TY  - BOOK
AU  - Roy,Anik
TI  - Statistical Monitoring of Image Data under Jump Regression Framework
U1  - SB:621.367 23rd
PY  - 2024///
CY  - Kolkata
PB  - Indian Statistical Institute
KW  - Image Procesing
KW  - Statistical Application
KW  - Anomaly Detection
KW  - Shewhart-Type Chart
KW  - Edge Preserving Smoothing
KW  - Sparse Changes
KW  - Image Registration
KW  - Satellite Imaging
N1  - Thesis (Ph.D) - Indian Statistcal Institute, 2024; Includes bibliography; Introduction -- Intensity-based Image Monitoring: Upper-q-Quantile CUSUM Control Chart -- Monitoring Images Using Jump
Location Curves -- Image Comparison Based on Local Pixel Clustering† -- Shape and Size Monitoring in Presence of Rigid-body Image Transformation -- Concluding Remarks and Future Directions --; Guided by Prof. Partha Sarathi Mukherjee
N2  - Image monitoring is an important research problem that has wide applications in various fields, including manufacturing industries, satellite imaging, medical diagnostics, and so forth. This problem, however, presents a challenging big data issue in the sense that, (i) it is characterized by high velocity and high volume of the data streams, (ii) observed im- age intensity functions are discontinuous in nature, have spatial structures, and it often contains noise, (iii) a typical grayscale image has a large number of pixels, implying high- dimensional nature of the data, (iv) in some applications, image surface often contains artifacts and insignificant anomalies (e.g., shadows, clouds, etc.), (v) sequence of im- ages are often not geometrically aligned. In this dissertation, image monitoring schemes are developed on the basis of image intensity values, edges, and other complex features from the image surface. This dissertation aims to bridge the gap between the research fields of image processing and statistical process control and effectively address all the aforementioned issues. Our proposed methods in this dissertation make use of various state-of-the-art techniques from both research domains and help the research field of image monitoring stride forward. Numerical examples and statistical properties show that the proposed image comparison and monitoring methods in this dissertation perform well in various real-life scenarios. Furthermore, the novel methodological advancements proposed in this dissertation will be highly beneficial to the practitioners in various fields
UR  - http://hdl.handle.net/10263/7490
ER  -