46 research outputs found
Combining feature aggregation and geometric similarity for re-identification of patterned animals
Image-based re-identification of animal individuals allows gathering of
information such as migration patterns of the animals over time. This, together
with large image volumes collected using camera traps and crowdsourcing, opens
novel possibilities to study animal populations. For many species, the
re-identification can be done by analyzing the permanent fur, feather, or skin
patterns that are unique to each individual. In this paper, we address the
re-identification by combining two types of pattern similarity metrics: 1)
pattern appearance similarity obtained by pattern feature aggregation and 2)
geometric pattern similarity obtained by analyzing the geometric consistency of
pattern similarities. The proposed combination allows to efficiently utilize
both the local and global pattern features, providing a general
re-identification approach that can be applied to a wide variety of different
pattern types. In the experimental part of the work, we demonstrate that the
method achieves promising re-identification accuracies for Saimaa ringed seals
and whale sharks.Comment: Camera traps, AI, and Ecology, 3rd International Worksho
Towards Phytoplankton Parasite Detection Using Autoencoders
Phytoplankton parasites are largely understudied microbial components with a
potentially significant ecological impact on phytoplankton bloom dynamics. To
better understand their impact, we need improved detection methods to integrate
phytoplankton parasite interactions in monitoring aquatic ecosystems. Automated
imaging devices usually produce high amount of phytoplankton image data, while
the occurrence of anomalous phytoplankton data is rare. Thus, we propose an
unsupervised anomaly detection system based on the similarity of the original
and autoencoder-reconstructed samples. With this approach, we were able to
reach an overall F1 score of 0.75 in nine phytoplankton species, which could be
further improved by species-specific fine-tuning. The proposed unsupervised
approach was further compared with the supervised Faster R-CNN based object
detector. With this supervised approach and the model trained on plankton
species and anomalies, we were able to reach the highest F1 score of 0.86.
However, the unsupervised approach is expected to be more universal as it can
detect also unknown anomalies and it does not require any annotated anomalous
data that may not be always available in sufficient quantities. Although other
studies have dealt with plankton anomaly detection in terms of non-plankton
particles, or air bubble detection, our paper is according to our best
knowledge the first one which focuses on automated anomaly detection
considering putative phytoplankton parasites or infections
Towards Phytoplankton Parasite Detection Using Autoencoders
Phytoplankton parasites are largely understudied microbial components with a potentially significant ecological influence on phytoplankton bloom dynamics. To better understand the impact of phytoplankton parasites, improved detection methods are needed to integrate phytoplankton parasite interactions into monitoring of aquatic ecosystems. Automated imaging devices commonly produce vast amounts of phytoplankton image data, but the occurrence of anomalous phytoplankton data in such datasets is rare. Thus, we propose an unsupervised anomaly detection system based on the similarity between the original and autoencoder-reconstructed samples. With this approach, we were able to reach an overall F1 score of 0.75 in nine phytoplankton species, which could be further improved by species-specific fine-tuning. The proposed unsupervised approach was further compared with the supervised Faster R-CNN-based object detector. Using this supervised approach and the model trained on plankton species and anomalies, we were able to reach a highest F1 score of 0.86. However, the unsupervised approach is expected to be more universal as it can also detect unknown anomalies and it does not require any annotated anomalous data that may not always be available in sufficient quantities. Although other studies have dealt with plankton anomaly detection in terms of non-plankton particles or air bubble detection, our paper is, according to our best knowledge, the first that focuses on automated anomaly detection considering putative phytoplankton parasites or infections
Tuberoosiskleroosi - suomalainen diagnoosi- ja seurantasuositus
•Tuberoosiskleroosia sairastavat potilaat tarvitsevat systemaattista, monen erikoisalan seurantaa läpi elämän. •Tavoitteena on haitallisten tai jopa hengenvaarallisten elinmuutosten varhainen toteaminen ja hoito. •Epilepsian tehokkaalla hoidolla ja kehityksen oikea-aikaisella tuella pyritään vaikuttamaan potilaiden kehitys¬ennusteeseen ja neuropsykiatristen häiriöiden esiintymiseen. •Tässä suosituksessa esitetään diagnosointi- ja seurantavaiheessa tarvittavat tutkimukset ja niiden toteutus, jossa hyödynnetään sekä erikoissairaanhoidon että perusterveydenhuollon palveluja.Peer reviewe