Moments and Moment Invariants in Pattern Recognition (Hardcover)

Moments as projections of an image’s intensity onto a proper polynomial basis can be applied to many different aspects of image processing. These include invariant pattern recognition, image normalization, image registration, focus/ defocus measurement, and watermarking. This book presents a survey of both recent and traditional image analysis and pattern recognition methods, based on image moments, and offers new concepts of invariants to linear filtering and implicit invariants. In addition to the theory, attention is paid to efficient algorithms for moment computation in a discrete domain, and to computational aspects of orthogonal moments. The authors also illustrate the theory through practical examples, demonstrating moment invariants in real applications across computer vision, remote sensing and medical imaging.

 

Key features:

 

• Presents a systematic review of the basic definitions and properties of moments covering geometric moments and complex moments.
• Considers invariants to traditional transforms – translation, rotation, scaling, and affine transform - from a new point of view, which offers new possibilities of designing optimal sets of invariants.
• Reviews and extends a recent field of invariants with respect to convolution/blurring.
• Introduces implicit moment invariants as a tool for recognizing elastically deformed objects.
• Compares various classes of orthogonal moments (Legendre, Zernike, Fourier-Mellin, Chebyshev, among others) and demonstrates their application to image reconstruction from moments.
• Offers comprehensive advice on the construction of various invariants illustrated with practical examples.
• Includes an accompanying website providing efficient numerical algorithms for moment computation and for constructing invariants of various kinds, with about 250 slides suitable for a graduate university course.

Moments and Moment Invariants in Pattern Recognition is ideal for researchers and engineers involved in pattern recognition in medical imaging, remote sensing, robotics and computer vision. Post graduate students in image processing and pattern recognition will also find the book of interest.

將影像強度投影到適當的多項式基底上的瞬間，可以應用於影像處理的許多不同方面。這些包括不變模式識別、影像正規化、影像配準、焦點/模糊度測量和浮水印。本書介紹了基於影像瞬間的最新和傳統影像分析和模式識別方法的綜述，並提供了對線性濾波和隱式不變量的新概念。除了理論外，還關注在離散域中進行瞬間計算的高效算法，以及正交瞬間的計算方面。作者還通過實際示例演示了理論在計算機視覺、遙感和醫學影像等實際應用中的瞬間不變性。

主要特點：
- 系統地回顧了瞬間的基本定義和性質，包括幾何瞬間和複雜瞬間。
- 從新的角度考慮傳統變換（平移、旋轉、縮放和仿射變換）的不變量，提供了設計最佳不變量集合的新可能性。
- 對於卷積/模糊方面的不變量進行了回顧和擴展。
- 引入了隱式瞬間不變量作為識別彈性變形物體的工具。
- 比較了各種類型的正交瞬間（Legendre、Zernike、Fourier-Mellin、Chebyshev等），並演示了它們在從瞬間重建影像方面的應用。
- 提供了關於構建各種不變量的全面建議，並附有實際示例。
- 包含一個附帶網站，提供高效的數值算法，用於瞬間計算和構建各種不同類型的不變量，並提供約250張適合研究生課程的投影片。

《模式識別中的瞬間和瞬間不變量》適合從事醫學影像、遙感、機器人和計算機視覺中的模式識別的研究人員和工程師。影像處理和模式識別的研究生學生也會對本書感興趣。