A Hybrid Approach to Parallel Connected Component Labeling Using CUDA
نویسندگان
چکیده
Connected component labeling (CCL) is a mandatory step in image segmentation where each object in an image is identified and uniquely labeled. Sequential CCL is a time-consuming operation and thus is often implemented within parallel processing framework to reduce execution time. Several parallel CCL methods have been proposed in the literature. Among them are NSZ label equivalence (NSZLE) method and modified 8 directional label selection (M8DLS) method. It was shown that M8DLS outperforms NSZ-LE and M8DLS is by far the best. In this paper we propose a new parallel CCL algorithm termed as HYBRID1 that hybridizes M8DLS and Kernel C method with some modification and show that it runs faster than M8DLS for various kinds of images.
منابع مشابه
Fast Parallel Connected Component Labeling Algorithms Using Cuda Based on 8-directional Label Selection
Connected component labeling (CCL) is a key step in image segmentation where foreground pixels are extracted and labeled. Sequential CCL is a computationally expensive operation and thus is often done within parallel processing framework to reduce execution time. Various parallel CCL methods have been proposed in the literature. Among them NSZ label equivalence (NSZ-LE) method seemed to perform...
متن کاملEfficient Parallel Connected Components Labeling with a Coarse-to-fine Strategy
This paper proposes a new parallel approach to solve connected components on a 2D binary image implemented with CUDA. We employ the following strategies to accelerate neighborhood exploration after dividing an input image into independent blocks. In the local labeling stage, a coarse-labeling algorithm, including row-column connection and label-equivalence list unification, is applied first to ...
متن کاملConnected component labeling on a 2D grid using CUDA
Connected component labeling is an important but computationally expensive operation required in many fields of research. The goal in the present work is to label connected components on a 2D binary map. Two different iterative algorithms for doing this task are presented. The first algorithm (Row–Col Unify) is based upon the directional propagation labeling, whereas the second algorithm uses t...
متن کاملConnected Component Labeling in CUDA
Connected component labeling (CCL) is a task of detecting connected regions in input data, and it finds its applications in pattern recognition, computer vision, and image processing. We present a new algorithm for connected component labeling in 2-D images implemented in CUDA. We first provide a brief overview of the CCL problem together with existing CPU-oriented algorithms. The rest of the c...
متن کاملParallelization of Rich Models for Steganalysis of Digital Images using a CUDA-based Approach
There are several different methods to make an efficient strategy for steganalysis of digital images. A very powerful method in this area is rich model consisting of a large number of diverse sub-models in both spatial and transform domain that should be utilized. However, the extraction of a various types of features from an image is so time consuming in some steps, especially for training pha...
متن کامل