Jelenlegi hely

Discrete Image Reconstruction from Uncertain and Insufficient Data

Lifetime from: 
2009
Lifetime to: 
2013
Short description: 
The goal of the project is to develop efficient image reconstruction methods to gain visual information from incorrect, noisy, uncertain projections. We also investigate how structural or geometrical prior information can facilitate an improve the reconstruction if the number of projections is insufficent to get a unique and exact reconstruction.
Description: 

The goal of the project is to develop efficient image acquisition methods to gain visual information from incorrect, noisy, and uncertain projections. Such projections typically arise in sensor-network applications. Projection signals can be detected in various forms (X-ray, gamma-ray, radar, ultrasound, electric or electromagnetic sensors, etc.). Due to the large variety in the number, modality and position of the detectors, there is no general method to obtain an accurate reconstruction in each application. In the past years it turned out, that discrete tomography can guarantee reconstructions of good quality even when the classical reconstruction methods are hardly applicable due to the small number of possibly noisy projections. Our aim is to investigate the theoretical background of reconstruction problems where projections are noisy, and/or their direction is not known precisely. We study uniqueness and stability questions. We also investigate how structural or geometrical prior information can facilitate an improve the reconstruction if the number of projections is insufficent to get a unique and exact reconstruction. We design and implement discrete tomographic reconstruction methods for different applications and analyse the performance of those algorithms from the viewpoint of speed, accuracy, and noise-sensitivity, both theoretically and numerically.

 

Publications: 
Balázs P. Reconstruction of binary images with disjoint components from horizontal and vertical projections. In: Chetverikov D, Sziranyi T, editors. A Képfeldolgozók és Alakfelismerők Társaságának konferenciája - KÉPAF 2009. Budapest: Akaprint; 2009.
Hantos N, Balázs P. Image enhancement by median filters in algebraic reconstruction methods: an experimental study. In: Bebis G, Boyle R, Parvin B, Koracin D, Chung R, Hammound R et al., editors. Advances in Visual Computing. Las Vegas, NV, USA: Springer Verlag; 2010. 3. p. 339-348p. (Lecture Notes in Computer Science).
Hantos N, Balázs P, Palágyi K. Binary image reconstruction from two projections and skeletal information. In: Barneva RP, Brimkov VE, Aggarwal JK, editors. Combinatorial Image Analysis. Berlin; Heidelberg; New York; London; Paris; Tokyo: Springer Verlag; 2012. 2. p. 263-273p. (Lecture Notes in Computer Science).
Hantos N, Balázs P. A uniqueness result for reconstructing hv-convex polyominoes from horizontal and vertical projections and morphological skeleton. In: Ramponi G, Lončarić S, Carini A, Egiazarian K, editors. Proceedings of International Symposium on Image and Signal Processing and Analysis (ISPA). Trieste: IEEE; 2013. 7. p. 788-793p.
Hantos N, Balázs P. Reconstruction and Enumeration of hv-Convex Polyominoes with Given Horizontal Projection. In: Ruiz-Shulcloper J, Sanniti di Baja G, editors. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (CIARP). Heidelberg; London; New York: Springer; 2013. 1. p. 100-107p. (LNCS).
Kategória: 
Tomography - Discrete Tomography