Pattern recognition with moment invariants: A comparative study and new results

Belkasim, Saeid; Shridhar, M.; Ahmadi, Majid

doi:10.1016/0031-3203(91)90140-z

Cited by 453 publications

(180 citation statements)

References 20 publications

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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: 99%

“…There are several insect identification systems that have been developed including: Digital Automated Identification System (DAISY) (Watson et al, 2003), Automated Bee Identification System (ABIS) (Arbuckle et al, 2001), Species Identification Automated and Web Accessible (SPIWA) (Do and Harp, 1999) and the Automated Insect Identification through Concatenated Histograms of Local Appearance (AIICHLA) (Larios et al, 2007). However, these systems have some limitations and may not be applicable for identifying pecan weevils.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Object Recognition By Zernike Momentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of Pecan Weevils through Image Processing

Al-Saqer¹

2011

American Journal of Agricultural and Biological Sciences

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“…A pattern may be represented by a density distribution function, moments can be obtained for a pattern representing an object, and they can be used to discriminate between objects (or classes) [6]. This technique has been previously used in pattern and object recognition as far as the early 60s [7,6,8,9]. Nevertheless, they were usually applied on measures from an RGB image and the classifiers used were simple.…”

Section: Introductionmentioning

confidence: 99%

3D Representation for Object Detection and Verification

Villavicencio

López-Franco

Arana‐Daniel

et al. 2013

Lecture Notes in Computer Science

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Abstract. In this paper we introduce a representation for object verification and a system for object recognition based on local features, invariant moments, silhouette creation and a 'net' reduction for depth information. The results are then compared with some of the most recent approaches for object detection such as local features and orientation histograms. Additionally, we used depth information to create descriptors that can be used for 3D verification of detected objects. Moments are computed from a 3D set of points which are arranged to create a descriptive object model. This information showed to be of matter in the decision whether the object is present within the analyzed image segment, or not.

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“…Since moment invariants have generated a substantial literature, e.g. [25], [45], [46], [7], [18], [4], [19] it seems appropriate to comment on the relation between the present treatise and these concepts. We notice again that the polynomial functions appear in both domains in (12) and (50) and that the linear combinations A brings about very similar orthogonal basis sets.…”

mentioning

confidence: 99%