Comparative Study of Hu Moments and Zernike Moments in Object Recognition

Sabhara, Reza Kasyauqi; Lee, Chin Poo; Lim, Kian Ming

doi:10.6029/smartcr.2013.03.003

Cited by 20 publications

(8 citation statements)

References 5 publications

(8 reference statements)

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“…The integration of Hu moments and Euclidian similarity as pattern classifier has dramatically decreased the processing time while enhancing QR code recognition accuracy compared to the traditional methods which use the whole patterns images to perfume comparisons. Although its efficiency, Hu moments technique still less efficient compared to Zernike moments which are more efficient, flexible, in addition that their coefficients are easier to reconstruct than Hu ones [24]. Zernike moments and other settings will be integrated in our future work as to further enhance the performance of proposed system.…”

Section: Discussionmentioning

confidence: 99%

QR Code Patterns Localization based on Hu Invariant Moments

Tribak¹,

Zaz²

2017

ijacsa

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Abstract-The widespread utilization of QR code and its coincidence with the swift growth of e-commerce transactions have imposed the computer vision researchers to continuously devise a variety of QR code recognition algorithms. The latter performances are generally limited due to two main factors. Firstly, most of them are computationally expensive because of the implemented feature descriptor complexities. Secondly, the evoked algorithms are often sensitive to pattern geometric deformations. In this paper a robust approach is proposed, in which the architecture is based on three distinct treatments among others: 1) An image quality assessment stage which evaluates the quality of the captured image in consideration that the presence of blur decreases significantly the recognition accuracy. 2) This stage is followed by an image segmentation based on an achromatic filter through which only the regions of interest are highlighted and consequently the execution time is reduced. 3) Finally, the Hu invariant moments technique is used as feature descriptor permitting removing false positives. This technique is implemented to filter out the set of extracted candidate QR code patterns, which have been roughly extracted by a scanning process. The Hu moments descriptor is able to recognize patterns independently of the geometric transformations they undergo. The experiments show that the incorporation of the aforementioned three stages enhances significantly the recognition accuracy along with a notable diminution of processing time. This makes the proposed approach adapted to embedded systems and devices with limited performances.

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Section: Discussionmentioning

confidence: 99%

QR Code Patterns Localization based on Hu Invariant Moments

Tribak¹,

Zaz²

2017

ijacsa

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“…Thus moments are uniquely quantified based on their orders ( [6,7]). The distinguishing feature of ZM is the invariance of its magnitude with respect to rotation ( [8,9,10,11]). If we are given the ordered pair (m, n) which represents the order of the Zernike polynomial and the multiplicity of its phase angle, then the ZM, can be defined as (3.3)…”

Section: Zernike Momentmentioning

confidence: 99%

Zernike Moments and Genetic Algorithm: Tutorial and Application

Babatunde¹,

Leisa²,

Liu³

et al. 2014

BJMCS

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Aims/ objectives: To demontrate effectiveness of Zernike Moments for Image Classification. Zernike moment(ZM) is an excellent region-based moment which has attracted the attentions of many image processing researchers since its first application to image analysis. Many papers have been published on several works done on ZM but no single paper ever give a detailed information of how the computation of ZM is done from the time the image is captured to the computation of ZM. This work showed how to effectively apply ZM on RGB images. We have demonstrated the effectiveness of Zernike moment in image classification system. A neuro-genetic intelligent system has been built with PNN classifier. The feature extracted viz ZM and Geometric features were further subjected to GA to bring the best combinatorial features for optimal accuracy. The algebraic structure of our novel fitness function enabled the GA to select the best results. The 10-fold CV used enabled the whole system to be unbiased giving a classification accuracy of 90.05%. A demonstration of affine properties of ZM are comprehensively stated and explained. In summary, the ZM enabled the classifier to have improved accuracy of 91% as compared with Geometric features with 89 % accuracy.

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“…In our case we will use the seven moment's invariants Hu (Sabhara et al, 2013), these signatures constructed a set of vectors descriptors associated with the 3D object.…”

Section: Vectors Descriptors Extractionmentioning

confidence: 99%

A New Method for 3d Shape Indexing and Retrieval in Large Database by Using the Level Cut

Elkhal¹,

Lakehal²,

Satori³

2014

Journal of Computer Science

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In this study, we propose a new method for indexing and retrieval of 3D models in large databases based on binary images extracted from the 3D object called "level cut" LC. These cuts are obtained by the intersection of the set of the plans with the 3D object. A set of equidistant parallel plans generates by the intersection with the 3D object a set of cuts that used to indexing the 3D model. We are based on these cuts to describe the 3D object by using the vectors descriptors based on these cuts. To validate our descriptor we extract a test database from the NTU base. The robustness of our descriptor is well demonstrated by the comparison with the two will known descriptors, the 3D Zernike descriptor and the 3D invariant moment's descriptor. The topological problem of the external surfaces that representing the 3D object has been confronted, that shows the superiority of the cut method, because it is keeping the external surfaces details of the 3D object during the cutting step.Keywords: Characteristics Level Cut, Vector Descriptor, 3D Shape Indexing and Retrieval, X-means Algorithm, Similarity Measuring INTROCUCTIONRecently, the technological development of 3D modeling tools is forever exploited. Many 3D models are now downloadable for free thanks to the internet, as well as the large databases of 3D models have become available in the market. The retrieval of these models in databases demands sophisticated methods for a rapid and effective response.Among the indexation approaches of 3D models, we find the 2D/3D approach (Haris et al., 2014;Jain and Singh, 2013;Li and Johan, 2013;Petre et al., 2010) which is based on a set of images (the characteristic views, images of depths, the slices...). The importance of this approach is extract for each image a descriptor vector for construct a set of vectors descriptors associated with the object to index. The similarity measure between two 3D objects then returns to calculate the distance between the two sets of descriptors representing them. The extraction of information of the 3D object in a robust and efficient way Recently, the technological development of 3D has become a real challenge. The proposed method permits to deal with this problem. Given a 3D object, the method starts by a normalization step of the processed object using the CPCA technique. Then, we extract a set of cuts based on our cutting method. This set of cuts (binary images) will be indexed by a 2D image descriptor to construct the descriptor vector associated to the 3D model, using the Hausdorff distance to calculate the distance between the vectors descriptors which allows us to measure the similarity between the 3D objects.The proposed descriptor shows the robustness in terms of response to the query provided by the user and the comparative study between the proposed descriptor and two other descriptors well known of the 3D/3D approach, the 3D Zernike moments and 3D invariant moments shows considerable results.

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Comparative Study of Hu Moments and Zernike Moments in Object Recognition

Cited by 20 publications

References 5 publications

QR Code Patterns Localization based on Hu Invariant Moments

QR Code Patterns Localization based on Hu Invariant Moments

Zernike Moments and Genetic Algorithm: Tutorial and Application

A New Method for 3d Shape Indexing and Retrieval in Large Database by Using the Level Cut

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