Transformation Invariance in Hand Shape Recognition

Coogan, Thomas A.; Sutherland, Alistair

doi:10.1109/icpr.2006.1134

Cited by 4 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous approaches have tackled these difficulties by different methodologies including exploiting the skin color [2,3], discriminative learning of hand classifiers [4,5] and incorporating constraints from arms [6] and from TV subtitles [7,8], etc. For hand modeling and recognition, principal component analysis (PCA) has also been applied and achieved promising results [9,10]. However, clean hand segmentation and clean backgrounds are needed as classical PCA is known to be sensitive to outliers.…”

Section: Introductionmentioning

confidence: 99%

Hand Modeling and Tracking for Video-Based Sign Language Recognition by Robust Principal Component Analysis

Piater

2012

Trends and Topics in Computer Vision

View full text Add to dashboard Cite

Hand modeling and tracking are essential in video-based sign language recognition. The high reformability and the large number of degrees of freedom of hands render the problem difficult. To tackle these challenges, a novel approach based on robust principal component analysis (PCA) is proposed. The robust PCA incorporates an L1 norm objective function to deal with background clutter, and a projection pursuit strategy to deal with the lack of alignment due to the deformation of hands. The learning algorithm of the robust PCA is very simple, involving only a search for the solutions in a finite set constructed from the training data, which leads to the learning of much more representative and interpretable bases. The incorporation of the L1 regularization in the fitting of the learned robust PCA models results in cleaner reconstructions and more stable fitting. Based on the robust PCA, a hand tracking system is developed that contains a skin-color region segmentation based on graph cuts and template matching in the framework of particle filtering. Experiments on a publicly available sign-language video database demonstrates the strength of the method.

show abstract

Section: Introductionmentioning

confidence: 99%

Hand Modeling and Tracking for Video-Based Sign Language Recognition by Robust Principal Component Analysis

Piater

2012

Trends and Topics in Computer Vision

View full text Add to dashboard Cite

show abstract

“…This means that the labels did not necessarily correspond to known sign hand shapes, nor did a label contain shapes which are actually the same, only those which look the same according to the clustering distance metric. Coogan and Sutherland [17] used a similar principle when they created a hierarchical decision tree, the leaf nodes of which contained the exemplar of a hand shape class, defined by fuzzy k-means clustering of the Eigenvalues resulting from performing PCA on the artificially constructed training images. Using gloved data to give good segmentation of the hands allowed Pahlevanzadeh et al to use a generic cosine detector to describe basic hand shapes [84] though the system is unlikely to be tractable.…”

Section: Hand Shapementioning

confidence: 99%

Sign Language Recognition

Cooper

Holt

Bowden

2011

Visual Analysis of Humans

176

View full text Add to dashboard Cite

This chapter covers the key aspects of Sign Language Recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a précis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gestures) is then discussed from a tracking and non-tracking viewpoint before summarising some of the approaches to the non-manual aspects of sign languages. Methods for combining the sign classification results into full SLR are given showing the progression towards speech recognition techniques and the further adaptations required for the sign specific case. Finally the current frontiers are discussed and the recent research presented. This covers the task of continuous sign recognition, the work towards true signer independence, how to effectively combine the different modalities of sign, making use of the current linguistic research and adapting to larger more noisy data sets.

show abstract

RETRACTED ARTICLE: Machine learning based sign language recognition: a review and its research frontier

Elakkiya

2020

J Ambient Intell Human Comput

View full text Add to dashboard Cite

Transformation Invariance in Hand Shape Recognition

Abstract: In

Cited by 4 publications

References 7 publications

Hand Modeling and Tracking for Video-Based Sign Language Recognition by Robust Principal Component Analysis

Hand Modeling and Tracking for Video-Based Sign Language Recognition by Robust Principal Component Analysis

Sign Language Recognition

RETRACTED ARTICLE: Machine learning based sign language recognition: a review and its research frontier

Contact Info

Product

Resources

About