Combined kernel for fast GPU computation of Zernike moments

Zernike moments (ZMs) are a set of orthogonal moments which have been successfully used in the fields of image processing and pattern recognition. A combination of edge blurring with ZMs computation was introduced. In this study, several kinds of artificial binary stripe images were used to investigate the effects of edge blurring on the absolute mean error of reconstructed image from high-order ZMs. After the blurring process, the reconstruction errors were increased dramatically at edge pixels, but decreased on non-edge pixels. The experimental results demonstrated that 2-pixel blurring approach provided better performance for reducing reconstruction error. Finally, a template matching between two real images was simulated to illustrate the effectiveness of the proposed method.

Image analysis with logarithmic Zernike moments

Zernike moments (ZMs) are widely used in many image analysis and pattern recognition problems because of their superiority compared with other moments. However, they suffer from high computation cost and inherent error. Previous researches have shown that the algorithm, computing ZMs in polar system, improves the ZMs accuracy of the reconstruction and invariance properties dramatically. In this study, the authors firstly modify a direct method for computing ZMs in polar coordinates and present a recursive relation. Then, this study presents an algorithm for fast computation of ZMs, based on the improved polar pixel tiling scheme. Owing to the symmetrical property, ZMs can be obtained by computing only one-sixteenth circle of the radial polynomials, which means that the number of pixels involved in the computation of ZMs is only 6.25% of the previous method. This leads to a significant reduction in the computational complexity requirements. A comparison with the other conventional method is performed in detail. The obtained results show the superiority of the proposed method.

The central theme of this paper is information extraction from remote sensing data with the aid of software developed in the fields of image processing, pattern recognition and artificial intelligence, more specifically of expert systems.

Rotation and translation invariants of Gaussian–Hermite moments

The fast computation of Zernike moments from normalized geometric moments has been developed in this paper. The computation is multiplication free and only additions are needed to generate Zernike moments. Geometric moments are generated using Hatamian's filter up to high orders by a very simple and straightforward computation scheme. Other kinds of moments (e.g., Legendre, pseudo Zernike) can be computed using the same algorithm after giving the proper transformations that state their relations to geometric moments. Proper normalizations of geometric moments are necessary so that the method can be used in the efficient computation of Zernike moments. To ensure fair comparisons, recursive algorithms are used to generate Zernike polynomials and other coefficients. The computational complexity model and test programs show that the speed-up factor of the proposed algorithm is superior with respect to other fast and/or direct computations. It perhaps is the first time that Zernike moments can be computed in real time rates, which encourages the use of Zernike moment features in different image retrieval systems that support huge databases such as the XM experimental model stated for the MPEG-7 experimental core. It is concluded that choosing direct computation would be impractical.

Explicit Separable two dimensional Moment Invariants for object recognition

The shape features of objects (connected components) in binary images are very important for image analysis, pattern (object) recognition, and computer vision. Conventional algorithms for calculating the shape features of objects in binary images can only calculate the shape features of objects without holes. This paper presents an algorithm or calculating the shape features of objects in binary images with holes. Based on a contour-tracing-based connected-component labeling algorithm, our proposed algorithm can calculate the number of objects, the number of holes, the Euler number in a binary image, extract the contours of objects, and calculate the area, perimeter, circularity, centroid of each object. Our proposed algorithm is able to make a contribution to image analysis, pattern recognition, and computer vision.

Zernike Moments (ZMs) have been widely used in pattern recognition and image analysis due to their merits of near-zero information redundancy and rotation invariance. However, the discrete sampling error introduced by state-of-the-art algorithms degrades their rotation invariance. In this paper, a novel algorithm for computing the accurate ZMs of binary images is presented. The new algorithm is based on Non-Symmetry Anti-Packing Image Representation (NAIR) and it computes the accurate ZMs of the whole image by summing up the ZMs of all rectangular regions without introducing the discrete sampling error. The experimental results in this paper show that the NAIR-based algorithm for accurate ZMs is faster than the state-of-the-art accurate algorithms.

This paper proposes an approach combining the set of Krawtchouk moments and HMMs (Hidden Markov Models) for unconstrained offline handwritten Chinese character recognition. The Krawtchouk moments can be effectively used as pattern features in analysis of two-dimensional images. Since the basis set is orthogonal in the discrete domain of the image coordinate space, the implementation of Krawtchouk moments doesn't involve any numerical approximation or coordinate transformation. This property makes Krawtchouk moments superior to conventional, more continuous moments such as Legendre moments and Zernike moments. In the field of character recognition, HMMs architecture has been used widely because of its strong adaptability to written distortion and variability. In this paper, Krawtchouk moments and HMMs are combined to recognize unconstrained offline handwritten Chinese amount in words. Experiments show that the average recognition of Krawtchouk is higher than Zernike and Legendre moments within the HMM framework.

As an orthogonal moment, Zernike moment (ZM) is an attractive image feature in a number of application scenarios due to its distinguishing properties. However, we find that for digital images, the commonly used Cartesian method for ZM computation has compromised the advantages of ZMs because of their non-ideal accuracy stemming from two inherent sources of errors, i.e., the geometric error and the integral error. There exists considerable errors in image reconstruction using ZMs calculated with the Cartesian method. In this paper, we propose a polar coordinate based algorithm for the computation of ZMs, which avoids the two kinds of errors and greatly improves the accuracy of ZM computation. We present solutions to the key issues in ZM computation under polar coordinate system, including the derivation of computation formulas, the polar pixel arrangement scheme, and the interpolation-based image conversion etc. As a result, ZM-based image reconstruction can be performed much more accurately.

Finite precision error analysis of Zernike moments computation schemes and a new, efficient, robust recursive algorithm

In this paper we present briefly the highlights of the projects undertaken at the National Nodal Centre for the Knowledge-Based Computer Science at ISI, Calcutta and the related sub-problems being tackled in order to make meaningful application of knowledge-based approach on some image processing, pattern recognition and computer vision to some real life problems. Application areas are interpretation of remotely sensed images for mineral location & targetting and hydro-carbon exploration problems, industrial inspection and verification system, such as cold rolling steel inspection, computer vision-based extraction and validation of PCB lay out, expert CAD system for electronic board inspection and test and medical imaging and expert systems. In relation to the above areas of applications there are several theoretical and experimental problems of fundamental importance which are being investigated such as 2D and 3D shape analysis recognition and positioning of partially occluded object, binary contour coding scheme, KBCS approach to cluster analysis, neural modelling & simulation and learning algorithms, semantic network and architectural mapping, connectivity vertification scheme and mathematical morphology.

Bio-Inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition comprises papers on diverse aspects of bio-inspired models, soft computing and hybrid intelligent systems. The articles are divided into four main parts. The first one consists of papers that propose new fuzzy and bio-inspired models to solve general problems. The second part deals with the main theme of modular neural networks in pattern recognition, which are basically papers using bio-inspired techniques. The third part contains papers that apply hybrid intelligent systems to the problem of time series analysis and prediction, while the fourth one shows papers dealing with bio-inspired models in optimization and robotics applications. An edited book in which both theoretical and application aspects are covered.

Text recognition has been one of the most active and challenging research areas in the field of image processing and pattern recognition. This paper presents a method to extract various important data from government documents and store it in a JSON file and link different documents of a person so that different data can be used when required. The approach we have taken here is that first the user will have to upload the scanned copy of the document into the GUI and the data is extracted from the photo. We made sure to capture all the necessary data from the image so that even if some of the data in the image is in circular shape, the software captures all the data and from the captured data, we select the important data and store it in a JSON file.