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

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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...

引用

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...

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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

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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

引用

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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Automatic Identification of the American Sign Language Using Machine Learning

Automatic Identification of the American Sign Language Using...

引用

Machine Learning and Autonomous systems (ICMLAS), International conference on

作者： Ravi Kishore Kodali Arindam Pattanayak Department of Electronics and Communication Engineering National Institute of Technology Warangal Warangal India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Communication is essential, providing a means to express thoughts, emotions, and ideas. Sign language is a fundamental form of communication for those who are deaf, have hearing impairments, or are non-verbal. This research uses machine learning to develop an effective algorithm for identifying the alphabet in American Sign Language (ASL) through natural hand movements. The system combines image processing, machine learning, and artificial intelligence, especially a convolutional neural network (CNN), to translate ASL gestures into accurate and understandable output. After training the model on a dataset that includes sign language images, the results demonstrate the system's ability to identify hand gestures related to the English alphabet accurately.

关键词： Hands Training Deep learning Sign language Translation Accuracy Machine learning algorithms image processing Predictive models Convolutional neural networks

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A Comparative Study of the Performance of Personnel Detection based on Visible and Infrared images 4

A Comparative Study of the Performance of Personnel Detectio...

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4th International conference on Video, Signal and image processing, VSIP 2022

作者： Liu, Futao Li, Zhenling Wan, Yuezhong Cao, Weida Ma, Duanwei Li, Xiaolei Yan, Peng Liaocheng Power Supply Company State Grid Corporation of China China Shandong University School of Control Science and Engineering China Shandong TCCA Information Technology Research Institute China

ISBN: (纸本)9781450397810

Personnel security has always been a hot topic of research especially in power systems. Effective detection of people in power scenes has become a high priority. Currently, algorithms for personnel detection have been developed considerably, and they are mainly used for visible images. Since infrared images imaged by thermal radiation can detect living blood creatures easily, they can be used in personnel detection at night. To investigate the effect of visible and infrared images on the performance of personnel detection algorithms in different environments, this thesis uses two object detection algorithms, YOLOv5 and Faster RCNN, to train personnel detection models for visible daytime, visible nighttime, infrared daytime and infrared nighttime datasets respectively. Comparative experiments are conducted to quantitatively and qualitatively analyze the performance advantages and disadvantages of the personnel detection algorithms for visible images and infrared images during the day and night. The experimental results for both models consistently show that the visible images are similar to the infrared images during daylight hours with a good target detection model, the visible image is slightly better in performance. While the infrared image is significantly better than the visible image at night, and the mAP@0.5 can be improved by 0.2. For small human target detection, the infrared image has a significant advantage over the visible image at night, with an average detection size reduction of 48.0% and a minimum detection size reduction of 64.6%. © 2022 ACM.

关键词： image enhancement

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Performance Optimization of Machine Learning-Driven image Recognition algorithms for iOS Mobile Platforms

Performance Optimization of Machine Learning-Driven Image Re...

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International conference on Control systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Ibragim Mamadaev Muslim Mamadaev Bauman Moscow State Technical University Moscow Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

Today., mobile devices play an important role in the daily life of every person., without which many can no longer imagine their lives. One of the key technologies leading to significant benefits for mobile applications is machine learning. Optimization of machine learning algorithms for mobile devices is an urgent and important task., it is aimed at developing and applying methods that will effectively use the limited computing resources of mobile devices. The paper discusses various ways to optimize image recognition algorithms on mobile devices., such as quantization and compression of models., optimization of initial calculations. In addition to ways to optimize the machine learning model itself., various libraries and tools for using this technology on mobile devices are also being considered. Each of the described methods has its advantages and disadvantages., and therefore., in the results of the work., it is proposed to use not only a combination of the described options., but also an additional method of parallelization of image processing processes.

关键词： Performance evaluation Support vector machines Machine learning algorithms image recognition Recurrent neural networks Optimization methods Generative adversarial networks Mobile handsets Libraries Convolutional neural networks

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FPGA Based image processing: A Comparative Study of Software-Centric and Hardware-Accelerated Approaches

FPGA Based Image Processing: A Comparative Study of Software...

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Smart Electronics and Communication systems (ISENSE), International conference on

作者： P N Parameswaran S Janardan Siddharth R Nair Thejaswitha T K Nikhil M Adersh V R Dept of Electronics and Communication College of Engineering Trivandrum Thiruvananthapuram India

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528133

This work compares two different approaches to image processing algorithm implementation in Zynq Zybo and Zedboard Field Programmable Gate Array (FPGA) boards. There are three main phases for the study, namely, Hardware-Accelerated image processing (HAIP), Software-Centric image processing (SCIP), and an in-depth analysis of their corresponding performance metrics. image processing algorithms were implemented in the SCIP phase, exclusively using the Zynq processing System (PS). On the other hand, the HAIP phase comprised creating distinct Intellectual Properties (IPs) for image processing tasks and integrating them with the Zynq PS through Direct Memory Access (DMA) and the Advanced eXtensible Interface (AXI) *** all assessed techniques, HAIP considerably outperformed SCIP in terms of processing time, for different image sizes, across both Zynq Zybo and Zedboard. On the other hand, as SCIP is software-centric, it showed minimal resource utilization and lower power consumption than HAIP, which showed higher resource utilization and power consumption. However, both methods produced processed images that were similar, highlighting the stability and dependability of FPGA-based image processing implementations. A comparative analysis between Zynq Zybo and Zedboard reveals insights into how different FPGA architectures impact the performance and resource utilization of image processing implementations. This work offers insights into the trade-offs between hardware-accelerated and software-centric approaches to image processing on FPGA platforms, guiding future design decisions for practical uses.

关键词： Hands Measurement Power demand image processing Intellectual property Logic gates Stability analysis Robustness Resource management Field programmable gate arrays

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