Robust Estimation of Albedo for Illumination-invariant Matching and Shape Recovery

Biswas, Soma; Aggarwal, Gaurav; Chellappa, Rama

doi:10.1109/iccv.2007.4409027

Cited by 27 publications

(37 citation statements)

References 12 publications

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“…Planetary natural images such as the ones taken on the Moon and Mars have a restricted set of object properties, acute albedo variations and negligible reflectance diversities (Wilhelms , 1970;Taguchi et al, 2014;Biswas et al, 2007). These constraints make it an ideal scenario for reconstructing the scene's DTM using SfS techniques.…”

Section: Modern Methodsmentioning

confidence: 99%

Lunar soil strength estimation based on Chang’E-3 images

Gao

Spiteri

et al. 2016

Advances in Space Research

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Section: Modern Methodsmentioning

confidence: 99%

Lunar soil strength estimation based on Chang’E-3 images

Gao

Spiteri

et al. 2016

Advances in Space Research

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“…Other popular face recognition methods, based on elastic bunch graph matching (EBGM) [12], active appearance model (AAM) [13], active shape model (ASM) [14] and albedo estimation [15], will also face some difficulties, since the intensive feature cannot be extracted from binary images. Although local binary pattern (LBP) based methods [16] extract features through local binarization, it also encounters problems on a binary image obtained by a global threshold…”

Section: Introductionmentioning

confidence: 99%

Recognition of quantized still face images

Chellappa

2009

2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems

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Abstract-In applications such as document understanding, only binary face images may be available as inputs to a face recognition (FR) algorithm. In this paper, we investigate the effects of the number of grey levels on PCA, multiple exemplar discriminant analysis (MEDA) and the elastic bunch graph matching (EBGM) FR algorithms. The inputs to these FR algorithms are quantized images (binary images or images with small number of grey levels) modified by distance and Box-Cox transforms. The performances of PCA and MEDA algorithms are at 87.66% for images in FRGC version 1 experiment 1 after they are thresholded and transformed while the EBGM algorithm achieves only 37.5%. In many document understanding applications, it is also required to verify a degraded low-quality image against a high-quality image, both of which are from the same source. For this problem, the performances of PCA and MEDA are stable when the images were degraded by noise, downsampling or different thresholding parameters.

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“…Although access-control and community protection were programs widely mentioned by it, face-recognition has additionally confirmed use-total in additional media information places that are processing. Whilst multimedia's surge has been seen in the previous decade, the running and evaluation of media information, including movies and pictures, are of curiosity that is wide [17], [18]. Among these information, movies and encounter pictures have a portion that is big.…”

Section: Introductionmentioning

confidence: 99%