48,550 research outputs found
A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images
Semantic segmentation is the pixel-wise labelling of an image. Since the
problem is defined at the pixel level, determining image class labels only is
not acceptable, but localising them at the original image pixel resolution is
necessary. Boosted by the extraordinary ability of convolutional neural
networks (CNN) in creating semantic, high level and hierarchical image
features; excessive numbers of deep learning-based 2D semantic segmentation
approaches have been proposed within the last decade. In this survey, we mainly
focus on the recent scientific developments in semantic segmentation,
specifically on deep learning-based methods using 2D images. We started with an
analysis of the public image sets and leaderboards for 2D semantic
segmantation, with an overview of the techniques employed in performance
evaluation. In examining the evolution of the field, we chronologically
categorised the approaches into three main periods, namely pre-and early deep
learning era, the fully convolutional era, and the post-FCN era. We technically
analysed the solutions put forward in terms of solving the fundamental problems
of the field, such as fine-grained localisation and scale invariance. Before
drawing our conclusions, we present a table of methods from all mentioned eras,
with a brief summary of each approach that explains their contribution to the
field. We conclude the survey by discussing the current challenges of the field
and to what extent they have been solved.Comment: Updated with new studie
Semantically Informed Multiview Surface Refinement
We present a method to jointly refine the geometry and semantic segmentation
of 3D surface meshes. Our method alternates between updating the shape and the
semantic labels. In the geometry refinement step, the mesh is deformed with
variational energy minimization, such that it simultaneously maximizes
photo-consistency and the compatibility of the semantic segmentations across a
set of calibrated images. Label-specific shape priors account for interactions
between the geometry and the semantic labels in 3D. In the semantic
segmentation step, the labels on the mesh are updated with MRF inference, such
that they are compatible with the semantic segmentations in the input images.
Also, this step includes prior assumptions about the surface shape of different
semantic classes. The priors induce a tight coupling, where semantic
information influences the shape update and vice versa. Specifically, we
introduce priors that favor (i) adaptive smoothing, depending on the class
label; (ii) straightness of class boundaries; and (iii) semantic labels that
are consistent with the surface orientation. The novel mesh-based
reconstruction is evaluated in a series of experiments with real and synthetic
data. We compare both to state-of-the-art, voxel-based semantic 3D
reconstruction, and to purely geometric mesh refinement, and demonstrate that
the proposed scheme yields improved 3D geometry as well as an improved semantic
segmentation
People, Penguins and Petri Dishes: Adapting Object Counting Models To New Visual Domains And Object Types Without Forgetting
In this paper we propose a technique to adapt a convolutional neural network
(CNN) based object counter to additional visual domains and object types while
still preserving the original counting function. Domain-specific normalisation
and scaling operators are trained to allow the model to adjust to the
statistical distributions of the various visual domains. The developed
adaptation technique is used to produce a singular patch-based counting
regressor capable of counting various object types including people, vehicles,
cell nuclei and wildlife. As part of this study a challenging new cell counting
dataset in the context of tissue culture and patient diagnosis is constructed.
This new collection, referred to as the Dublin Cell Counting (DCC) dataset, is
the first of its kind to be made available to the wider computer vision
community. State-of-the-art object counting performance is achieved in both the
Shanghaitech (parts A and B) and Penguins datasets while competitive
performance is observed on the TRANCOS and Modified Bone Marrow (MBM) datasets,
all using a shared counting model.Comment: 10 page
ImageSpirit: Verbal Guided Image Parsing
Humans describe images in terms of nouns and adjectives while algorithms
operate on images represented as sets of pixels. Bridging this gap between how
humans would like to access images versus their typical representation is the
goal of image parsing, which involves assigning object and attribute labels to
pixel. In this paper we propose treating nouns as object labels and adjectives
as visual attribute labels. This allows us to formulate the image parsing
problem as one of jointly estimating per-pixel object and attribute labels from
a set of training images. We propose an efficient (interactive time) solution.
Using the extracted labels as handles, our system empowers a user to verbally
refine the results. This enables hands-free parsing of an image into pixel-wise
object/attribute labels that correspond to human semantics. Verbally selecting
objects of interests enables a novel and natural interaction modality that can
possibly be used to interact with new generation devices (e.g. smart phones,
Google Glass, living room devices). We demonstrate our system on a large number
of real-world images with varying complexity. To help understand the tradeoffs
compared to traditional mouse based interactions, results are reported for both
a large scale quantitative evaluation and a user study.Comment: http://mmcheng.net/imagespirit
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