A Segmentation System for Soccer Robot Based on Neural Networks

Amoroso, C.; Chella, Antonio; Morreale, Vito; Storniolo, Pietro

doi:10.1007/3-540-45327-x_10

Cited by 12 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[5] used a simple EA to evolve a robust colour classifier for robot soccer by chrominance space transformation. [1] used a neural net-work to determine the thresholds for object classes in a hue histogram. [3] utilized decision trees to perform the same task of thresholding for classification.…”

Section: Object Detection In Robot Soccermentioning

confidence: 99%

Robust method of detecting moving objects in videos evolved by genetic programming

Song

Fang

2008

Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation

View full text Add to dashboard Cite

In this paper we investigated the use of Genetic Programming (GP) to evolve programs which could detect moving objects in videos. Two main approaches under the paradigm were proposed and investigated, single-frame approach and multi-frame approach. The former is based on analyzing individual video frames and treat them independently while the latter approach consider a sequence of frames. In the single-frame approach, three methods are investigated including using pixel intensity, pixel hue value and feature values. The experiments on Robosoccer field show that GP could detect the target under different lighting conditions and could even handle arbitrary camera positions. Although there was no domain knowledge had been provided during evolution, GP was able to produce moving object detectors that were robust and fast.

show abstract

Section: Object Detection In Robot Soccermentioning

confidence: 99%

Robust method of detecting moving objects in videos evolved by genetic programming

Song

Fang

2008

Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation

View full text Add to dashboard Cite

show abstract

“…An ANN was chosen because: it can approximate any function [4]; it works with noisy data; minimum knowledge about the problem is required [5], and also a minimum knowledge of the input statistical distribution [6] is sufficient. As a proof of concept, this work classifies only three entities, namely: soccer field, ball and "other elements".…”

Section: Introductionmentioning

confidence: 99%

Neural Network in Computer Vision for RoboCup Middle Size League

Ribeiro¹,

Lopes²,

Ribeiro³

2016

JSEA

View full text Add to dashboard Cite

Robot World Cup Initiative (RoboCup) is a worldwide competition proposed to advance research in robotics and artificial intelligence. It has a league called RoboCup soccer devoted for soccer robots, which is a challenge because robots are mobile, fully autonomous, multi-agents, and they play on a dynamic environment. Moreover, robots must recognize the game entities, which is a crucial task during a game. A camera is usually used as an input system to recognize ball, opponents, soccer field, and so on. These elements may be recognized applying some tools of computational intelligence, for example an artificial neural network. This paper describes the application of an artificial neural network on middle size robotic football league, where a multilayer perceptron neural network is trained with the backpropagation algorithm, to classify elements on the image. Each output neuron represents an entity and its output value depends on the current entity that is present on the image. The results show that an artificial neural network successfully classified the entities. They were recognized even when similar color entities were present on the image.

show abstract

“…Color image segmentation techniques are classified into five types; among them, edge detection [4,5], region growing, neural network-based [6][7][8][9][10] and fuzzy based [11] methods are either based on neural network classification or have complicated algorithms and therefore are not suitable for real time applications. On the other hand, histogram thresholding methods, due to their speed and robustness on detecting multi-color objects, are more widely used in real time situations [12,13].…”

Section: Introductionmentioning

confidence: 99%

Real time object detection using a novel adaptive color thresholding method

Bagheri¹,

Madani

Sahba³

et al. 2011

Proceedings of the 2011 International ACM Workshop on Ubiquitous Meta User Interfaces

View full text Add to dashboard Cite

Object detection is an important area of research in computer vision. One of the challenges in this domain is to detect objects in real time using the minimum resources possible. In this paper, we describe a robust method for real time object detection that can be used on low-profile hardware and needs little training. This approach is based on a discrete adaptive color thresholding method. By applying a redistribution algorithm based on color specifications on the training data, the system would be able to detect colors that may appear with small changes in lighting conditions in the scene. The detection algorithm uses a spatial voting method to improve the accuracy of the result. These characteristics make this method a robust tool in ubiquitous computing and also help intelligent environments to act/react more properly by increasing their awareness of the environment.

show abstract

A Segmentation System for Soccer Robot Based on Neural Networks

Cited by 12 publications

References 10 publications

Robust method of detecting moving objects in videos evolved by genetic programming

Robust method of detecting moving objects in videos evolved by genetic programming

Neural Network in Computer Vision for RoboCup Middle Size League

Real time object detection using a novel adaptive color thresholding method

Contact Info

Product

Resources

About