The median filter is a valuable imageprocessing tool that can be used in many applications to advance picture quality, and inferior noise, and acquire data ready for more analysis. The median filter, a non-linear ima...
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ISBN:
(数字)9798331518523
ISBN:
(纸本)9798331518530
The median filter is a valuable imageprocessing tool that can be used in many applications to advance picture quality, and inferior noise, and acquire data ready for more analysis. The median filter, a non-linear imageprocessing technique, has excessive application in many diverse fields because of its capacity to remove noise while preserving edges. The filter is significant for both simple and complex imageprocessing occupations since it is together easy to comprehend and fast to use. An active method to attain real-time performance is to devise a median filter on a FieldProgrammable Gate Array (FPGA) for image signal processing and machinevisionapplications. FPGAs are well-suited for photo filtering due to their capability to achieve parallel processing, which is one of their prominent advantages. The research article meets with the design and simulation of the average filter in HDL and synthesis on the FPGA for assessing the performance indices.
Automatic caption generation from images has become an active research topic in the field of Computer vision (CV) and Natural Language processing (NLP). machine generated image caption plays a vital role for the visua...
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Automatic caption generation from images has become an active research topic in the field of Computer vision (CV) and Natural Language processing (NLP). machine generated image caption plays a vital role for the visually impaired people by converting the caption to speech to have a better understanding of their surrounding. Though significant amount of research has been conducted for automatic caption generation in other languages, far too little effort has been devoted to Bangla image caption generation. In this paper, we propose an encoder-decoder based model which takes an image as input and generates the corresponding Bangla caption as output. The encoder network consists of a pretrained image feature extractor called ResNet-50, while the decoder network consists of Bidirectional LSTMs for caption generation. The model has been trained and evaluated using a Bangla image captioning dataset named BanglaLekhaimageCaptions. The proposed model achieved a training accuracy of 91% and BLEU-1, BLEU-2, BLEU-3, BLEU-4 scores of 0.81, 0.67, 0.57, and 0.51 respectively. Moreover, a comparative study for different pretrained feature extractors such as VGG-16 and Xception is presented. Finally, the proposed model has been deployed on an embedded device for analysing the inference time and power consumption.
The detection and identification of imperfect wheat grains are of great significance in evaluating their quality. Manual inspection and separation of imperfect grains in wheat are time-consuming and expensive. Therefo...
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High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range of exposures, which is important in imageprocessing, computer graphics, and computer vision. In recent years, there has been a si...
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High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range of exposures, which is important in imageprocessing, computer graphics, and computer vision. In recent years, there has been a significant advancement in HDR imaging using deep learning (DL). This study conducts a comprehensive and insightful survey and analysis of recent developments in deep HDR imaging methodologies. We hierarchically and structurally group existing deep HDR imaging methods into five categories based on (1) number/domain of input exposures, (2) number of learning tasks, (3) novel sensor data, (4) novel learning strategies, and (5) applications. Importantly, we provide a constructive discussion on each category regarding its potential and challenges. Moreover, we review some crucial aspects of deep HDR imaging, such as datasets and evaluation metrics. Finally, we highlight some open problems and point out future research directions.
INTRODUCTION: Due to the advancement in the field of Artificial Intelligence (AI), the ability to tackle entire problems of machine intelligence. Nowadays, machine learning (ML) is becoming a hot topic due to the dire...
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INTRODUCTION: Due to the advancement in the field of Artificial Intelligence (AI), the ability to tackle entire problems of machine intelligence. Nowadays, machine learning (ML) is becoming a hot topic due to the direct training of machines with less interaction with a human. The scenario of manual feeding of the machine is changed in the modern era, it will learn automatically. Supervised and unsupervised ML techniques are used as a distinct purpose like feature extraction, pattern recognition, object detection, and classification. OBJECTIVES: In Computer vision (CV), ML performs a significant role to extract crucial information from images. CV successfully contributes to multiple domains, surveillance system, optical character recognition, robotics, suspect detection, and many more. The direction of CV research is going toward healthcare realm, medical imaging (MI) is the emerging technology, play a vital role to enhance image quality and recognized critical features of binary medical image, covert original image into grayscale and set the threshold values for segmentation. CONTRIBUTION: This paper will address the importance of machine learning, state-of-the-art, and how ML is utilized in computer vision and imageprocessing. This survey will provide details about the type of tools and applications, datasets, and techniques. Limitations of previous work and challenges of future work also discussed. Further, we identify and discuss a set of open issues yet to be addressed, for efficiently applying of ML in Computer vision and image process. METHODS, RESULTS, AND CONCLUSION: In this review paper, we have discussed the techniques and various types of supervised and unsupervised algorithms of ML, general overview of imageprocessing and the results based on the impact;neural network enabled models, limitations, tools and application of CV, moreover, highlight the critical open research areas of ML in CV.
Automatic detection of the healthy and unhealthy maize plant leaf is a prevalent machinevision learning task and has significant applications in the Food Industry. In this paper, effective machine learning technique ...
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This research work aims to develop an AI-based plant growth monitoring system using computer vision. By leveraging computer vision algorithms and artificial intelligence techniques, the system will enable real-time an...
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Convolutional neural networks (CNNs) are a widely researched neural network architecture that has demonstrated exemplary performance in imageprocessing tasks and applications compared to other popular deep learning a...
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Brain-computer interfaces (BCIs) have shown promise in supporting communication for individuals with motor or speech impairments. Recent advancements such as brain-to-speech or brain-to-image technology aim to reconst...
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Failures of tailings dams have been happening lately. Due to the lack of laws on particular design criteria and stability requirements related monitoring during construction and maintenance, they are thought to be mor...
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