With the increased use of closed-circuit television (CCTv) footage for security and surveillance purposes as well as for object or person recognition and efficiency monitoring, high-quality CCTvvideos are necessary. ...
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ISBN:
(纸本)9781450395670
With the increased use of closed-circuit television (CCTv) footage for security and surveillance purposes as well as for object or person recognition and efficiency monitoring, high-quality CCTvvideos are necessary. In this paper, we propose Corgi Eye, a moving object removal + super-resolution framework for enhancing CCTv footages to remove ghosting artifacts caused by performing multiframe super-resolution (MISR) on moving objects. Our method extends the framework of Eagle Eye, which is an existing MISR framework tailored for mobile devices. Our results demonstrate that the system can completely remove ghosting effects caused by moving objects while performing MISR on CCTv footage. Our proposed method demonstrates competitive performance when compared to Eagle Eye, achieving a 16% increase in terms of PSNR metric. Additionally, our method can produce clear images, on par with deep learning approaches such as ESPCN and SOF-vSR.
Hand Recognition and Gesture Control For Dino Game Using Computer vision to control the popular Chrome Dino game using hand recognition and gesture control through computer vision techniques. The system leverages real...
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ISBN:
(数字)9798331515683
ISBN:
(纸本)9798331515690
Hand Recognition and Gesture Control For Dino Game Using Computer vision to control the popular Chrome Dino game using hand recognition and gesture control through computer vision techniques. The system leverages real-time imageprocessing and machine learning algorithms to detect and interpret hand gestures, allowing for an intuitive and interactive gaming experience. The proposed method utilizes a webcam to capture live video feed, from which hand landmarks are extracted using a pre-trained neural network model. various hand gestures, such as swipe and hold, are then mapped to corresponding in-game actions such as jumping and ducking. This gesture-based control mechanism not only enhances user engagement but also demonstrates the potential of computer vision in creating touchless interfaces for gaming applications.
The use of machine learning models, particularly deep learning models, for the analysis of remote sensing products, especially multispectral satellite images, has recently experienced exponential development. Therefor...
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ISBN:
(纸本)9781665452519
The use of machine learning models, particularly deep learning models, for the analysis of remote sensing products, especially multispectral satellite images, has recently experienced exponential development. Therefore, this article will present a protocol for processing multispectral satellite images by deep learning through the latest methods used in neural networks for computer vision, exploring all the methods used and proposed. In this study, we present the main methods of deep learning adapted to the processing of multispectral satellite images in the form of an efficient processing protocol. Our methodology proceeds with a systematic analysis of all the deep learning concepts by testing the applicability of multispectral satellite images and the contribution of the concept to the accuracy and performance of the model. In addition, each method introduced in this study has been tested in a real use case of remote sensing products especially satellite imagery for spatial analysis tasks such as semantic segmentation, object and pixel classification, object detection, image fusion, and land use and land cover classification (LULC). Thus, a discussion of the use of this protocol and some open challenges in this technological field are presented.
Anomaly detection in remotely sensed imagery constitutes a pivotal endeavor with a multitude of applications, encompassing areas such as environmental surveillance and precision agriculture. A variety of methodologies...
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The development of automated methods capable of detecting and localizing actions is crucial for a variety of applications, ranging from surveillance and autonomous driving to content moderation. This thesis focuses on...
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The development of automated methods capable of detecting and localizing actions is crucial for a variety of applications, ranging from surveillance and autonomous driving to content moderation. This thesis focuses on creating action detection methods that deliver robust performances. At the heart of these methods’ robustness lie two fundamental elements: the detection of atomic actions and the ability for compositional understanding. Atomic actions are those that are identifiable from a single image or a short video. In this research, we developed innovative methods to detect and localize such actions that achieve state-of-the art performance. The key strength of these methods lies in their ability to refine visual features both spatially and semantically, enabling precise identification of action-specific regions. For scalability, we further developed a multi-branch network to recognize new composition of objects and actions. Our design ensures that each branch learns decoupled features, allowing the network to transfer previously learned concepts to identify new compositions. This approach outperforms existing methods by a good margin as our extensive experiments on benchmark datasets demonstrate. Further, the correct identification of the attributes of the participating objects in actions helps to detect unknown compositions. Therefore, we have created a network utilizing spatially localized learning to correctly associate objects and attributes. This network achieves state-of-the-art performance in object-attribute association on cluttered scenes. The developed methods in this thesis can do robust action detection at scale and serve as a base for numerous future applications.
Solar Energy is one of the important source of clean energy and a proven reliable source for future advancement in energy management sector. The International Energy Agency- IEA published a report stating, the power g...
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Document imageprocessing is one of the growing research fields in the digital world for applications like data base indexing, text recognition, signature verification, web-searching engines, etc. Segmenting intermixe...
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In recent years, the country has proposed the strategic development goal of 'Made in China 2025', and the intelligent manufacturing industry has gradually received national attention. Intelligent robots rely o...
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Gesture recognition technology is a natural human-computer interaction method with a wide range of applications, among which using bionic robotic hands to achieve gesture recognition is more meaningful and valuable fo...
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