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	Author	Luca Ginanni Corradini; Simone Balocco; Luciano Maresca; Silvio Vitale; Matteo Stefanini
	Title	Anatomical Modifications After Stent Implantation: A Comparative Analysis Between CGuard, Wallstent, and Roadsaver Carotid Stents			Type	Journal Article
	Year	2023	Publication	Journal of Endovascular Therapy	Abbreviated Journal
	Volume	30	Issue	1	Pages	18-24
	Keywords	Ginanni Corradini L, Balocco S, Maresca L, Vitale S, Stefanini M.
	Abstract	Abstract Purpose: Carotid revascularization can be associated with modifications of the vascular geometry, which may lead to complications. The changes on the vessel angulation before and after a carotid WallStent (WS) implantation are compared against 2 new dual-layer devices, CGuard (CG) and RoadSaver (RS). Materials and Methods: The study prospectively recruited 217 consecutive patients (112 GC, 73 WS, and 32 RS, respectively). Angiography projections were explored and the one having a higher arterial angle was selected as a basal view. After stent implantation, a stent control angiography was performed selecting the projection having the maximal angle. The same procedure is followed in all the 3 stent types to guarantee comparable conditions. The angulation changes on the stented segments were quantified from both angiographies. The statistical analysis quantitatively compared the pre-and post-angles for the 3 stent types. The results are qualitatively illustrated using boxplots. Finally, the relation between pre- and post-angles measurements is analyzed using linear regression. Results: For CG, no statistical difference in the axial vessel geometry between the basal and postprocedural angles was found. For WS and RS, statistical difference was found between pre- and post-angles. The regression analysis shows that CG induces lower changes from the original curvature with respect to WS and RS. Conclusion: Based on our results, CG determines minor changes over the basal morphology than WS and RS stents. Hence, CG respects better the native vessel anatomy than the other stents. Level of Evidence: Level 4, Case Series.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	xxx			Approved	no
	Call Number	Admin @ si @ GBM2023			Serial	4006
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	Author	Daniel Marczak; Sebastian Cygert; Tomasz Trzcinski; Bartlomiej Twardowski
	Title	Revisiting Supervision for Continual Representation Learning			Type	Miscellaneous
	Year	2023	Publication	Arxiv	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	In the field of continual learning, models are designed to learn tasks one after the other. While most research has centered on supervised continual learning, recent studies have highlighted the strengths of self-supervised continual representation learning. The improved transferability of representations built with self-supervised methods is often associated with the role played by the multi-layer perceptron projector. In this work, we depart from this observation and reexamine the role of supervision in continual representation learning. We reckon that additional information, such as human annotations, should not deteriorate the quality of representations. Our findings show that supervised models when enhanced with a multi-layer perceptron head, can outperform self-supervised models in continual representation learning.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	xxx			Approved	no
	Call Number	Admin @ si @ MCT2023			Serial	4013
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	Author	Olivier Penacchio; Xavier Otazu; Arnold J Wilkings; Sara M. Haigh
	Title	A mechanistic account of visual discomfort			Type	Journal Article
	Year	2023	Publication	Frontiers in Neuroscience	Abbreviated Journal	FN
	Volume	17	Issue		Pages
	Keywords
	Abstract	Much of the neural machinery of the early visual cortex, from the extraction of local orientations to contextual modulations through lateral interactions, is thought to have developed to provide a sparse encoding of contour in natural scenes, allowing the brain to process efficiently most of the visual scenes we are exposed to. Certain visual stimuli, however, cause visual stress, a set of adverse effects ranging from simple discomfort to migraine attacks, and epileptic seizures in the extreme, all phenomena linked with an excessive metabolic demand. The theory of efficient coding suggests a link between excessive metabolic demand and images that deviate from natural statistics. Yet, the mechanisms linking energy demand and image spatial content in discomfort remain elusive. Here, we used theories of visual coding that link image spatial structure and brain activation to characterize the response to images observers reported as uncomfortable in a biologically based neurodynamic model of the early visual cortex that included excitatory and inhibitory layers to implement contextual influences. We found three clear markers of aversive images: a larger overall activation in the model, a less sparse response, and a more unbalanced distribution of activity across spatial orientations. When the ratio of excitation over inhibition was increased in the model, a phenomenon hypothesised to underlie interindividual differences in susceptibility to visual discomfort, the three markers of discomfort progressively shifted toward values typical of the response to uncomfortable stimuli. Overall, these findings propose a unifying mechanistic explanation for why there are differences between images and between observers, suggesting how visual input and idiosyncratic hyperexcitability give rise to abnormal brain responses that result in visual stress.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	NEUROBIT			Approved	no
	Call Number	Admin @ si @ POW2023			Serial	3886
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	Author	Patricia Suarez; Dario Carpio; Angel Sappa
	Title	A Deep Learning Based Approach for Synthesizing Realistic Depth Maps			Type	Conference Article
	Year	2023	Publication	22nd International Conference on Image Analysis and Processing	Abbreviated Journal
	Volume	14234	Issue		Pages	369–380
	Keywords
	Abstract	This paper presents a novel cycle generative adversarial network (CycleGAN) architecture for synthesizing high-quality depth maps from a given monocular image. The proposed architecture uses multiple loss functions, including cycle consistency, contrastive, identity, and least square losses, to enable the generation of realistic and high-fidelity depth maps. The proposed approach addresses this challenge by synthesizing depth maps from RGB images without requiring paired training data. Comparisons with several state-of-the-art approaches are provided showing the proposed approach overcome other approaches both in terms of quantitative metrics and visual quality.
	Address	Udine; Italia; Setember 2023
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICIAP
	Notes	MSIAU			Approved	no
	Call Number	Admin @ si @ SCS2023a			Serial	3968
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	Author	Armin Mehri; Parichehr Behjati; Angel Sappa
	Title	TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution			Type	Journal Article
	Year	2023	Publication	IEEE Access	Abbreviated Journal	ACCESS
	Volume	11	Issue		Pages	11529-11540
	Keywords
	Abstract	Image Super Resolution is a potential approach that can improve the image quality of low-resolution optical sensors, leading to improved performance in various industrial applications. It is important to emphasize that most state-of-the-art super resolution algorithms often use a single channel of input data for training and inference. However, this practice ignores the fact that the cost of acquiring high-resolution images in various spectral domains can differ a lot from one another. In this paper, we attempt to exploit complementary information from a low-cost channel (visible image) to increase the image quality of an expensive channel (infrared image). We propose a dual stream Transformer-based super resolution approach that uses the visible image as a guide to super-resolve another spectral band image. To this end, we introduce Transformer in Transformer network for Guidance super resolution, named TnTViT-G, an efficient and effective method that extracts the features of input images via different streams and fuses them together at various stages. In addition, unlike other guidance super resolution approaches, TnTViT-G is not limited to a fixed upsample size and it can generate super-resolved images of any size. Extensive experiments on various datasets show that the proposed model outperforms other state-of-the-art super resolution approaches. TnTViT-G surpasses state-of-the-art methods by up to 0.19∼2.3dB , while it is memory efficient.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MSIAU			Approved	no
	Call Number	Admin @ si @ MBS2023			Serial	3876
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	Author	Armin Mehri; Parichehr Behjati; Dario Carpio; Angel Sappa
	Title	SRFormer: Efficient Yet Powerful Transformer Network for Single Image Super Resolution			Type	Journal Article
	Year	2023	Publication	IEEE Access	Abbreviated Journal	ACCESS
	Volume	11	Issue		Pages
	Keywords
	Abstract	Recent breakthroughs in single image super resolution have investigated the potential of deep Convolutional Neural Networks (CNNs) to improve performance. However, CNNs based models suffer from their limited fields and their inability to adapt to the input content. Recently, Transformer based models were presented, which demonstrated major performance gains in Natural Language Processing and Vision tasks while mitigating the drawbacks of CNNs. Nevertheless, Transformer computational complexity can increase quadratically for high-resolution images, and the fact that it ignores the original structures of the image by converting them to the 1D structure can make it problematic to capture the local context information and adapt it for real-time applications. In this paper, we present, SRFormer, an efficient yet powerful Transformer-based architecture, by making several key designs in the building of Transformer blocks and Transformer layers that allow us to consider the original structure of the image (i.e., 2D structure) while capturing both local and global dependencies without raising computational demands or memory consumption. We also present a Gated Multi-Layer Perceptron (MLP) Feature Fusion module to aggregate the features of different stages of Transformer blocks by focusing on inter-spatial relationships while adding minor computational costs to the network. We have conducted extensive experiments on several super-resolution benchmark datasets to evaluate our approach. SRFormer demonstrates superior performance compared to state-of-the-art methods from both Transformer and Convolutional networks, with an improvement margin of 0.1∼0.53dB . Furthermore, while SRFormer has almost the same model size, it outperforms SwinIR by 0.47% and inference time by half the time of SwinIR. The code will be available on GitHub.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MSIAU			Approved	no
	Call Number	Admin @ si @ MBC2023			Serial	3887
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	Author	Patricia Suarez; Henry Velesaca; Dario Carpio; Angel Sappa
	Title	Corn kernel classification from few training samples			Type	Journal
	Year	2023	Publication	Artificial Intelligence in Agriculture	Abbreviated Journal
	Volume	9	Issue		Pages	89-99
	Keywords
	Abstract	This article presents an efficient approach to classify a set of corn kernels in contact, which may contain good, or defective kernels along with impurities. The proposed approach consists of two stages, the first one is a next-generation segmentation network, trained by using a set of synthesized images that is applied to divide the given image into a set of individual instances. An ad-hoc lightweight CNN architecture is then proposed to classify each instance into one of three categories (ie good, defective, and impurities). The segmentation network is trained using a strategy that avoids the time-consuming and human-error-prone task of manual data annotation. Regarding the classification stage, the proposed ad-hoc network is designed with only a few sets of layers to result in a lightweight architecture capable of being used in integrated solutions. Experimental results and comparisons with previous approaches showing both the improvement in accuracy and the reduction in time are provided. Finally, the segmentation and classification approach proposed can be easily adapted for use with other cereal types.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MSIAU			Approved	no
	Call Number	Admin @ si @ SVC2023			Serial	3892
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	Author	Spencer Low; Oliver Nina; Angel Sappa; Erik Blasch; Nathan Inkawhich
	Title	Multi-Modal Aerial View Image Challenge: Translation From Synthetic Aperture Radar to Electro-Optical Domain Results-PBVS 2023			Type	Conference Article
	Year	2023	Publication	Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops	Abbreviated Journal
	Volume		Issue		Pages	515-523
	Keywords
	Abstract	This paper unveils the discoveries and outcomes of the inaugural iteration of the Multi-modal Aerial View Image Challenge (MAVIC) aimed at image translation. The primary objective of this competition is to stimulate research efforts towards the development of models capable of translating co-aligned images between multiple modalities. To accomplish the task of image translation, the competition utilizes images obtained from both synthetic aperture radar (SAR) and electro-optical (EO) sources. Specifically, the challenge centers on the translation from the SAR modality to the EO modality, an area of research that has garnered attention. The inaugural challenge demonstrates the feasibility of the task. The dataset utilized in this challenge is derived from the UNIfied COincident Optical and Radar for recognitioN (UNICORN) dataset. We introduce an new version of the UNICORN dataset that is focused on enabling the sensor translation task. Performance evaluation is conducted using a combination of measures to ensure high fidelity and high accuracy translations.
	Address	Vancouver; Canada; June 2023
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CVPRW
	Notes	MSIAU			Approved	no
	Call Number	Admin @ si @ LNS2023a			Serial	3913
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	Author	Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Chenyang Wang; Junjun Jiang; Xianming Liu; Zhiwei Zhong; Dai Bin; Li Ruodi; Li Shengye
	Title	Thermal Image Super-Resolution Challenge Results-PBVS 2023			Type	Conference Article
	Year	2023	Publication	Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops	Abbreviated Journal
	Volume		Issue		Pages	470-478
	Keywords
	Abstract	This paper presents the results of two tracks from the fourth Thermal Image Super-Resolution (TISR) challenge, held at the Perception Beyond the Visible Spectrum (PBVS) 2023 workshop. Track-1 uses the same thermal image dataset as previous challenges, with 951 training images and 50 validation images at each resolution. In this track, two evaluations were conducted: the first consists of generating a SR image from a HR thermal noisy image downsampled by four, and the second consists of generating a SR image from a mid-resolution image and compare it with its semi-registered HR image (acquired with another camera). The results of Track-1 outperformed those from last year’s challenge. On the other hand, Track-2 uses a new acquired dataset consisting of 160 registered visible and thermal images of the same scenario for training and 30 validation images. This year, more than 150 teams participated in the challenge tracks, demonstrating the community’s ongoing interest in this topic.
	Address	Vancouver; Canada; June 2023
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CVPRW
	Notes	MSIAU			Approved	no
	Call Number	Admin @ si @ RSV2023			Serial	3914
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	Author	Spencer Low; Oliver Nina; Angel Sappa; Erik Blasch; Nathan Inkawhich
	Title	Multi-Modal Aerial View Object Classification Challenge Results-PBVS 2023			Type	Conference Article
	Year	2023	Publication	Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops	Abbreviated Journal
	Volume		Issue		Pages	412-421
	Keywords
	Abstract	This paper presents the findings and results of the third edition of the Multi-modal Aerial View Object Classification (MAVOC) challenge in a detailed and comprehensive manner. The challenge consists of two tracks. The primary aim of both tracks is to encourage research into building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) imagery. Participating teams are encouraged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge demonstrated the feasibility of combining both modalities, the 2022 challenge expanded on the capability of multi-modal models. The 2023 challenge introduces a refined version of the UNICORN dataset and demonstrates significant improvements made. The 2023 challenge adopts an updated UNIfied CO-incident Optical and Radar for recognitioN (UNICORN V2) dataset and competition format. Two tasks are featured: SAR classification and SAR + EO classification. In addition to measuring accuracy of models, we also introduce out-of-distribution measures to encourage model robustness.The majority of this paper is dedicated to discussing the top performing methods and evaluating their performance on our blind test set. It is worth noting that all of the top ten teams outperformed the Resnet-50 baseline. The top team for SAR classification achieved a 173% performance improvement over the baseline, while the top team for SAR + EO classification achieved a 175% improvement.
	Address	Vancouver; Canada; June 2023
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CVPRW
	Notes	MSIAU			Approved	no
	Call Number	Admin @ si @ LNS2023b			Serial	3915
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	Author	Patricia Suarez; Angel Sappa
	Title	Toward a Thermal Image-Like Representation			Type	Conference Article
	Year	2023	Publication	Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications	Abbreviated Journal
	Volume		Issue		Pages	133-140
	Keywords
	Abstract	This paper proposes a novel model to obtain thermal image-like representations to be used as an input in any thermal image compressive sensing approach (e.g., thermal image: filtering, enhancing, super-resolution). Thermal images offer interesting information about the objects in the scene, in addition to their temperature. Unfortunately, in most of the cases thermal cameras acquire low resolution/quality images. Hence, in order to improve these images, there are several state-of-the-art approaches that exploit complementary information from a low-cost channel (visible image) to increase the image quality of an expensive channel (infrared image). In these SOTA approaches visible images are fused at different levels without paying attention the images acquire information at different bands of the spectral. In this paper a novel approach is proposed to generate thermal image-like representations from a low cost visible images, by means of a contrastive cycled GAN network. Obtained representations (synthetic thermal image) can be later on used to improve the low quality thermal image of the same scene. Experimental results on different datasets are presented.
	Address	Lisboa; Portugal; February 2023
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	VISIGRAPP
	Notes	MSIAU			Approved	no
	Call Number	Admin @ si @ SuS2023b			Serial	3927
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	Author	Gisel Bastidas-Guacho; Patricio Moreno; Boris X. Vintimilla; Angel Sappa
	Title	Application on the Loop of Multimodal Image Fusion: Trends on Deep-Learning Based Approaches			Type	Conference Article
	Year	2023	Publication	13th International Conference on Pattern Recognition Systems	Abbreviated Journal
	Volume	14234	Issue		Pages	25–36
	Keywords
	Abstract	Multimodal image fusion allows the combination of information from different modalities, which is useful for tasks such as object detection, edge detection, and tracking, to name a few. Using the fused representation for applications results in better task performance. There are several image fusion approaches, which have been summarized in surveys. However, the existing surveys focus on image fusion approaches where the application on the loop of multimodal image fusion is not considered. On the contrary, this study summarizes deep learning-based multimodal image fusion for computer vision (e.g., object detection) and image processing applications (e.g., semantic segmentation), that is, approaches where the application module leverages the multimodal fusion process to enhance the final result. Firstly, we introduce image fusion and the existing general frameworks for image fusion tasks such as multifocus, multiexposure and multimodal. Then, we describe the multimodal image fusion approaches. Next, we review the state-of-the-art deep learning multimodal image fusion approaches for vision applications. Finally, we conclude our survey with the trends of task-driven multimodal image fusion.
	Address	Guayaquil; Ecuador; July 2023
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICPRS
	Notes	MSIAU			Approved	no
	Call Number	Admin @ si @ BMV2023			Serial	3932
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	Author	Xavier Soria; Yachuan Li; Mohammad Rouhani; Angel Sappa
	Title	Tiny and Efficient Model for the Edge Detection Generalization			Type	Conference Article
	Year	2023	Publication	Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Most high-level computer vision tasks rely on low-level image operations as their initial processes. Operations such as edge detection, image enhancement, and super-resolution, provide the foundations for higher level image analysis. In this work we address the edge detection considering three main objectives: simplicity, efficiency, and generalization since current state-of-the-art (SOTA) edge detection models are increased in complexity for better accuracy. To achieve this, we present Tiny and Efficient Edge Detector (TEED), a light convolutional neural network with only 58K parameters, less than 0:2% of the state-of-the-art models. Training on the BIPED dataset takes less than 30 minutes, with each epoch requiring less than 5 minutes. Our proposed model is easy to train and it quickly converges within very first few epochs, while the predicted edge-maps are crisp and of high quality. Additionally, we propose a new dataset to test the generalization of edge detection, which comprises samples from popular images used in edge detection and image segmentation. The source code is available in https://github.com/xavysp/TEED.
	Address	Paris; France; October 2023
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICCVW
	Notes	MSIAU			Approved	no
	Call Number	Admin @ si @ SLR2023			Serial	3941
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	Author	Armin Mehri
	Title	Deep learning based architectures for cross-domain image processing			Type	Book Whole
	Year	2023	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Human vision is restricted to the visual-optical spectrum. Machine vision is not. Cameras sensitive to diverse infrared spectral bands can improve the capacities of autonomous systems and provide a comprehensive view. Relevant scene content can be made visible, particularly in situations when sensors of other modalities, such as a visual-optical camera, require a source of illumination. As a result, increasing the level of automation not only avoids human errors but also reduces machine-induced errors. Furthermore, multi-spectral sensor systems with infrared imagery as one modality are a rich source of information and can conceivably increase the robustness of many autonomous systems. Robotics, automobiles, biometrics, security, surveillance, and the military are some examples of fields that can profit from the use of infrared imagery in their respective applications. Although multimodal spectral sensors have come a long way, there are still several bottlenecks that prevent us from combining their output information and using them as comprehensive images. The primary issue with infrared imaging is the lack of potential benefits due to their cost influence on sensor resolution, which grows exponentially with greater resolution. Due to the more costly sensor technology required for their development, their resolutions are substantially lower than thoseof regular digital cameras. This thesis aims to improve beyond-visible-spectrum machine vision by integrating multi-modal spectral sensors. The emphasis is on transforming the produced images to enhance their resolution to match expected human perception, bring the color representation close to human understanding of natural color, and improve machine vision application performance. This research focuses mainly on two tasks, image Colorization and Image Super resolution for both single- and cross-domain problems. We first start with an extensive review of the state of the art in both tasks, point out the shortcomings of existing approaches, and then present our solutions to address their limitations. Our solutions demonstrate that low-cost channel information (i.e., visible image) can be used to improve expensive channel information (i.e., infrared image), resulting in images with higher quality and closer to human perception at a lower cost than a high-cost infrared camera.
	Address
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	IMPRIMA	Place of Publication		Editor	Angel Sappa
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-126409-1-5	Medium
	Area		Expedition		Conference
	Notes	MSIAU			Approved	no
	Call Number	Admin @ si @ Meh2023			Serial	3959
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	Author	Iban Berganzo-Besga; Hector A. Orengo; Felipe Lumbreras; Aftab Alam; Rosie Campbell; Petrus J Gerrits; Jonas Gregorio de Souza; Afifa Khan; Maria Suarez Moreno; Jack Tomaney; Rebecca C Roberts; Cameron A Petrie
	Title	Curriculum learning-based strategy for low-density archaeological mound detection from historical maps in India and Pakistan			Type	Journal Article
	Year	2023	Publication	Scientific Reports	Abbreviated Journal	ScR
	Volume	13	Issue		Pages	11257
	Keywords
	Abstract	This paper presents two algorithms for the large-scale automatic detection and instance segmentation of potential archaeological mounds on historical maps. Historical maps present a unique source of information for the reconstruction of ancient landscapes. The last 100 years have seen unprecedented landscape modifications with the introduction and large-scale implementation of mechanised agriculture, channel-based irrigation schemes, and urban expansion to name but a few. Historical maps offer a window onto disappearing landscapes where many historical and archaeological elements that no longer exist today are depicted. The algorithms focus on the detection and shape extraction of mound features with high probability of being archaeological settlements, mounds being one of the most commonly documented archaeological features to be found in the Survey of India historical map series, although not necessarily recognised as such at the time of surveying. Mound features with high archaeological potential are most commonly depicted through hachures or contour-equivalent form-lines, therefore, an algorithm has been designed to detect each of those features. Our proposed approach addresses two of the most common issues in archaeological automated survey, the low-density of archaeological features to be detected, and the small amount of training data available. It has been applied to all types of maps available of the historic 1″ to 1-mile series, thus increasing the complexity of the detection. Moreover, the inclusion of synthetic data, along with a Curriculum Learning strategy, has allowed the algorithm to better understand what the mound features look like. Likewise, a series of filters based on topographic setting, form, and size have been applied to improve the accuracy of the models. The resulting algorithms have a recall value of 52.61% and a precision of 82.31% for the hachure mounds, and a recall value of 70.80% and a precision of 70.29% for the form-line mounds, which allowed the detection of nearly 6000 mound features over an area of 470,500 km2, the largest such approach to have ever been applied. If we restrict our focus to the maps most similar to those used in the algorithm training, we reach recall values greater than 60% and precision values greater than 90%. This approach has shown the potential to implement an adaptive algorithm that allows, after a small amount of retraining with data detected from a new map, a better general mound feature detection in the same map.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MSIAU			Approved	no
	Call Number	Admin @ si @ BOL2023			Serial	3976
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