experimentally established. An algorithm for identifying R waves in an ECG has been developed and its operability has been verified. Using the algorithm developed, a graph of the dynamics of the heartbeat period is plotted as a function of time (period number). To identify the P and T waves, the wavelet image of the real ECG is constructed, and the presence of distortion of the shape of this image due to the influence of QRS complex and neighboring waves is established. Computer removal (filtering) of QRS complexes from the ECG is carried out, wavelet image of the remaining part is calculated. The possibility of determining the positions of the maximum P- and T-waves has been established from the wavelet image of the filtered ECG. The theoretical and practical value of this study consists in a new direction of wavelet analysis of cardiac signals, by successively applying several wavelets of a special form, to identify characteristic sections of the ECG in order to improve diagnostics. The proposed method also assumes intermediate filtering of the identified sections when moving from wavelet to wavelet. The development was carried out within the framework of computer generation of arrays of digital functions of the state of the heart, suitable for training artificial intelligence systems, and diagnostics using such systems. The practical results obtained can be applied in the development of a digital expert diagnostic system or a specific technical device.

关键词： Training Correlation Shape Heart beat Filtering Heuristic algorithms Signal processing algorithms Electrocardiography Wavelet analysis Distortion

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Edge-type High-speed Intelligent Infrared Pre-processor

Edge-type High-speed Intelligent Infrared Pre-processor

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IEEE International conference on Integrated Circuits, Technologies and Applications (ICTA)

作者： Yuxiao Wen Runjiang Dou Yongchao Deng Chi Zhang Huanhui Zhang Shuangming Yu Jian Liu Nanjian Wu Liyuan Liu Chinese Academy of Sciences Institute of Semiconductors Beijing China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China School of Integrated Circuits University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331530723

ISBN: (纸本)9798331530730

This paper proposes an edge-type high-speed intelligent infrared pre-processor, including the pre-processing algorithm and the corresponding hardware architecture. At algorithm level, guided filtering method and neural network method are combined to propose a non-uniformity correction algorithm with stronger scene adaptability. By combining unsharpen mask and adaptive gray stretching algorithms, the processing effect of image details is effectively improved. At hardware level, this paper optimizes the above algorithm. The structure adopts modular pipeline design, which can realize dynamic switching between algorithms through control instructions, and can effectively meet the real-time pre-processing requirements of infrared images at edge end.

关键词： Heuristic algorithms image edge detection Software algorithms Pipelines Computer architecture Switches Hardware Software Real-time systems image enhancement

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