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Physics Informed Capsule Enhanced Variational AutoEncoder for Underwater Image Enhancement Open
We present a novel dual-stream architecture that achieves state-of-the-art underwater image enhancement by explicitly integrating the Jaffe-McGlamery physical model with capsule clustering-based feature representation learning. Our method …
Performance of Computer Vision Algorithms for Fine‐Grained Classification Using Crowdsourced Insect Images Open
With fine‐grained classification, we identify unique characteristics to distinguish among classes of the same super‐class. We are focusing on species recognition in Insecta as they are critical for biodiversity monitoring and at the base o…
AI Species Identification Using Image and Sound Recognition for Citizen Science, Collection Management and Biomonitoring: From Training Pipeline to Large-Scale Models Open
Biodiversity data are currently being generated at an unprecedented rate from deployed field monitoring sensors (e.g., wildlife and insect cameras, sound recorders, radars), citizen science observations, digitised museum collections, and b…
CE-VAE: Capsule Enhanced Variational AutoEncoder for Underwater Image Enhancement Open
Unmanned underwater image analysis for marine monitoring faces two key challenges: (i) degraded image quality due to light attenuation and (ii) hardware storage constraints limiting high-resolution image collection. Existing methods primar…
Performance of computer vision algorithms for fine-grained classification using crowdsourced insect images Open
With fine-grained classification, we identify unique characteristics to distinguish among classes of the same super-class. We are focusing on species recognition in Insecta, as they are critical for biodiversity monitoring and at the base …
Comparison between transformers and convolutional models for fine-grained classification of insects Open
Fine-grained classification is challenging due to the difficulty of finding discriminatory features. This problem is exacerbated when applied to identifying species within the same taxonomical class. This is because species are often shari…
UW-ProCCaps: UnderWater Progressive Colourisation with Capsules Open
Underwater images are fundamental for studying and understanding the status of marine life. We focus on reducing the memory space required for image storage while the memory space consumption in the collecting phase limits the time lasting…
UW-CVGAN: UnderWater Image Enhancement with Capsules Vectors Quantization Open
The degradation in the underwater images is due to wavelength-dependent light attenuation, scattering, and to the diversity of the water types in which they are captured. Deep neural networks take a step in this field, providing autonomous…
TUCaN: Progressively Teaching Colourisation to Capsules Open
Automatic image colourisation is the computer vision research path that studies how to colourise greyscale images (for restoration). Deep learning techniques improved image colourisation yielding astonishing results. These differ by variou…
Collaboration among Image and Object Level Features for Image Colourisation Open
Image colourisation is an ill-posed problem, with multiple correct solutions which depend on the context and object instances present in the input datum. Previous approaches attacked the problem either by requiring intense user interaction…
Is It a Plausible Colour? UCapsNet for Image Colourisation Open
Human beings can imagine the colours of a grayscale image with no particular effort thanks to their ability of semantic feature extraction. Can an autonomous system achieve that? Can it hallucinate plausible and vibrant colours? This is th…
Stir to Pour: Efficient Calibration of Liquid Properties for Pouring Actions Open
Humans use simple probing actions to develop intuition about the physical behaviour of common objects. Such intuition is particularly useful for adaptive estimation of favourable manipulation strategies of those objects in novel contexts. …
WhoAmI: An Automatic Tool for Visual Recognition of Tiger and Leopard Individuals in the Wild Open
Photographs of wild animals in their natural habitats can be recorded unobtrusively via cameras that are triggered by motion nearby. The installation of such camera traps is becoming increasingly common across the world. Although this is a…
View article: Machine learning approaches for identifying prey handling activity in otariid pinnipeds
Machine learning approaches for identifying prey handling activity in otariid pinnipeds Open
Systems developed in wearable devices with sensors onboard are widely used to collect data of humans and animals activities with the perspective of an on-board automatic classification of data. An interesting application of these systems i…
An Exploration of the Interaction Between capsules with ResNetCaps models Open
Image recognition is an open challenge in computer vision since its early stages. The application of deep neural networks yielded significant improvements towards its solution. Despite their classification abilities, deep networks need dat…
To Stir or Not to Stir: Online Estimation of Liquid Properties for Pouring Actions Open
Our brains are able to exploit coarse physical models of fluids to solve everyday manipulation tasks. There has been considerable interest in developing such a capability in robots so that they can autonomously manipulate fluids adapting t…
Localizing Tortoise Nests by Neural Networks Open
The goal of this research is to recognize the nest digging activity of tortoises using a device mounted atop the tortoise carapace. The device classifies tortoise movements in order to discriminate between nest digging, and non-digging act…
Human activity recognition using multisensor data fusion based on Reservoir Computing Open
Activity recognition plays a key role in providing activity assistance and care for users in smart homes. In this work, we present an activity recognition system that classifies in the near real-time a set of common daily activities exploi…