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Xavier Soria; Angel Sappa; Patricio Humanante; Arash Akbarinia |
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Title |
Dense extreme inception network for edge detection |
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Journal Article |
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2023 |
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Pattern Recognition |
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PR |
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139 |
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109461 |
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Edge detection is the basis of many computer vision applications. State of the art predominantly relies on deep learning with two decisive factors: dataset content and network architecture. Most of the publicly available datasets are not curated for edge detection tasks. Here, we address this limitation. First, we argue that edges, contours and boundaries, despite their overlaps, are three distinct visual features requiring separate benchmark datasets. To this end, we present a new dataset of edges. Second, we propose a novel architecture, termed Dense Extreme Inception Network for Edge Detection (DexiNed), that can be trained from scratch without any pre-trained weights. DexiNed outperforms other algorithms in the presented dataset. It also generalizes well to other datasets without any fine-tuning. The higher quality of DexiNed is also perceptually evident thanks to the sharper and finer edges it outputs. |
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MSIAU |
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Admin @ si @ SSH2023 |
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3982 |
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Author |
Iban Berganzo-Besga; Hector A. Orengo; Felipe Lumbreras; Paloma Aliende; Monica N. Ramsey |
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Title |
Automated detection and classification of multi-cell Phytoliths using Deep Learning-Based Algorithms |
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Journal Article |
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Year |
2022 |
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Journal of Archaeological Science |
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JArchSci |
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148 |
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105654 |
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This paper presents an algorithm for automated detection and classification of multi-cell phytoliths, one of the major components of many archaeological and paleoenvironmental deposits. This identification, based on phytolith wave pattern, is made using a pretrained VGG19 deep learning model. This approach has been tested in three key phytolith genera for the study of agricultural origins in Near East archaeology: Avena, Hordeum and Triticum. Also, this classification has been validated at species-level using Triticum boeoticum and dicoccoides images. Due to the diversity of microscopes, cameras and chemical treatments that can influence images of phytolith slides, three types of data augmentation techniques have been implemented: rotation of the images at 45-degree angles, random colour and brightness jittering, and random blur/sharpen. The implemented workflow has resulted in an overall accuracy of 93.68% for phytolith genera, improving previous attempts. The algorithm has also demonstrated its potential to automatize the classification of phytoliths species with an overall accuracy of 100%. The open code and platforms employed to develop the algorithm assure the method's accessibility, reproducibility and reusability. |
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December 2022 |
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MSIAU; MACO; 600.167 |
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Admin @ si @ BOL2022 |
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3753 |
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Author |
Henry Velesaca; Patricia Suarez; Raul Mira; Angel Sappa |
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Title |
Computer Vision based Food Grain Classification: a Comprehensive Survey |
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Journal Article |
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2021 |
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Computers and Electronics in Agriculture |
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CEA |
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187 |
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106287 |
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This manuscript presents a comprehensive survey on recent computer vision based food grain classification techniques. It includes state-of-the-art approaches intended for different grain varieties. The approaches proposed in the literature are analyzed according to the processing stages considered in the classification pipeline, making it easier to identify common techniques and comparisons. Additionally, the type of images considered by each approach (i.e., images from the: visible, infrared, multispectral, hyperspectral bands) together with the strategy used to generate ground truth data (i.e., real and synthetic images) are reviewed. Finally, conclusions highlighting future needs and challenges are presented. |
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MSIAU; 600.130; 600.122 |
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Admin @ si @ VSM2021 |
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3576 |
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Author |
Patricia Suarez; Dario Carpio; Angel Sappa |
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Title |
Enhancement of guided thermal image super-resolution approaches |
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Journal Article |
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Year |
2024 |
Publication |
Neurocomputing |
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NEUCOM |
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573 |
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127197 |
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1-17 |
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Guided image processing techniques are widely used to extract meaningful information from a guiding image and facilitate the enhancement of the guided one. This paper specifically addresses the challenge of guided thermal image super-resolution, where a low-resolution thermal image is enhanced using a high-resolution visible spectrum image. We propose a new strategy that enhances outcomes from current guided super-resolution methods. This is achieved by transforming the initial guiding data into a representation resembling a thermal-like image, which is more closely in sync with the intended output. Experimental results with upscale factors of 8 and 16, demonstrate the outstanding performance of our approach in guided thermal image super-resolution obtained by mapping the original guiding information to a thermal-like image representation. |
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MSIAU |
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Admin @ si @ SCS2024 |
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3998 |
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