Recognition of Hand Posture Based on a Point Cloud Descriptor and a Feature of Extended Fingers

Warchoł, Dawid; Wysocki, Marian

doi:10.14313/jamris_1-2016/7

Cited by 4 publications

(9 citation statements)

References 19 publications

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“…To increase the distinctiveness of the descriptor, we divide the bounding boxes into several cells of equal sizes, and the descriptor values are calculated for each of them. The experiments undertaken in [9,36] showed that dividing the bounding box into three horizontal cells yields the best results in the hand posture recognition problem in most cases (nine cubic cells and three vertical cells were also verified). Therefore, the box is divided to three horizontal cells in this work (see Fig.…”

Section: Hand Posture Recognition Methodsmentioning

confidence: 89%

“…The algorithm is based on finding the hand center point using a Gaussian blur with a relatively large kernel on a binarized hand image and fitting the largest possible circle in the palm region. This is described in detail in [36].…”

Section: Hand Posture Recognition Methodsmentioning

confidence: 99%

“…However, such an algorithm does not outperform the most accurate methods found in the literature. Therefore, we propose to combine GRSD-C's and ESF2's most effective features and configurations with the Viewpoint Feature Histogram (VFH) descriptor used in our previous works [9,36] for hand-gesture recognition. Table 4 Comparison between recognition rates of our method using VFH(all features), ESF2(A3), GRSD-C(2-cat.…”

Section: Methodsmentioning

confidence: 99%

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Hand Posture Recognition Using Modified Ensemble of Shape Functions and Global Radius-Based Surface Descriptor

Warchoł

2018

csci

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This paper presents an approach to the recognition of static hand gestures based on data acquired from 3D cameras and point cloud descriptors: Ensemble of Shape Functions and Global Radius-based Surface Descriptor. We describe a recognition algorithm consisting of hand segmentation, noise removal and downsampling of point clouds, dividing point cloud bounding boxes to cells, feature extraction and normalization, and gesture classification. Modifications to the descriptors are proposed in order to increase the hand posture recognition rates while decreasing the quantity of used features as well as the computational cost of the algorithm. Experiments performed on four challenging datasets using cross-validation tests prove the usefulness of our approach.

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Section: Hand Posture Recognition Methodsmentioning

confidence: 89%

Section: Hand Posture Recognition Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Hand Posture Recognition Using Modified Ensemble of Shape Functions and Global Radius-Based Surface Descriptor

Warchoł

2018

csci

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“…Future works will consider dimensionality reduction of obtained binary strings, as well as experiments with other real-valued, local descriptors, or other descriptor types [37]. …”

Section: Resultsmentioning

confidence: 99%

A Binary Representation for Real-Valued, Local Feature Descriptors

Oszust

2017

JAMRIS

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“…Additionally, users performing gestures are not required to wear any gloves or to keep their hands very close to the camera, as is the case with Leap Motion cameras. In addition to the listed advantages, the main motivation for using 3D data is the good results obtained for the recognition of static hand postures in single images with representative datasets [16,17,18].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Fingerspelling Sequences in Polish Sign Language Using Point Clouds Obtained from Depth Images

Warchoł

Kapuściński

Wysocki

2019

Sensors

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The paper presents a method for recognizing sequences of static letters of the Polish finger alphabet using the point cloud descriptors: viewpoint feature histogram, eigenvalues-based descriptors, ensemble of shape functions, and global radius-based surface descriptor. Each sequence is understood as quick highly coarticulated motions, and the classification is performed by networks of hidden Markov models trained by transitions between postures corresponding to particular letters. Three kinds of the left-to-right Markov models of the transitions, two networks of the transition models—independent and dependent on a dictionary—as well as various combinations of point cloud descriptors are examined on a publicly available dataset of 4200 executions (registered as depth map sequences) prepared by the authors. The hand shape representation proposed in our method can also be applied for recognition of hand postures in single frames. We confirmed this using a known, challenging American finger alphabet dataset with about 60,000 depth images.

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Recognition of Hand Posture Based on a Point Cloud Descriptor and a Feature of Extended Fingers

Abstract: Abstract

Cited by 4 publications

References 19 publications

Hand Posture Recognition Using Modified Ensemble of Shape Functions and Global Radius-Based Surface Descriptor

Hand Posture Recognition Using Modified Ensemble of Shape Functions and Global Radius-Based Surface Descriptor

A Binary Representation for Real-Valued, Local Feature Descriptors

Recognition of Fingerspelling Sequences in Polish Sign Language Using Point Clouds Obtained from Depth Images

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