Extended Hopfield Network for Sequence Learning: Application to Gesture Recognition

نویسندگان

  • André Maurer
  • Micha Hersch
  • Aude Billard
چکیده

In this paper, we extend the Hopfield Associative Memory for storing multiple sequences of varying duration. We apply the model for learning, recognizing and encoding a set of human gestures. We measure systematically the performance of the model against noise.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Improving Phoneme Sequence Recognition using Phoneme Duration Information in DNN-HSMM

Improving phoneme recognition has attracted the attention of many researchers due to its applications in various fields of speech processing. Recent research achievements show that using deep neural network (DNN) in speech recognition systems significantly improves the performance of these systems. There are two phases in DNN-based phoneme recognition systems including training and testing. Mos...

متن کامل

Feature Learning for Conditional Random Fields and its Application to Gesture Recognition

Conditional random fields (CRFs) have been successful in many sequence labeling tasks, which conventionally rely on a hand-craft feature representation of input data. However, a powerful data representation could be another determining factor of the performance, which has not attracted enough attention yet. We describe a novel sequence labeling framework for gesture recognition, which builds a ...

متن کامل

EMG-based wrist gesture recognition using a convolutional neural network

Background: Deep learning has revolutionized artificial intelligence and has transformed many fields. It allows processing high-dimensional data (such as signals or images) without the need for feature engineering. The aim of this research is to develop a deep learning-based system to decode motor intent from electromyogram (EMG) signals. Methods: A myoelectric system based on convolutional ne...

متن کامل

Hand Gesture Recognition from RGB-D Data using 2D and 3D Convolutional Neural Networks: a comparative study

Despite considerable enhances in recognizing hand gestures from still images, there are still many challenges in the classification of hand gestures in videos. The latter comes with more challenges, including higher computational complexity and arduous task of representing temporal features. Hand movement dynamics, represented by temporal features, have to be extracted by analyzing the total fr...

متن کامل

Continuous Body and Hand Gesture Recognition for Natural Human-Computer Interaction: Extended Abstract

We present a new approach to gesture recognition that tracks body and hands simultaneously and recognizes gestures continuously from an unsegmented and unbounded input stream. Our system estimates 3D coordinates of upper body joints and classifies the appearance of hands into a set of canonical shapes. A novel multi-layered filtering technique with a temporal sliding window is developed to enab...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2005