Adobe, Inc.

Anuj Shara

Prasenjit Mondal

Ankit Bal

Ram Bhushan Agrawal

Deepanshu Arora

Siddharth Kumar

Ruppesh Nalwaya

Abhijeet Gaiha

While a user holds a camera positioned relative to an object, a first image of the object and a second image of the object, as captured by the camera, may be obtained. Intensity variations between a first intensity map of the first image and a combination intensity map obtained from the first intensity map and a second intensity map of the second image may be compared. Then, a shadow may be identified within the first image, based on the intensity variations.

Image shadow detection using multiple images

Techniques are disclosed for document boundary detection (BD) from an input image using a combination of deep learning model and image processing algorithms. Quadrilaterals approximating the document boundaries in the input image are determined and rated separately using both these approaches: deep leaning using convolutional neural network (CNN) and heuristics using image processing algorithms. Thereafter, the best rated quadrilateral is selected from the quadrilaterals obtained from both the approaches.

Document boundary detection using deep learning model and image processing algorithms

There are described mobile devices, and methods thereof, for real time analyses of captured images. The device determines whether a light level of a first image meets or exceeds a low light threshold. The device also determines whether a shadow level of the first image meets or exceeds a shadow threshold in response to determining the light level does not meet or exceed the low light threshold. The light source is activated in response to determining the light level meets or exceeds the low light threshold or the shadow level meets or exceeds the shadow threshold. The device further determines whether a glare level of a second image meets or exceeds a glare threshold in response to identifying the second image. If so, then a final image is captured with the light source inactive. Otherwise, the final image is captured with the light source active.

Real time shadow and glare analysis for document capture

Techniques are disclosed for generating a shadow map of a digital image. In some examples, a method may include generating a shadow mask of a digital image, generating a dilated de-noised binarized gradient image based on the shadow mask, generating a binarized median-filtered gray image based on the digital image and the dilated de-noised binarized gradient image, and generating a shadow map based on the shadow mask and the binarized median-filtered gray image. The generated shadow map can then be used to remove shadows from the digital image without degrading the quality of the image content in the digital image.

Removal of shadows from document images while preserving fidelity of image contents

Systems and methods are disclosed for content aware digital image enhancement. In particular, in one or more embodiments, the disclosed systems and methods analyze content of a digital image portraying a document with graphics and/or text and generate a cleaning confidence map. Specifically, in one or more embodiments, the disclosed systems and methods generate a cleaning confidence map indicating a likelihood that each pixel in the digital image portrays text or a graphic. Moreover, in one or more embodiments, the disclosed systems and methods utilize the cleaning confidence map as a reflection of how aggressively to modify digital images. In particular, in one or more embodiments, the disclosed systems and methods utilize the cleaning confidence map to remove shadows, identify and clean background pixels, and correct contrast in relation to the digital image.

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Delhi, India

Anuj Shara