Accurate object reconstruction by statistical moments

Statistical moments can offer a powerful means for object description in object sequences. Moments used in this way provide a description of the changing shape of the object with time. Using these descriptions to predict temporal views of the object requires efficient and accurate reconstruction of the object from a limited set of moments, but accurate reconstruction from moments has as yet received only limited attention. We show how we can improve accuracy not only by consideration of formulation, but also by a new adaptive thresholding technique that removes one parameter needed in reconstruction. Both approaches are equally applicable for Legendre and other orthogonal moments to improve accuracy in reconstruction

Shape descriptors for mode-shape recognition and model updating

The most widely used method for comparing mode shapes from finite elements and experimental measurements is the Modal Assurance Criterion (MAC), which returns a single numerical value and carries no explicit information on shape features. New techniques, based on image processing (IP) and pattern recognition (PR) are described in this paper. The Zernike moment descriptor (ZMD), Fourier descriptor (FD), and wavelet descriptor (WD), presented in this article, are the most popular shape descriptors having properties that include efficiency of expression, robustness to noise, invariance to geometric transformation and rotation, separation of local and global shape features and computational efficiency. The comparison of mode shapes is readily achieved by assembling the shape features of each mode shape into multi-dimensional shape feature vectors (SFVs) and determining the distances separating them. © 2009 IOP Publishing Ltd

Inteligentna detekcija greške u sustavu distribucije električne energije korištenjem termalnih slika i grupe klasifikatora

In today\u27s world, many companies use the thermal imaging (infrared), in order to prevent failures and improve the reliability of the electrical networks. In fact, the technical inspection of the electrical equipment using thermal cameras, is the most effective method for preventive defect detection. This contribution deals with, a systematic method in which, areas suspected of failure, are identified through computer-aided thermal image processing. To this end, the candidate areas are determined, using adaptive threshold and, a number of features are extracted from them. Next, using a genetic algorithm (GA), the irrelevant features are omitted. Finally, by means of a hybrid classifier, the pattern of positive and false positive areas, have been identified. This classifier can also be used as a filter, after extracting the candidate areas. This method is tested on images taken from Tehran northwest substations. As a result, applying the feature selection algorithm leads to a faster intelligent fault detection and higher Reliability, especially in widespread networks, which is known as an effective validation for the proposed method.U suvremenom svijetu mnoga poduzeća koriste termalne slike (infracrvene) kako bi se spriječili kvarovi i popravila pouzdanost električne mreže. Zapravo, tehnički pregled električke opreme korištenjem termalnih kamera je najučinkovitija metoda za detekciju i prevenciju kvarova. U ovom radu proučava se sistematična metoda u kojoj se sumnjiva područja identificiraju korištenjem računalne obrade termalne slike. Nakon što se odrede moguća područja korištenjem prilagodljivih pragova iz njih se izvode mnoga svojstva. Zatim se korištenjem genetičkih algoritama izostavljaju nevažna svojstva. Konačno, korištenjem hibridnog klasifikatora identificiraju se uzorci pozitivnih i lažno pozitivnih područja. Taj klasifikator može se koristiti i kao filter nakon izdvajanja mogućih područja. Ova metoda testirana je na slikama teheranske sjevernozapadne stanice. Korištenje algoritma za selekciju svojstava dovodi do brže i inteligentne detekcije te veće pouzdanosti, posebno kod rasprostranjenih mreža koje služe za učinkovitu validaciju predloženih metoda

Local Descriptor by Zernike Moments for Real-time Keypoint Matching

This paper presents a real-time keypoint matching algorithm using a local descriptor derived by Zernike moments. From an input image, we find a set of keypoints by using an existing corner detection algorithm. At each keypoint we extract a fixed size image patch and compute a local descriptor derived by Zernike moments. The proposed local descriptor is invariant to rotation and illumination changes. In order to speed up the computation of Zernike moments, we compute the Zernike basis functions in advance and store them in a set of lookup tables. The matching is performed with an Approximate Nearest Neighbor (ANN) method and refined by a RANSAC algorithm. In the experiments we confirmed that videos of frame size 320×240 with the scale, rotation, illumination and even 3D viewpoint changes are processed at 25~30Hz using the proposed method. Unlike existing keypoint matching algorithms, our approach also works in realtime for registering a reference image

Rotation Invariant Indexing For Image Using Zernike Moments and R–Tree

The Zernike moment algorithm and R-Tree algorithm are known as state of the art in the recognition of images and in the multimedia database respectively. The methods of storing the images and retrieving the similar images based on a query image automatically are the problems in the image database. This paper proposes the method to combine the Zernike moments algorithm and the R–tree algorithm in the image database. The indices of images which are retrieved from the extraction process using Zernike moments algorithm are used as the multidimensional indices to recognize the images. The multidimensional indices of Zernike moments which are stored in the R–tree are compared to the magnitudes of Zernike moments of a query image for searching the similar images. The result shows that the combination of these algorithms can be used efficiently in the image database because the recognition accuracy rate using Zernike moments algorithm is 95.20%

Fast Region of Interest detection for fetal genital organs in B-mode ultrasound images

Genital organ detection of fetus in B-mode ultrasound images has a considerable significance. It is useful to know any malformations present in the genital organs and also to determine the sex of the fetus. In this paper we propose a Feature from Accelerated Segment Test (FAST) technique for approximate detection of fetal genitals in ultrasound images. FAST algorithm is capable of producing the corner points at a higher speed which falls on the fetal genital organs. A window of size 60×60 pixels being corner point as a center is considered as Region of Interest (ROI), where genital organ of fetus is anticipated. The efficiency of the algorithm is calculated as the ratio of number of images where corner points are placed on the fetus genital organ to the total number of images tested. FAST algorithm is robust to speckles present in the image, machine independent, fast and also computationally less intensive to implement in real time with an efficiency of 96.7%