Graph Spectral Domain Feature Learning With Application to in-Air Hand-Drawn Number and Shape Recognition

Alwaely, Basheer; Abhayaratne, Charith

doi:10.1109/access.2019.2950643

Cited by 11 publications

(10 citation statements)

References 58 publications

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“…The appropriate approach also has the characteristics of quick operation and rotational and flipping invariance. [5] Brand-new air-handwritten password-based authentication mechanism for IoT devices is proposed. This method is an application of computer vision where it identifies the line which is drawn on the air by making use of a camera, pair light-weighted deep CNN models and a Kalman signal processing filter.…”

Section: Literature Surveymentioning

confidence: 99%

A Review on Dynamic Hand Gesture Recognition Techniques

A¹,

Harish²,

Sambhrama³

et al. 2023

IJRASET

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Body language is one of the nonverbal methodsof communication, and it comprises hand gestures, arm movements, posturing, and gestures and facial expressions. One way to communicate information through the movement of the body is through gestures. HGR is a smart, intuitive, and easy method of human-computer interaction (HCI). HGR systems have two key applications: SLR and GBC. To help the deaf communicate with the hearing community, SLR tries to automaticallyinterpret SLs via a computer. The idea that SL is a highly ordered and primarily symbolic collection of human gestures is what led to the development of universal gesture-based HCI.

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Section: Literature Surveymentioning

confidence: 99%

A Review on Dynamic Hand Gesture Recognition Techniques

A¹,

Harish²,

Sambhrama³

et al. 2023

IJRASET

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“…In our recent work, we proposed graph spectral domain feature-based recognition for numbers [46]. The graph spectral feature-based methods that are primarily based on a fully connected graph formed by the shape were successful in recognizing global shape structures, but not on accurately representing local variations, as shown in FIGURE 1.…”

Section: Related Workmentioning

confidence: 99%

“…The existing graph node connectivity can be categorized into three types: 1) Full connectivity: when each node is connected to all other nodes in the graph. [46]. This type of connectivity provides an efficient characterization of the global outline of the shape.…”

Section: Related Workmentioning

confidence: 99%

“…As discussed in Section II, although a fully connected graph provides an efficient representation of the global outline of the shape [46], the major drawback of this type is that local details are not reliably captured compared to the global outline. In addition, K-Nearest Neighbour connectivity does not reflect the topology structure, especially when using a small value of K, where nodes are only connected to their neighbours.…”

Section: The Proposed Adaptively Connected Graph Formulationmentioning

confidence: 99%

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AGSF: Adaptive Graph Formulation and Hand-Crafted Graph Spectral Features for Shape Representation

Alwaely

Abhayaratne

2020

IEEE Access

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Addressing intra-class variation in high similarity shapes is a challenging task in shape representation due to highly common local and global shape characteristics. Therefore, this paper proposes a new set of hand-crafted features for shape recognition by exploiting spectral features of the underlying graph adaptive connectivity formed by the shape characteristics. To achieve this, the paper proposes a new method for formulating an adaptively connected graph on the nodes of the shape outline. The adaptively connected graph is analysed in terms of its spectral bases followed by extracting hand-crafted adaptive graph spectral features (AGSF) to represent both global and local characteristics of the shape. Experimental evaluation using five 2D shape datasets and four challenging 3D shape datasets shows improvements with respect to the existing hand-crafted feature methods up to 9.14% for 2D shapes and up to 14.02% for 3D shapes. Also for 2D datasets, the proposed AGSF has outperformed the deep learning methods by 17.3%.INDEX TERMS Shape recognition, graph signal processing, graph spectral theory, adaptive graph connectivity, hand-crafted features, graph spectral feature learning.

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“…A recent study on human vision suggests that the visual cortex perceives and understands shapes by representing the shape boundary as a connected set of nodes [42]. Inspired by these human vision literature, our recent work demonstrated how to consider a shape as a connected graph to capture both the global outline [3] and the protrusions for shape representation [5].…”

Section: Introductionmentioning

confidence: 95%

GHOSM: Graph-based Hybrid Outline and Skeleton Modelling for Shape Recognition

Alwaely

Abhayaratne

2023

ACM Trans. Multimedia Comput. Commun. Appl.

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An efficient and accurate shape detection model plays a major role in many research areas.With the emergence of more complex shapes in real life applications, shape recognition models need to capture the structure with more effective features in order to achieve high accuracy rates for shape recognition. This paper presents a new method for 2D/3D shape recognition based on graph spectral domain handcrafted features, which are formulated by exploiting both an outline and a skeleton shape through the global outline and internal details. A fully connected graph is generated over the shape outline to capture the global outline representation while a hierarchically clustered graph with adaptive connectivity is formed on the skeleton to capture the structural descriptions of the shape. We demonstrate the ability of the Fiedler vector to provide the graph partitioning of the skeleton graph. The performance evaluation demonstrates the efficiency of the proposed method compared to state-of-the-art studies with increments of 4.09%, 2.2% and 14.02% for 2D static hand gestures, 2D shapes and 3D shapes, respectively.

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Graph Spectral Domain Feature Learning With Application to in-Air Hand-Drawn Number and Shape Recognition

Cited by 11 publications

References 58 publications

A Review on Dynamic Hand Gesture Recognition Techniques

A Review on Dynamic Hand Gesture Recognition Techniques

AGSF: Adaptive Graph Formulation and Hand-Crafted Graph Spectral Features for Shape Representation

GHOSM: Graph-based Hybrid Outline and Skeleton Modelling for Shape Recognition

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