Feature Extraction Methods for Character Recognition

Not Include

Face recognition based on curvelets, invariant moments features and SVM

Recent studies highlighted on face recognition methods. In this paper, a new algorithm is proposed for face recognition by combining Fast Discrete Curvelet Transform (FDCvT) and Invariant Moments with Support vector machine (SVM), which improves rate of face recognition in various situations. The reason of using this approach depends on two things. first, Curvelet transform which is a multi-resolution method, that can efficiently represent image edge discontinuities; Second, the Invariant Moments analysis which is a statistical method that meets with the translation, rotation and scale invariance in the image. Furthermore, SVM is employed to classify the face image based on the extracted features. This process is applied on each of ORL and Yale databases to evaluate the performance of the suggested method. Experimentally, the proposed method results show that our system can compose efficient and reasonable face recognition feature, and obtain useful recognition accuracy, which is able to face and side-face states detection of persons to decrease fault rate of production

An integrated formulation of zernike invariant for mining insect images

This paper presents mathematical integration of Zernike Moments and United Moment Invariant for extracting printed insect images.These features are further mining for granular information by investigating the variance of Interclass and intra-class. The results reveal that the proposed integrated formulation yield better analysis compared to convectional Zernike moments and United Moment Invariant

Handwritten Devanagari numeral recognition

Optical character recognition (OCR) plays a very vital role in today’s modern world. OCR can be useful for solving many complex problems and thus making human’s job easier. In OCR we give a scanned digital image or handwritten text as the input to the system. OCR can be used in postal department for sorting of the mails and in other offices. Much work has been done for English alphabets but now a day’s Indian script is an active area of interest for the researchers. Devanagari is on such Indian script. Research is going on for the recognition of alphabets but much less concentration is given on numerals. Here an attempt was made for the recognition of Devanagari numerals. The main part of any OCR system is the feature extraction part because more the features extracted more is the accuracy. Here two methods were used for the process of feature extraction. One of the method was moment based method. There are many moment based methods but we have preferred the Tchebichef moment. Tchebichef moment was preferred because of its better image representation capability. The second method was based on the contour curvature. Contour is a very important boundary feature used for finding similarity between shapes. After the process of feature extraction, the extracted feature has to be classified and for the same Artificial Neural Network (ANN) was used. There are many classifier but we preferred ANN because it is easy to handle and less error prone and apart from that its accuracy is much higher compared to other classifier. The classification was done individually with the two extracted features and finally the features were cascaded to increase the accuracy

An Integrated Formulation of Zernike Invariant for Mining Insect Images

This paper presents mathematical integration of Zernike Moments and United Moment Invariant for extracting printed insect images.  These features are further mining for granular information by investigating the variance  of Interclass and intra-class. The results reveal that the proposed integrated formulation yield better analysis compared to conventional Zernike moments and United Moment Invarian

Automatic Segmentation and Classification of Red and White Blood cells in Thin Blood Smear Slides

In this work we develop a system for automatic detection and classification of cytological images which plays an increasing important role in medical diagnosis. A primary aim of this work is the accurate segmentation of cytological images of blood smears and subsequent feature extraction, along with studying related classification problems such as the identification and counting of peripheral blood smear particles, and classification of white blood cell into types five. Our proposed approach benefits from powerful image processing techniques to perform complete blood count (CBC) without human intervention. The general framework in this blood smear analysis research is as follows. Firstly, a digital blood smear image is de-noised using optimized Bayesian non-local means filter to design a dependable cell counting system that may be used under different image capture conditions. Then an edge preservation technique with Kuwahara filter is used to recover degraded and blurred white blood cell boundaries in blood smear images while reducing the residual negative effect of noise in images. After denoising and edge enhancement, the next step is binarization using combination of Otsu and Niblack to separate the cells and stained background. Cells separation and counting is achieved by granulometry, advanced active contours without edges, and morphological operators with watershed algorithm. Following this is the recognition of different types of white blood cells (WBCs), and also red blood cells (RBCs) segmentation. Using three main types of features: shape, intensity, and texture invariant features in combination with a variety of classifiers is next step. The following features are used in this work: intensity histogram features, invariant moments, the relative area, co-occurrence and run-length matrices, dual tree complex wavelet transform features, Haralick and Tamura features. Next, different statistical approaches involving correlation, distribution and redundancy are used to measure of the dependency between a set of features and to select feature variables on the white blood cell classification. A global sensitivity analysis with random sampling-high dimensional model representation (RS-HDMR) which can deal with independent and dependent input feature variables is used to assess dominate discriminatory power and the reliability of feature which leads to an efficient feature selection. These feature selection results are compared in experiments with branch and bound method and with sequential forward selection (SFS), respectively. This work examines support vector machine (SVM) and Convolutional Neural Networks (LeNet5) in connection with white blood cell classification. Finally, white blood cell classification system is validated in experiments conducted on cytological images of normal poor quality blood smears. These experimental results are also assessed with ground truth manually obtained from medical experts

Image Description using Radial Associated Laguerre Moments

This study proposes a new set of moment functions for describing gray-level and color images based on the associated Laguerre polynomials, which are orthogonal over the whole right-half plane. Moreover, the mathematical frameworks of radial associated Laguerre moments (RALMs) and associated rotation invariants are introduced. The proposed radial Laguerre invariants retain the basic form of disc-based moments, such as Zernike moments (ZMs), pseudo-Zernike moments (PZMs), Fourier-Mellin moments (OFMMs), and so on. Therefore, the rotation invariants of RALMs can be easily obtained. In addition, the study extends the proposed moments and invariants defined in a gray-level image to a color image using the algebra of quaternion to avoid losing some significant color information. Finally, the paper verifies the feature description capacities of the proposed moment function in terms of image reconstruction and invariant pattern recognition accuracy. Experimental results confirmed that the associated Laguerre moments (ALMs) perform better than orthogonal OFMMs in both noise-free and noisy conditions

Uniscale and multiscale gait recognition in realistic scenario

The performance of a gait recognition method is affected by numerous challenging factors that degrade its reliability as a behavioural biometrics for subject identification in realistic scenario. Thus for effective visual surveillance, this thesis presents five gait recog- nition methods that address various challenging factors to reliably identify a subject in realistic scenario with low computational complexity. It presents a gait recognition method that analyses spatio-temporal motion of a subject with statistical and physical parameters using Procrustes shape analysis and elliptic Fourier descriptors (EFD). It introduces a part- based EFD analysis to achieve invariance to carrying conditions, and the use of physical parameters enables it to achieve invariance to across-day gait variation. Although spatio- temporal deformation of a subject’s shape in gait sequences provides better discriminative power than its kinematics, inclusion of dynamical motion characteristics improves the iden- tification rate. Therefore, the thesis presents a gait recognition method which combines spatio-temporal shape and dynamic motion characteristics of a subject to achieve robust- ness against the maximum number of challenging factors compared to related state-of-the- art methods. A region-based gait recognition method that analyses a subject’s shape in image and feature spaces is presented to achieve invariance to clothing variation and carry- ing conditions. To take into account of arbitrary moving directions of a subject in realistic scenario, a gait recognition method must be robust against variation in view. Hence, the the- sis presents a robust view-invariant multiscale gait recognition method. Finally, the thesis proposes a gait recognition method based on low spatial and low temporal resolution video sequences captured by a CCTV. The computational complexity of each method is analysed. Experimental analyses on public datasets demonstrate the efficacy of the proposed methods

Human-Centric Machine Vision

Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans