Run-Based Connected Components Labeling Using Double-Row Scan

2020 25th International Conference on Pattern Recognition (ICPR)

et al. 2021

Connected Components Labeling represents a fundamental step for many Computer Vision and Image Processing pipelines. Since the first appearance of the task in the sixties, many algorithmic solutions to optimize the computational load needed to label an image have been proposed. Among them, block-based scan approaches and decision trees revealed to be some of the most valuable strategies.However, due to the cost of the manual construction of optimal decision trees and the computational limitations of automatic strategies employed in the past, the application of blocks and decision trees has been restricted to small masks, and thus to 2D algorithms.With this paper we present a novel heuristic algorithm based on decision tree learning methodology, called Entropy Partitioning Decision Tree (EPDT). It allows to compute nearoptimal decision trees for large scan masks. Experimental results demonstrate that algorithms based on the generated decision trees outperform state-of-the-art competitors.

Section: Resultsmentioning

confidence: 99%

“…For this reason, an open-source framework that allows to fairly benchmark and compare new proposals on CCL has been released in 2016 [35]. The framework, named YACCLAB (Yet Another Connected Components Labeling Benchmark), has been employed by many authors since its first appearance [36], [37], [38], [39], [40], [41].…”

Section: Introductionmentioning

confidence: 99%

A Heuristic-Based Decision Tree for Connected Components Labeling of 3D Volumes

Söchting

2020 25th International Conference on Pattern Recognition (ICPR)

et al. 2021

“…The inclusion of algorithms in OpenCV has been done after a careful and really open comparison of the execution times, evaluated using YACCLAB [12,22], a widely used [34,17] open source C++ benchmarking framework for CCL algorithms. YACCLAB allows researchers to test state-of-the-art algorithms on real and synthetic generated datasets.…”

Section: Discussionmentioning

confidence: 99%

Quest for Speed: The Epic Saga of Record-Breaking on OpenCV Connected Components Extraction

Lecture Notes in Computer Science

Grana

2022

Connected Components Labeling (CCL) represents an essential part of many Image Processing and Computer Vision pipelines. Given its relevance on the field, it has been part of most cutting-edge Computer Vision libraries. In this paper, all the algorithms included in the OpenCV during the years are reviewed, from sequential to parallel/GPU-based implementations. Our goal is to provide a better understanding of what has changed and why one algorithm should be preferred to another both in terms of memory usage and execution speed.

“…In this section the benefits of the proposed strategy are evaluated using YACCLAB [26], [32], [33], a widely used [34], [35] open source C++ benchmarking framework for CCL algorithms. YACCLAB allows researchers to test state-of-theart algorithms on real and synthetic generated datasets.…”

Section: Comparative Evaluationmentioning

confidence: 99%

Spaghetti Labeling: Directed Acyclic Graphs for Block-Based Connected Components Labeling

IEEE Trans. on Image Process.

Baraldi

et al. 2020

Connected Components Labeling is an essential step of many Image Processing and Computer Vision tasks. Since the first proposal of a labeling algorithm, which dates back to the sixties, many approaches have optimized the computational load needed to label an image. In particular, the use of decision forests and state prediction have recently appeared as valuable strategies to improve performance. However, due to the overhead of the manual construction of prediction states and the size of the resulting machine code, the application of these strategies has been restricted to small masks, thus ignoring the benefit of using a block-based approach. In this paper, we combine a block-based mask with state prediction and code compression: the resulting algorithm is modeled as a Directed Rooted Acyclic Graph with multiple entry points, which is automatically generated without manual intervention. When tested on synthetic and real datasets, in comparison with optimized implementations of state-of-the-art algorithms, the proposed approach shows superior performance, surpassing the results obtained by all compared approaches in all settings.