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Image Segmentation - Principles, Techniques, and Applications (Hardcover)
Loot Price: R3,186
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Image Segmentation - Principles, Techniques, and Applications (Hardcover)
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Total price: R3,206
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Image Segmentation Summarizes and improves new theory, methods, and
applications of current image segmentation approaches, written by
leaders in the field The process of image segmentation divides an
image into different regions based on the characteristics of
pixels, resulting in a simplified image that can be more
efficiently analyzed. Image segmentation has wide applications in
numerous fields ranging from industry detection and bio-medicine to
intelligent transportation and architecture. Image Segmentation:
Principles, Techniques, and Applications is an up-to-date
collection of recent techniques and methods devoted to the field of
computer vision. Covering fundamental concepts, new theories and
approaches, and a variety of practical applications including
medical imaging, remote sensing, fuzzy clustering, and watershed
transform. In-depth chapters present innovative methods developed
by the authors--such as convolutional neural networks, graph
convolutional networks, deformable convolution, and model
compression--to assist graduate students and researchers apply and
improve image segmentation in their work. Describes basic
principles of image segmentation and related mathematical methods
such as clustering, neural networks, and mathematical morphology.
Introduces new methods for achieving rapid and accurate image
segmentation based on classic image processing and machine learning
theory. Presents techniques for improved convolutional neural
networks for scene segmentation, object recognition, and change
detection, etc. Highlights the effect of image segmentation in
various application scenarios such as traffic image analysis,
medical image analysis, remote sensing applications, and material
analysis, etc. Image Segmentation: Principles, Techniques, and
Applications is an essential resource for undergraduate and
graduate courses such as image and video processing, computer
vision, and digital signal processing, as well as researchers
working in computer vision and image analysis looking to improve
their techniques and methods.
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