7,586 research outputs found
A survey of exemplar-based texture synthesis
Exemplar-based texture synthesis is the process of generating, from an input
sample, new texture images of arbitrary size and which are perceptually
equivalent to the sample. The two main approaches are statistics-based methods
and patch re-arrangement methods. In the first class, a texture is
characterized by a statistical signature; then, a random sampling conditioned
to this signature produces genuinely different texture images. The second class
boils down to a clever "copy-paste" procedure, which stitches together large
regions of the sample. Hybrid methods try to combine ideas from both approaches
to avoid their hurdles. The recent approaches using convolutional neural
networks fit to this classification, some being statistical and others
performing patch re-arrangement in the feature space. They produce impressive
synthesis on various kinds of textures. Nevertheless, we found that most real
textures are organized at multiple scales, with global structures revealed at
coarse scales and highly varying details at finer ones. Thus, when confronted
with large natural images of textures the results of state-of-the-art methods
degrade rapidly, and the problem of modeling them remains wide open.Comment: v2: Added comments and typos fixes. New section added to describe
FRAME. New method presented: CNNMR
Particle-by-Particle Reconstruction of Ultrafiltration Cakes in 3D from Binarized TEM Images
Transmission electron microscopy (TEM) imaging is one of the few techniques available for direct observation of the microstructure of ultrafiltration cakes. TEM images yield local microstructural information in the form of two-dimensional grayscale images of slices a few particle diameters in thickness. This work presents an innovative particle-by-particle reconstruction scheme for simulating ultrafiltration cake microstructure in three dimensions from TEM images. The scheme uses binarized TEM images, thereby permitting use of lesser-quality images. It is able to account for short- and long-range order within ultrafiltration cake structure by matching the morphology of simulated and measured microstructures at a number of resolutions and scales identifiable within the observed microstructure. In the end, simulated microstructures are intended for improving our understanding of the relationships between cake morphology, ultrafiltration performance, and operating conditions
Contour Detection from Deep Patch-level Boundary Prediction
In this paper, we present a novel approach for contour detection with
Convolutional Neural Networks. A multi-scale CNN learning framework is designed
to automatically learn the most relevant features for contour patch detection.
Our method uses patch-level measurements to create contour maps with
overlapping patches. We show the proposed CNN is able to to detect large-scale
contours in an image efficienly. We further propose a guided filtering method
to refine the contour maps produced from large-scale contours. Experimental
results on the major contour benchmark databases demonstrate the effectiveness
of the proposed technique. We show our method can achieve good detection of
both fine-scale and large-scale contours.Comment: IEEE International Conference on Signal and Image Processing 201
The Multiscale Morphology Filter: Identifying and Extracting Spatial Patterns in the Galaxy Distribution
We present here a new method, MMF, for automatically segmenting cosmic
structure into its basic components: clusters, filaments, and walls.
Importantly, the segmentation is scale independent, so all structures are
identified without prejudice as to their size or shape. The method is ideally
suited for extracting catalogues of clusters, walls, and filaments from samples
of galaxies in redshift surveys or from particles in cosmological N-body
simulations: it makes no prior assumptions about the scale or shape of the
structures.}Comment: Replacement with higher resolution figures. 28 pages, 17 figures. For
Full Resolution Version see:
http://www.astro.rug.nl/~weygaert/tim1publication/miguelmmf.pd
A robust nonlinear scale space change detection approach for SAR images
In this paper, we propose a change detection approach based on nonlinear scale space analysis of change images for robust detection of various changes incurred by natural phenomena and/or human activities in Synthetic Aperture Radar (SAR) images using Maximally Stable Extremal Regions (MSERs). To achieve this, a variant of the log-ratio image of multitemporal images is calculated which is followed by Feature Preserving Despeckling (FPD) to generate nonlinear scale space images exhibiting different trade-offs in terms of speckle reduction and shape detail preservation. MSERs of each scale space image are found and then combined through a decision level fusion strategy, namely "selective scale fusion" (SSF), where contrast and boundary curvature of each MSER are considered. The performance of the proposed method is evaluated using real multitemporal high resolution TerraSAR-X images and synthetically generated multitemporal images composed of shapes with several orientations, sizes, and backscatter amplitude levels representing a variety of possible signatures of change. One of the main outcomes of this approach is that different objects having different sizes and levels of contrast with their surroundings appear as stable regions at different scale space images thus the fusion of results from scale space images yields a good overall performance
Multi-scale Discriminant Saliency with Wavelet-based Hidden Markov Tree Modelling
The bottom-up saliency, an early stage of humans' visual attention, can be
considered as a binary classification problem between centre and surround
classes. Discriminant power of features for the classification is measured as
mutual information between distributions of image features and corresponding
classes . As the estimated discrepancy very much depends on considered scale
level, multi-scale structure and discriminant power are integrated by employing
discrete wavelet features and Hidden Markov Tree (HMT). With wavelet
coefficients and Hidden Markov Tree parameters, quad-tree like label structures
are constructed and utilized in maximum a posterior probability (MAP) of hidden
class variables at corresponding dyadic sub-squares. Then, a saliency value for
each square block at each scale level is computed with discriminant power
principle. Finally, across multiple scales is integrated the final saliency map
by an information maximization rule. Both standard quantitative tools such as
NSS, LCC, AUC and qualitative assessments are used for evaluating the proposed
multi-scale discriminant saliency (MDIS) method against the well-know
information based approach AIM on its released image collection with
eye-tracking data. Simulation results are presented and analysed to verify the
validity of MDIS as well as point out its limitation for further research
direction.Comment: arXiv admin note: substantial text overlap with arXiv:1301.396
Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networks
Early and accurate diagnosis of interstitial lung diseases (ILDs) is crucial
for making treatment decisions, but can be challenging even for experienced
radiologists. The diagnostic procedure is based on the detection and
recognition of the different ILD pathologies in thoracic CT scans, yet their
manifestation often appears similar. In this study, we propose the use of a
deep purely convolutional neural network for the semantic segmentation of ILD
patterns, as the basic component of a computer aided diagnosis (CAD) system for
ILDs. The proposed CNN, which consists of convolutional layers with dilated
filters, takes as input a lung CT image of arbitrary size and outputs the
corresponding label map. We trained and tested the network on a dataset of 172
sparsely annotated CT scans, within a cross-validation scheme. The training was
performed in an end-to-end and semi-supervised fashion, utilizing both labeled
and non-labeled image regions. The experimental results show significant
performance improvement with respect to the state of the art
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