13,447 research outputs found
Learning Discriminative Multilevel Structured Dictionaries for Supervised Image Classification
Sparse representations using overcomplete dictionaries have proved to be a
powerful tool in many signal processing applications such as denoising,
super-resolution, inpainting, compression or classification. The sparsity of
the representation very much depends on how well the dictionary is adapted to
the data at hand. In this paper, we propose a method for learning structured
multilevel dictionaries with discriminative constraints to make them well
suited for the supervised pixelwise classification of images. A multilevel
tree-structured discriminative dictionary is learnt for each class, with a
learning objective concerning the reconstruction errors of the image patches
around the pixels over each class-representative dictionary. After the initial
assignment of the class labels to image pixels based on their sparse
representations over the learnt dictionaries, the final classification is
achieved by smoothing the label image with a graph cut method and an erosion
method. Applied to a common set of texture images, our supervised
classification method shows competitive results with the state of the art
Adaptive diffusion constrained total variation scheme with application to `cartoon + texture + edge' image decomposition
We consider an image decomposition model involving a variational
(minimization) problem and an evolutionary partial differential equation (PDE).
We utilize a linear inhomogenuous diffusion constrained and weighted total
variation (TV) scheme for image adaptive decomposition. An adaptive weight
along with TV regularization splits a given image into three components
representing the geometrical (cartoon), textural (small scale - microtextures),
and edges (big scale - macrotextures). We study the wellposedness of the
coupled variational-PDE scheme along with an efficient numerical scheme based
on Chambolle's dual minimization method. We provide extensive experimental
results in cartoon-texture-edges decomposition, and denoising as well compare
with other related variational, coupled anisotropic diffusion PDE based
methods
Global Variational Method for Fingerprint Segmentation by Three-part Decomposition
Verifying an identity claim by fingerprint recognition is a commonplace
experience for millions of people in their daily life, e.g. for unlocking a
tablet computer or smartphone. The first processing step after fingerprint
image acquisition is segmentation, i.e. dividing a fingerprint image into a
foreground region which contains the relevant features for the comparison
algorithm, and a background region. We propose a novel segmentation method by
global three-part decomposition (G3PD). Based on global variational analysis,
the G3PD method decomposes a fingerprint image into cartoon, texture and noise
parts. After decomposition, the foreground region is obtained from the non-zero
coefficients in the texture image using morphological processing. The
segmentation performance of the G3PD method is compared to five
state-of-the-art methods on a benchmark which comprises manually marked ground
truth segmentation for 10560 images. Performance evaluations show that the G3PD
method consistently outperforms existing methods in terms of segmentation
accuracy
Linearized Alternating Direction Method with Adaptive Penalty and Warm Starts for Fast Solving Transform Invariant Low-Rank Textures
Transform Invariant Low-rank Textures (TILT) is a novel and powerful tool
that can effectively rectify a rich class of low-rank textures in 3D scenes
from 2D images despite significant deformation and corruption. The existing
algorithm for solving TILT is based on the alternating direction method (ADM).
It suffers from high computational cost and is not theoretically guaranteed to
converge to a correct solution. In this paper, we propose a novel algorithm to
speed up solving TILT, with guaranteed convergence. Our method is based on the
recently proposed linearized alternating direction method with adaptive penalty
(LADMAP). To further reduce computation, warm starts are also introduced to
initialize the variables better and cut the cost on singular value
decomposition. Extensive experimental results on both synthetic and real data
demonstrate that this new algorithm works much more efficiently and robustly
than the existing algorithm. It could be at least five times faster than the
previous method.Comment: Accepted by International Journal of Computer Vision (IJCV
Segmentation of Scanning Tunneling Microscopy Images Using Variational Methods and Empirical Wavelets
In the fields of nanoscience and nanotechnology, it is important to be able
to functionalize surfaces chemically for a wide variety of applications.
Scanning tunneling microscopes (STMs) are important instruments in this area
used to measure the surface structure and chemistry with better than molecular
resolution. Self-assembly is frequently used to create monolayers that redefine
the surface chemistry in just a single-molecule-thick layer. Indeed, STM images
reveal rich information about the structure of self-assembled monolayers since
they convey chemical and physical properties of the studied material.
In order to assist in and to enhance the analysis of STM and other images, we
propose and demonstrate an image-processing framework that produces two image
segmentations: one is based on intensities (apparent heights in STM images) and
the other is based on textural patterns. The proposed framework begins with a
cartoon+texture decomposition, which separates an image into its cartoon and
texture components. Afterward, the cartoon image is segmented by a modified
multiphase version of the local Chan-Vese model, while the texture image is
segmented by a combination of 2D empirical wavelet transform and a clustering
algorithm. Overall, our proposed framework contains several new features,
specifically in presenting a new application of cartoon+texture decomposition
and of the empirical wavelet transforms and in developing a specialized
framework to segment STM images and other data. To demonstrate the potential of
our approach, we apply it to actual STM images of cyanide monolayers on
Au\{111\} and present their corresponding segmentation results
Survey of Computer Vision and Machine Learning in Gastrointestinal Endoscopy
This paper attempts to provide the reader a place to begin studying the
application of computer vision and machine learning to gastrointestinal (GI)
endoscopy. They have been classified into 18 categories. It should be be noted
by the reader that this is a review from pre-deep learning era. A lot of deep
learning based applications have not been covered in this thesis
Directional Global Three-part Image Decomposition
We consider the task of image decomposition and we introduce a new model
coined directional global three-part decomposition (DG3PD) for solving it. As
key ingredients of the DG3PD model, we introduce a discrete multi-directional
total variation norm and a discrete multi-directional G-norm. Using these novel
norms, the proposed discrete DG3PD model can decompose an image into two parts
or into three parts. Existing models for image decomposition by Vese and Osher,
by Aujol and Chambolle, by Starck et al., and by Thai and Gottschlich are
included as special cases in the new model. Decomposition of an image by DG3PD
results in a cartoon image, a texture image and a residual image. Advantages of
the DG3PD model over existing ones lie in the properties enforced on the
cartoon and texture images. The geometric objects in the cartoon image have a
very smooth surface and sharp edges. The texture image yields oscillating
patterns on a defined scale which is both smooth and sparse. Moreover, the
DG3PD method achieves the goal of perfect reconstruction by summation of all
components better than the other considered methods. Relevant applications of
DG3PD are a novel way of image compression as well as feature extraction for
applications such as latent fingerprint processing and optical character
recognition
Fast Multi-Layer Laplacian Enhancement
A novel, fast and practical way of enhancing images is introduced in this
paper. Our approach builds on Laplacian operators of well-known edge-aware
kernels, such as bilateral and nonlocal means, and extends these filter's
capabilities to perform more effective and fast image smoothing, sharpening and
tone manipulation. We propose an approximation of the Laplacian, which does not
require normalization of the kernel weights. Multiple Laplacians of the
affinity weights endow our method with progressive detail decomposition of the
input image from fine to coarse scale. These image components are blended by a
structure mask, which avoids noise/artifact magnification or detail loss in the
output image. Contributions of the proposed method to existing image editing
tools are: (1) Low computational and memory requirements, making it appropriate
for mobile device implementations (e.g. as a finish step in a camera pipeline),
(2) A range of filtering applications from detail enhancement to denoising with
only a few control parameters, enabling the user to apply a combination of
various (and even opposite) filtering effects
Depth Sequence Coding with Hierarchical Partitioning and Spatial-domain Quantisation
Depth coding in 3D-HEVC for the multiview video plus depth (MVD) architecture
(i) deforms object shapes due to block-level edge-approximation; (ii) misses an
opportunity for high compressibility at near-lossless quality by failing to
exploit strong homogeneity (clustering tendency) in depth syntax, motion vector
components, and residuals at frame-level; and (iii) restricts interactivity and
limits responsiveness of independent use of depth information for "non-viewing"
applications due to texture-depth coding dependency. This paper presents a
standalone depth sequence coder, which operates in the lossless to
near-lossless quality range while compressing depth data superior to lossy
3D-HEVC. It preserves edges implicitly by limiting quantisation to the
spatial-domain and exploits clustering tendency efficiently at frame-level with
a novel binary tree based decomposition (BTBD) technique. For mono-view coding
of standard MVD test sequences, on average, (i) lossless BTBD achieved compression-ratio and coding gain against the pseudo-lossless
3D-HEVC, using the lowest quantisation parameter , and (ii)
near-lossless BTBD achieved and dB Bj{\o}ntegaard delta
bitrate (BD-BR) and distortion (BD-PSNR), respectively, against 3D-HEVC. In
view-synthesis applications, decoded depth maps from BTBD rendered superior
quality synthetic-views, compared to 3D-HEVC, with depth BD-BR and
dB synthetic-texture BD-PSNR on average.Comment: Submitted to IEEE Transactions on Image Processing. 13 pages, 5
figures, and 5 table
Image Cartoon-Texture Decomposition Using Isotropic Patch Recurrence
Aiming at separating the cartoon and texture layers from an image,
cartoon-texture decomposition approaches resort to image priors to model
cartoon and texture respectively. In recent years, patch recurrence has emerged
as a powerful prior for image recovery. However, the existing strategies of
using patch recurrence are ineffective to cartoon-texture decomposition, as
both cartoon contours and texture patterns exhibit strong patch recurrence in
images. To address this issue, we introduce the isotropy prior of patch
recurrence, that the spatial configuration of similar patches in texture
exhibits the isotropic structure which is different from that in cartoon, to
model the texture component. Based on the isotropic patch recurrence, we
construct a nonlocal sparsification system which can effectively distinguish
well-patterned features from contour edges. Incorporating the constructed
nonlocal system into morphology component analysis, we develop an effective
method to both noiseless and noisy cartoon-texture decomposition. The
experimental results have demonstrated the superior performance of the proposed
method to the existing ones, as well as the effectiveness of the isotropic
patch recurrence prior.Comment: 13 pages, 10 figure
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