Kullback-Leibler Divergence based Marker Detection in Augmented Reality

Susan, Seba; Tandon, Shivendra; Seth, Surabhi; Mudassir, Mohd. Tariq; Chaudhary, Ritika; Baisoya, Nikhil

doi:10.1109/ccaa.2018.8777570

Cited by 3 publications

(2 citation statements)

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“…Susan et al [ 71 ] investigated the possibility of using the Kullback-Leibler (KL) divergence measure [ 72 ] for marker detection. The authors compare their work with ARToolKit [ 6 ], which allows users to set custom markers and later detect them using correlation with the markers’ predefined database.…”

Section: Related Workmentioning

confidence: 99%

Designing a Simple Fiducial Marker for Localization in Spatial Scenes Using Neural Networks

Kostak

Slaby

2021

Sensors

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The paper describes the process of designing a simple fiducial marker. The marker is meant for use in augmented reality applications. Unlike other systems, it does not encode any information, but it can be used for obtaining the position, rotation, relative size, and projective transformation. Also, the system works well with motion blur and is resistant to the marker’s imperfections, which could theoretically be drawn only by hand. Previous systems put constraints on colors that need to be used to form the marker. The proposed system works with any saturated color, leading to better blending with the surrounding environment. The marker’s final shape is a rectangular area of a solid color with three lines of a different color going from the center to three corners of the rectangle. Precise detection can be achieved using neural networks, given that the training set is very varied and well designed. A detailed literature review was performed, and no such system was found. Therefore, the proposed design is novel for localization in the spatial scene. The testing proved that the system works well both indoor and outdoor, and the detections are precise.

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Section: Related Workmentioning

confidence: 99%

Designing a Simple Fiducial Marker for Localization in Spatial Scenes Using Neural Networks

Kostak

Slaby

2021

Sensors

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“…The symmetric Kullback-Leibler(KL) divergence loss of these two distributions is added to the original cross-entropy loss to achieve joint backpropagation and parameter update [ 10 , 11 ]. By minimizing the divergence loss, the expressive ability and generalization ability of remote sensing image segmentation are enhanced [ 12 ]. Aiming at the problem of low segmentation accuracy in the original model, this paper takes advantage of multiscale convolution kernels and mixes multiple convolution kernels in one convolution operation.…”

Section: Introductionmentioning

confidence: 99%

A Method for Extracting Building Information from Remote Sensing Images Based on Deep Learning

Lian-ying

Chen²,

2022

Computational Intelligence and Neuroscience

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Semantic segmentation of remote sensing images is an important issue in remote sensing tasks. Existing algorithms can extract information more accurately, but it is difficult to capture the contours of objects and further reveal the interaction information between different objects in the image. Therefore, a deep learning-based method for extracting building information from remote sensing images is proposed. First, the deep learning semantic segmentation model DeepLabv3+ and Mixconv2d are combined, and convolution kernels of different sizes are used for feature recognition. Then, the regularization method based on Rdrop Loss improves the accuracy and efficiency of contour capture for objects of different resolutions, and at the same time improves the consistency of dataset fitting. Finally, the proposed remote sensing image information extraction method is verified based on the self-built dataset. The experimental results show that the proposed algorithm can effectively improve the algorithm efficiency and result accuracy, and has good segmentation performance.

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