2016
1.
Azzopardi, George; Robles, Laura Fernandez; Alegre, Enrique; Petkov, Nicolai
Increased Generalization Capability of Trainable COSFIRE Filters with Application to Machine Vision Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, machine vision, trainable filters
@inproceedings{Azzopardi2016,
title = {Increased Generalization Capability of Trainable COSFIRE Filters with Application to Machine Vision},
author = {George Azzopardi and Laura Fernandez Robles and Enrique Alegre and Nicolai Petkov},
doi = {10.1109/ICPR.2016.7900152},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
booktitle = {23rd International Conference on Pattern Recognition (ICPR)},
publisher = {IEEE},
abstract = {The recently proposed trainable COSFIRE filters are highly effective in a wide range of computer vision applications, including object recognition, image classification, contour detection and retinal vessel segmentation. A COSFIRE filter is selective for a collection of contour parts in a certain spatial arrangement. These contour parts and their spatial arrangement are determined in an automatic configuration procedure from a single user-specified pattern of interest. The traditional configuration, however, does not guarantee the selection of the most distinctive contour parts. We propose a genetic algorithm-based optimization step in the configuration of COSFIRE filters that determines the minimum subset of contour parts that best characterize the pattern of interest. We use a public dataset of images of an edge milling head machine equipped with multiple cutting tools to demonstrate the effectiveness of the proposed optimization step for the detection and localization of such tools. The optimization process that we propose yields COSFIRE filters with substantially higher generalization capability. With an average of only six COSFIRE filters we achieve high precision P and recall R rates (P = 91.99%; R = 96.22%). This outperforms the original COSFIRE filter approach (without optimization) mostly in terms of recall. The proposed optimization procedure increases the efficiency of COSFIRE filters with little effect on the selectivity.},
keywords = {brain-inspired, machine vision, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
The recently proposed trainable COSFIRE filters are highly effective in a wide range of computer vision applications, including object recognition, image classification, contour detection and retinal vessel segmentation. A COSFIRE filter is selective for a collection of contour parts in a certain spatial arrangement. These contour parts and their spatial arrangement are determined in an automatic configuration procedure from a single user-specified pattern of interest. The traditional configuration, however, does not guarantee the selection of the most distinctive contour parts. We propose a genetic algorithm-based optimization step in the configuration of COSFIRE filters that determines the minimum subset of contour parts that best characterize the pattern of interest. We use a public dataset of images of an edge milling head machine equipped with multiple cutting tools to demonstrate the effectiveness of the proposed optimization step for the detection and localization of such tools. The optimization process that we propose yields COSFIRE filters with substantially higher generalization capability. With an average of only six COSFIRE filters we achieve high precision P and recall R rates (P = 91.99%; R = 96.22%). This outperforms the original COSFIRE filter approach (without optimization) mostly in terms of recall. The proposed optimization procedure increases the efficiency of COSFIRE filters with little effect on the selectivity.
2015
2.
Fernández-Robles, Laura; Azzopardi, George; Alegre, Enrique; Petkov, Nicolai
Cutting Edge Localisation in an Edge Profile Milling Head Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: machine vision, trainable filters
@inproceedings{Fern\'{a}ndez-Robles2015,
title = {Cutting Edge Localisation in an Edge Profile Milling Head},
author = {Laura Fern\'{a}ndez-Robles and George Azzopardi and Enrique Alegre and Nicolai Petkov},
doi = {10.1007/978-3-319-23117-4_29},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {Proceedings Part II of CAIP2015, LNCS 9257
},
pages = {336-347},
publisher = {Springer},
abstract = {Wear evaluation of cutting tools is a key issue for prolonging their lifetime and ensuring high quality of products. In this paper, we present a method for the effective localisation of cutting edges of inserts in digital images of an edge profile milling head. We introduce a new image data set of 144 images of an edge milling head that contains 30 inserts. We use a circular Hough transform to detect the screws that fasten the inserts. In a cropped area around a detected screw, we use Canny’s edge detection algorithm and Standard Hough Transform to localise line segments that characterise insert edges. We use this information and the geometry of the insert to identify which of these line segments is the cutting edge. The output of our algorithm is a set of quadrilateral regions around the identified cutting edges. These regions can then be used as input to other algorithms for the quality assessment of the cutting edges. Our results show that the proposed method is very effective for the localisation of the cutting edges of inserts in an edge profile milling machine.},
keywords = {machine vision, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
Wear evaluation of cutting tools is a key issue for prolonging their lifetime and ensuring high quality of products. In this paper, we present a method for the effective localisation of cutting edges of inserts in digital images of an edge profile milling head. We introduce a new image data set of 144 images of an edge milling head that contains 30 inserts. We use a circular Hough transform to detect the screws that fasten the inserts. In a cropped area around a detected screw, we use Canny’s edge detection algorithm and Standard Hough Transform to localise line segments that characterise insert edges. We use this information and the geometry of the insert to identify which of these line segments is the cutting edge. The output of our algorithm is a set of quadrilateral regions around the identified cutting edges. These regions can then be used as input to other algorithms for the quality assessment of the cutting edges. Our results show that the proposed method is very effective for the localisation of the cutting edges of inserts in an edge profile milling machine.