1,299 research outputs found
Vector extension of monogenic wavelets for geometric representation of color images
14 pagesInternational audienceMonogenic wavelets offer a geometric representation of grayscale images through an AM/FM model allowing invariance of coefficients to translations and rotations. The underlying concept of local phase includes a fine contour analysis into a coherent unified framework. Starting from a link with structure tensors, we propose a non-trivial extension of the monogenic framework to vector-valued signals to carry out a non marginal color monogenic wavelet transform. We also give a practical study of this new wavelet transform in the contexts of sparse representations and invariant analysis, which helps to understand the physical interpretation of coefficients and validates the interest of our theoretical construction
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Composition-guided image acquisition
textTo make a picture more appealing, professional photographers apply a wealth of photographic composition rules, of which amateur photographers are of- ten unaware. This dissertation aims at providing in-camera feedback to the amateur photographer while taking pictures. The proposed algorithms do not depend on prior knowledge of the indoor/outdoor setting or scene, and are amenable to software implementation on fixed-point programmable digital signal processors available in digital still cameras.
The key enabling step in automating photographic composition rules is to locate the main subject. Digital still image acquisition maps the 3-D world onto a 2-D picture. By using the 2-D picture alone, segmenting the main subject without prior knowledge of the scene is ill-posed. Even with prior knowledge, segmentation is often computationally intensive and error prone.
This dissertation defends the idea that reliable main subject segmenta- tion without prior knowledge of scene and setting may be achieved by acquiring a single picture, in which the optical system blurs objects not in the plane of
focus. After segmentation, photographic composition rules may be automated. In this context, segmentation only needs to approximately and not precisely locate the main subject.
In this dissertation, I combine optical and digital image processing to perform the segmentation of the main subject without prior knowledge of the scene. In particular, I propose to acquire a picture in which the main subject is in focus, and the shutter aperture is fully open. The lens optics will blur any object not in the plane of focus. For the acquired picture, I develop a computationally simple one-pass algorithm to segment the main subject.
The post segmentation objective is to automate selected photographic composition rules. The algorithms can either be applied on the picture taken with the objects not in the plane of focus blurred, or on a user-intended picture with the same focal length settings. This way, in-camera feedback can be provided to the amateur photographer, in the form of alternate compositions of the same scene.
I automate three photographic composition rules: (1) placement of the main subject obeying the rule-of-thirds, (2) background blurring to simulate the main subject being in motion or decrease the depth-of-field of the picture, and (3) merger detection and mitigation when equally focused main subject and background objects merge as one object.
The primary contributions of the dissertation are in digital still image processing. The first is the automation of segmentation of the main subject in a single still picture assisted by optical pre-processing. The second is the automation of main subject placement, artistic background blur, and merger detection and mitigation to try to improve photographic composition.Electrical and Computer Engineerin
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
Robust iris recognition under unconstrained settings
Tese de mestrado integrado. Bioengenharia. Faculdade de Engenharia. Universidade do Porto. 201
Color monogenic wavelet representation based on a tensor-like use of the riesz transform: application to image coding
11 pagesInternational audienceWe propose a new extension of monogenic analysis to multi-valued signals like color images. This generalization is based on an analogy between the Riesz transform and structure tensors and takes advantage of the well defined vector differential geometry. Our color wavelet transform is non-marginal and its coefficients - separated into amplitude, phase, orientation and local color axis - have interesting physical interpretation in terms of local energy, contour model, and colorimetric features. An image coding application is proposed as a practical study
Multiscale structure of meanders
This is the final version of the article. Available from the publisher via the DOI in this record..River meander planforms can be described based on wavelet analysis, but an objective method to identify the main characteristics of a meander planform over all spatial scales is yet to be found. Here we show how a set of simple metrics representing meander shape can be retrieved from a continuous wavelet transform of a planform geometry. We construct a synoptic multiple looping tree to establish the meander structure, revealing the embedding of dominant meander scales in larger-scale loops. The method can be applied beyond the case of rivers to unravel the meandering structure of lava flows, turbidity currents, tidal channels, rivulets, supraglacial streams, and extraterrestrial flows.This research was supported by
the Royal Netherlands Academy of
Arts and Sciences (KNAW), project
SPIN3-JRP-29, and by NWO-WOTRO
Science for Global Development,
project WT76-269. We thank Meinhard
Bayani Cardenas, the Associate Editor,
Efi Foufoula-Georgiou, Jon Schwenk,
and one anonymous reviewer for
their comments and suggestions.
The data used in this study can be
obtained by contacting the corresponding
author. The processing
routines can be downloaded at
https://github.com/bartverm/
meanderscribe.git
Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review
Interest point detection is one of the most fundamental and critical problems
in computer vision and image processing. In this paper, we carry out a
comprehensive review on image feature information (IFI) extraction techniques
for interest point detection. To systematically introduce how the existing
interest point detection methods extract IFI from an input image, we propose a
taxonomy of the IFI extraction techniques for interest point detection.
According to this taxonomy, we discuss different types of IFI extraction
techniques for interest point detection. Furthermore, we identify the main
unresolved issues related to the existing IFI extraction techniques for
interest point detection and any interest point detection methods that have not
been discussed before. The existing popular datasets and evaluation standards
are provided and the performances for eighteen state-of-the-art approaches are
evaluated and discussed. Moreover, future research directions on IFI extraction
techniques for interest point detection are elaborated
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