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共 127 条记录，以下是121-130 订阅

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A Color-Aware Unsupervised Segmentation Network for Nuclei in Histopathology images

A Color-Aware Unsupervised Segmentation Network for Nuclei i...

引用

Electronic Information Engineering and Computer Communication (EIECC), international conference on

作者： Hongyan Ruan School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Hefei China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

Precise nuclei segmentation is essential in digital pathology, yet it faces difficulties due to the wide existence and morphological diversity of nuclei, staining protocols, and imaging conditions. the process of annotating nuclei instances demands skilled pathologists to carefully trace the outlines of the nuclear structures, which is both labor-intensive and expensive. Consequently, deep learning-based segmentation techniques, which heavily depend on large datasets for training, face challenges in maximizing their effectiveness in this domain. Here our study introduces an unsupervised method for segmenting nuclei instances in whole slide histopathology images. Our approach is divided into two primary phases: self-supervised training and clustering-based segmentation. In the first phase, mask pre-training allows the encoding network to capture the essential color features of pathology images, enabling it to extract more comprehensive and detailed information about the nuclei. In the second stage, the encoder backbone that has been trained is used to derive clustering features from the images. the network learning and clustering processes are optimized jointly, and the final results of instance segmentation are achieved by means of subsequent post-processing techniques. the results indicate that the proposed method exhibits enhanced performance compared to other unsupervised nuclei segmentation approaches.

关键词： Training Measurement Instance segmentation image segmentation Protocols Histopathology Clustering algorithms Feature extraction Faces Unsupervised learning

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A Novel Model for Recognising Handwritten Devanagari Numerals using Machine Learning

A Novel Model for Recognising Handwritten Devanagari Numeral...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Ch. Prathima Ramprakash Reddy Arava K Sevitha G Manikanth D Vinay C Surya School of Computing Mohan Babu University (formerly Sree Vidyanikethan Engineering College) Tirupati India Department of CSE KSRM College of Engineering Department of CSSE Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

this research study performs a comprehensive comparative analysis aimed at developing effective machine learning models for classifying handwritten Devanagari numerals. the research focuses on evaluating the performance of various models to determine the most accurate classification approach. Initiating with data pre-processing, including feature extraction and normalization, the data is prepared for model training and assessment. A diverse range of machine learning models, from traditional methods like Support Vector Machines (SVM) to advanced techniques such as Random Forests, K-Nearest Neighbors, and Convolutional Neural Networks, are considered for the comparative analysis, ensuring a thorough assessment of classification capabilities. Cross-validation techniques are employed during model training and testing to enhance reliability. Statistical tests are utilized to assess the performance variations among models, enhancing the robustness of the analysis. Visual representations of performance metrics and comparison results offer clear insights. this research study aims to identify the most suitable machine learning model for handwritten Devanagari numeral classification, potentially advancing character recognition systems and linguistic applications.

关键词： Support vector machines Training Handwriting recognition Analytical models Machine learning algorithms image recognition Nearest neighbor methods

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Adaptive Contrast Based Real-Time image and Video Dehazing

Adaptive Contrast Based Real-Time Image and Video Dehazing

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international conference on Circuits, Communication, Control and Computing

作者： Rohin Joshi Dhruv Dange Kavya Ks Sharath R Maiya Department of Information Science and Engineering Ramaiah Institute of Technology Bangalore India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

Dehazing algorithms have been developed in re-sponse to the need for effectively and instantaneously removing atmospheric turbidities such as mist, haze, and fog from media. the removal of haze from an image or video enables the extraction of additional details from the scene. this paper presents the development of a real-time, memory-optimized dehazing system that utilizes digital image processing techniques and NVIDIA's CUDA architecture for efficient parallel computing. the methodology incorporates a quad-tree search algorithm for efficient atmospheric light estimation, which significantly enhances dehazing accuracy. advanced contrast enhancement techniques are employed for transmission estimation to ensure clarity and visibility in dehazed images, addressing challenges such as non-uniform illumination and varying haze densities. For image restoration, the system dynamically clips values to improve brightness and minimize information loss. the transmission map's artifacts are then smoothed using a Gaussian filter, resulting in more reliable dehazing. Extensive testing on a variety of publicly accessible datasets demonstrated that the proposed model achieved comparable accuracy to numerous existing techniques, while also restoring high-quality dehazed images and videos. the system achieves a running time of 35ms per image and up to 10ms per frame for video sequences. Performance was objectively assessed using the Peak signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), yielding a PSNR of 28.75 and an SSIM of 0.80.

关键词： PSNR Accuracy Video sequences Estimation Turbidity Streaming media Real-time systems image restoration Reliability Testing

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A Novel Framework for Satellite image Dehazing Using advanced Computational Techniques

A Novel Framework for Satellite Image Dehazing Using Advance...

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Innovation in Technology (ASIANCON), Asian conference on

作者： Sandeep Vishwakarma Anuradha Deepika Punj J.C. Bose University of Science and Technology Faridabad India Department of Computer Science and Engineering Symbiosis Institute of Technology Pune India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Satellite imagery information is used in many different ways; some of the more common ones are environmental and urban planning and disaster response. that is why haze has a highly negative impact on the quality and visibility of such images and their further usage. the following paper proposes an innovative approach to satellite images dehazing to enhance its value to professionals and researchers in the field. the ideas for the proposed method involve incorporating the use of machine learning algorithms into image enhancement procedures to adequately dehaze the images, increase image resolution, and enhance the efficiency of getting information from satellite images. Our approach involves a three-step process: seen as consisting of preprocessing, a dehazing network, and postprocessing. the preliminary processing generally includes initial enhancement operations such as histogram equalization and color stretch. the foundation of our framework is a deep learning-based dehazing network that uses CNNs, residual blocks, attention mechanisms, and GANs to dehaze and restore the images’ quality. Additional operations are performed in postprocessing; they include details enlargement, the removal of noise , and color correction so as to achieve natural and quality results. We evaluate our framework using two standard satellite image datasets: the users of the mentioned services are SateHaze1k and RICE. the effectiveness of the proposed method is evaluated by means of two quality measures, namely Peak signal-to-Noise Ratio and Structural Similarity Index. Test results portray the efficiency of the proposed technique in dehazing with enhanced PSNR and SSIM values over other conventional and state-of-art works on machine learning-based dehazing methods. that is why our framework can provide the workable and scalable solution for the improvement of the satellite images’ clarity that would be the beneficial for its usage in numerous applications.

关键词： Technological innovation image resolution Satellites image color analysis Urban planning Stability criteria Satellite images Colored noise Standards Remote sensing

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Implementation of Genetic Algorithm Optimization based Artificial Neural Network on Raspberry Pi for Fault Diagnosis

Implementation of Genetic Algorithm Optimization based Artif...

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international conference on Control, Decision and Information Technologies (CoDIT)

作者： Amal Hichri Wajdi Saadaoui Mansour Hajji Majdi Mansouri Mohamed Nounou Kais Bouzrara Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Qatar Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir University of Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

this study presents an embedded system (ES) designed for fault detection and diagnosis in grid-connected photovoltaic (GCPV) systems using transient regime analysis. the primary aim of transient regime analysis is to facilitate real-time decision-making, especially during critical faults. A neural network classifier, incorporating a Genetic Algorithm for automated hyperparameter optimization, is developed for GCPV fault classification. these classifiers are seamlessly integrated into a Raspberry Pi 4 platform for fault diagnosis in GCPV systems. Both simulation and experimental results substantiate the ES's viability for fault diagnosis in the examined GCPV system, achieving high accuracy and enabling prompt decision-making to enhance the reliability and safety of GCPV systems.

关键词： Fault diagnosis Photovoltaic systems Embedded systems Accuracy Fault detection Decision making Artificial neural networks Hyperparameter optimization Transient analysis Genetic algorithms

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Improving Block Matching and 3 Dimensions (BM3D) Filtering for image Noise Removal Using Discrete Wavelet Transformation (DWT)

Improving Block Matching and 3 Dimensions (BM3D) Filtering f...

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Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, IEEE international Maghreb Meeting of the conference on

作者： Warda Kshem Asem Khmag Department of Computer Engineering Faculty of Engineering University of Zawia Az zawia Libya

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372649

In the field of image denoising, Block Matching 3 Dimensions technique (BM3D) stated as a powerful technique that works to remove white Gaussian noise (A WGN). Despite BM3D shows its great success in comparison with the best denoising methods, still its visual quality decreases as the noise density escalates in noisy digital images. therefore, the need to create a powerful image denoising method is necessary to accomplish the processing of image enhancement applications. the proposed method is introduced by. In this study, discrete wavelet transformation is applied on the LL level of the wavelet decomposition due to its ability to keep the smallest change in the original image. Furthermore, the coefficients of LL will be then divided to small blocks that contain contaminated pixels that suffer from additive white Gaussian noise (A WGN). Comparing with conventional block matching 3D (BM3D) that works well only on the similar patches, our proposed method has the ability to work on similar and dissimilar patches in order to find the inter and intra- correlations between dissimilar pixels. the proposed technique is examined on several digital benchmark images with different noise levels in terms of the quantity of the lost in the original and resulted pixels. In this regard, the performance of the tested images is estimated and then compared proposed with several high quality noise removal techniques according to several assessments such as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and the figure of merits (FOM). the high amount of these factors reflects the success of the proposed technique. Further, it is interesting to observe the improvement in the running time where it was far less than the investigated noise removal algorithms.

关键词： Visualization Correlation PSNR three-dimensional displays Noise Estimation Discrete wavelet transforms

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IoT-Integrated Door Sensor Solution for Enhancing Smart Home Security

IoT-Integrated Door Sensor Solution for Enhancing Smart Home...

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

作者： Ishwari Kakade Renuka Wadetwar Kritika Nimje Sagarkumar S. Badhiye Pradnya Borkar Snehal Shinde Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Indian Institute of Information Technology Nagpur Maharashtra India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Although the field of IoT-powered home security systems has seen some great strides, there are still quite a few gray areas. As of now available solutions mainly rely on elementary motion detection and simple biometric systems, which usually fail to properly differentiate between everyday activities and real threats (leading to numerous false alarms) as well security is often at risk. Furthermore, most current systems do not have powerful recognition algorithms and can only recognize specific persons under very stable circumstances. the installation of a variety of sensors can improve detection, but the problem arises when compatibility issues across multiple chips and signals from interfering with each other make them unreliable. In addition, visual verification requiring cameras (camera technology) are present in the space but these systems do not deploy number of advanced image processing techniques thus failing to improve threat detection/categorization capabilities. this type of comprehensive and coherent security solution can only be built upon multimodal sensing, but the state-of-the-art in sensor data fusion research is woefully inadequate to support effective situational awareness due to a lack of scalable cross-sensor data synthesis methods. Filling these gaps will have important implications for future smart home security, making systems more reliable and trustworthy.

关键词： Visualization Smart homes Transforms Sensor fusion Cameras Real-time systems Security Reliability Intelligent sensors Testing

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