Removing Redundancies in Binary Images

Banaeyan, Majid; Batavia, Darshan; Kropatsch, Walter G.

doi:10.1007/978-3-031-08277-1_19

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…The height of the built pyramid and the complexity of the construction depends on how the CKs are selected. In order to achieve an efficient and a unique selection of the CKs a total order is defined over the vertices [17]. Consider 𝐺 as the neighborhood graph of an binary input image with 𝑀 by 𝑁 vertices.…”

Section: Selecting the Cks Using A Total Ordermentioning

confidence: 99%

“…Graphs as a versatile representative tool may have many unnecessary (redundant) edges [17]. Through the construction of the pyramid, contracting edges results in a smaller induced graph at the upper level.…”

Section: Redundant Edgesmentioning

confidence: 99%

“…Generally, the definition of the redundant edges depends on the applications and to what extend the reconstruction needs to be performed. For example, Banaeyan et al [6,17,19] defined the concept of the redundant edges in a binary graph pyramid in order to do the connected component labeling task where the fully reconstruction is performed. They showed that the redundant edges can be detected (predicted) before performing the contraction of edges.…”

Section: Definition 8 (Redundant Edges) In a Hierarchical Structure T...mentioning

confidence: 99%

“…Selecting an efficient total order effects on selecting the CKs where the number of CKs determines the height of the pyramid. Pyramids with logarithmic height reduce the parallel computational complexity of fundamental operations such as connected component labeling [19,17] and distance transform [20]. Therefore, a proper selection of the total order must result in constructing the pyramid with logarithmic height.…”

mentioning

confidence: 99%

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Fast Labeled Spanning Tree in Binary Irregular Graph Pyramids

Banaeyan¹,

Kropatsch²

2022

J. Engg. Res. & Sci.

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Irregular Pyramids are powerful hierarchical structures in pattern recognition and image processing. They have high potential of parallel processing that makes them useful in processing of a huge amount of digital data generated every day. This paper presents a fast method for constructing an irregular pyramid over a binary image where the size of the images is more than 2000 in each of 2/3 dimensions. Selecting the contraction kernels (CKs) as the main task in constructing the pyramid is investigated. It is shown that the proposed fast labeled spanning tree (FLST) computes the equivalent contraction kernels (ECKs) in only two steps. To this purpose, first, edges of the corresponding neighborhood graph of the binary input image are classified. Second, by using a total order an efficient function is defined to select the CKs. By defining the redundant edges, further edge classification is performed to partition all the edges in each level of the pyramid. Finally, two important applications are presented : connected component labeling (CCL) and distance transform (DT) with lower parallel complexity 𝒪(𝑙𝑜 𝑔(𝛿)) where the 𝛿 is the diameter of the largest connected component in the image.

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Section: Selecting the Cks Using A Total Ordermentioning

confidence: 99%

Section: Redundant Edgesmentioning

confidence: 99%