Using Expectation-Maximization for exposing image forgeries by revealing inconsistencies in shadow geometry

Nasiri, Mojtaba; Behrad, Alireza

doi:10.1016/j.jvcir.2018.12.007

Cited by 4 publications

(1 citation statement)

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“…This technique performed quite well but uses structured illumination generated by a projector and scene preparation is required with illumination sensors. [20] used inconsistencies in shadow geometry for image forgery detection. They computed the correspondence of an object and its cast shadow, but the object and shadowed points were set manually, by the user.…”

Section: Related Workmentioning

confidence: 99%

Identification of Shadowed Areas to Improve Ragweed Leaf Segmentation

2021

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As part of a project targeting geometrical structure analysis and identification of ragweed leaves, sample images were created. Even though images were taken under near optimal conditions, the samples still contain noise of cast shadow. The proposed method improves chromaticity based primary shape segmentation efficiency by identification and re-classification of the shadowed areas. The primary classification of each point is done generally based on thresholding the Hue channel of Hue/Saturation/Value color space. In this work, the primary classification is enhanced by thresholding an intra-class normalized weight computed from the class specific Value channel. The corrective step is the removal of areas marked as shadow from the object class. The idea is based on the assumption that the image contains a single, flat leaf in front of a homogeneous background, but there are no color and illumination restrictions. Thus, parameters of the imaging system and the light sources have influence on homogeneity of image parts; however vague shadows differ only in intensity, and hard shadows can only be dropped on the background.

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Section: Related Workmentioning

confidence: 99%