An Equal Homological Spanning Forest Structure for 2D Topological Picture Examination

Journal of Generalized Lie Theory and Applications

ISSN: 1736-4337

Open Access

Opinion - (2022) Volume 16, Issue 4

An Equal Homological Spanning Forest Structure for 2D Topological Picture Examination

Palle Jorgensen*
*Correspondence: Palle Jorgensen, Deparment of Mathematics, University of Iowa, USA, Email:
Deparment of Mathematics, University of Iowa, USA

Received: 04-Apr-2022, Manuscript No. glta-22-69773; Editor assigned: 06-Apr-2022, Pre QC No. P-69773; Reviewed: 18-Apr-2022, QC No. Q-69773; Revised: 22-Apr-2022, Manuscript No. R-69773; Published: 26-Apr-2022 , DOI: 10.37421/1736-4337.2022.16.335
Citation: Jorgensen, Palle “An Equal Homological Spanning Forest Structure for 2D Topological Picture Examination.” J Generalized Lie Theory App 16 (2022): 335.
Copyright: © 2022 Jorgensen P. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


In a topologically predictable system to help equal topological examination and acknowledgment for 2D computerized objects was presented. In view of this hypothetical work, we centre on the issue of tracking down effective algorithmic answers for topological cross examination of a 2D computerized object of interest D of a pre-segmented advanced picture I, utilizing 4-nearness between pixels of D. To amplify the level of parallelization of the topological cycles, we use as numerous rudimentary unit handling as pixels the picture I has. The numerical model hidden this structure is a proper expansion of the traditional idea of conceptual cell complex: a base double dynamic cell complex (pACC for short). This adaptable information structure envelops the thought of Homological Spanning Forest cultivated in. Beginning from a symmetric pACC related with I, the usual methodology is to build by means of combinatorial tasks one more hilter kilter one introducing the maximal number of non-invalid basic rudimentary co-operations between the cells of D. The key topological apparatuses have been changed in order to advance a proficient equal execution in any equal arranged design (GPUs, multi-strung PCs, SIMD portions, etc.). A product model demonstrating such an equal system is fabricated.


The topological consistency proof of such systems is provided in most of the cases by means of a mathematical model of digital images and objects, under which all theoretical formulae related to topology are true and there is no room for paradoxes. Moreover, this framework must substantially simplify the algorithmic design of an advanced topological calculus and recognition of the objects of interest. We aim to achieve parallel architectures compatible with this framework and to reduce drastically the time complexity in topological computations. In order to avoid segmentation issues and noise which are common to mathematically ill-posed problems ubiquitous in the area of Digital Imagery, the input data are 2-dimensional integer-valued matrices associated with a binary or gray-level pre-segmented 2D digital image I. Our interest here is todesign and to implement a parallel framework providing efficient and fast algorithmic answer to any topological interrogation for a region of interest (ROI for short) D of I, using 4-adjacency between pixels of D [1].

Roughly speaking, topology helps to understand the different “degrees of connectivity” a geometric object has. To deal with topological isomorphisms or homeomorphisms between continuous geometric objects is a very hard task and discretization strategies, such as triangulations, are employed for reducing the computational complexity of the topological interrogation. Within a semicontinuous context, geometric subdivided objects are commonly represented by cell or CW-complexes. Finally, within a purely discrete level, combinatorial versions of CW-complexes, called abstract cell complexes (ACC, for short), can be used for a correct algorithmic development. They are formed of basic elements (representing the cells using topological coordinates) of different dimension together with a bounding function describing the combinatorial relationship “to be in the boundary of”. Different definitions of ACCs can be found in the literature. Concerning the computability of topological features and invariants, there are two main ways for computing n-dimensional “(co)holes” measuring the lack of connectivity: (co)homology and (co) homotopy. Homology considers the notion of hole in linear algebra terms and homotopy in purely combinatorial terms. All the previous descriptions of ACCs are convenient for codifying chain complexes (algebraic versions of cell complexes) and designing homological computational techniques. Nevertheless, homotopy computation is much more harder in general than homology computation and cannot be appropriately developed in a parallel framework exclusively based on this ACC coding. An exception to this is given by the Euler number that can be computed exclusively using local information on pixels [2].

We use as model of our parallel combinatorial framework an extension of the classical ACC notion called primal–dual abstract cell complex, in the sense that two bounding functions are employed for specifying the connectivity of the structure. This notion encompasses the concept of HSF developed in which ensures the reliability of topological interrogation both at homology and homotopy levels. In fact, an HSF of a cell complex D can be seen as an asymmetric pACC strongly “connected” (in combinatorial terms) to a fully symmetric pACC defining D [3,4]. From this topological model in 2D digital ambiance, we can reach homotopy-type features and characteristics like topological trees or thinning. There are numerous contributions in the literature parallel algorithms computing a concrete topological invariant or feature propose a theoretical topologically-consistent framework in this digital context. To our knowledge, the present paper is the first one implementing such a parallel framework for advanced topology computation [5]. The combinatorial technique used here for constructing asymmetric pACCs encoding faithful topological information takes some inspiration from methods developed in Simple Homotopy Theory, Discrete Morse Theory, Effective Homology and, mainly Algebraic-Topological models.


The handiness and legitimacy of the Homological Spanning Forest methodology inside the setting of the 2D Digital Imagery is additionally united in this paper under the arrangement of basic double unique cell complex. We convey an equal system for topological calculation in view of HSF designs of pictures and items. Both homology and homotopy type data of a ROI D of a computerized picture I can be proficiently extricated from HSF-models for I. A HSF woods of D is constructed by means of equal and consecutive iterative correction of a HSF of the climate space I, expanding in each step the thickness of cells of the HSF having a place with Cell(D). The strategy utilized in every one of the rudimentary advances (aside from the development of the underlying MrSF) of this combinatorial improvement process depends on the pACC-homology idea of base double way. The time intricacy request of this structure is near the logarithm of the amount of the width and the level of the picture. Just a straight term (assuming not exactly its 15% of this total in the mean) shows up on the last advances. Besides, the product system is completely equal, so it is normal to scale well for any equal engineering (GPUs, SIMD parts, multithreaded, and so on.). At last, in a not so distant future we need to face to the accompanying difficulties: the expansion of loads to the phones of our theoretical basic double cell complex definition, since it has all the earmarks of being a suitable answer for manage issues of topological elements and genuinely based recreation; fostering a comparative system for higher layered pictures and ROIs with any sort of nearness connection.


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