19 research outputs found
Object Contour and Edge Detection with RefineContourNet
A ResNet-based multi-path refinement CNN is used for object contour
detection. For this task, we prioritise the effective utilization of the
high-level abstraction capability of a ResNet, which leads to state-of-the-art
results for edge detection. Keeping our focus in mind, we fuse the high, mid
and low-level features in that specific order, which differs from many other
approaches. It uses the tensor with the highest-levelled features as the
starting point to combine it layer-by-layer with features of a lower
abstraction level until it reaches the lowest level. We train this network on a
modified PASCAL VOC 2012 dataset for object contour detection and evaluate on a
refined PASCAL-val dataset reaching an excellent performance and an Optimal
Dataset Scale (ODS) of 0.752. Furthermore, by fine-training on the BSDS500
dataset we reach state-of-the-art results for edge-detection with an ODS of
0.824.Comment: Keywords: Object Contour Detection, Edge Detection, Multi-Path
Refinement CN
Seeing Tree Structure from Vibration
Humans recognize object structure from both their appearance and motion;
often, motion helps to resolve ambiguities in object structure that arise when
we observe object appearance only. There are particular scenarios, however,
where neither appearance nor spatial-temporal motion signals are informative:
occluding twigs may look connected and have almost identical movements, though
they belong to different, possibly disconnected branches. We propose to tackle
this problem through spectrum analysis of motion signals, because vibrations of
disconnected branches, though visually similar, often have distinctive natural
frequencies. We propose a novel formulation of tree structure based on a
physics-based link model, and validate its effectiveness by theoretical
analysis, numerical simulation, and empirical experiments. With this
formulation, we use nonparametric Bayesian inference to reconstruct tree
structure from both spectral vibration signals and appearance cues. Our model
performs well in recognizing hierarchical tree structure from real-world videos
of trees and vessels.Comment: ECCV 2018. The first two authors contributed equally to this work.
Project page: http://tree.csail.mit.edu
The Unreasonable Effectiveness of Encoder-Decoder Networks for Retinal Vessel Segmentation
We propose an encoder-decoder framework for the segmentation of blood vessels
in retinal images that relies on the extraction of large-scale patches at
multiple image-scales during training. Experiments on three fundus image
datasets demonstrate that this approach achieves state-of-the-art results and
can be implemented using a simple and efficient fully-convolutional network
with a parameter count of less than 0.8M. Furthermore, we show that this
framework - called VLight - avoids overfitting to specific training images and
generalizes well across different datasets, which makes it highly suitable for
real-world applications where robustness, accuracy as well as low inference
time on high-resolution fundus images is required
A semantics-guided warping for semi-supervised video object instance segmentation
International audienceIn the semi-supervised video object instance segmentation domain, the mask warping technique, which warps the mask of the target object to flow vectors frame by frame, is widely used to extract target object. The big issue with this approach is that the generated warped map is not always of high accuracy, where the background or other objects may be wrongly detected as the target object. To cope with this problem, we propose to use the semantics of the target object as a guidance during the warping process. The warping confidence computation firstly judges the confidence of the generated warped map. Then a semantic selection is introduced to optimize the warped map with low confidence, where the target object is re-identified using the semantics-labels of the target object. The proposed method is assessed on the recently published large-scale Youtube-VOS dataset and compared to some state-of-the-art methods. The experimental results show that the proposed approach has a promising performance. © Springer Nature Switzerland AG 2020
Large Receptive Field Fully Convolutional Network for Semantic Segmentation of Retinal Vasculature in Fundus Images
Glaucoma Diagnosis from Eye Fundus Images Based on Deep Morphometric Feature Estimation
International audienc
Left Atrial Segmentation in a Few Seconds Using Fully Convolutional Network and Transfer Learning
International audienceIn this paper, we propose a fast automatic method that segments left atrial cavity from 3D GE-MRIs without any manual assistance , using a fully convolutional network (FCN) and transfer learning. This FCN is the base network of VGG-16, pre-trained on ImageNet for natural image classification, and fine tuned with the training dataset of the MICCAI 2018 Atrial Segmentation Challenge. It relies on the "pseudo-3D" method published at ICIP 2017, which allows for segmenting objects from 2D color images which contain 3D information of MRI volumes. For each n th slice of the volume to segment, we consider three images, corresponding to the (n − 1) th , n th , and (n + 1) th slices of the original volume. These three gray-level 2D images are assembled to form a 2D RGB color image (one image per channel). This image is the input of the FCN to obtain a 2D segmentation of the n th slice. We process all slices, then stack the results to form the 3D output segmentation. With such a technique, the segmentation of the left atrial cavity on a 3D volume takes only a few seconds. We reached a dice of 0.911 on the training set
Bayesian semantic instance segmentation in open set world
This paper addresses the semantic instance segmentation task in the open-set conditions, where input images can contain known and unknown object classes. The training process of existing semantic instance segmentation methods requires annotation masks for all object instances, which is expensive to acquire or even infeasible in some realistic scenarios, where the number of categories may increase boundlessly. In this paper, we present a novel open-set semantic instance segmentation approach capable of segmenting all known and unknown object classes in images, based on the output of an object detector trained on known object classes. We formulate the problem using a Bayesian framework, where the posterior distribution is approximated with a simulated annealing optimization equipped with an efficient image partition sampler. We show empirically that our method is competitive with state-of-the-art supervised methods on known classes, but also performs well on unknown classes when compared with unsupervised methods
Toward global calibration of a robot-assisted vitreo-retinal surgery based on OCT C-scan image registration
status: publishe