17,757 research outputs found
Representing and retrieving regions using binary partition trees
This paper discusses the interest of Binary Partition Trees for image and region representation in the context of indexing and similarity based retrieval. Binary Partition Trees concentrate in a compact and structured way the set of regions that compose an image. Since the tree is able to represent images in a multiresolution way, only simple descriptors need to be attached to the nodes. Moreover, this representation is used for similarity based region retrieval.Peer ReviewedPostprint (published version
Shape-based defect classification for Non Destructive Testing
The aim of this work is to classify the aerospace structure defects detected
by eddy current non-destructive testing. The proposed method is based on the
assumption that the defect is bound to the reaction of the probe coil impedance
during the test. Impedance plane analysis is used to extract a feature vector
from the shape of the coil impedance in the complex plane, through the use of
some geometric parameters. Shape recognition is tested with three different
machine-learning based classifiers: decision trees, neural networks and Naive
Bayes. The performance of the proposed detection system are measured in terms
of accuracy, sensitivity, specificity, precision and Matthews correlation
coefficient. Several experiments are performed on dataset of eddy current
signal samples for aircraft structures. The obtained results demonstrate the
usefulness of our approach and the competiveness against existing descriptors.Comment: 5 pages, IEEE International Worksho
ASTErIsM - Application of topometric clustering algorithms in automatic galaxy detection and classification
We present a study on galaxy detection and shape classification using
topometric clustering algorithms. We first use the DBSCAN algorithm to extract,
from CCD frames, groups of adjacent pixels with significant fluxes and we then
apply the DENCLUE algorithm to separate the contributions of overlapping
sources. The DENCLUE separation is based on the localization of pattern of
local maxima, through an iterative algorithm which associates each pixel to the
closest local maximum. Our main classification goal is to take apart elliptical
from spiral galaxies. We introduce new sets of features derived from the
computation of geometrical invariant moments of the pixel group shape and from
the statistics of the spatial distribution of the DENCLUE local maxima
patterns. Ellipticals are characterized by a single group of local maxima,
related to the galaxy core, while spiral galaxies have additional ones related
to segments of spiral arms. We use two different supervised ensemble
classification algorithms, Random Forest, and Gradient Boosting. Using a sample
of ~ 24000 galaxies taken from the Galaxy Zoo 2 main sample with spectroscopic
redshifts, and we test our classification against the Galaxy Zoo 2 catalog. We
find that features extracted from our pipeline give on average an accuracy of ~
93%, when testing on a test set with a size of 20% of our full data set, with
features deriving from the angular distribution of density attractor ranking at
the top of the discrimination power.Comment: 20 pages, 13 Figures, 8 Tables, Accepted for publication in the
Monthly Notices of the Royal Astronomical Societ
A comparative evaluation of interactive segmentation algorithms
In this paper we present a comparative evaluation of four popular interactive segmentation algorithms. The evaluation was carried out as a series of user-experiments, in which participants were tasked with extracting 100 objects from a common dataset: 25 with each algorithm, constrained within a time limit of 2 min for each object. To facilitate the experiments, a âscribble-drivenâ segmentation tool was developed to enable interactive image segmentation by simply marking areas of foreground and background with the mouse. As the participants refined and improved their respective segmentations, the corresponding updated segmentation mask was stored along with the elapsed time. We then collected and evaluated each recorded mask against a manually segmented ground truth, thus allowing us to gauge segmentation accuracy over time. Two benchmarks were used for the evaluation: the well-known Jaccard index for measuring object accuracy, and a new fuzzy metric, proposed in this paper, designed for measuring boundary accuracy. Analysis of the experimental results demonstrates the effectiveness of the suggested measures and provides valuable insights into the performance and characteristics of the evaluated algorithms
Traffic monitoring using image processing : a thesis presented in partial fulfillment of the requirements for the degree of Master of Engineering in Information and Telecommunications Engineering at Massey University, Palmerston North, New Zealand
Traffic monitoring involves the collection of data describing the characteristics of vehicles and their movements. Such data may be used for automatic tolls, congestion and incident detection, law enforcement, and road capacity planning etc. With the recent advances in Computer Vision technology, videos can be analysed automatically and relevant information can be extracted for particular applications. Automatic surveillance using video cameras with image processing technique is becoming a powerful and useful technology for traffic monitoring. In this research project, a video image processing system that has the potential to be developed for real-time application is developed for traffic monitoring including vehicle tracking, counting, and classification. A heuristic approach is applied in developing this system. The system is divided into several parts, and several different functional components have been built and tested using some traffic video sequences. Evaluations are carried out to show that this system is robust and can be developed towards real-time applications
Entropy reduction via simplified image contourization
The process of contourization is presented which converts a raster image into a set of plateaux or contours. These contours can be grouped into a hierarchical structure, defining total spatial inclusion, called a contour tree. A contour coder has been developed which fully describes these contours in a compact and efficient manner and is the basis for an image compression method. Simplification of the contour tree has been undertaken by merging contour tree nodes thus lowering the contour tree's entropy. This can be exploited by the contour coder to increase the image compression ratio. By applying general and simple rules derived from physiological experiments on the human vision system, lossy image compression can be achieved which minimizes noticeable artifacts in the simplified image
Surface networks
© Copyright CASA, UCL. The desire to understand and exploit the structure of continuous surfaces is common to researchers in a range of disciplines. Few examples of the varied surfaces forming an integral part of modern subjects include terrain, population density, surface atmospheric pressure, physico-chemical surfaces, computer graphics, and metrological surfaces. The focus of the work here is a group of data structures called Surface Networks, which abstract 2-dimensional surfaces by storing only the most important (also called fundamental, critical or surface-specific) points and lines in the surfaces. Surface networks are intelligent and ânatural â data structures because they store a surface as a framework of âsurface â elements unlike the DEM or TIN data structures. This report presents an overview of the previous works and the ideas being developed by the authors of this report. The research on surface networks has fou
A graph-based mathematical morphology reader
This survey paper aims at providing a "literary" anthology of mathematical
morphology on graphs. It describes in the English language many ideas stemming
from a large number of different papers, hence providing a unified view of an
active and diverse field of research
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