Recent studies point to an accuracy gap between humans and Artificial Neural Network (ANN) models when classifying blurred images, with humans outperforming ANNs. To bridge this gap, we introduce a spectral channel-ba...
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image classification is one of the main parts of computer vision, which is important in applications like self-driving automotives/vehicle systems. While working with image/video data it needs huge amount of resources...
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The integration of human-robot interaction (HRI) technologies with industrial automation has become increasingly essential for enhancing productivity and safety in manufacturing environments. In this paper, we propose...
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The proceedings contain 39 papers. The topics discussed include: performance analysis of several CNN based models for brain MRI in tumor classification;MRI-based lumbar sagittal alignment classification system;3D mapp...
ISBN:
(纸本)9798350352368
The proceedings contain 39 papers. The topics discussed include: performance analysis of several CNN based models for brain MRI in tumor classification;MRI-based lumbar sagittal alignment classification system;3D mapping and landing zone identification in complex terrains using DSM and photogrammetry;vision language models for oil palm fresh fruit bunch ripeness classification;towards no shadow: region-based shadow compensation on low-altitude urban aerial images;comparative analysis of deep learning architectures for blood cancer classification;exploration of group and shuffle module for semantic segmentation of sea ice concentration;on handcrafted machine learning features for art authentication;and acoustic signature modelling of marine vessels in various environmental and operational conditions.
image stabilization plays a crucial role in providing accurate and reliable visual information for machinevisionapplications. In maritime applications, such as unmanned ship navigation, where six degrees of freedom ...
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ISBN:
(纸本)9798350388350;9798350388343
image stabilization plays a crucial role in providing accurate and reliable visual information for machinevisionapplications. In maritime applications, such as unmanned ship navigation, where six degrees of freedom (DOF) motion and harsh maritime conditions prevail, the efficacy of image stabilization technology is vital for robust imageprocessing algorithms. This paper offers a comprehensive review of image stabilization techniques tailored for maritime environments, developed over the past two decades. We analyzed a total of 39 research articles on the subject, sourced from Web-of-Science, SCOPUS, and the Engineering Index databases, discussing potential research directions to address the limitations of current image stabilization methods, with special consideration for the unique requirements of ship-borne cameras. It provides an up-to-date overview of the techniques, limitations, and algorithms of ship-borne cameras for maritime applications, identifying current knowledge gaps and areas requiring further research. This review aims to guide the development of new technologies and methods to improve the performance of image stabilization systems in maritime contexts.
In recent years, with the development of artificial intelligence technology, intelligent robots are more and more widely used in many fields. In this paper, an intelligent patrol wheeled robot based on image recogniti...
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Automated text-to-image (image synthesis) and image-to-text (image captioning) generation are two of the most challenging and cutting-edge fields of study in Computer vision (CV) in conjunction with Natural Language P...
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This examination intends to enhance the overall performance of welding operations through picture processing. It's going to use an aggregate of PC vision and gadgets, getting to know to perceive better and tune we...
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In the past years, machine learning (ML) and deep learning (DL) have led to the advancement of several applications, including computer vision, natural language processing, and audio processing. These complex tasks re...
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ISBN:
(纸本)9798400716164
In the past years, machine learning (ML) and deep learning (DL) have led to the advancement of several applications, including computer vision, natural language processing, and audio processing. These complex tasks require large models, which is a challenge to deploy in devices with limited resources. These resource-constrained devices have limited computation power and memory. Hence, the neural networks must be optimized through network acceleration and compression techniques. This paper proposes a novel method to compress and accelerate neural networks from a small set of spatial convolution kernels. Firstly, a novel pruning algorithm is proposed based on the density-based clustering method that identifies and removes redundancy in CNNs while maintaining the accuracy and throughput tradeoff. Secondly, a novel pruning algorithm based on the grid-based clustering method is proposed to identify and remove redundancy in CNNs. The performance of the three pruning algorithms (density-based, grid-based, and partitional-based clustering algorithms) is evaluated against each other. The experiments were conducted using the deep CNN compression technique on the VGG-16 and ResNet models to achieve higher accuracy on image classification than the original model at a higher compression ratio and speedup.
This article proposes a simple and effective method for image subject segmentation. Our research mainly focuses on the characteristics of material images in the experimental platform. Through in-depth research, we hav...
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