Orientation Invariant Object Recognitions Using Geometric Moments Invariants and Color Histograms (original) (raw)
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Object Recognition using SVM-KNN based on Geometric Moment Invariant
ijcttjournal.org
In this paper, a framework for recognizing an object from the given image is discussed. The proposed method is fusion of two popular methods in the literature, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). We propose the use of KNN to find closest neighbors to a query image and train a local SVM that preserves the distance function on the collection of neighbors. The proposed method is implemented in two steps. The first one concerns KNN to compute distances of the query to all training and pick the nearest K neighbors. The second step is to recognize the object using SVM classifier. For feature vector formation, Hu's Moment Invariant is computed to represent the image, which is invariant to translation, rotation and scaling. Experimental results are shown for COIL-100 database. Comparative analysis of proposed method with SVM and KNN is also given for each experiment.
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.
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2006
Geometric moment invariant produces a set of feature vectors that are invariant under shifting, scaling and rotation. The technique is widely used to extract the global features for pattern recognition due to its discrimination power and robustness. In this paper, moment invariant is used to identify the object from the captured image using the first invariant (Ø1). The recognition rate for this technique is 90% after the image undergoes suitable processing and segmentation process.
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.
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/object-recognition-using-higher-order-moments-and-comparative-study-between-shape-space-and-moment-invariant-method https://www.ijert.org/research/object-recognition-using-higher-order-moments-and-comparative-study-between-shape-space-and-moment-invariant-method-IJERTV3IS030831.pdf In this paper, for boundary representation of the image some invariable features are extracted using higher order moments. Moment based invariants, in various forms, have been widely used over the years as features for recognition in many areas of image analysis. Here the moment based method combines the original moment invariants and the contour moment invariant, which is called a relative contour moment invariant. This algorithm is discussed and tested with scaling, translation and rotation invariance. As all the possible views of an object caused by translation, scaling and rotation are represented as a single point here we also describe shape space method to represent objects as points on a high-dimensional surface for efficient recognition of object. Then we compare both the relative contour invariant method and shape space method.
A comparative study of Motion Descriptors and Zernike moments in color object recognition
2007
Classification and object recognition is one of the most important tasks in image processing. Most applications deal with the classification of definite shapes, for example identifying a particular type aircraft. In these applications, compact visual descriptors are necessary to describe image content. Fourier descriptors are widely used in image processing to describe and classify object. Several techniques have proved useful moment's invariants. In this paper, we studied Motion descriptors (MD) introduced recently by Gauthier et al.; combined with Zernike Moments (ZM). Experiments are conducted using three databases: COIL-100, which consists of 3D objects, A R faces and cellular phones database. Recognition is performed by a Support Vector Machine as supervised classification method.
Pattern Recognition with Rotation Invariant Multiresolution Features
2004
We propose new rotation moment invariants based on multiresolution filter bank techniques. The multiresolution pyramid motivates our simple but efficient feature selection procedure based on the fuzzy C-mean clustering, combined with the Mahalanobis distance. The procedure verifies an impact of random noise as well as an interesting and less known impact of noise due to spatial transformations. The recognition accuracy of the proposed techniques has been tested with the preceding moment invariants as well as with some wavelet based schemes. The numerical experiments, with more than 30,000 images, demonstrate a tangible accuracy increase of about 3% for low noise, 8% for the average noise and 15% for high level noise.
A New Approach of Local Feature Descriptors Using Moment Invariants
Journal of Computer Science, 2014
Moment invariants have been widely introduced in recognizing planar objects for a few decades. This is due the robustness of moment function in distinguishing the original identity of object under various two Dimensional (2D) transformations. A set of moments computed from a planar images, represents the global description of an object's shape and geometrical features of an image. Since global descriptor utilizes the information of a whole object or shape to describe the features of an object, it does not tolerate occlusion. If there is a mixture of regions that do not belong to the object of the interest, an additional task of segmentation is required to isolate the object for recognition. Hence, moment invariants are proposed to be employed as local descriptors for object recognition since local descriptors do not suffer from the drawbacks caused by image clutter and occlusion. A new approach of local feature descriptors using moment invariants is presented. The preliminary framework is divided into three different stages. Interest points are firstly detected in the entire image. The local descriptors are then produced by applying moment invariants on the region around the interest points. Cross-correlation is finally carried out for feature matching.