4,942 research outputs found
Recovering Faces from Portraits with Auxiliary Facial Attributes
Recovering a photorealistic face from an artistic portrait is a challenging
task since crucial facial details are often distorted or completely lost in
artistic compositions. To handle this loss, we propose an Attribute-guided Face
Recovery from Portraits (AFRP) that utilizes a Face Recovery Network (FRN) and
a Discriminative Network (DN). FRN consists of an autoencoder with residual
block-embedded skip-connections and incorporates facial attribute vectors into
the feature maps of input portraits at the bottleneck of the autoencoder. DN
has multiple convolutional and fully-connected layers, and its role is to
enforce FRN to generate authentic face images with corresponding facial
attributes dictated by the input attribute vectors. %Leveraging on the spatial
transformer networks, FRN automatically compensates for misalignments of
portraits. % and generates aligned face images. For the preservation of
identities, we impose the recovered and ground-truth faces to share similar
visual features. Specifically, DN determines whether the recovered image looks
like a real face and checks if the facial attributes extracted from the
recovered image are consistent with given attributes. %Our method can recover
high-quality photorealistic faces from unaligned portraits while preserving the
identity of the face images as well as it can reconstruct a photorealistic face
image with a desired set of attributes. Our method can recover photorealistic
identity-preserving faces with desired attributes from unseen stylized
portraits, artistic paintings, and hand-drawn sketches. On large-scale
synthesized and sketch datasets, we demonstrate that our face recovery method
achieves state-of-the-art results.Comment: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV
Context-based Normalization of Histological Stains using Deep Convolutional Features
While human observers are able to cope with variations in color and
appearance of histological stains, digital pathology algorithms commonly
require a well-normalized setting to achieve peak performance, especially when
a limited amount of labeled data is available. This work provides a fully
automated, end-to-end learning-based setup for normalizing histological stains,
which considers the texture context of the tissue. We introduce Feature Aware
Normalization, which extends the framework of batch normalization in
combination with gating elements from Long Short-Term Memory units for
normalization among different spatial regions of interest. By incorporating a
pretrained deep neural network as a feature extractor steering a pixelwise
processing pipeline, we achieve excellent normalization results and ensure a
consistent representation of color and texture. The evaluation comprises a
comparison of color histogram deviations, structural similarity and measures
the color volume obtained by the different methods.Comment: In: 3rd Workshop on Deep Learning in Medical Image Analysis (DLMIA
2017
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