With the continuous advancement of smart city construction, autonomous driving technology is playing an increasingly important role in urban traffic systems. This study aims to explore the development and optimization...
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Deep Learning is a technology developed with GPU Acceleration that has a good ability to process image computations. One of the deep learning methods widely used in classifying two-dimensional objects is the Convoluti...
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ISBN:
(纸本)9783031214370;9783031214387
Deep Learning is a technology developed with GPU Acceleration that has a good ability to process image computations. One of the deep learning methods widely used in classifying two-dimensional objects is the Convolutional Neural Network algorithm. Just like other imageprocessingalgorithms, the classification process is very dependent on the quality of the image used. Therefore, it is concerned that pre-processing is done. This study aimed to find a scenario for image data pre-processing by comparing the threshold types used. By using two scenarios, the first scenario using Simple Threshold and the second scenario using threshold Canny. The first scenario begins with collecting data from an X-ray image after the established dataset is advanced to pre-process the data set. In this pre-processing data, several things were done to increase the level of data accuracy by changing and equalizing the pixel size in the dataset, changing the color of the image on the dataset to grayscale, distributing the histogram or commonly known as histogram equalization, and finally applying a simple threshold. Unlike the second scenario, which does not use a simple threshold but uses a threshold canny. After completion of the pre-processing stage and then the continued training phase. At this stage, the dataset will be trained using CNN. After the dataset is trained, it enters the testing stage. The testing stage shows the results that the data is classified properly. The validation obtained from the two scenarios shows that the simple threshold gives better results than the canny threshold, with a value that shows a simple threshold of 97% and a canny threshold of 89%. This result shows that the dataset's treatment differences greatly affect the results' accuracy.
In the field of agriculture being able to identify plant diseases is extremely important as it can lead to crop loss and negatively impact food security. Detecting these diseases early on is crucial, to prevent their ...
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This study explores the application of computer vision technology to improve the efficiency of maintaining cleanliness and order in high-traffic commercial areas. Given the growing importance of automation in managing...
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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, qualif...
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L Color cast, an aberration common in digital images, poses challenges in various imageprocessing applications, affecting image quality and visual perception. This research investigates diverse methodologies for colo...
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The field of image manipulation is dynamic, exploiting a range of algorithms to analyze, manipulate and enhance digital images. Our study focuses on a crucial application of imageprocessing, which is the elimination ...
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ISBN:
(纸本)9783031821523;9783031821530
The field of image manipulation is dynamic, exploiting a range of algorithms to analyze, manipulate and enhance digital images. Our study focuses on a crucial application of imageprocessing, which is the elimination of blind Gaussian noise in order to improve image quality and facilitate image analysis by preserving essential details. In this research, we explore the use of different convolutional neural network (CNN) architectures to tackle the problem of blind Gaussian noise, applying different noise levels, ranging from low to high. We present an in-depth comparative analysis of the three main CNN architectures: DnCNN, DRNet and RIDNet, highlighting the quantitative and qualitative experimental results of these different approaches. These methods have demonstrated remarkable performance in imageprocessing tasks, particularly denoising, using various techniques built into CNNs, such as batch normalization and residual learning. Our results show that these techniques bring significant improvements to all three CNN approaches, as evidenced by the remarkable performance observed in the experimental results. These findings underline the robustness of CNN architectures in the face of complex noise scenarios, such as the blind noise scenario addressed in our study.
With the rapid development of big data and Internet of Things (IoT), more and more digital products are emerging. However, this has also brought about a growing problem of copyright violation. Digital image robust wat...
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作者：
Seitaj, OltianaMechanics
University of Roma Tre Department of Industrial Engineering Electronics Rome Italy
This paper evaluates the impact of hybrid deep learning approaches on lung tumor segmentation by combining traditional imageprocessing techniques with advanced AI-driven models. The study integrates Convolutional Neu...
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The Coati Optimization Algorithm (COA) is a promising metaheuristic inspired by coati hunting behaviors, demonstrating strong performance in complex optimization tasks. However, COA encounters challenges with converge...
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