Hand Gesture Recognition Using by Sohal Asha (7 results)

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Taschenbuch. Condition: Neu. Hand Gesture Recognition using Machine learning in Python | Asha Sohal | Taschenbuch | Englisch | 2023 | LAP LAMBERT Academic Publishing | EAN 9786207450374 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 52 pp. Englisch.

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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Hand gesture-controlled presentations using Machine Learning (ML) involve using a computer vision-based system to interpret hand movements and gestures as commands to control presentations. Gather a dataset of hand gesture images or videos, capturing various hand movements and gestures that correspond to different presentation commands (e.g., next slide, previous slide, zoom in, zoom out). Clean and preprocess the collected data by resizing, normalizing, and augmenting images or videos to enhance the model's robustness. Utilize Machine Learning techniques, often employing Convolutional Neural Networks (CNNs) or other deep learning architectures, to train a model on the collected dataset. This model learns to recognize and classify different hand gestures. Once trained, the model is capable of recognizing specific hand gestures in real-time. It can identify gestures such as open palm for next slide, closed fist for previous slide, pinch for zoom in, spreading fingers for zoom out, etc.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 52 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Hand gesture-controlled presentations using Machine Learning (ML) involve using a computer vision-based system to interpret hand movements and gestures as commands to control presentations. Gather a dataset of hand gesture images or videos, capturing various hand movements and gestures that correspond to different presentation commands (e.g., next slide, previous slide, zoom in, zoom out). Clean and preprocess the collected data by resizing, normalizing, and augmenting images or videos to enhance the model's robustness. Utilize Machine Learning techniques, often employing Convolutional Neural Networks (CNNs) or other deep learning architectures, to train a model on the collected dataset. This model learns to recognize and classify different hand gestures. Once trained, the model is capable of recognizing specific hand gestures in real-time. It can identify gestures such as open palm for next slide, closed fist for previous slide, pinch for zoom in, spreading fingers for zoom out, etc.