Multiscale image segmentation using wavelet-domain hidden Markov models

By Hyeokho Choi, Richard Baraniuk. IEEE Transactions on Image Processing, 2001

424 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ee4bf82e9c3e013acf5a25e6e89435f1dc564515

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In Computer Vision and Pattern Recognition

Citations in the corpus

An information fidelity criterion for image quality assessment using natural scene statistics (2005, methodology): “NSS have been explicitly incorporated into a number of image processing algorithms: in compression algorithms [22]–[25], denoising algorithms [26]–[28], image modeling [29], image segmentation [30], and texture analysis and synthesis [31].”
Multiresolution Markov models for signal and image processing (2002, methodology): “For this latter set of methods, the general idea is to exploit the localization properties of wavelets to allow much easier and more transparent adaptive processing in order to minimize distortion of important image features while removing noise.”
No-reference quality assessment using natural scene statistics: JPEG2000 (2005, methodology): “NSS have been explicitly incorporated into a number of image processing algorithms: in compression algorithms [10] –[12], [25], denoising algorithms [23], [34], [35], image modeling [36], image segmentation [37], and texture analysis and synthesis [38].”
Statistics of real-world hyperspectral images (2011, methodology): “These low-level statistics have also found use as building blocks for higher-level visual tasks such as segmentation and object detection [7, 23, 36].”
Wavelet-based multiscale texture segmentation: Application to stromal compartment characterization on virtual slides (2010, methodology): “This context vector can correspond to the value of the father and of the neighbors of the father (Choi and Baraniuk, 2001), of the neighbors of the son, or a combination of several context vectors (Fan and Xia, 2001) [Fig.5].”
Image information and visual quality (2006, methodology): “NSS have been explicitly incorporated into a number of image processing algorithms: in compression algorithms [21]–[24], denoising algorithms [25]–[27], image modeling [28], image segmentation [29], and texture analysis and synthesis [30].”