A comparative study of Motion Descriptors and Zernike moments in color object recognition (original) (raw)
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Colour Object recognition combining Motion Descriptors, Zernike Moments and Support Vector Machine
IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics, 2006
Fourier descriptors have been used successfully in the past to grey-level images, rigid bodied object. Here we used Motion Descriptors (MD) introduced recently by Gauthier et al., combined with Zernike Moments (ZM), in order to perform a recognition task in colour images. The feature vector for the MD obtained for each object appears to be unique and can be used for shape recognition. The MD, alone or combined with ZM, are used as an input of a Support Vector Machine (SVM) based classifier. We illustrate results on three available datasets: ORL faces database, COIL-100, which consists of 3D objects and A R faces.
Object Classification Using Sequences of Zernike Moments
Computer Information Systems and Industrial Management, 2017
In this paper we propose a method of object classification based on the sequences of Zernike moments. The method makes use of the pattern recognition properties of Zernike moments and expands it to the problem of classification. Since the distinctive features of the classified objects are carried over to the Zernike moments, the proposed method allows for a robust, rotation and translation invariant classification of complex objects in grayscale images. In this approach, each object class has defined a reference Zernike moment sequence that is used as the prototype of the class. The object's affiliation to the class is decided with the MSE criterion calculated for the object's Zernike moments sequence and the reference Zernike moments sequence of the class. The method is tested using grayscale images of handwritten digits and microscopic sections.
Object Recognition Using Local Characterisation and Zernike Moments
Lecture Notes in Computer Science, 2005
Even if lots of object invariant descriptors have been proposed in the literature, putting them into practice in order to obtain a robust system face to several perturbations is still a studied problem. Comparative studies between the most commonly used descriptors put into obviousness the invariance of Zernike moments for simple geometric transformations and their ability to discriminate objects. Whatever, these moments can reveal themselves insufficiently robust face to perturbations such as partial object occultation or presence of a complex background. In order to improve the system performances, we propose in this article to combine the use of Zernike descriptors with a local approach based on the detection of image points of interest. We present in this paper the Zernike invariant moments, Harris keypoint detector and the support vector machine. Experimental results present the contribution of the local approach face to the global one in the last part of this article.
Orientation Invariant Object Recognitions Using Geometric Moments Invariants and Color Histograms
International Journal of Computer and Electrical Engineering, 2015
Object recognition is a very challenging task in artificial intelligence and robotics. Many approaches have been implemented to achieve this task with greater precision and accuracy. In this paper we have implemented the approach of detecting objects in images undergo with the change in scale, rotation, and orientation. Extracting Geometric moments invariant which are extensively use to extort global features and using color histogram approach we have improved the previously recognition rate to a significant measure. The accuracy of classification is increased by adding the new feature of color Histogram which is also an invariant feature for change in scale rotation, translation, and orientation of objects and using support vector machine learning algorithm for classification.
A comparative study of moment invariants and Fourier descriptors in planar shape recognition
Proceedings of MELECON '94. Mediterranean Electrotechnical Conference, 1994
Rxcent developments in the area of pattern recognition have brought up various methodologies to the problem of planar shape recognition. In this study, two popular feature sets, namely Moment Invariants and Fourier Descriptors are applied to the problem of classification of 2-D images of airplanes, and their performance is compared vis-a-vis computational load and accuracy. An alternative form of the Moment Invariant technique, Zernike Moments, is also compared with ordinary Moment Invariants .
aZIBO: A New Descriptor Based in Shape Moments and Rotational Invariant Features
2014 22nd International Conference on Pattern Recognition, 2014
In this work, a descriptor called aZIBO (absolute Zernike moments with Invariant Boundary Orientation) that describes the shape of objects using the module of Zernike moments and the edge features obtained from an almost rotational invariant version of the Edge Gradient Co-occurrence Matrix (EGCM) is proposed. The two descriptors obtained, the Zernike module as global descriptor and the new version of EGCM as local one, are used to characterize images from three different datasets, Kimia99, MPEG2 and MPEG7. Later on, the concatenation of both local and global descriptors was evaluated using kNN with Cityblock and Chi-square distance metrics. Also, the descriptors are assessed separately with a weightbased method, being the results obtained compared with the ones reached by the baseline method, ZMEG (Zernike Moment Edge Gradient). Using MPEG7, which is the most challenging dataset, and the weight-based classifier, this proposal obtained a success rate of 78.29%, outperforming the 75.86% achieved by ZMEG method. With the MPEG2 dataset, results were even better with an 81.00% of success rate against 77.25% of ZMEG.
Real object recognition using moment invariants
Sadhana, 2005
Moments and functions of moments have been extensively employed as invariant global features of images in pattern recognition. In this study, a flexible recognition system that can compute the good features for high classification of 3-D real objects is investigated. For object recognition, regardless of orientation, size and position, feature vectors are computed with the help of nonlinear moment invariant functions. Representations of objects using two-dimensional images that are taken from different angles of view are the main features leading us to our objective. After efficient feature extraction, the main focus of this study, the recognition performance of classifiers in conjunction with moment-based feature sets, is introduced.
IET Computer Vision, 2011
The magnitude of Zernike moments (ZMs) has been used as rotation invariant features for classification problems in the past. Their individual real and imaginary components and phase coefficients are ignored, because they change with rotation. This study presents a new method to modify the individual real and imaginary components of ZMs which change due to image rotation. The modified real and imaginary components are then used as invariant image descriptors. The performance of the proposed method and magnitude-based ZM method is analysed on grayscale face images and binary character images in application to the fields of face recognition and character recognition, respectively. Experimental results show that the proposed method is robust to image rotation. For classification, the authors use L 1 -norm as the similarity measure. It is shown that the proposed method gives better recognition rate over the magnitude-based ZM method, comparatively at low orders of moment and thus it is recommended for pose invariant face recognition and also for rotation invariant character recognition. This has been proved by comparing the results of the proposed method with existing prominent methods of feature extraction in face and character recognition. On ORL database, the proposed method achieves the highest recognition rate of 96.5%, whereas a recognition rate of 99.7% is obtained on binary Roman character images.