Principles of Digital Image Processing: Advanced Methods (Undergraduate Topics in Computer Science)

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Publisher ‏ : ‎ Springer; 2013th edition (May 22, 2013)
Language ‏ : ‎ English
Paperback ‏ : ‎ 382 pages
ISBN-10 ‏ : ‎ 1848829183
ISBN-13 ‏ : ‎ 978-1848829183
Item Weight ‏ : ‎ 1.95 pounds
Dimensions ‏ : ‎ 7.01 x 0.91 x 9.25 inches

Principles of Digital Image Processing: Advanced Methods (Undergraduate Topics in Computer Science)

Digital image processing is a crucial field in computer science that involves manipulating digital images through various algorithms and techniques. In this post, we will explore some advanced methods in digital image processing that are commonly studied in undergraduate courses in computer science.

1. Image segmentation: Image segmentation is the process of partitioning an image into multiple segments to simplify the representation of an image. This is a challenging task that involves identifying boundaries within an image and separating different objects or regions. Advanced methods in image segmentation include clustering algorithms, graph-based segmentation, and deep learning-based approaches.

2. Object recognition: Object recognition is the process of identifying and classifying objects within an image. Advanced methods in object recognition include convolutional neural networks (CNNs), which have revolutionized the field of computer vision. CNNs are able to learn hierarchical representations of images and can be used for tasks such as object detection, localization, and classification.

3. Image restoration: Image restoration is the process of recovering degraded or corrupted images to improve their quality. Advanced methods in image restoration include deconvolution techniques, super-resolution algorithms, and deep learning-based approaches. These methods can be used to remove noise, blur, and other imperfections from images.

4. Image synthesis: Image synthesis involves generating new images based on existing images or data. Advanced methods in image synthesis include generative adversarial networks (GANs), which are able to generate realistic images that are indistinguishable from real images. GANs have been used for tasks such as image-to-image translation, style transfer, and image generation.

Overall, the field of digital image processing is constantly evolving, with new methods and techniques being developed to address various challenges in image analysis and manipulation. By studying advanced methods in digital image processing, undergraduate students in computer science can gain a deeper understanding of this important field and develop the skills needed to work on cutting-edge research and applications.
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