CNN Based Posture-Free Hand Detection

09/27/2018
by   Richard Adiguna, et al.
0

Although many studies suggest high performance hand detection methods, those methods are likely to be overfitting. Fortunately, the Convolution Neural Network (CNN) based approach provides a better way that is less sensitive to translation and hand poses. However the CNN approach is complex and can increase computational time, which at the end reduce its effectiveness on a system where the speed is essential.In this study we propose a shallow CNN network which is fast, and insensitive to translation and hand poses. It is tested on two different domains of hand datasets, and performs in relatively comparable performance and faster than the other state-of-the-art hand CNN-based hand detection method. Our evaluation shows that the proposed shallow CNN network performs at 93.9 competitors.

READ FULL TEXT

page 2

page 3

research
03/06/2018

CNN-Based Automatic Urinary Particles Recognition

The urine sediment analysis of particles in microscopic images can assis...
research
10/03/2022

Introducing Vision Transformer for Alzheimer's Disease classification task with 3D input

Many high-performance classification models utilize complex CNN-based ar...
research
05/12/2021

A one-armed CNN for exoplanet detection from light curves

We propose Genesis, a one-armed simplified Convolutional Neural Network ...
research
01/13/2020

Towards Interpretable and Robust Hand Detection via Pixel-wise Prediction

The lack of interpretability of existing CNN-based hand detection method...
research
01/12/2021

Two-stage CNN-based wood log recognition

The proof of origin of logs is becoming increasingly important. In the c...
research
05/14/2014

Return of the Devil in the Details: Delving Deep into Convolutional Nets

The latest generation of Convolutional Neural Networks (CNN) have achiev...
research
08/25/2022

CNN-based Prediction of Network Robustness With Missing Edges

Connectivity and controllability of a complex network are two important ...

Please sign up or login with your details

Forgot password? Click here to reset