Deep Learning based Segmentation of Fish in Noisy Forward Looking MBES Images

In this work, we investigate a Deep Learning (DL) approach to fish segmentation in a small dataset of noisy low-resolution images generated by a forward-looking multibeam echosounder (MBES). We build on recent advances in DL and Convolutional Neural Networks (CNNs) for semantic segmentation and demonstrate an end-to-end approach for a fish/non-fish probability prediction for all range-azimuth positions projected by an imaging sonar. We use self-collected datasets from the Danish Sound and the Faroe Islands to train and test our model and present techniques to obtain satisfying performance and generalization even with a low-volume dataset. We show that our model proves the desired performance and has learned to harness the importance of semantic context and take this into account to separate noise and non-targets from real targets. Furthermore, we present techniques to deploy models on low-cost embedded platforms to obtain higher performance fit for edge environments - where compute and power are restricted by size/cost - for testing and prototyping

Single Image Super-Resolution for Domain-Specific Ultra-Low Bandwidth Image Transmission

Low-bandwidth communication, such as underwater acoustic communication, is limited by best-case data rates of 30--50 kbit/s. This renders such channels unusable or inefficient at best for single image, video, or other bandwidth-demanding sensor-data transmission. To combat data-transmission bottlenecks, we consider practical use-cases within the maritime domain and investigate the prospect of Single Image Super-Resolution methodologies. This is investigated on a large, diverse dataset obtained during years of trawl fishing where cameras have been placed in the fishing nets. We propose down-sampling images to a low-resolution low-size version of about 1 kB that satisfies underwater acoustic bandwidth requirements for even several frames per second. A neural network is then trained to perform up-sampling, trying to reconstruct the original image. We aim to investigate the quality of reconstructed images and prospects for such methods in practical use-cases in general. Our focus in this work is solely on learning to reconstruct the high-resolution images on "real-world" data. We show that our method achieves better perceptual quality and superior reconstruction than generic bicubic up-sampling and motivates further work in this area for underwater applications

Familial aggregation of atrial fibrillation: a study in Danish twins

BACKGROUND: Heritability may play a role in non-familial atrial fibrillation (AF). We hypothesized that a monozygotic (MZ) twin whose co-twin was diagnosed with AF would have an increased risk of the disease compared to a dizygotic (DZ) twin in the same situation. METHODS AND RESULTS: A sample of 1137 same-sex twin pairs (356 MZ and 781 DZ pairs) where one or both members were diagnosed with AF were identified in The Danish Twin Registry. Concordance rates were twice as high for MZ pairs than for DZ pairs regardless of gender, 22.0% vs. 11.6% (p<0.0001). In a Cox regression of event free survival times, we compared the time span between occurrences of disease in MZ and DZ twins. The unaffected twin was included, when his or her twin-sibling (the index twin) was diagnosed with AF. After adjustment for age at entry, MZ twins had a significantly shorter event free survival time (hazard ratio: 2.0 (95% confidence interval (CI): 1.3 – 3.0)) thereby indicating a genetic component. Using biometric models, we estimated the heritability of AF to be 62 % (55 % – 68 %), due to additive genetics. There were no significant differences across genders. CONCLUSION: All the analyses of twin similarities in the present study indicate that genetic factors play a substantial role in the risk of AF for both genders. The recurrence risk for co-twins (12–22%) is clinically relevant and suggests that co-twins of AF-affected twins belong to a high-risk group for AF