• Corpus ID: 113442873

Neural Learning Methods for Human-Computer Interaction

  title={Neural Learning Methods for Human-Computer Interaction},
  author={Thomas Kopinski},
This thesis aims at improving the complex task of hand gesture recognition by utilizing machine learning techniques to learn from features calculated from 3D point cloud data. The main contributions of this work are embedded in the domains of machine learning and in the human-machine interaction. Since the goal is to demonstrate that a robust real-time capable system can be set up which provides a supportive means of interaction, the methods researched have to be light-weight in the sense that… 

Hand Gesture Recognition in Automotive Human–Machine Interaction Using Depth Cameras

This review describes current Machine Learning approaches to hand gesture recognition with depth data from time-of-flight sensors and confirms that Convolutional Neural Networks and Long Short-Term Memory yield most reliable results.



Intelligent Approaches to interact with Machines using Hand Gesture Recognition in Natural way: A Survey

Work done in the area of hand gesture recognition is discussed where focus is on the intelligent approaches including soft computing based methods like artificial neural network, fuzzy logic, genetic algorithms etc.

Neural Network Based Data Fusion for Hand Pose Recognition with Multiple ToF Sensors

3D based hand pose recognition using a new generation of low-cost time-of-flight sensors intended for outdoor use in automotive human-machine interaction is presented.

Human gesture recognition using Kinect camera

Experimental results have shown that the backpropagation neural network method outperforms other classification methods and can achieve recognition with 100% accuracy, which confirms the high potential of using the Kinect camera in human body recognition applications.

Comparing gesture recognition accuracy using color and depth information

This paper proposes a method that accommodates such challenging conditions by detecting the hands using scene depth information from the Kinect using Dynamic Time Warping (DTW) and can be generalized to recognize a wider range of gestures.

Depth camera based hand gesture recognition and its applications in Human-Computer-Interaction

This paper compares the performance in terms of speed and accuracy between FEMD and traditional corresponding-based shape matching algorithm, Shape Context and demonstrates that this robust hand gesture recognition system can be a key enabler for numerous hand gesture based HCI systems.

A light-weight real-time applicable hand gesture recognition system for automotive applications

A sophisticated temporal fusion technique boosts the overall robustness of recognition by taking into account data coming from previous classification steps, especially when it comes to generalization on previously unknown persons.

Vision based hand gesture recognition for human computer interaction: a survey

An analysis of comparative surveys done in the field of gesture based HCI and an analysis of existing literature related to gesture recognition systems for human computer interaction by categorizing it under different key parameters are provided.

The Power Is in Your Hands: 3D Analysis of Hand Gestures in Naturalistic Video

  • Eshed Ohn-BarM. Trivedi
  • Computer Science
    2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops
  • 2013
A two-stage method for hand and hand-object interaction detection is developed and significantly outperforms a state-of-the-art baseline on the dataset for hand detection.

Time-of-flight based multi-sensor fusion strategies for hand gesture recognition

The presented contribution illustrates that real-time capability can be maintained with such a setup as the used 3D descriptors, the fusion strategy as well as the online confidence measures are computationally efficient.

Robust Part-Based Hand Gesture Recognition Using Kinect Sensor

A novel distance metric, Finger-Earth Mover's Distance (FEMD), is proposed, which only matches the finger parts while not the whole hand, it can better distinguish the hand gestures of slight differences.