| | ARTICLES | Am. J. innov. res. appl. sci. Volume 2, Issue 1, Pages 1-36 (January 2016) |
| Research Article THE CAUSAL NEXUS BETWEEN GOVERNMENT EXPENDITURE AND ECONOMIC GROWTH IN NIGERIA: EVIDENCE FROM A BOOTSTRAP ROLLING WINDOW APPROACH | Usman, Gambo Ahmad | and | Nanthakumar Loganathan |. Am. J. innov. res. appl. sci. 2016; 2(1):16-24. | PDF FULL TEXT | | Received | 30 December 2015| | Accepted | 8 January 2016| | Published | 18 January 2016 | |
This article is made freely available as part of this journal's Open Access: | Ghali ManuscriptRef.4-AJIRAS301215 |
Affiliation :
1.Faculty of Informatics and Computing | Universiti Sultan Zainal Abidin (UniSZA) | Tembila Campus, 22200 Besut,Terengganu | Malaysia |
Authors Copyright © 2015:
| Muhammad Ghali Aliyu1 | Mohd Fadzil Abdul Kadir1 |Mumtazimah Mohamad 1 | and |Abd Rasid Mamat1 |
| Muhammad Ghali Aliyu1 | Mohd Fadzil Abdul Kadir1 |Mumtazimah Mohamad 1 | and |Abd Rasid Mamat1 |
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ABSTRACT
Background: Plant identification based on leaf shape is an active area of research in image processing. This is because each leaf carries substantial information that can be used to identify and classify the type of a plant. However, the task is complex due to existence of noise in the features of a leaf during image acquisition that can be influenced by other leaves that have similar features but with different categories or classes. To overcome this problem, an efficient preprocessing stage needs to be considered. Objectives: This paper aim to compare five different de-noising techniques such as mean filtering technique (MFT), median filtering technique (MDFT) , adaptive (wiener) filtering technique (WFT), rank order filtering technique (ROFT) and adaptive two-pass rank order filtering technique (ATRFT) for noise removal during preprocessing stage. Material and Methods: The five different filtering techniques were applied to various categories of plant leaf and their performance was evaluated using mean square error (MSE) and peak signal to noise ratio (PSNR). The Wu’s Standard database was used to test the proposed algorithm. Results: the results showed that Adaptive (wiener) filtering technique (WFT) presents the best performance in terms of noise removal with higher average PSNR values of 40.64 dB and least MSE average result of 6.10 compare to other techniques mention above. While as Median filter give the best processing time. Conclusion: These results could be applicable to plant identification and classification in the preprocessing stage.
Keywords: Image preprocessing, peak signal to noise ratio, mean square error, noise removal
Background: Plant identification based on leaf shape is an active area of research in image processing. This is because each leaf carries substantial information that can be used to identify and classify the type of a plant. However, the task is complex due to existence of noise in the features of a leaf during image acquisition that can be influenced by other leaves that have similar features but with different categories or classes. To overcome this problem, an efficient preprocessing stage needs to be considered. Objectives: This paper aim to compare five different de-noising techniques such as mean filtering technique (MFT), median filtering technique (MDFT) , adaptive (wiener) filtering technique (WFT), rank order filtering technique (ROFT) and adaptive two-pass rank order filtering technique (ATRFT) for noise removal during preprocessing stage. Material and Methods: The five different filtering techniques were applied to various categories of plant leaf and their performance was evaluated using mean square error (MSE) and peak signal to noise ratio (PSNR). The Wu’s Standard database was used to test the proposed algorithm. Results: the results showed that Adaptive (wiener) filtering technique (WFT) presents the best performance in terms of noise removal with higher average PSNR values of 40.64 dB and least MSE average result of 6.10 compare to other techniques mention above. While as Median filter give the best processing time. Conclusion: These results could be applicable to plant identification and classification in the preprocessing stage.
Keywords: Image preprocessing, peak signal to noise ratio, mean square error, noise removal