Structured light 3D depth map enhancement and gesture recognition using image content adaptive filtering

2014 ◽  
Author(s):  
Vikas Ramachandra ◽  
James Nash ◽  
Kalin Atanassov ◽  
Sergio Goma
Keyword(s):  
Gesture Recognition ◽  
Adaptive Filtering ◽  
Structured Light ◽  
Depth Map ◽  
Image Content ◽  
Content Adaptive

scholarly journals Robust hand gesture recognition using multiple shape-oriented visual cues

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Samy Bakheet ◽  
Ayoub Al-Hamadi
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Pose Estimation ◽  
Depth Map ◽  
Hand Gesture Recognition ◽  
Support Vector ◽  
Hand Gesture ◽  
Hand Pose Estimation ◽  
Time Operation ◽  
Hand Pose

AbstractRobust vision-based hand pose estimation is highly sought but still remains a challenging task, due to its inherent difficulty partially caused by self-occlusion among hand fingers. In this paper, an innovative framework for real-time static hand gesture recognition is introduced, based on an optimized shape representation build from multiple shape cues. The framework incorporates a specific module for hand pose estimation based on depth map data, where the hand silhouette is first extracted from the extremely detailed and accurate depth map captured by a time-of-flight (ToF) depth sensor. A hybrid multi-modal descriptor that integrates multiple affine-invariant boundary-based and region-based features is created from the hand silhouette to obtain a reliable and representative description of individual gestures. Finally, an ensemble of one-vs.-all support vector machines (SVMs) is independently trained on each of these learned feature representations to perform gesture classification. When evaluated on a publicly available dataset incorporating a relatively large and diverse collection of egocentric hand gestures, the approach yields encouraging results that agree very favorably with those reported in the literature, while maintaining real-time operation.

Angular Depth Map Generation for Gesture Recognition

2012 ◽  
Vol 3 (2) ◽  
pp. 1-8
Author(s):  
Pusik Park ◽  
Rakhimov Rustam Igorevich ◽  
Jongchan Choi ◽  
Dugki Min ◽  
Jongho Yoon
Keyword(s):  
Gesture Recognition ◽  
Depth Map ◽  
Map Generation

scholarly journals Single-Shot Structured Light Sensor for 3D Dense and Dynamic Reconstruction

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1094 ◽  
Author(s):  
Feifei Gu ◽  
Zhan Song ◽  
Zilong Zhao
Keyword(s):  
Stereo Matching ◽  
Speckle Pattern ◽  
Structured Light ◽  
Optical Element ◽  
Depth Map ◽  
Point Clouds ◽  
Calibration Method ◽  
Acquisition Time ◽  
Single Shot ◽  
Dynamic Reconstruction

Structured light (SL) has a trade-off between acquisition time and spatial resolution. Temporally coded SL can produce a 3D reconstruction with high density, yet it is not applicable to dynamic reconstruction. On the contrary, spatially coded SL works with a single shot, but it can only achieve sparse reconstruction. This paper aims to achieve accurate 3D dense and dynamic reconstruction at the same time. A speckle-based SL sensor is presented, which consists of two cameras and a diffractive optical element (DOE) projector. The two cameras record images synchronously. First, a speckle pattern was elaborately designed and projected. Second, a high-accuracy calibration method was proposed to calibrate the system; meanwhile, the stereo images were accurately aligned by developing an optimized epipolar rectification algorithm. Then, an improved semi-global matching (SGM) algorithm was proposed to improve the correctness of the stereo matching, through which a high-quality depth map was achieved. Finally, dense point clouds could be recovered from the depth map. The DOE projector was designed with a size of 8 mm × 8 mm. The baseline between stereo cameras was controlled to be below 50 mm. Experimental results validated the effectiveness of the proposed algorithm. Compared with some other single-shot 3D systems, our system displayed a better performance. At close range, such as 0.4 m, our system could achieve submillimeter accuracy.

DIBR with content-adaptive filtering for 3D view

2014 ◽  
Author(s):  
Che-Wei Liu ◽  
Shao-En Li ◽  
Jia-Liang Syu ◽  
Hsin-Ting Li ◽  
Wei-Han Cheng ◽  
...  
Keyword(s):  
Adaptive Filtering ◽  
Content Adaptive

scholarly journals Single-Shot 3D Shape Reconstruction Using Structured Light and Deep Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3718 ◽  
Author(s):  
Hieu Nguyen ◽  
Yuzeng Wang ◽  
Zhaoyang Wang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
3D Imaging ◽  
Structured Light ◽  
Depth Map ◽  
Shape Reconstruction ◽  
Single Shot ◽  
Deep Convolutional Neural Networks ◽  
3D Shape ◽  
3D Shape Reconstruction

Single-shot 3D imaging and shape reconstruction has seen a surge of interest due to the ever-increasing evolution in sensing technologies. In this paper, a robust single-shot 3D shape reconstruction technique integrating the structured light technique with the deep convolutional neural networks (CNNs) is proposed. The input of the technique is a single fringe-pattern image, and the output is the corresponding depth map for 3D shape reconstruction. The essential training and validation datasets with high-quality 3D ground-truth labels are prepared by using a multi-frequency fringe projection profilometry technique. Unlike the conventional 3D shape reconstruction methods which involve complex algorithms and intensive computation to determine phase distributions or pixel disparities as well as depth map, the proposed approach uses an end-to-end network architecture to directly carry out the transformation of a 2D image to its corresponding 3D depth map without extra processing. In the approach, three CNN-based models are adopted for comparison. Furthermore, an accurate structured-light-based 3D imaging dataset used in this paper is made publicly available. Experiments have been conducted to demonstrate the validity and robustness of the proposed technique. It is capable of satisfying various 3D shape reconstruction demands in scientific research and engineering applications.

scholarly journals Fast Facial Detection by Depth Map Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Ming-Yuan Shieh ◽  
Tsung-Min Hsieh
Keyword(s):  
Image Processing ◽  
Facial Recognition ◽  
Structured Light ◽  
Depth Map ◽  
Infrared Light ◽  
Light Sources ◽  
Detection Scheme ◽  
Detection Techniques ◽  
The Face ◽  
Map Analysis

In order to obtain correct facial recognition results, one needs to adopt appropriate facial detection techniques. Moreover, the effects of facial detection are usually affected by the environmental conditions such as background, illumination, and complexity of objectives. In this paper, the proposed facial detection scheme, which is based on depth map analysis, aims to improve the effectiveness of facial detection and recognition under different environmental illumination conditions. The proposed procedures consist of scene depth determination, outline analysis, Haar-like classification, and related image processing operations. Since infrared light sources can be used to increase dark visibility, the active infrared visual images captured by a structured light sensory device such as Kinect will be less influenced by environmental lights. It benefits the accuracy of the facial detection. Therefore, the proposed system will detect the objective human and face firstly and obtain the relative position by structured light analysis. Next, the face can be determined by image processing operations. From the experimental results, it demonstrates that the proposed scheme not only improves facial detection under varying light conditions but also benefits facial recognition.

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