Comparative analysis of pattern reconstruction using orthogonal moments

Padilla-Vivanco, Alfonso; Urcid, Gonzalo; Granados-Agustín, Fermín; Cornejo-Rodriguez, Alejandro

doi:10.1117/1.2432878

Cited by 11 publications

(3 citation statements)

References 23 publications

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“…The outperformance of Zernike Moments is already observed in many comparative studies such as (Belkasim et al, 1991;Ezer et al, 1994;Liao and Pawlak, 1996;Lin and Chou, 2003;Padilla-Vivanco et al, 2007;Park and Kim, 2005;The and Chin, 1998;Zhang and Lu, 2004). …”

Section: Zernike Moments Methodmentioning

confidence: 75%

Red Palm Weevil (<i>Rynchophorus Ferrugineous, Olivier</i>) Recognition by Image Processing Techniques

Al-Saqer¹

2011

American Journal of Agricultural and Biological Sciences

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Problem statement: Red palm weevil is the most destructive insect for palm trees all over the world. This research is part of developing an automated wireless red palm weevil detection and control system. The focus for this study was to develop red palm Weevil recognition system which can detect RPW in an image and can be used in wireless image sensor network which will be part of entire proposed system. Approach: Template based recognition techniques were used. Two general recognition methods i.e., Zernike and Regional Properties and an algorithm combining them were used. Besides that, a novel technique for detecting Rostrum of RPW named as 'Rostrum Analysis' was proposed and used for recognition, a conclusive algorithm based on all three techniques was also proposed, 319 test images of RPW and 93 images of other insects which found in RPW habitat were used. Results: It was found that both general techniques i.e., Regional Properties and Zernike Moments methods perform reasonably in recognizing RPW. The algorithm based on both these methods performs better than individual methods. The Rostrum Analysis outperforms better than both the earlier methods and proposed algorithm using all three analytical techniques gives best results among all discussed techniques in recognizing RPW as well as other insects. Conclusion: The most balanced and efficient recognition technique is to use the proposed conclusive algorithm which is combination of Regional Properties, Zernike Moments and Rostrum Analysis techniques. The maximum time for processing an image is 0.47 sec and the results obtained in recognizing the RPW and other insects are 97 and 88% respectively.

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Section: Zernike Moments Methodmentioning

confidence: 75%

Red Palm Weevil (<i>Rynchophorus Ferrugineous, Olivier</i>) Recognition by Image Processing Techniques

Al-Saqer¹

2011

American Journal of Agricultural and Biological Sciences

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“…In this case, each moment will be a unique and independent representation to a given image. In many comparison studies of moments based methods (Teh and Chin, 1988;Lin and Chou, 2003;Belkasim and Shridhar, 1991;Zhang and Lu, 2004;Park and Kim, 2004;Ezer et al, 1994;Padilla-Vivanco and Urcid-Serrano, 2007;Liao and Pawlak, 1996), Zernike moments outperformed the others methods.…”

Section: Object Recognition By Zernike Momentsmentioning

confidence: 98%

Identification of Pecan Weevils through Image Processing

Al-Saqer¹

2011

American Journal of Agricultural and Biological Sciences

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Problem Statement: Pecan weevil is one of the most destructive pests of Oklahoma. The scope of this study is to develop a recognition system that can serve in a wireless imaging network for monitoring pecan weevils. Approach: The recognition methods used in this study are based on template matching. Five recognition methods were implemented: Normalized cross-correlation, Fourier descriptors, Zernike moments, String matching and Regional properties. The training set consisted of 205 pecan weevils and the testing set included 30 randomly selected pecan weevils and 74 other insects which typically exist in pecan habitat. Results: It was found that Region-based methods were better in representing and recognizing biological objects such as insects. Different recognition rates were obtained at different order of Zernike moments. The optimum result among the tested orders of Zernike moments was found to be at the order 3. The results also showed that using different number of Fourier descriptors may not significantly increase the recognition rate of this method. Conclusion:The most robust and reliable recognition rate was achieved when the Zernike moments and Region properties recognition methods were used in a combination. A positive match from either of these two independent tests would yield reliable results. Therefore, 100% recognition could be achieved by adopting the proposed algorithm. The processing time for such recognition is 0.44 sec.

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“…The 2D discrete Zernike Moments of an image function of radial order n and angular repetition l are, 25,26…”

Section: Zernike Momentsmentioning

confidence: 99%

Vein pattern classification using convolutional neuronal network and moment invariants

Vargas Barona,

Espejel Rivera,

Castro-Ortega

et al. 2023

Applications of Digital Image Processing XLVI

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Vein pattern recognition is a novel method to reliably identify or authenticate a person’s safety. It uses infrared images from the palm, wrist, or fingers, which shows the network of veins under the skin. This paper presents a Convolutional Neural Network (CNN) to classify infrared images of the hand vein pattern. The public PolyU Database is used to train the CNN. The CNN can classify 6000 vein patterns of the hand with an accuracy of 92.81%. Even more, its performance is compared with the invariant moment descriptors. In this case, vein pattern recognition is carried out on the raw images using k-Nearest Neighbors (k-NN) and invariant Zernike moments. An accuracy of 99.97% is obtained.

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Comparative analysis of pattern reconstruction using orthogonal moments

Cited by 11 publications

References 23 publications

Red Palm Weevil (<i>Rynchophorus Ferrugineous, Olivier</i>) Recognition by Image Processing Techniques

Red Palm Weevil (<i>Rynchophorus Ferrugineous, Olivier</i>) Recognition by Image Processing Techniques

Identification of Pecan Weevils through Image Processing

Vein pattern classification using convolutional neuronal network and moment invariants

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