Publications by authors named "Niladri B Puhan"

Acoustic sensors have been in commercial use for more than 60 years [...

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The modeling of driver behavior plays an essential role in developing Advanced Driver Assistance Systems (ADAS) to support the driver in various complex driving scenarios. The behavior estimation of surrounding vehicles is crucial for an autonomous vehicle to safely navigate through an unsignalized intersection. This work proposes a novel kernelized convolutional transformer network (KCTN) with multi-head attention (MHA) mechanism to estimate driver behavior at a challenging unsignalized three-way roundabout.

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Automatic machine classification of concrete structural defects in images poses significant challenges because of multitude of problems arising from the surface texture, such as presence of stains, holes, colors, poster remains, graffiti, marking and painting, along with uncontrolled weather conditions and illuminations. In this paper, we propose an interleaved deep artifacts-aware attention mechanism (iDAAM) to classify multi-target multi-class and single-class defects from structural defect images. Our novel architecture is composed of interleaved fine-grained dense modules (FGDM) and concurrent dual attention modules (CDAM) to extract local discriminative features from concrete defect images.

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Glaucoma is a progressive optic neuropathy characterized by peripheral visual field loss, which is caused by degeneration of retinal nerve fibers. The peripheral vision loss due to glaucoma is asymptomatic. If not detected and treated at an early stage, it leads to complete blindness, which is irreversible in nature.

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Computer-assisted automated exudate detection is crucial for large-scale screening of diabetic retinopathy (DR). The motivation of this work is robust and accurate detection of low contrast and isolated hard exudates using fundus imaging. Gabor filtering is first performed to enhance exudate visibility followed by Tsallis entropy thresholding.

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