Search Results for author: Debleena Sengupta

Found 4 papers, 1 papers with code

End-to-End Trainable Deep Active Contour Models for Automated Image Segmentation: Delineating Buildings in Aerial Imagery

no code implementations ECCV 2020 Ali Hatamizadeh, Debleena Sengupta, Demetri Terzopoulos

The automated segmentation of buildings in remote sensing imagery is a challenging task that requires the accurate delineation of multiple building instances over typically large image areas.

Image Segmentation Segmentation +1

End-to-End Deep Convolutional Active Contours for Image Segmentation

no code implementations29 Sep 2019 Ali Hatamizadeh, Debleena Sengupta, Demetri Terzopoulos

The Active Contour Model (ACM) is a standard image analysis technique whose numerous variants have attracted an enormous amount of research attention across multiple fields.

Image Segmentation Instance Segmentation +2

Deep learning architectures for automated image segmentation

no code implementations19 Sep 2019 Debleena Sengupta

Image segmentation is widely used in a variety of computer vision tasks, such as object localization and recognition, boundary detection, and medical imaging.

Boundary Detection Image Segmentation +5

Deep Active Lesion Segmentation

1 code implementation19 Aug 2019 Ali Hatamizadeh, Assaf Hoogi, Debleena Sengupta, Wuyue Lu, Brian Wilcox, Daniel Rubin, Demetri Terzopoulos

Lesion segmentation is an important problem in computer-assisted diagnosis that remains challenging due to the prevalence of low contrast, irregular boundaries that are unamenable to shape priors.

Lesion Segmentation Segmentation

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