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Research on Multipoint Collaborative Target Recognition and Tracking Algorithm

Research on Multipoint Collaborative Target Recognition and ...

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Electronic Information Engineering and Computer Science (EIECS), 2021 International conference on

作者： Yang Zhang Guang Yang Siqi Gao School of Electronic Information Engineering Changchun University of Science and Technology Changchun China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

Frame differencing method can adapt well to dynamic environments with changes in lighting and complex background transformations. However, its characteristic of determining whether an object is in motion by extracting the object's contour leads to misdetection and missed detection when detecting moving objects. Meanwhile, frame difference operation also increases the calculation processing time and thus generates a higher delay. To address the mentioned issues, the article proposes a multi-point collaborative object recognition algorithm. Firstly, perform grayscale processing on the image. Secondly, it employs an improved BM3D (Block Matching 3D) algorithm for denoising. then the image data after noise removal is identified and tracked by an improved adaptive interframe difference algorithm. Finally, a multi-point collaborative FPGA-based object recognition and tracking system is designed to validate the algorithm. Experimental results show that, compared to traditional frame differencing methods, the improved algorithm reduces latency by approximately 200 microseconds and significantly enhances the accuracy of object recognition.

关键词： wavelet transforms Target tracking three-dimensional displays Target recognition Noise reduction Noise Collaboration Gray-scale Approximation algorithms Delays

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Comprehensive Weather Recognition Using ResNet-50 and a Novel Weather image Dataset

Comprehensive Weather Recognition Using ResNet-50 and a Nove...

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signal processing, Communications and Engineering Management conference (ISPCEM), International

作者： Jun Wang Yunhan Zhou Min Fu School of Electrical and Electronics Engineering UESTC Chengdu China Cyrinn Science & Technology Ltd. Guangzhou China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

Weather conditions play a crucial role in both daily human activities and industrial operations. For example, recognizing different weather patterns is critical for outdoor automation systems. With the development of AI technology, scientific researchers have trained many models which receive weather images as inputs and output the weather in these images fast and accurately. However, many existing weather recognition models are limited in scope, typically identifying only four types of weather with varying degrees of accuracy. In this study, the two key contributions of this research are: first, the creation of a custom dataset designed specifically for recognizing seven distinct weather conditions, and second, the exploration of multiple machine learning techniques to identify the most effective model for this task. By developing our own dataset, we ensure a broader representation of weather types, allowing the models to train and be tested on more diverse and realistic scenarios. We evaluate five different algorithms: Support Vector Machine (SVM), Random Forest, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and ResNet50. To enhance model generalization and reduce overfitting, we employ data augmentation techniques and add dropout layers in the CNN and ResNet50 architectures. through extensive experimentation, ResNet50 emerges as the best-performing model, achieving an accuracy of approximately 87% on the custom dataset. this work demonstrates the importance of creating domain-specific datasets and using a range of machine learning methods to develop accurate and reliable weather recognition models. Our approach offers a robust solution for outdoor vision systems, with potential applications in autonomous vehicles, surveillance systems, and other environments where accurate weather detection is critical for safety and performance.

关键词： Support vector machines Accuracy image recognition Surveillance signal processing algorithms signal processing Safety Convolutional neural networks Meteorology Residual neural networks

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Research on super-resolution reconstruction of single-frame image of infrared focal plane polarization imaging 8

Research on super-resolution reconstruction of single-frame ...

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8th Symposium on Novel Photoelectronic Detection Technology and applications

作者： Yuan, Hongwu Wang, Yiqing Xu, Guoming Wang, Feng School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei China Key Laboratory of Polarization Imaging Detection Technology in Anhui Province Hefei China Institute of Intelligent Information Anhui Xinhua University Hefei China School of Internet Anhui University Hefei China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

Since the low resolution of infrared focal plane arrays may degrade the performance of polarization imaging significantly, it is necessary to study the super-resolution reconstruction method for superior image resolution and contrast. Four typical single-frame image reconstruction methods are studied in this paper, and the comparison of reconstruction result of these methods is conducted by using subjective and objective evaluation. the experiments show that the reconstruction method based on generative adversarial network performs poorly in the evaluation indexes such as peak signal-to-noise ratio and structural similarity, but its reconstructed image has good visual effects, rich texture and details, and has a strong ability to suppress background noise, and using the reconstructed images for polarization information parsing can significantly improve the accuracy of polarization information parsing and the effect of polarization image fusion. © 2022 SPIE

关键词： image fusion

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Deep Learning-Based Bayoud Disease Recognition Using Palm Fronds Database From Biskra Region, Algeria

Deep Learning-Based Bayoud Disease Recognition Using Palm Fr...

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International conference on image and signal processing and their applications (ISPA)

作者： Betka Abir Hendaoui Mounira Noureddine Samia Toumi Abida Medaoukh Saadia Departement of Electrical Engineering University Mohamed Khider Biskra Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Early Recognition of plant diseases is required to provide appropriate treatments and protect agricultural production. Automated image detection techniques can provide early, fast, accurate, and inexpensive detection results. However, scientific research on date palm diseases is even rare due to the limited number of databases, including the Bayoud disease. In this paper, a new database, Biskra Algeria Palm Fronds Database (BAPFD), containing 400 images of date palm fronds is proposed, it includes images of both healthy and Bayoud disease-affected fronds. the Deep Learning (DL) technique is then used to classify our database and recognize Bayoud disease in date palm fronds. the performance of our approach is measured in terms of accuracy, loss, precision, and recall. the mentioned experimental results show that the proposed approach based on DL achieved high performance in recognizing Bayoud disease in date palm fronds, up to 100% for precision and recall and in order of 4×10 –4 for loss. the results show also that the model is capable to accurately classify infected and not infected fronds images even with a small percentage of bayoud. this demonstrates that our database contains significant information concerning bayoud diseases, which enables DL to design an accurate model that aids in correctly classifying palm fronds and identifying Bayoud disease. We further acknowledge the effectiveness of the DL approach in the recognition of healthy and not healthy fronds.

关键词： Deep learning Plant diseases Databases signal processing algorithms Production signal processing Loss measurement

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Time Based image Synthesis: Light and Color Conversion Using Machine Learning Techniques

Time Based Image Synthesis: Light and Color Conversion Using...

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Technological Advancements in Computational Sciences (ICTACS), International conference on

作者： Jyoti Ranjan Labh R.K. Dwivedi College of Computing Sciences and Information Technology Teerthanker Mahaveer University Moradabad India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

the progress made in camera technology and computational imaging has been astounding when it comes to photography. A specialized neural image signal processor is currently used by many top smartphone cameras and DSLR to convert chaotic raw sensor photos into the final, polished output. A sizable dataset of image pairs-one with a noisy, short-exposure raw picture and another with a clean, long-exposure ground-truth raw image-is needed to train these neural ISPs for low-light, processing It takes a lot of effort and time to capture these aligned image pairs, and motion blur in the long-exposure ground truth frequently makes the process difficult. Artificial nighttime datasets can be used to train various image processing algorithms and neural ISPs that are more resilient and effective. In order to overcome this difficulty, we proposed a model that uses to convert image colour and lighting of a raw picture to artificially create realistic image of different time zone raw photos, brighter photos are typically easier to take, have less noise, and are less likely to suffer from motion blur. the objective of the suggested processing framework is to transform raw photographs taken during the different time into simulated images of different timing with different levels of noise and other low-light features. More ease and flexibility in training neural ISPs for time-based rendering is made possible by this artificial time- based picture dataset. Additionally, in our proposed method the performance of the trained neural ISPs can resemble that of models trained only on real different timings photos if they mix their synthetic data with a tiny percentage of real data. the potential to produce accurate raw photos of midnight and different night time in a synthetic manner holds significant implications for computational photography and image processing applications and even object recognition under difficult nocturnal conditions.

关键词： Photography Training image color analysis Training data Transforms Streaming media Cameras Timing Noise measurement Synthetic data

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A Non-stationary signal Preprocessing Method based on STFT for CW Radio Doppler signal 2020

A Non-stationary Signal Preprocessing Method based on STFT f...

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Proceedings of the 2020 4th International conference on Vision, image and signal processing

作者： Zhengshun Wang Ping Song Qiang Tang Yan Rui Beijing Institute of Technology Beijing China Xi'an Institute of Electromechanical Information Technology Xi'an China

ISBN: (纸本)9781450389532

In this paper, several common time-frequency analysis methods of nonstationary signals are discussed, and a mixed signal with noise is used in the analysis of these methods, whose advantages and disadvantages are analyzed in detail. We propose an improved method time-frequency analysis based on STFT, which is more suitable for analyzing the time-frequency of continuous wave radio Doppler signal generated in the meeting of missile and target. After short-time Fourier transforming of the signal, reassign the frequency and time distribution points to the gravity center of each PSD estimated point in order to ensure the accuracy of estimation as far as possible. then filter and estimate the reassigned spectrogram of the signal with a suitable threshold. the method mentioned above was used to process the real experimental data, and good results were obtained, which proves the feasibility and effectiveness of it, and it will support subsequent information processing or calculation strongly.

关键词： Radio Doppler signal STFT Time-frequency Analysis

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Super-Resolution Generative Adversarial Network with Modified Architecture for Single image Super-Resolution

Super-Resolution Generative Adversarial Network with Modifie...

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International conference on Computer, Communication and signal processing (ICCCSP)

作者： N. Amruth Gowtham Shashikanth Deepakq Dipti Patra National Institute of Technology Rourkela India

ISBN: (数字)9781728165097

ISBN: (纸本)9781728165103

Recently, Single image Super-Resolution (SISR) has become an attractive research area in image processing which generates a High-Resolution (HR) image by using Single Low-Resolution (LR) image. Deep learningbased SISR approaches have achieved better Super-Resolved (SR) results by using mean squared error (MSE) as an objective function that increases the quality of SR results over performance metrics like peak-signal-to-noise-ratio (PSNR) and structural similarity index (SSIM). Nevertheless, MSE based approaches lead to generate over smoothed images with less high-frequency texture information at larger upscaling factors. Recent experiments have proved that Generative Adversarial Networks (GAN) generates perceptually convincing SR images through efficient extraction of high-frequency information from single LR image. In this paper, we propose a GAN based approach for SISR with modified deep-residual network architecture. In our proposed technique, we introduce the bottle-neck convolutional (CN) layer in the network structure of the Generator. Adding bottle-neck layers improves the network performance through 1 x 1 convolutional layers which extract complex features from the input and also reduces the computational complexity compared to 3 x 3 convolution layers. We further improve the model performance by removing batch normalization layer from the entire generator to overcome the unpleasant artifacts and improves GPU usage while training.

关键词： Generators Training Generative adversarial networks Feature extraction Superresolution Gallium nitride Convolution

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Design of a Complex Binary Associative Dataflow Processor

Design of a Complex Binary Associative Dataflow Processor

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2012 4th International conference on Computer Engineering and Technology(ICCET 2012)

作者： Tariq Jamil Department of Electrical and Computer Engineering Sultan Qaboos University AlKhod 123-Muscat-OMAN

Complex numbers in engineering applications such as digital signal processing and image processing deserve formulation of an efficient method for their representation and processing in a computer system. Complex Binary Number System (CBNS) allows both real and imaginary parts of a complex number to be represented as a single unit (instead of two separate units as in today's microprocessors), thus allowing for reduced number of arithmetic operations dealing with complex numbers and, hence, increase in overall system performance. Incorporating CBNS within a parallel processing environment, such as associative dataflow processor (ADP), provides an ideal amalgamation of novelty in computer arithmetic with the use of associative memory in computer architecture. In this paper, an introduction to CBNS has been presented followed by the presentation of the associative dataflow concept and the design of a complex binary associative dataflow processor (CBADP). this design has been granted an innovation patent no. 2010100706 by the Australian Patent Office in July 2010.

关键词： Complex binary, Associative dataflow, Microprocessor, Parallel processing.

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Design of High-Speed Multipliers using Counter based 4:2 Compressor with Pre-processing

Design of High-Speed Multipliers using Counter based 4:2 Com...

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International conference on Information Technology and Electrical Engineering (ICITEE)

作者： Pavitra Y J Mahanta Talakal Department of Electronics and Communication Engineering PES University Bangalore India

ISBN: (数字)9798350375817

ISBN: (纸本)9798350375824

Adders and multipliers form the basic building blocks in most of the system designs. Multipliers are widely used in arithmetic and logic units, digital signal processing, audio processing, and image processing applications. Multipliers have complex structures and optimization of multiplier designs plays a vital role in improving the performance parameters like area, power, and delay. the proposed work aims to design compressor-based multipliers using counter logic for power and delay-efficient models. A novel 4:2 counter-based compressor with pre-processing is proposed to design multipliers to improve performance parameters like area power and delay. the proposed designs are synthesized on Cadence Genus with 90nm technology. Experiments carried out reveal that the proposed multipliers save a maximum of 21.71% of area, and 75.63% of power and improve speed by 24.28% over multipliers reported in the literature.

关键词： Electrical engineering image coding Digital signal processing Delays Logic Information technology System analysis and design Optimization Field programmable gate arrays Arithmetic

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Cellular automata based color image watermarking using NSCT and SVD

Cellular automata based color image watermarking using NSCT ...

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IEEE UP Section conference on Electrical Computer and Electronics (UPCON)

作者： Chandan Saha Md. Foisal Hossain Department of Electronics and Communication Engineering Khulna University of Engineering & Technology Khulna Bangladesh

ISBN: (纸本)9781538630051

Cellular automata based color image watermarking using non sub sampled contourlet transform (NSCT) and singular value decomposition (SVD) is presented in this paper. Game of life (GOL) cellular automata is used to scramble binary watermark so that no one can recover the watermark without secret scrambling keys. Because of the salient features like multiscale, multidirection and shift invariance of NSCT, 2-level NSCT is applied to red, green and blue component of RGB cover image. Most of the perceptual content of an image focuses on low frequency subband of NSCT and the singular values taken from low frequency subband have certain stability. So a modest change in the singular values taken from low frequency subband of NSCT is applicable. In this proposed scheme, each singular value of scrambled binary watermark is embedded in fixed location of singular value matrix of low frequency subbands of NSCT of red, green and blue components. the scheme offers security, imperceptibility and robustness against various image processing and geometric attacks like Gaussian white noise, salt and pepper noise, median filtering, JPEG compression, cropping and rotation. the proposed scheme is compared with existing watermarking schemes in terms of Peak signal to Noise ratio (PSNR), Normalized Correlation (NC)and provides satisfactory performance than other schemes.

关键词： Watermarking Automata Color Games Transforms Robustness Singular value decomposition

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