140 research outputs found
Registration Loss Learning for Deep Probabilistic Point Set Registration
Probabilistic methods for point set registration have interesting theoretical
properties, such as linear complexity in the number of used points, and they
easily generalize to joint registration of multiple point sets. In this work,
we improve their recognition performance to match state of the art. This is
done by incorporating learned features, by adding a von Mises-Fisher feature
model in each mixture component, and by using learned attention weights. We
learn these jointly using a registration loss learning strategy (RLL) that
directly uses the registration error as a loss, by back-propagating through the
registration iterations. This is possible as the probabilistic registration is
fully differentiable, and the result is a learning framework that is truly
end-to-end. We perform extensive experiments on the 3DMatch and Kitti datasets.
The experiments demonstrate that our approach benefits significantly from the
integration of the learned features and our learning strategy, outperforming
the state-of-the-art on Kitti. Code is available at
https://github.com/felja633/RLLReg.Comment: 3DV 202
Learning Fast and Robust Target Models for Video Object Segmentation
Video object segmentation (VOS) is a highly challenging problem since the
initial mask, defining the target object, is only given at test-time. The main
difficulty is to effectively handle appearance changes and similar background
objects, while maintaining accurate segmentation. Most previous approaches
fine-tune segmentation networks on the first frame, resulting in impractical
frame-rates and risk of overfitting. More recent methods integrate generative
target appearance models, but either achieve limited robustness or require
large amounts of training data.
We propose a novel VOS architecture consisting of two network components. The
target appearance model consists of a light-weight module, which is learned
during the inference stage using fast optimization techniques to predict a
coarse but robust target segmentation. The segmentation model is exclusively
trained offline, designed to process the coarse scores into high quality
segmentation masks. Our method is fast, easily trainable and remains highly
effective in cases of limited training data. We perform extensive experiments
on the challenging YouTube-VOS and DAVIS datasets. Our network achieves
favorable performance, while operating at higher frame-rates compared to
state-of-the-art. Code and trained models are available at
https://github.com/andr345/frtm-vos.Comment: CVPR 2020. arXiv admin note: substantial text overlap with
arXiv:1904.0863
Learning What to Learn for Video Object Segmentation
Video object segmentation (VOS) is a highly challenging problem, since the
target object is only defined during inference with a given first-frame
reference mask. The problem of how to capture and utilize this limited target
information remains a fundamental research question. We address this by
introducing an end-to-end trainable VOS architecture that integrates a
differentiable few-shot learning module. This internal learner is designed to
predict a powerful parametric model of the target by minimizing a segmentation
error in the first frame. We further go beyond standard few-shot learning
techniques by learning what the few-shot learner should learn. This allows us
to achieve a rich internal representation of the target in the current frame,
significantly increasing the segmentation accuracy of our approach. We perform
extensive experiments on multiple benchmarks. Our approach sets a new
state-of-the-art on the large-scale YouTube-VOS 2018 dataset by achieving an
overall score of 81.5, corresponding to a 2.6% relative improvement over the
previous best result.Comment: First two authors contributed equall
Is Markerless More or Less? Comparing a Smartphone Computer Vision Method for Equine Lameness Assessment to Multi-Camera Motion Capture
Lameness, an alteration of the gait due to pain or dysfunction of the locomotor system, is the most common disease symptom in horses. Yet, it is difficult for veterinarians to correctly assess by visual inspection. Objective tools that can aid clinical decision making and provide early disease detection through sensitive lameness measurements are needed. In this study, we describe how an AI-powered measurement tool on a smartphone can detect lameness in horses without the need to mount equipment on the horse. We compare it to a state-of-the-art multi-camera motion capture system by simultaneous, synchronised recordings from both systems. The mean difference between the systems' output of lameness metrics was below 2.2 mm. Therefore, we conclude that the smartphone measurement tool can detect lameness at relevant levels with easy-of-use for the veterinarian. Computer vision is a subcategory of artificial intelligence focused on extraction of information from images and video. It provides a compelling new means for objective orthopaedic gait assessment in horses using accessible hardware, such as a smartphone, for markerless motion analysis. This study aimed to explore the lameness assessment capacity of a smartphone single camera (SC) markerless computer vision application by comparing measurements of the vertical motion of the head and pelvis to an optical motion capture multi-camera (MC) system using skin attached reflective markers. Twenty-five horses were recorded with a smartphone (60 Hz) and a 13 camera MC-system (200 Hz) while trotting two times back and forth on a 30 m runway. The smartphone video was processed using artificial neural networks detecting the horse's direction, action and motion of body segments. After filtering, the vertical displacement curves from the head and pelvis were synchronised between systems using cross-correlation. This rendered 655 and 404 matching stride segmented curves for the head and pelvis respectively. From the stride segmented vertical displacement signals, differences between the two minima (MinDiff) and the two maxima (MaxDiff) respectively per stride were compared between the systems. Trial mean difference between systems was 2.2 mm (range 0.0-8.7 mm) for head and 2.2 mm (range 0.0-6.5 mm) for pelvis. Within-trial standard deviations ranged between 3.1-28.1 mm for MC and between 3.6-26.2 mm for SC. The ease of use and good agreement with MC indicate that the SC application is a promising tool for detecting clinically relevant levels of asymmetry in horses, enabling frequent and convenient gait monitoring over time
Analyzing the reflections from single ommatidia in the butterfly compound eye with Voronoi diagrams
Cytochrome P450 2C19 Polymorphism is Associated with Reduced Clopidogrel Response in Cerebrovascular Disease
Swan foraging shapes spatial distribution of two submerged plants, favouring the preferred prey species
Compared to terrestrial environments, grazing intensity on belowground plant parts may be particularly strong in aquatic environments, which may have great effects on plant-community structure. We observed that the submerged macrophyte, Potamogeton pectinatus, which mainly reproduces with tubers, often grows at intermediate water depth and that P. perfoliatus, which mainly reproduces with rhizomes and turions, grows in either shallow or deep water. One mechanism behind this distributional pattern may be that swans prefer to feed on P. pectinatus tubers at intermediate water depths. We hypothesised that when swans feed on tubers in the sediment, P. perfoliatus rhizomes and turions may be damaged by the uprooting, whereas the small round tubers of P. pectinatus that escaped herbivory may be more tolerant to this bioturbation. In spring 2000, we transplanted P. perfoliatus rhizomes into a P. pectinatus stand and followed growth in plots protected and unprotected, respectively, from bird foraging. Although swan foraging reduced tuber biomass in unprotected plots, leading to lower P. pectinatus density in spring 2001, this species grew well both in protected and unprotected plots later that summer. In contrast, swan grazing had a dramatic negative effect on P. perfoliatus that persisted throughout the summer of 2001, with close to no plants in the unprotected plots and high densities in the protected plots. Our results demonstrate that herbivorous waterbirds may play a crucial role in the distribution and prevalence of specific plant species. Furthermore, since their grazing benefitted their preferred food source, the interaction between swans and P. pectinatus may be classified as ecologically mutualistic
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