50,744 research outputs found
Iterative Instance Segmentation
Existing methods for pixel-wise labelling tasks generally disregard the
underlying structure of labellings, often leading to predictions that are
visually implausible. While incorporating structure into the model should
improve prediction quality, doing so is challenging - manually specifying the
form of structural constraints may be impractical and inference often becomes
intractable even if structural constraints are given. We sidestep this problem
by reducing structured prediction to a sequence of unconstrained prediction
problems and demonstrate that this approach is capable of automatically
discovering priors on shape, contiguity of region predictions and smoothness of
region contours from data without any a priori specification. On the instance
segmentation task, this method outperforms the state-of-the-art, achieving a
mean of 63.6% at 50% overlap and 43.3% at 70% overlap.Comment: 13 pages, 10 figures; IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 201
Recurrent Contour-based Instance Segmentation with Progressive Learning
Contour-based instance segmentation has been actively studied, thanks to its
flexibility and elegance in processing visual objects within complex
backgrounds. In this work, we propose a novel deep network architecture, i.e.,
PolySnake, for contour-based instance segmentation. Motivated by the classic
Snake algorithm, the proposed PolySnake achieves superior and robust
segmentation performance with an iterative and progressive contour refinement
strategy. Technically, PolySnake introduces a recurrent update operator to
estimate the object contour iteratively. It maintains a single estimate of the
contour that is progressively deformed toward the object boundary. At each
iteration, PolySnake builds a semantic-rich representation for the current
contour and feeds it to the recurrent operator for further contour adjustment.
Through the iterative refinements, the contour finally progressively converges
to a stable status that tightly encloses the object instance. Moreover, with a
compact design of the recurrent architecture, we ensure the running efficiency
under multiple iterations. Extensive experiments are conducted to validate the
merits of our method, and the results demonstrate that the proposed PolySnake
outperforms the existing contour-based instance segmentation methods on several
prevalent instance segmentation benchmarks. The codes and models are available
at https://github.com/fh2019ustc/PolySnake
Iterative Next Boundary Detection for Instance Segmentation of Tree Rings in Microscopy Images of Shrub Cross Sections
We address the problem of detecting tree rings in microscopy images of shrub
cross sections. This can be regarded as a special case of the instance
segmentation task with several unique challenges such as the concentric
circular ring shape of the objects and high precision requirements that result
in inadequate performance of existing methods. We propose a new iterative
method which we term Iterative Next Boundary Detection (INBD). It intuitively
models the natural growth direction, starting from the center of the shrub
cross section and detecting the next ring boundary in each iteration step. In
our experiments, INBD shows superior performance to generic instance
segmentation methods and is the only one with a built-in notion of
chronological order. Our dataset and source code are available at
http://github.com/alexander-g/INBD.Comment: CVPR 202
Bounding Box-Free Instance Segmentation Using Semi-Supervised Learning for Generating a City-Scale Vehicle Dataset
Vehicle classification is a hot computer vision topic, with studies ranging
from ground-view up to top-view imagery. In remote sensing, the usage of
top-view images allows for understanding city patterns, vehicle concentration,
traffic management, and others. However, there are some difficulties when
aiming for pixel-wise classification: (a) most vehicle classification studies
use object detection methods, and most publicly available datasets are designed
for this task, (b) creating instance segmentation datasets is laborious, and
(c) traditional instance segmentation methods underperform on this task since
the objects are small. Thus, the present research objectives are: (1) propose a
novel semi-supervised iterative learning approach using GIS software, (2)
propose a box-free instance segmentation approach, and (3) provide a city-scale
vehicle dataset. The iterative learning procedure considered: (1) label a small
number of vehicles, (2) train on those samples, (3) use the model to classify
the entire image, (4) convert the image prediction into a polygon shapefile,
(5) correct some areas with errors and include them in the training data, and
(6) repeat until results are satisfactory. To separate instances, we considered
vehicle interior and vehicle borders, and the DL model was the U-net with the
Efficient-net-B7 backbone. When removing the borders, the vehicle interior
becomes isolated, allowing for unique object identification. To recover the
deleted 1-pixel borders, we proposed a simple method to expand each prediction.
The results show better pixel-wise metrics when compared to the Mask-RCNN (82%
against 67% in IoU). On per-object analysis, the overall accuracy, precision,
and recall were greater than 90%. This pipeline applies to any remote sensing
target, being very efficient for segmentation and generating datasets.Comment: 38 pages, 10 figures, submitted to journa
Weakly- and Semi-Supervised Panoptic Segmentation
We present a weakly supervised model that jointly performs both semantic- and
instance-segmentation -- a particularly relevant problem given the substantial
cost of obtaining pixel-perfect annotation for these tasks. In contrast to many
popular instance segmentation approaches based on object detectors, our method
does not predict any overlapping instances. Moreover, we are able to segment
both "thing" and "stuff" classes, and thus explain all the pixels in the image.
"Thing" classes are weakly-supervised with bounding boxes, and "stuff" with
image-level tags. We obtain state-of-the-art results on Pascal VOC, for both
full and weak supervision (which achieves about 95% of fully-supervised
performance). Furthermore, we present the first weakly-supervised results on
Cityscapes for both semantic- and instance-segmentation. Finally, we use our
weakly supervised framework to analyse the relationship between annotation
quality and predictive performance, which is of interest to dataset creators.Comment: ECCV 2018. The first two authors contributed equall
Pseudo Mask Augmented Object Detection
In this work, we present a novel and effective framework to facilitate object
detection with the instance-level segmentation information that is only
supervised by bounding box annotation. Starting from the joint object detection
and instance segmentation network, we propose to recursively estimate the
pseudo ground-truth object masks from the instance-level object segmentation
network training, and then enhance the detection network with top-down
segmentation feedbacks. The pseudo ground truth mask and network parameters are
optimized alternatively to mutually benefit each other. To obtain the promising
pseudo masks in each iteration, we embed a graphical inference that
incorporates the low-level image appearance consistency and the bounding box
annotations to refine the segmentation masks predicted by the segmentation
network. Our approach progressively improves the object detection performance
by incorporating the detailed pixel-wise information learned from the
weakly-supervised segmentation network. Extensive evaluation on the detection
task in PASCAL VOC 2007 and 2012 [12] verifies that the proposed approach is
effective
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