An extreme helping hand for handicap people: Using computer vision

Menghani, Piyush; Barthwal, Saurabh; Bansal, S.

doi:10.1109/icraie.2016.7939564

Cited by 3 publications

(1 citation statement)

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“…Te computer vision's primary goal is to analyze, process, and give meaning to digital images. To achieve this feat, machine-learning (ML) algorithms as per diferent methods are being developed day by day [1]. For text-based models or 1D inputs, K-nearest neighbor (KNN) models are being used with profciency [2,3].…”

Section: Introductionmentioning

confidence: 99%

A State-of-the-Art Computer Vision Adopting Non-Euclidean Deep-Learning Models

Chowdhury,

Sany,

Ahamed

et al. 2023

International Journal of Intelligent Systems

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A distance metric known as non-Euclidean distance deviates from the laws of Euclidean geometry, which is the geometry that governs most physical spaces. It is utilized when Euclidean distance is inappropriate, for as when dealing with curved surfaces or spaces with complex topologies. The ability to apply deep learning techniques to non-Euclidean domains including graphs, manifolds, and point clouds is made possible by non-Euclidean deep learning. The use of non-Euclidean deep learning is rapidly expanding to study real-world datasets that are intrinsically non-Euclidean. Over the years, numerous novel techniques have been introduced, each with its benefits and drawbacks. This paper provides a categorized archive of non-Euclidean approaches used in computer vision up to this point. It starts by outlining the context, pertinent information, and the development of the field’s history. Modern state-of-the-art methods have been described briefly and categorized by application fields. It also highlights the model’s shortcomings in tables and graphs and shows different real-world applicability. Overall, this work contributes to a collective information and performance comparison that will help enhance non-Euclidean deep-learning research and development in the future.

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Section: Introductionmentioning

confidence: 99%