A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Fundamentals of digital image processing
Fundamentals of digital image processing
Performance study of several global thresholding techniques for segmentation
Computer Vision, Graphics, and Image Processing
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Digital image processing: principles and applications
Digital image processing: principles and applications
The image processing handbook (2nd ed.)
The image processing handbook (2nd ed.)
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Real-time detection of steam in video images
Pattern Recognition
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Detection of big rocks is an important, even critical, problem in the mining industry due to the risk of machine blockage causing high costs. This paper presents a computer-vision-based method to detect big rocks in a real mining industry. Our system, based on a mixture of image processing techniques and neural networks, works as follows: once the image is taken, a pre-processing step is performed, filtering the image and extracting a det of candidate rocks. Then a neural networks processes the candidate rocks to ensure correct detection. A tracking algorithm is then aplied to avoid false detection due to rock grouping. Using geometrical information, it is possible to estimate the real dimensions of the rocks. Our computer vision system satisfies time constrains imposed by the imdustry to work in real time and is currently operating. The algorithm presented is independent of the rocks shape. Results obtained during nine months of unsupervised work are provided, showing that our system is able to work under different light conditions and is robust enough to face real work conditions.