Coin Value Counter

The coins which are required to be counted manually, collected in huge amount, for example the coins collected at donation box in the temples is very hard. Counting of coins be made easy with the method which is given here for coin recognition, and here scanning of the coin from both the sides is considered. The main aim of this project is counting the value of coins as well as the total number of coins using image processing with MATLAB. Coin segmentation and cropping is done using codes in image processing. The Eigen value of coins is calculated using MATLAB .Eigen values and Eigen vectors are used to create the Eigen faces of the coins which will help in Coin Recognition. Real-time image is captured and identified and thus coin recognition is done. DOI: 10.17762/ijritcc2321-8169.150520

Spatio-structural Symbol Description with Statistical Feature Add-on

The original publication is available at www.springerlink.comInternational audienceIn this paper, we present a method for symbol description based on both spatio-structural and statistical features computed on elementary visual parts, called 'vocabulary'. This extracted vocabulary is grouped by type (e.g., circle, corner ) and serves as a basis for an attributed relational graph where spatial relational descriptors formalise the links between the vertices, formed by these types, labelled with global shape descriptors. The obtained attributed relational graph description has interesting properties that allows it to be used efficiently for recognising structure and by comparing its attribute signatures. The method is experimentally validated in the context of electrical symbol recognition from wiring diagrams

Walsh Transform based Feature vector generation for Image Database Classification

Thousands of images are generated everyday, which implies the need to build an easy, faster, automated classifier to classify and organize these images. Classification means selecting an appropriate class for a given image from a set of pre-defined classes. The main objective of this work is to explore feature vector generation using Walsh transform for classification. In the first method, we applied Walsh transform on the columns of an image to generate feature vectors. In second method, Walsh wavelet matrix is used for feature vector generation. In third method we proposed to apply vector quantization (VQ) on feature vectors generated by earlier methods. It gives better accuracy, fast computation and less storage space as compared with the earlier methods. Nearest neighbor and nearest mean classification algorithms are used to classify input test image. Image database used for the experimentation contains 2000 images. All these methods generate large number of outputs for single test image by considering four similarity measures, six sizes of feature vector, two ways of classification, four VQ techniques, three sizes of codebook, and five combinations of wavelet transform matrix generation. We observed improvement in accuracy from 63.22% to 74% (55% training data) through the series of techniques

Human Activity Recognition Based on R Transform

This paper addresses human activity recognition based on a new feature descriptor. For a binary human silhouette, an extended radon transform, transform, is employed to represent low-level features. The advantage of the trans-form lies in its low computational complexity and geomet-ric invariance. Then a set of HMMs based on the extracted features are trained to recognize activities. Compared with other commonly-used feature descriptors, transform is robust to frame loss in video, disjoint silhouettes and holes in the shape, and thus achieves better performance in rec-ognizing similar activities. Rich experiments have proved the efficiency of the proposed method. 1

A Novel Medical Freehand Sketch 3D Model Retrieval Method by Dimensionality Reduction and Feature Vector Transformation

To assist physicians to quickly find the required 3D model from the mass medical model, we propose a novel retrieval method, called DRFVT, which combines the characteristics of dimensionality reduction (DR) and feature vector transformation (FVT) method. The DR method reduces the dimensionality of feature vector; only the top M low frequency Discrete Fourier Transform coefficients are retained. The FVT method does the transformation of the original feature vector and generates a new feature vector to solve the problem of noise sensitivity. The experiment results demonstrate that the DRFVT method achieves more effective and efficient retrieval results than other proposed methods

NAVIDOMASS: Structural-based approaches towards handling historical documents

ISSN: 1051-4651 Print ISBN: 978-1-4244-7542-1International audienceIn the context of the NAVIDOMASS project, the problematic of this paper concerns the clustering of historical document images. We propose a structural-based framework to handle the ancient ornamental letters data-sets. The contribution, firstly, consists of examining the structural (i.e. graph) representation of the ornamental letters, secondly, the graph matching problem is applied to the resulted graph-based representations. In addition, a comparison between the structural (graphs) and statistical (generic Fourier descriptor) techniques is drawn