Abstract: Computacional recognition of sign languages aims to
allow a greater social and digital inclusion of deaf people through
interpretation of their language by computer. This article presents
a model of recognition of two of global parameters from sign
languages; hand configurations and hand movements. Hand motion
is captured through an infrared technology and its joints are built
into a virtual three-dimensional space. A Multilayer Perceptron
Neural Network (MLP) was used to classify hand configurations and
Dynamic Time Warping (DWT) recognizes hand motion. Beyond
of the method of sign recognition, we provide a dataset of
hand configurations and motion capture built with help of fluent
professionals in sign languages. Despite this technology can be
used to translate any sign from any signs dictionary, Brazilian
Sign Language (Libras) was used as case study. Finally, the model
presented in this paper achieved a recognition rate of 80.4%.
Abstract: To explore how the brain may recognise objects in its
general,accurate and energy-efficient manner, this paper proposes the
use of a neuromorphic hardware system formed from a Dynamic
Video Sensor (DVS) silicon retina in concert with the SpiNNaker
real-time Spiking Neural Network (SNN) simulator. As a first step
in the exploration on this platform a recognition system for dynamic
hand postures is developed, enabling the study of the methods used
in the visual pathways of the brain. Inspired by the behaviours of
the primary visual cortex, Convolutional Neural Networks (CNNs)
are modelled using both linear perceptrons and spiking Leaky
Integrate-and-Fire (LIF) neurons.
In this study’s largest configuration using these approaches, a
network of 74,210 neurons and 15,216,512 synapses is created and
operated in real-time using 290 SpiNNaker processor cores in parallel
and with 93.0% accuracy. A smaller network using only 1/10th of the
resources is also created, again operating in real-time, and it is able
to recognise the postures with an accuracy of around 86.4% - only
6.6% lower than the much larger system. The recognition rate of the
smaller network developed on this neuromorphic system is sufficient
for a successful hand posture recognition system, and demonstrates
a much improved cost to performance trade-off in its approach.
Abstract: Methods for measuring or estimating ground shape by a laser range finder and a vision sensor (Exteroceptive sensors) have critical weaknesses in terms that these methods need a prior database built to distinguish acquired data as unique surface conditions for driving. Also, ground information by Exteroceptive sensors does not reflect the deflection of ground surface caused by the movement of UGVs. Therefore, this paper proposes a method of recognizing exact and precise ground shape using an Inertial Measurement Unit (IMU) as a proprioceptive sensor. In this paper, firstly this method recognizes the attitude of a robot in real-time using IMU and compensates attitude data of a robot with angle errors through analysis of vehicle dynamics. This method is verified by outdoor driving experiments of a real mobile robot.
Abstract: Information on weed distribution within the field is
necessary to implement spatially variable herbicide application.
Since hand labor is costly, an automated weed control system could be
feasible. This paper deals with the development of an algorithm for
real time specific weed recognition system based on Histogram
Analysis of an image that is used for the weed classification. This
algorithm is specifically developed to classify images into broad and
narrow class for real-time selective herbicide application. The
developed system has been tested on weeds in the lab, which have
shown that the system to be very effectiveness in weed identification.
Further the results show a very reliable performance on images of
weeds taken under varying field conditions. The analysis of the results
shows over 95 percent classification accuracy over 140 sample images
(broad and narrow) with 70 samples from each category of weeds.
Abstract: Information on weed distribution within the field is necessary to implement spatially variable herbicide application. Since hand labor is costly, an automated weed control system could be feasible. This paper deals with the development of an algorithm for real time specific weed recognition system based on Histogram Maxima with threshold of an image that is used for the weed classification. This algorithm is specifically developed to classify images into broad and narrow class for real-time selective herbicide application. The developed system has been tested on weeds in the lab, which have shown that the system to be very effectiveness in weed identification. Further the results show a very reliable performance on images of weeds taken under varying field conditions. The analysis of the results shows over 95 percent classification accuracy over 140 sample images (broad and narrow) with 70 samples from each category of weeds.