2011
DOI: 10.3844/jcssp.2011.736.743
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Improved Vertex Chain Code Based Mapping Algorithm for Curve Length Estimation

Abstract: Problem statement: Image representation has always been an important and interesting topic in image processing and pattern recognition. However, curve tracing and its relative operations are the main bottleneck. Approach: This research presents the mapping algorithm that covers one of the vertex chain code cells, the rectangular-VCC cell. The mapping algorithm consists of a cell-representation algorithm that represents a thinned binary image in rectangular cells, a transcribing algorithm that transcribes the c… Show more

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Cited by 19 publications
(6 citation statements)
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“…They include human factors and design factors. Human factors include personality Characteristics [9,10], learning styles [11][12][13][14], and instructor's attributes [15]). Design factors include a wide range of constructs that affect effectiveness of e-learning systems such as technology [5,[16][17][18], learner control, learning model [19,20], course contents and structure [21][22][23], and interaction [23][24][25][26].…”
Section: E-learning Systems and Outcomesmentioning
confidence: 99%
“…They include human factors and design factors. Human factors include personality Characteristics [9,10], learning styles [11][12][13][14], and instructor's attributes [15]). Design factors include a wide range of constructs that affect effectiveness of e-learning systems such as technology [5,[16][17][18], learner control, learning model [19,20], course contents and structure [21][22][23], and interaction [23][24][25][26].…”
Section: E-learning Systems and Outcomesmentioning
confidence: 99%
“…Features extraction is an important stage of image classification; selecting the best feature descriptor leads to classification accuracy (Haron, Rehman, Wulandhari, & Saba, 2011; Harouni et al, 2014; Sadad, Munir, Saba, & Hussain, 2018) and sometimes only few features perform best (Mughal, Muhammad, et al, 2018; Mughal, Sharif, et al, 2018; Mughal, Muhammad, Sharif, Saba, & Rehman, 2017; Neamah, Mohamad, Saba, & Rehman, 2014). Several features descriptors, such as LBP, HOG, SURF, and SIFT, ORB, and so forth.…”
Section: Proposed Methodologymentioning
confidence: 99%
“…A dense group has an extremely specific angle, made of an interwoven of hues, that is the main inspiration of this work to consider this feature to detect crowd in a scenario. While color features have been used for object detection, but the need to visualize crowd as a cluster of the high variance color textured region needs to be studied and explored [49][50][51]. Fig.…”
Section: Related Workmentioning
confidence: 99%