2,017 research outputs found
Photorealistic Style Transfer with Screened Poisson Equation
Recent work has shown impressive success in transferring painterly style to
images. These approaches, however, fall short of photorealistic style transfer.
Even when both the input and reference images are photographs, the output still
exhibits distortions reminiscent of a painting. In this paper we propose an
approach that takes as input a stylized image and makes it more photorealistic.
It relies on the Screened Poisson Equation, maintaining the fidelity of the
stylized image while constraining the gradients to those of the original input
image. Our method is fast, simple, fully automatic and shows positive progress
in making a stylized image photorealistic. Our results exhibit finer details
and are less prone to artifacts than the state-of-the-art.Comment: presented in BMVC 201
3D Matting: A Soft Segmentation Method Applied in Computed Tomography
Three-dimensional (3D) images, such as CT, MRI, and PET, are common in
medical imaging applications and important in clinical diagnosis. Semantic
ambiguity is a typical feature of many medical image labels. It can be caused
by many factors, such as the imaging properties, pathological anatomy, and the
weak representation of the binary masks, which brings challenges to accurate 3D
segmentation. In 2D medical images, using soft masks instead of binary masks
generated by image matting to characterize lesions can provide rich semantic
information, describe the structural characteristics of lesions more
comprehensively, and thus benefit the subsequent diagnoses and analyses. In
this work, we introduce image matting into the 3D scenes to describe the
lesions in 3D medical images. The study of image matting in 3D modality is
limited, and there is no high-quality annotated dataset related to 3D matting,
therefore slowing down the development of data-driven deep-learning-based
methods. To address this issue, we constructed the first 3D medical matting
dataset and convincingly verified the validity of the dataset through quality
control and downstream experiments in lung nodules classification. We then
adapt the four selected state-of-the-art 2D image matting algorithms to 3D
scenes and further customize the methods for CT images. Also, we propose the
first end-to-end deep 3D matting network and implement a solid 3D medical image
matting benchmark, which will be released to encourage further research.Comment: 12 pages, 7 figure
Modeling of 2D and 3D Assemblies Taking Into Account Form Errors of Plane Surfaces
The tolerancing process links the virtual and the real worlds. From the
former, tolerances define a variational geometrical language (geometric
parameters). From the latter, there are values limiting those parameters. The
beginning of a tolerancing process is in this duality. As high precision
assemblies cannot be analyzed with the assumption that form errors are
negligible, we propose to apply this process to assemblies with form errors
through a new way of allowing to parameterize forms and solve their assemblies.
The assembly process is calculated through a method of allowing to solve the 3D
assemblies of pairs of surfaces having form errors using a static equilibrium.
We have built a geometrical model based on the modal shapes of the ideal
surface. We compute for the completely deterministic contact points between
this pair of shapes according to a given assembly process. The solution gives
an accurate evaluation of the assembly performance. Then we compare the results
with or without taking into account the form errors. When we analyze a batch of
assemblies, the problem is to compute for the nonconformity rate of a pilot
production according to the functional requirements. We input probable errors
of surfaces (position, orientation, and form) in our calculus and we evaluate
the quality of the results compared with the functional requirements. The pilot
production then can or cannot be validated
Fast Preprocessing for Robust Face Sketch Synthesis
Exemplar-based face sketch synthesis methods usually meet the challenging
problem that input photos are captured in different lighting conditions from
training photos. The critical step causing the failure is the search of similar
patch candidates for an input photo patch. Conventional illumination invariant
patch distances are adopted rather than directly relying on pixel intensity
difference, but they will fail when local contrast within a patch changes. In
this paper, we propose a fast preprocessing method named Bidirectional
Luminance Remapping (BLR), which interactively adjust the lighting of training
and input photos. Our method can be directly integrated into state-of-the-art
exemplar-based methods to improve their robustness with ignorable computational
cost.Comment: IJCAI 2017. Project page:
http://www.cs.cityu.edu.hk/~yibisong/ijcai17_sketch/index.htm
Deep Image Matting: A Comprehensive Survey
Image matting refers to extracting precise alpha matte from natural images,
and it plays a critical role in various downstream applications, such as image
editing. Despite being an ill-posed problem, traditional methods have been
trying to solve it for decades. The emergence of deep learning has
revolutionized the field of image matting and given birth to multiple new
techniques, including automatic, interactive, and referring image matting. This
paper presents a comprehensive review of recent advancements in image matting
in the era of deep learning. We focus on two fundamental sub-tasks: auxiliary
input-based image matting, which involves user-defined input to predict the
alpha matte, and automatic image matting, which generates results without any
manual intervention. We systematically review the existing methods for these
two tasks according to their task settings and network structures and provide a
summary of their advantages and disadvantages. Furthermore, we introduce the
commonly used image matting datasets and evaluate the performance of
representative matting methods both quantitatively and qualitatively. Finally,
we discuss relevant applications of image matting and highlight existing
challenges and potential opportunities for future research. We also maintain a
public repository to track the rapid development of deep image matting at
https://github.com/JizhiziLi/matting-survey
Privileged Prior Information Distillation for Image Matting
Performance of trimap-free image matting methods is limited when trying to
decouple the deterministic and undetermined regions, especially in the scenes
where foregrounds are semantically ambiguous, chromaless, or high
transmittance. In this paper, we propose a novel framework named Privileged
Prior Information Distillation for Image Matting (PPID-IM) that can effectively
transfer privileged prior environment-aware information to improve the
performance of students in solving hard foregrounds. The prior information of
trimap regulates only the teacher model during the training stage, while not
being fed into the student network during actual inference. In order to achieve
effective privileged cross-modality (i.e. trimap and RGB) information
distillation, we introduce a Cross-Level Semantic Distillation (CLSD) module
that reinforces the trimap-free students with more knowledgeable semantic
representations and environment-aware information. We also propose an
Attention-Guided Local Distillation module that efficiently transfers
privileged local attributes from the trimap-based teacher to trimap-free
students for the guidance of local-region optimization. Extensive experiments
demonstrate the effectiveness and superiority of our PPID framework on the task
of image matting. In addition, our trimap-free IndexNet-PPID surpasses the
other competing state-of-the-art methods by a large margin, especially in
scenarios with chromaless, weak texture, or irregular objects.Comment: 15 pages, 7 figure
Recovering the lost gold of the developing world : bibliographic database
This report contains a library of 181 references, including abstracts, prepared for Project
R 7120 "Recovering the lost gold of the developing world" funded by the UK' s
Department for International Development (DFID) under the Knowledge and Research
(KAR) programme. As part of an initial desk study, a literature review of gold processing
methods used by small-scale miners was carried out using the following sources; the lSI
Science Citation Index accessed via Bath Information and Data Services (BIDS), a
licensed GEOREF CD-ROM database held at the BGS's Library in Keyworth and
IMMage a CD-ROM database produced by the Institution of Mining and Metallurgy held
by the Minerals group ofBGS. Information on the search terms used is available from the
author
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