1,667 research outputs found
GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysis
Histopathological cancer diagnosis is based on visual examination of stained
tissue slides. Hematoxylin and eosin (H\&E) is a standard stain routinely
employed worldwide. It is easy to acquire and cost effective, but cells and
tissue components show low-contrast with varying tones of dark blue and pink,
which makes difficult visual assessments, digital image analysis, and
quantifications. These limitations can be overcome by IHC staining of target
proteins of the tissue slide. IHC provides a selective, high-contrast imaging
of cells and tissue components, but their use is largely limited by a
significantly more complex laboratory processing and high cost. We proposed a
conditional CycleGAN (cCGAN) network to transform the H\&E stained images into
IHC stained images, facilitating virtual IHC staining on the same slide. This
data-driven method requires only a limited amount of labelled data but will
generate pixel level segmentation results. The proposed cCGAN model improves
the original network \cite{zhu_unpaired_2017} by adding category conditions and
introducing two structural loss functions, which realize a multi-subdomain
translation and improve the translation accuracy as well. % need to give
reasons here. Experiments demonstrate that the proposed model outperforms the
original method in unpaired image translation with multi-subdomains. We also
explore the potential of unpaired images to image translation method applied on
other histology images related tasks with different staining techniques
Changing the view:towards the theory of visualisation comprehension
The core problem of the evaluation of information visualisation is that the end product of visualisation - the comprehension of the information from the data - is difficult to measure objectively. This paper outlines a description of visualisation comprehension based on two existing theories of perception: principles of perceptual organisation and the reverse hierarchy theory. The resulting account of the processes involved in visualisation comprehension enables evaluation that is not only objective, but also non-comparative, providing an absolute efficiency classification. Finally, as a sample application of this approach, an experiment studying the benefits of interactivity in 3D scatterplots is presented
Attribute-Guided Face Generation Using Conditional CycleGAN
We are interested in attribute-guided face generation: given a low-res face
input image, an attribute vector that can be extracted from a high-res image
(attribute image), our new method generates a high-res face image for the
low-res input that satisfies the given attributes. To address this problem, we
condition the CycleGAN and propose conditional CycleGAN, which is designed to
1) handle unpaired training data because the training low/high-res and high-res
attribute images may not necessarily align with each other, and to 2) allow
easy control of the appearance of the generated face via the input attributes.
We demonstrate impressive results on the attribute-guided conditional CycleGAN,
which can synthesize realistic face images with appearance easily controlled by
user-supplied attributes (e.g., gender, makeup, hair color, eyeglasses). Using
the attribute image as identity to produce the corresponding conditional vector
and by incorporating a face verification network, the attribute-guided network
becomes the identity-guided conditional CycleGAN which produces impressive and
interesting results on identity transfer. We demonstrate three applications on
identity-guided conditional CycleGAN: identity-preserving face superresolution,
face swapping, and frontal face generation, which consistently show the
advantage of our new method.Comment: ECCV 201
Really: towards a photorealist ontology of facticity
The overall aim of this investigation is to present a more detailed reading and analysis of
1970s American Photorealism than has been offered by historians and theorists to date. To
this end, the thesis reveals and develops the ontological significance of the complex of
`mundane facts' which comprises Photorealist painting: a layered complex of facts which I
summarise throughout the thesis as the `facticity' of the Photorealist artwork.In order to develop a more comprehensive understanding of Photorealism on an
ontological level, the thesis attends to the four layers which make up all Photorealist
paintings, namely: i) the copied photographic `facts' which comprise the final painting; ii) the
plastic `facts' of the paintings and the methods of their construction; iii) the `matter -of- fact',
quotidian subject matter; and iv) the `(f)act' of beholding the paintings. This analysis is
founded on a critical discussion of the three seemingly conflicting art theory components
inherent in Photorealist painting: the `artless', `objective' photograph; the mechanistic
Minimalist construction; and the Pop iconography.By contending with the peculiar theoretical tensions within the layers of mundane
facts, this thesis demonstrates a deeper reading of these seemingly superficial paintings of
photographs, and argues for Photorealism to be regarded as a form of painting which
brilliantly, and critically, conjoins `the Real' & `the Minimal', the photographic & the
handmade: deliberate paradoxes which reveal as much about present visual ontologies as they
do the debates and frictions between the pictorial and the non -representational which
surrounded their making. At this level the investigation is ultimately concerned with the
extended meanings of that artwork which gives again, in meticulous, painstaking detail, the
quotidian world in which it and the viewer are situated
Manipulating Attributes of Natural Scenes via Hallucination
In this study, we explore building a two-stage framework for enabling users
to directly manipulate high-level attributes of a natural scene. The key to our
approach is a deep generative network which can hallucinate images of a scene
as if they were taken at a different season (e.g. during winter), weather
condition (e.g. in a cloudy day) or time of the day (e.g. at sunset). Once the
scene is hallucinated with the given attributes, the corresponding look is then
transferred to the input image while preserving the semantic details intact,
giving a photo-realistic manipulation result. As the proposed framework
hallucinates what the scene will look like, it does not require any reference
style image as commonly utilized in most of the appearance or style transfer
approaches. Moreover, it allows to simultaneously manipulate a given scene
according to a diverse set of transient attributes within a single model,
eliminating the need of training multiple networks per each translation task.
Our comprehensive set of qualitative and quantitative results demonstrate the
effectiveness of our approach against the competing methods.Comment: Accepted for publication in ACM Transactions on Graphic
Automated pebble mosaic stylization of images
Digital mosaics have usually used regular tiles, simulating the historical
"tessellated" mosaics. In this paper, we present a method for synthesizing
pebble mosaics, a historical mosaic style in which the tiles are rounded
pebbles. We address both the tiling problem, where pebbles are distributed over
the image plane so as to approximate the input image content, and the problem
of geometry, creating a smooth rounded shape for each pebble. We adapt SLIC,
simple linear iterative clustering, to obtain elongated tiles conforming to
image content, and smooth the resulting irregular shapes into shapes resembling
pebble cross-sections. Then, we create an interior and exterior contour for
each pebble and solve a Laplace equation over the region between them to obtain
height-field geometry. The resulting pebble set approximates the input image
while presenting full geometry that can be rendered and textured for a highly
detailed representation of a pebble mosaic
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