An efficient parameterless quadrilateral-based image segmentation method

This paper proposes a general quadrilateral-based framework for image segmentation, in which quadrilaterals are first constructed from an edge map, where neighboring quadrilaterals with similar features of interest are then merged together to form regions. Under the proposed framework, the quadrilaterals enable the elimination of local variations and unnecessary details for merging from which each segmented region is accurately and completely described by a set of quadrilaterals. To illustrate the effectiveness of the proposed framework, we derived an efficient and high-performance parameterless quadrilateral-based segmentation algorithm from the framework. The proposed algorithm shows that the regions obtained under the framework are segmented into multiple levels of quadrilaterals that accurately represent the regions without severely over or undersegmenting them. When evaluated objectively and subjectively, the proposed algorithm performs better than three other segmentation techniques, namely, seeded region growing, K-means clustering and constrained gravitational clustering, and offers an efficient description of the segmented objects conducive to content-based applications. © 2005 IEEE.published_or_final_versio

Automated Hierarchical Image Segmentation Based on Merging of Quadrilaterals

Proceedings of the 6th WSEAS International Conference on Signal Processing, Computational Geometry & Artifical Vision, 2006, p. 135-140This paper proposes a quadrilateral-based and automated hierarchical segmentation method, in which quadrilaterals are first constructed from an edge map, where neighboring quadrilaterals with similar features of interest are then merged together in a hierarchical mode to form regions. When evaluated qualitatively and quantitatively, the proposed method outperforms three traditional and commonly-used techniques, namely, K-means clustering, seeded region growing and quadrilateral-based segmentation. It is shown by experimental results that our proposed method is robust in both recovering missed important regions while preventing unnecessary over-segmentation, and offers an efficient description of the segmented objects conducive to content-based applications.postprintThe 6th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision (ISCGAV'06), Crete, Greece, August 2006. in Conference Proceedings, 2006, p. 135-14

Quadrilateral-based region segmentation for tracking

We propose a novel quadrilateral based region segmentation method that is favorable for object tracking. Instead of using groups of pixels or regular blocks, it uses groups of connected quadrilaterals to represent regions. The proposed method derives the vertices of each quadrilateral from the edge map using the concept of center of masses. By merging the quadrilaterals, regions can be represented. The proposed method offers better data reduction than pixelwise region representation and better boundary approximation than block-based segmentation methods. Experimental results show that it generates a more reasonable region map, which is more suitable for object tracking, and a smaller number of regions than the seeded region growing, K-means clustering, and constrained gravitational clustering methods. © 2002 Society of Photo-Optical Instrumentation Engineers.published_or_final_versio

A New Method for Colour Image Segmentation

This paper presents an unsupervised algorithm of colour image segmentation. This method combines the advantages of the approaches based on split and merge and region growing, and the use of the RGB and HSV colour representation models. The effectiveness of the proposed method has been verified by the implementation of the algorithm using three different testing images with homogeneous regions, spatially compact and continuous. It was observed that the proposed algorithm outperforms the other analysed techniques requiring shorter processing time when compared with the other methods

Man-made Surface Structures from Triangulated Point Clouds

Photogrammetry aims at reconstructing shape and dimensions of objects captured with cameras, 3D laser scanners or other spatial acquisition systems. While many acquisition techniques deliver triangulated point clouds with millions of vertices within seconds, the interpretation is usually left to the user. Especially when reconstructing man-made objects, one is interested in the underlying surface structure, which is not inherently present in the data. This includes the geometric shape of the object, e.g. cubical or cylindrical, as well as corresponding surface parameters, e.g. width, height and radius. Applications are manifold and range from industrial production control to architectural on-site measurements to large-scale city models. The goal of this thesis is to automatically derive such surface structures from triangulated 3D point clouds of man-made objects. They are defined as a compound of planar or curved geometric primitives. Model knowledge about typical primitives and relations between adjacent pairs of them should affect the reconstruction positively. After formulating a parametrized model for man-made surface structures, we develop a reconstruction framework with three processing steps: During a fast pre-segmentation exploiting local surface properties we divide the given surface mesh into planar regions. Making use of a model selection scheme based on minimizing the description length, this surface segmentation is free of control parameters and automatically yields an optimal number of segments. A subsequent refinement introduces a set of planar or curved geometric primitives and hierarchically merges adjacent regions based on their joint description length. A global classification and constraint parameter estimation combines the data-driven segmentation with high-level model knowledge. Therefore, we represent the surface structure with a graphical model and formulate factors based on likelihood as well as prior knowledge about parameter distributions and class probabilities. We infer the most probable setting of surface and relation classes with belief propagation and estimate an optimal surface parametrization with constraints induced by inter-regional relations. The process is specifically designed to work on noisy data with outliers and a few exceptional freeform regions not describable with geometric primitives. It yields full 3D surface structures with watertightly connected surface primitives of different types. The performance of the proposed framework is experimentally evaluated on various data sets. On small synthetically generated meshes we analyze the accuracy of the estimated surface parameters, the sensitivity w.r.t. various properties of the input data and w.r.t. model assumptions as well as the computational complexity. Additionally we demonstrate the flexibility w.r.t. different acquisition techniques on real data sets. The proposed method turns out to be accurate, reasonably fast and little sensitive to defects in the data or imprecise model assumptions.Künstliche Oberflächenstrukturen aus triangulierten Punktwolken Ein Ziel der Photogrammetrie ist die Rekonstruktion der Form und Größe von Objekten, die mit Kameras, 3D-Laserscannern und anderern räumlichen Erfassungssystemen aufgenommen wurden. Während viele Aufnahmetechniken innerhalb von Sekunden triangulierte Punktwolken mit Millionen von Punkten liefern, ist deren Interpretation gewöhnlicherweise dem Nutzer überlassen. Besonders bei der Rekonstruktion künstlicher Objekte (i.S.v. engl. man-made = „von Menschenhand gemacht“ ist man an der zugrunde liegenden Oberflächenstruktur interessiert, welche nicht inhärent in den Daten enthalten ist. Diese umfasst die geometrische Form des Objekts, z.B. quaderförmig oder zylindrisch, als auch die zugehörigen Oberflächenparameter, z.B. Breite, Höhe oder Radius. Die Anwendungen sind vielfältig und reichen von industriellen Fertigungskontrollen über architektonische Raumaufmaße bis hin zu großmaßstäbigen Stadtmodellen. Das Ziel dieser Arbeit ist es, solche Oberflächenstrukturen automatisch aus triangulierten Punktwolken von künstlichen Objekten abzuleiten. Sie sind definiert als ein Verbund ebener und gekrümmter geometrischer Primitive. Modellwissen über typische Primitive und Relationen zwischen Paaren von ihnen soll die Rekonstruktion positiv beeinflussen. Nachdem wir ein parametrisiertes Modell für künstliche Oberflächenstrukturen formuliert haben, entwickeln wir ein Rekonstruktionsverfahren mit drei Verarbeitungsschritten: Im Rahmen einer schnellen Vorsegmentierung, die lokale Oberflächeneigenschaften berücksichtigt, teilen wir die gegebene vermaschte Oberfläche in ebene Regionen. Unter Verwendung eines Schemas zur Modellauswahl, das auf der Minimierung der Beschreibungslänge beruht, ist diese Oberflächensegmentierung unabhängig von Kontrollparametern und liefert automatisch eine optimale Anzahl an Regionen. Eine anschließende Verbesserung führt eine Menge von ebenen und gekrümmten geometrischen Primitiven ein und fusioniert benachbarte Regionen hierarchisch basierend auf ihrer gemeinsamen Beschreibungslänge. Eine globale Klassifikation und bedingte Parameterschätzung verbindet die datengetriebene Segmentierung mit hochrangigem Modellwissen. Dazu stellen wir die Oberflächenstruktur in Form eines graphischen Modells dar und formulieren Faktoren basierend auf der Likelihood sowie auf apriori Wissen über die Parameterverteilungen und Klassenwahrscheinlichkeiten. Wir leiten die wahrscheinlichste Konfiguration von Flächen- und Relationsklassen mit Hilfe von Belief-Propagation ab und schätzen eine optimale Oberflächenparametrisierung mit Bedingungen, die durch die Relationen zwischen benachbarten Primitiven induziert werden. Der Prozess ist eigens für verrauschte Daten mit Ausreißern und wenigen Ausnahmeregionen konzipiert, die nicht durch geometrische Primitive beschreibbar sind. Er liefert wasserdichte 3D-Oberflächenstrukturen mit Oberflächenprimitiven verschiedener Art. Die Leistungsfähigkeit des vorgestellten Verfahrens wird an verschiedenen Datensätzen experimentell evaluiert. Auf kleinen, synthetisch generierten Oberflächen untersuchen wir die Genauigkeit der geschätzten Oberflächenparameter, die Sensitivität bzgl. verschiedener Eigenschaften der Eingangsdaten und bzgl. Modellannahmen sowie die Rechenkomplexität. Außerdem demonstrieren wir die Flexibilität bzgl. verschiedener Aufnahmetechniken anhand realer Datensätze. Das vorgestellte Rekonstruktionsverfahren erweist sich als genau, hinreichend schnell und wenig anfällig für Defekte in den Daten oder falsche Modellannahmen

Image Segmentation by Energy and Related Functional Minimization Methods

Effective and efficient methods for partitioning a digital image into image segments, called ¿image segmentation,¿ have a wide range of applications that include pattern recognition, classification, editing, rendering, and compressed data for image search. In general, image segments are described by their geometry and similarity measures that identify them. For example, the well-known optimization model proposed and studied in depth by David Mumford and Jayant Shah is based on an L2 total energy functional that consists of three terms that govern the geometry of the image segments, the image fidelity (or closeness to the observed image), and the prior (or image smoothness). Recent work in the field of image restoration suggests that a more suitable choice for the fidelity measure is, perhaps, the l1 norm. This thesis explores that idea applied to the study of image segmentation along the line of the Mumford and Shah optimization model, but eliminating the need of variational calculus and regularization schemes to derive the approximating Euler-Lagrange equations. The main contribution of this thesis is a formulation of the problem that avoids the need for the calculus of variation. The energy functional represents a global property of an image. It turns out to be possible, however, to predict how localized changes to the segmentation will affect its value. This has been shown previously in the case of the l2 norm, but no similar method is available for other norms. The method described here solves the problem for the l1 norm, and suggests how it would apply to other forms of the fidelity measure. Existing methods rely on a fixed initial condition. This can lead to an algorithm finding local instead of global optimizations. The solution given here shows how to specify the initial condition based on the content of the image and avoid finding local minima

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

An Efficient Parameterless Quadrilateral-Based Image Segmentation Method

Abstract—This paper proposes a general quadrilateral-based framework for image segmentation, in which quadrilaterals are first constructed from an edge map, where neighboring quadrilaterals with similar features of interest are then merged together to form regions. Under the proposed framework, the quadrilaterals enable the elimination of local variations and unnecessary details for merging from which each segmented region is accurately and completely described by a set of quadrilaterals. To illustrate the effectiveness of the proposed framework, we derived an efficient and high-performance parameterless quadrilateral-based segmentation algorithm from the framework. The proposed algorithm shows that the regions obtained under the framework are segmented into multiple levels of quadrilaterals that accurately represent the regions without severely over or undersegmenting them. When evaluated objectively and subjectively, the proposed algorithm performs better than three other segmentation techniques, namely, seeded region growing, K-means clustering and constrained gravitational clustering, and offers an efficient description of the segmented objects conducive to content-based applications. Index Terms—Approximate methods, object representations, region growing, quadrilateral-based segmentation.