Implementation of an interactive TV interface via gesture and handwritten numeral recognition

Sheu, Jia-Shing; Huang, Ya‐Ling

doi:10.1007/s11042-015-2739-6

Cited by 9 publications

(9 citation statements)

References 9 publications

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“…Consequently, two options have been defined in order to find the best mappings: (1) designers can rely on their own expertise or (2) organize user studies. However, the first option often leads to arbitrary gesture sets [36] that do not take into account users' mental model or opinions, resulting in misdesigns and frustrating user experiences [8]. Involving users into the design process represents a viable alternative for collecting important data to inform design.…”

Section: Related Workmentioning

confidence: 99%

Understanding Gesture Articulations Variability

Erazo

Rekik

Grisoni

et al. 2017

Lecture Notes in Computer Science

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Interfaces based on mid-air gestures often use a one-to-one mapping between gestures and commands, but most remain very basic. Actually, people exhibit inherent intrinsic variations for their gesture articulations because gestures carry dependency with both the person producing them and the specific context, social or cultural, in which they are being produced. We advocate that allowing applications to map many gestures to one command is a key step to give more flexibility, avoid penalizations, and lead to better user interaction experiences. Accordingly, this paper presents our results on mid-air gesture variability. We are mainly concerned with understanding variability in mid-air gesture articulations from a pure user-centric perspective. We describe a comprehensive investigation on how users vary the production of gestures under unconstrained articulation conditions. The conducted user study consisted in two tasks. The first one provides a model of user conception and production of gestures; from this study we also derive an embodied taxonomy of gestures. This taxonomy is used as a basis for the second experiment, in which we perform a fine grain quantitative analysis of gesture articulation variability. Based on these results, we discuss implications for gesture interface designs.

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Section: Related Workmentioning

confidence: 99%

Understanding Gesture Articulations Variability

Erazo

Rekik

Grisoni

et al. 2017

Lecture Notes in Computer Science

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show abstract

“…The first seven papers belong to the technique category of smart pattern recognition [4,7,10,13,14,18,22]. The first paper entitled BFeature design scheme for Kinect-based DTW human gesture recognition^by Ding and Chang [7] develops a feature design scheme involving hybridizations of joint positions and joint angles for dynamic time warping-based human gesture recognition with the Kinect camera.…”

Section: Summary Of 2accepted Papersmentioning

confidence: 99%

“…The first paper entitled BFeature design scheme for Kinect-based DTW human gesture recognition^by Ding and Chang [7] develops a feature design scheme involving hybridizations of joint positions and joint angles for dynamic time warping-based human gesture recognition with the Kinect camera. The second paper entitled BImplementation of an interactive TV interface via gesture and handwritten numeral recognition^by Sheu and Huang [14] considers the utilization of the Kinect sensor to recognize the dynamic gestures of users to achieve real-time interactive control of TV where a back-propagation neural network was also used to perform handwriting recognition to help the command recognition task. The third paper entitled BAn novel random forests and its application to the classification of mangroves remote sensing image^by Luo et al [13] proposes a novel random forests algorithm with variables random input and random combination machine to improve the weakness of low accuracy and an over-fitting phenomenon in a single decision tree.…”

Section: Summary Of 2accepted Papersmentioning

confidence: 99%

Guest Editorial: Smart Multimedia Communication Technologies for New-Generation Artificial Intelligence Applications

Hsieh

2016

Multimed Tools Appl

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With the development of the Internet of thing (IoT) mechanism nowadays, the required techniques for artificial intelligence (AI)-related applications will be equipped with a crucial property, which is Bsmart.^Smart multimedia communication technologies are composed of two main technique categories: the smart multimedia processing technique class and the smart communication transmission technique class. These will be the necessity for constructing a new-generation AI application. The technical category of multimedia processing involves lots of types of techniques where pattern recognition, video (or image) processing, and human machine interaction (HCI) are three kernel technique types to establish an AI application under the regulation of the IoT mechanism. Video coding and decoding (codecs) techniques, media streaming techniques, and hardware-based techniques for enhancing media communication play important roles for establishing an efficient communication transmission channel between the user end device and the remote media handling server.BSmart multimedia communication technologies^associated with the new-generation AI application will absolutely be an evolution tendency for the development of multimedia and communication technologies. Following this thought line, we deeply believe that the appropriate term Bsmart^will be given to pattern recognition, video processing, human machine interaction, media streaming, and hardware-based media communication enhancement techniques for building up a smart multimedia communication system.The special issue, BSmart Multimedia Communication Technologies for New-Generation Artificial Intelligence Applications,^acts as a path breaker for exploring new-generation AI applications using multimedia communication technologies with intelligence. We totally received around 30 submissions, and the vast majority of all submissions are significant in Multimed Tools Appl (2016) 75:9665-9668

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“…Recently, in-air hand-drawn number recognition has gained strong interest [4]- [20] due to applications-related importance and the interesting challenges in the problem. These methods are either based on image representation of numbers and shapes [4]- [13] followed by shape matching or the path representation of hand movement [14]- [20] followed by shape matching. Some methods are based on deep learning for shape matching [9]- [13].…”

Section: Introductionmentioning

confidence: 99%

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

Alwaely

Abhayaratne

2019

IEEE Access

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This paper addresses the problem of recognition of dynamic shapes by representing the structure in a shape as a graph and learning the graph spectral domain features. Our proposed method includes pre-processing for converting the dynamic shapes into a fully connected graph, followed by analysis of the eigenvectors of the normalized Laplacian of the graph adjacency matrix for forming the feature vectors. The method proposes to use the eigenvector corresponding to the lowest eigenvalue for formulating the feature vectors as it captures the details of the structure of the graph. The use of the proposed graph spectral domain representation has been demonstrated in an in-air hand-drawn number and symbol recognition applications. It has achieved average accuracy rates of 99.56% and 99.44%, for numbers and symbols, respectively, outperforming the existing methods for all datasets used. It also has the added benefits of fast real-time operation and invariance to rotation and flipping, making the recognition system robust to different writing and drawing variations.

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Implementation of an interactive TV interface via gesture and handwritten numeral recognition

Cited by 9 publications

References 9 publications

Understanding Gesture Articulations Variability

Understanding Gesture Articulations Variability

Guest Editorial: Smart Multimedia Communication Technologies for New-Generation Artificial Intelligence Applications

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

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