Publications

@article{chaves_automatic_2018,

title = {Automatic characterisation of chars from the combustion of pulverised coals using machine vision},

author = {Deisy Chaves and Laura Fernández-Robles and José Bernal and Enrique Alegre and María Trujillo},

url = {https://www.sciencedirect.com/science/article/pii/S0032591018304753},

year  = {2018},

date = {2018-01-01},

journal = {Powder technology},

volume = {338},

pages = {110–118},

abstract = {This paper presents a method for automatically characterizing char particles produced during pulverized coal combustion using machine vision. The study combines two approaches: morphological analysis and intensity distribution through texture features. By using bit-plane slicing, the method captures both fine and rough details of the char particles. The particles are then classified based on their reactivity (high, medium, or low). Tested on char images from Colombian coal regions, the method achieves precision comparable to manual analysis, offering a more efficient, automated solution for evaluating coal reactivity.},

note = {Publisher: Elsevier},

keywords = {char morphology, coal combustion, machine vision, reactivity classification},

pubstate = {published},

tppubtype = {article}

}

@article{fernandez-robles_machine-vision-based_2017,

title = {Machine-vision-based identification of broken inserts in edge profile milling heads},

author = {Laura Fernández-Robles and George Azzopardi and Enrique Alegre and Nicolai Petkov},

url = {https://www.sciencedirect.com/science/article/pii/S0736584515300806},

year  = {2017},

date = {2017-01-01},

journal = {Robotics and Computer-Integrated Manufacturing},

volume = {44},

pages = {276–283},

abstract = {This paper presents a machine vision system designed to detect and identify broken inserts in edge milling head tools used for aggressive machining of thick plates. The system localizes inserts by detecting the screws, calculates the expected position and orientation of the cutting edge using geometrical operations, and compares it with the real edge to detect any deviations. The proposed method was evaluated on a new publicly available dataset, achieving a harmonic mean of precision and recall of 91.43%. The results indicate that the system is effective and ready for real-time implementation in monitoring machining tools.},

note = {Publisher: Pergamon},

keywords = {Automatic Identification, Edge Milling, machine vision, Tool Breakage},

pubstate = {published},

tppubtype = {article}

}

@article{fernandez-robles_recognition_2017,

title = {Recognition and retrieval of objects in diverse applications},

author = {Laura Fernández-Robles},

url = {https://www.raco.cat/index.php/ELCVIA/article/view/v16-n2-fernandez},

year  = {2017},

date = {2017-01-01},

journal = {ELCVIA: electronic letters on computer vision and image analysis},

volume = {16},

number = {2},

pages = {21–24},

abstract = {This work focuses on object description and retrieval techniques applied to various real-world problems. It explores the classification of boar spermatozoa based on acrosome integrity using methods based on invariant local features. The paper also presents solutions for insert localization and recognition of broken inserts in milling heads, offering an automatic, in-process method for detection without interrupting machining operations. Additionally, it introduces a new descriptor, colour COSFIRE, for object retrieval in the context of the European project aimed at combating sexual exploitation of children.},

keywords = {COSFIRE Descriptor, Invariant Features, machine vision, Object Retrieval, Spermatozoa Classification},

pubstate = {published},

tppubtype = {article}

}

@article{alegre_tool_2005,

title = {Tool insert wear classification using statistical descriptors and neuronal networks},

author = {Enrique Alegre and Rocío Aláiz-Rodríguez and Joaquín Barreiro and M Viñuela},

url = {https://link.springer.com/chapter/10.1007/11578079_82},

year  = {2005},

date = {2005-01-01},

journal = {Progress in Pattern Recognition, Image Analysis and Applications: 10th Iberoamerican Congress on Pattern Recognition, CIARP 2005, Havana, Cuba, November 15-18, 2005. Proceedings 10},

pages = {786–793},

abstract = {This work proposes an automated method to determine tool insert wear levels using image analysis. Images of tungsten carbide inserts were acquired during machining of AISI SAE 1045 and 4140 steel bars. After pre-processing and wear area segmentation, statistical moment-based descriptors were extracted. Two classification experiments (binary and three-class) were conducted using Lp2, k-NN, and neural networks. Zernike and Legendre descriptors achieved the best results with a multilayer perceptron (MLP) neural network.},

note = {Publisher: Springer Berlin Heidelberg},

keywords = {image processing, machine vision, Manufacturing, neural networks, Tool wear},

pubstate = {published},

tppubtype = {article}

}