کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6855651 | 660831 | 2016 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Connected-component labeling based on hypercubes for memory constrained scenarios
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
The extraction and labeling of connected components in images play an important role in a wide range of fields, such as computer vision, remote sensing, medicine, biometrics, document analysis, robotics, among others. The automatic identification of relevant image regions allows for the development of intelligent systems to address complex problems for segmentation, classification and interpretation purposes. In this work, we present novel algorithms for labeling connected components that do not require any data structure on the labeling process. The algorithms are derived from other based upon independent spanning trees over the hypercube graph. Initially, the image coordinates are mapped to a binary Gray code axis, such that all pixels that are neighbors in the image are neighbors on the hypercube and each node that belongs to the hypercube represents a pixel in the image. We then use the algorithm proposed by Silva et al. (2013) to generate the logâN independent spanning trees over the image. The proposed methods for connected-component labeling are applied to a number of images to demonstrate its effectiveness.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 61, 1 November 2016, Pages 272-281
Journal: Expert Systems with Applications - Volume 61, 1 November 2016, Pages 272-281
نویسندگان
Eduardo Sant'Ana da Silva, Helio Pedrini,