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Removing Redundancies in Binary images 2nd

Removing Redundancies in Binary Images

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2nd International Conference on Intelligent Systems and pattern recognition, ISPR 2022

作者： Banaeyan, Majid Batavia, Darshan Kropatsch, Walter G. Pattern Recognition and Image Processing Group 193/03 TU Wien Vienna Austria

ISBN: (纸本)9783031082764

Every day a huge amount of digital data is generated. processing such big data encourages efficient data structure and parallelized operations. In this regard, this paper proposes a graph-based method reducing the memory requirement of the data storage. Graphs as a versatile representative tool in intelligent systems and pattern recognition may consist of many nonessential edges accumulating memory. This paper defines the structure of such redundant edges in the neighborhood graph of a 2D binary image. We introduce a novel approach for contracting the edges that simultaneously assists in determining the structurally redundant edges. In addition, finding a set of independent edges, the redundant edges are removed in parallel with the complexity O(1 ). Theoretically, we prove that the maximum number of redundant edges is bounded by half of all edges. Practical results show the memory requirement decreases significantly depending on the input data in different categories of binary image data sets. Using the combinatorial map as the data structure, first the topological structure of the graph is preserved. Second, the method can be extended to higher dimensions (nD). © 2022, Springer Nature Switzerland AG.

关键词： Binary images

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Parallel O(log(n) ) Computation of the Adjacency of Connected Components 3rd

Parallel O(log(n) ) Computation of the Adjacency of Conne...

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3rd International Conference on pattern recognition and Artificial Intelligence, ICPRAI 2022

作者： Banaeyan, Majid Kropatsch, Walter G. TU Wien Pattern Recognition and Image Processing Group 193/03 Vienna Austria

ISBN: (纸本)9783031092817

Connected Component Labeling (CCL) is a fundamental task in pattern recognition and image processing algorithms. It groups the pixels into regions, such that adjacent pixels have the same label while pixels belonging to distinct regions have different labels. The common linear-time raster scan CCL techniques have a complexity of O(image- size) in a 2D binary image. To speed up the procedure of the CCL, the paper proposes a new irregular graph pyramid. To construct this pyramid, we use a new formalism [1] that introduces an order of the pixels in the base grid to detect the redundant edges through the hierarchical structure. These redundant edges, unlike the usual methods of constructing the irregular pyramid, are removed before contracting the edges. This not only simplifies the construction processes but may decrease memory consumption by approximately half. To perform the CCL task efficiently the proposed parallel algorithm reduces the complexity to O(log(n) ) where the n is the diameter of the largest connected component in the image. In addition, using an efficient combinatorial structure the topological properties of the connected components including adjacency of CCs, multi-boundaries and inclusions are preserved. Finally, the mathematical proofs provide fully parallel implementations and lead to efficient results in comparison with the state-of-the-art. © 2022, Springer Nature Switzerland AG.

关键词： Pixels

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Pyramidal Connected Component Labeling by Irregular Graph Pyramid 5

Pyramidal Connected Component Labeling by Irregular Graph Py...

引用

5th International Conference on pattern recognition and image Analysis, IPRIA 2021

作者： Banaeyan, Majid Kropatsch, Walter G. Vienna University of Technology Pattern Recognition and Image Processing Group 193/03 Vienna Austria

ISBN: (纸本)9781665426596

This paper presents a new logarithmic-time algorithm which simultaneously assigns labels to all connected components of a binary image in parallel. The irregular graph pyramid of an input binary image is constructed based on the optimized combinatorial structure. The novel small built pyramid has only three levels and is created at worst case with O(log(N2)) complexity for a N× N-sized (2D) binary image. To assign a label to each connected component, instead of the common linear-time raster scan techniques (with complexity O(N2)), only two important movements, namely bottom-up and top-down traversing, are needed. First, in the bottom-up traversing, the redundant connections are removed and the contraction kernels are contracted. This results in a simpler graph at top of the pyramid each of its vertices has a unique label identifying corresponding connected component. Such reduced graph preserves all connecting relations including inclusions. Second, in the top-down traversing, each unique label propagates down into each individual corresponding pixel at the base level. The complexity of the labeling propagation procedure in worst cases is O (log(image - size)). The GPU implementation of the algorithm has high performance and the bottleneck is the bandwidth of the memory or equivalently the number of available independent processing elements. Finally, the experimental results show the proposed algorithm outperforms the other state-of-the-art methods. © 2021 IEEE.

关键词： Binary images

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image = Structure + Few Colors

Image = Structure + Few Colors

引用

Joint IAPR International Workshops on Structural, Syntactic and Statistical Techniques in pattern recognition, S+SSPR 2020

作者： Batavia, Darshan Gonzalez-Diaz, Rocio Kropatsch, Walter G. TU Wien Pattern Recognition and Image Processing Group 193/03 Vienna Austria Applied Math I University of Seville Seville Spain

ISBN: (纸本)9783030739720

Topology plays an important role in computer vision by capturing the structure of the objects. Nevertheless, its potential applications have not been sufficiently developed yet. In this paper, we combine the topological properties of an image with hierarchical approaches to build a topology preserving irregular image pyramid (TIIP). The TIIP algorithm uses combinatorial maps as data structure which implicitly capture the structure of the image in terms of the critical points. Thus, we can achieve a compact representation of an image, preserving the structure and topology of its critical points (maxima, the minima and the saddles). The parallel algorithmic complexity of building the pyramid is O(log d) where d is the diameter of the largest object. We achieve promising results for image reconstruction using only a few color values and the structure of the image, although preserving fine details including the texture of the image. © 2021, Springer Nature Switzerland AG.

关键词： image reconstruction

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