Fuzzy Latent-Dynamic Conditional Neural Fields for Gesture Recognition in Video

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

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

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

What's the point? Frame-wise Pointing Gesture Recognition with Latent-Dynamic Conditional Random Fields

We use Latent-Dynamic Conditional Random Fields to perform skeleton-based pointing gesture classification at each time instance of a video sequence, where we achieve a frame-wise pointing accuracy of roughly 83%. Subsequently, we determine continuous time sequences of arbitrary length that form individual pointing gestures and this way reliably detect pointing gestures at a false positive detec...

متن کامل

Object Recognition with Latent Conditional Random Fields

In this thesis we present a discriminative part-based approach for the recognition of object classes from unsegmented cluttered scenes. Objects are modelled as flexible constellations of parts conditioned on local observations. For each object class the probability of a given assignment of parts to local features is modelled by a Conditional Random Field (CRF). We propose an extension of the CR...

متن کامل

Investigating syllabic prominence with Conditional Random Fields and Latent-Dynamic Conditional Random Fields

The present study performs an investigation on several issues concerning the automatic detection of prominences. Its aim is to offer a better understanding of the prominence phenomenon in order to be able to improve existent prominence detection systems. The study is threefold: first, the presence of hidden dynamics in the sequence of prominent and non-prominent syllables is tested by comparing...

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

Segment-Level Neural Conditional Random Fields for Named Entity Recognition

We present Segment-level Neural CRF, which combines neural networks with a linear chain CRF for segment-level sequence modeling tasks such as named entity recognition (NER) and syntactic chunking. Our segment-level CRF can consider higher-order label dependencies compared with conventional word-level CRF. Since it is difficult to consider all possible variable length segments, our method uses s...

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

ژورنال

عنوان ژورنال: International Journal on Information and Communication Technology (IJoICT)

سال: 2017

ISSN: 2356-5462

DOI: 10.21108/ijoict.2016.22.124