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NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale
[article]
2021
arXiv
pre-print
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To tackle the challenges, we propose a novel hybrid-representation learning model that combines the merits of foreground mask, contour map, and signed distance transform to produce high-quality 3D masks ...
Segmenting 3D cell nuclei from microscopy image volumes is critical for biological and clinical analysis, enabling the study of cellular expression patterns and cell lineages. ...
We thank Daniel Franco-Barranco for setting up the challenge using NucMM. ...
arXiv:2107.05840v1
fatcat:zrcanxxsxrcwdhzssizyts3bcq
Learning sign language by watching TV (using weakly aligned subtitles)
2009
2009 IEEE Conference on Computer Vision and Pattern Recognition
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; (ii) we show that by optimizing a scoring function based on multiple instance learning, we are able to extract the sign of interest from hours of signing footage, despite the very weak and noisy supervision ...
The contributions are: (i) we propose a distance function to match signing sequences which includes the trajectory of both hands, the hand shape and orientation, and properly models the case of hands touching ...
Acknowledgements: We are grateful for financial support from RCUK, EPSRC, the Royal Academy of Engineering, and ONR MURI N00014-07-1-0182. ...
doi:10.1109/cvprw.2009.5206523
fatcat:drop7dorfffenlbevbswxgxj6y
Learning sign language by watching TV (using weakly aligned subtitles)
2009
2009 IEEE Conference on Computer Vision and Pattern Recognition
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; (ii) we show that by optimizing a scoring function based on multiple instance learning, we are able to extract the sign of interest from hours of signing footage, despite the very weak and noisy supervision ...
The contributions are: (i) we propose a distance function to match signing sequences which includes the trajectory of both hands, the hand shape and orientation, and properly models the case of hands touching ...
Acknowledgements: We are grateful for financial support from RCUK, EPSRC, the Royal Academy of Engineering, and ONR MURI N00014-07-1-0182. ...
doi:10.1109/cvpr.2009.5206523
dblp:conf/cvpr/BuehlerZE09
fatcat:3ya3rjsf3zafvppf7njx2vxpdm
PyTorch Connectomics: A Scalable and Flexible Segmentation Framework for EM Connectomics
[article]
2021
arXiv
pre-print
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We present PyTorch Connectomics (PyTC), an open-source deep-learning framework for the semantic and instance segmentation of volumetric microscopy images, built upon PyTorch. ...
Those functionalities can be easily realized in PyTC by changing the configuration options without coding and adapted to other 2D and 3D segmentation tasks for different tissues and imaging modalities. ...
model that additionally learns a signed distance map (Fig. 4 ). ...
arXiv:2112.05754v1
fatcat:jjdatdvbr5eypmxmqbvujmarc4
Recurrent Pixel Embedding for Instance Grouping
[article]
2017
arXiv
pre-print
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We introduce a differentiable, end-to-end trainable framework for solving pixel-level grouping problems such as instance segmentation consisting of two novel components. ...
We demonstrate substantial improvements over state-of-the-art instance segmentation for object proposal generation, as well as demonstrating the benefits of grouping loss for classification tasks such ...
Rahul Sukthankar for the helpful discussion, advice and encouragement. ...
arXiv:1712.08273v1
fatcat:77ohxblx3vgpjnwr5loalgqeam
A Multi-Task Network with Distance–Mask–Boundary Consistency Constraints for Building Extraction from Aerial Images
2021
Remote Sensing
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Based on the multi-scale features, one regression loss and two classification losses were used for predicting the distance-transform map, segmentation, and boundary. ...
In order to compensate for the loss of shape information, two shape-related auxiliary tasks (i.e., boundary prediction and distance estimation) were jointly learned with building segmentation task in our ...
Distance representations can also be used to supplement shape information for semantic segmentation. In [34] , a signed distance representation was introduced for building extraction. ...
doi:10.3390/rs13142656
fatcat:5lfaohfky5bmdhchqfbjvzmrcm
Recurrent Pixel Embedding for Instance Grouping
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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We introduce a differentiable, end-to-end trainable framework for solving pixel-level grouping problems such as instance segmentation consisting of two novel components. ...
We demonstrate substantial improvements over state-of-the-art instance segmentation for object proposal generation, as well as demonstrating the benefits of grouping loss for classification tasks such ...
Fig. 11 shows the embedding visualization, as well as predicted semantic segmentation and instance-level segmentation. ...
doi:10.1109/cvpr.2018.00940
dblp:conf/cvpr/KongF18a
fatcat:v5rgn7rtl5bhdpggrdzetkm7e4
Conv-MCD: A Plug-and-Play Multi-task Module for Medical Image Segmentation
[article]
2019
arXiv
pre-print
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For the task of medical image segmentation, fully convolutional network (FCN) based architectures have been extensively used with various modifications. ...
can be obtained from ground truth segmentation maps with no additional annotation costs. ...
Distance map D3 is obtained by applying signed distance transform to the contour. ...
arXiv:1908.05311v1
fatcat:s2btvguw5rcmha6p37rhog4q7q
ELLIPSDF: Joint Object Pose and Shape Optimization with a Bi-level Ellipsoid and Signed Distance Function Description
[article]
2021
arXiv
pre-print
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This paper proposes an expressive yet compact model for joint object pose and shape optimization, and an associated optimization algorithm to infer an object-level map from multi-view RGB-D camera observations ...
Acknowledgments The first author would like to thank Kejie Li at University of Adelaide for helpful discussions. ...
The coarse-level shape error function e φ (x, d, T, δz) is defined similarly, using a signed distance function for the coarse shape. ...
arXiv:2108.00355v1
fatcat:ufmebg4v6rd63c45ty67au7pte
DeepCSR: A 3D Deep Learning Approach for Cortical Surface Reconstruction
[article]
2020
arXiv
pre-print
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Towards this end, we train a neural network model with hypercolumn features to predict implicit surface representations for points in a brain template space. ...
Traditional frameworks for this task like FreeSurfer demand lengthy runtimes, while its accelerated variant FastSurfer still relies on a voxel-wise segmentation which is limited by its resolution to capture ...
For a given surface S, the function f S can be modeled, for instance, using occupancy field or signed distance function. ...
arXiv:2010.11423v1
fatcat:yuiaqrjx75gexcl3vhghblhfi4
Driving Scene Perception Network: Real-Time Joint Detection, Depth Estimation and Semantic Segmentation
2018
2018 IEEE Winter Conference on Applications of Computer Vision (WACV)
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2018 pre-print arXiv:1803.03778v1 fatcat:ngfrbr22rvgo7ngi4ts3olq32i
As the demand for enabling high-level autonomous driving has increased in recent years and visual perception is one of the critical features to enable fully autonomous driving, in this paper, we introduce ...
an efficient approach for simultaneous object detection, depth estimation and pixel-level semantic segmentation using a shared convolutional architecture. ...
In our preliminary experiment, without limiting the distance range of objects for detection at training stage, the network falls to detecting many imaginary traffic signs. ...
doi:10.1109/wacv.2018.00145
dblp:conf/wacv/ChenYML18
fatcat:rvsk22hyz5axrmrsppfgijebjm
Table of Contents
2021
2021 Swedish Artificial Intelligence Society Workshop (SAIS)
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Signed Distance Functions for Visual Instance Segmentation
Emil Brissman, Joakim Johnander, Michael Felsberg
5-10
3
Robot First Aid: Autonomous Vehicles Could Help in Emergencies
Martin Cooney, ...
Drill Core Analysis
Christian Günther, Nils Jansson, Marcus Liwicki, Foteini Simistira-Liwicki
19-24
6
Class-Incremental Learning for Semantic Segmentation -A study
Karl Holmquist, Lena Klasén, ...
doi:10.1109/sais53221.2021.9483990
fatcat:fk7g3pkivzcdpc56rimeyv6csq
A simple model of the vertical–horizontal illusion
2010
Vision Research
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In particular, we find that the '+'-sign figure suffers from a loss of sensitivity in comparing their vertical and horizontal segments when compared to the 'L'-figure. ...
These two factors, orientation anisotropy and length bisection, provide a very good account of various configurations of the illusion when the stimulus looks like a 'T', an 'L', or a '+'-sign, and for ...
Acknowledgments The authors thank Michael Landy and Peter Thompson for their comments on an earlier version of this manuscript. ...
doi:10.1016/j.visres.2010.03.005
pmid:20298713
fatcat:lsqkahaufnapvkfleqpnsc535y
Learning Metric Graphs for Neuron Segmentation In Electron Microscopy Images
[article]
2019
arXiv
pre-print
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We first show that seed-based postprocessing of the feature vectors, as originally proposed, produces inferior accuracy because it is difficult for the convolutional net to predict feature vectors that ...
In this case, segmentations from a "metric graph" turn out to be competitive or even superior to segmentations from a directly predicted affinity graph. ...
We can derive an affinity graph from a metric graph by simply inverting the signs of all the distances between objects (i.e. affinity is the negative distance between nodes). ...
arXiv:1902.00100v1
fatcat:35mbkttjxja7jaqabykc3tlkxu
Latent Partition Implicit with Surface Codes for 3D Representation
[article]
2022
arXiv
pre-print
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LPI represents a shape as Signed Distance Functions (SDFs) using surface codes. ...
LPI can be learned without ground truth signed distances, point normals or any supervision for part partition. ...
Visual comparison with DeepLS [8] , SIF [22] and Nglod [80] under D-FAUST.
Fig. 12 . 12 Fig. 12. Shape abstraction with instance segmentation. ...
arXiv:2207.08631v3
fatcat:pff5z6ruo5g3rne46apro7z7hu
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