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Scale-space corner detection (SSCD) has been drawing much attention in the past. Multi-scale corner detection (MSCD), which recognizes corners only at several scales, can be treated as a fast implementation of SSCD. In this paper, a new MSCD algorithm is proposed, which is based on an arithmetic mean (AM) of the k-cosine curvature values respectively computed at three scales. Compared to the existing...
The more complete the training set of an optical character recognition platform, the greater the chances of obtaining a better precision in transcription. The development of a database for such purpose is a task of paramount effort as it is performed manually and must be as extensive as possible in order to potentially cover all words in a language. Dealing with historic documents either handwritten,...
Recognizing breathing pattern is important in many fields of medicine. Ensemble empirical mode decomposition (an adaptive algorithm) was used to investigate breathing pattern, including thoracic breathing (TB) and abdominal breathing (AB). This study recognizes TB and AB by correlation coefficient and power proportion. Results indicate that the recognition accuracy of TB by correlation coefficient...
A novel feature extraction method is proposed in this paper. Dislike contour-based or region-based approaches, an object is first converted to a closed curve by extended central projection (ECP). The derived curve not only keeps the affine transform information, but also is very robust to noise. Then whitening transform is performed to the curve such that the affine transformation is simplified to...
We introduce a novel subspace segmentation method called Minimal Squared Frobenius Norm Representation (MSFNR). MSFNR performs data clustering by solving a convex optimization problem. We theoretically prove that in the noiseless case, MSFNR is equivalent to the classical Factorization approach and always classifies data correctly. In the noisy case, we show that on both synthetic and real-word datasets,...
Automation of HEp-2 cell pattern classification would drastically improve the accuracy and throughput of diagnostic services for many auto-immune diseases, but it has proven difficult to reach a sufficient level of precision. Correct diagnosis relies on a subtle assessment of texture type in microscopic images of indirect immunofluorescence (IIF), which so far has eluded reliable replication through...
For the problem of low resolution, camera movement and target's detail fuzzy in aerial video when detecting and recognizing pedestrian, this paper proposes weighted region matching algorithm based on saliency detection and Kalman filter(KS-WRM). In the preprocessing stage, the KS-WRM algorithm uses saliency detection algorithm, which adds human subjective consciousness to segment pedestrians. The...
Affine moment invariants are important shape descriptors in pattern recognition and computer vision. Existing affine invariants methods are based on geometric and complex moments. In this paper, we propose a set of affine invariants extracted from Legendre moments. These invariants are derived by the relationship between the Legendre moment of the affine transformed image and that of the original...
As orthogonal moments in the polar coordinate, radial orthogonal moments such as Zernike, pseudo-Zernike and orthogonal Fourier-Mellin moments have been successfully used in the field of pattern recognition. However, the scale and rotation invariant property of these moments has not been studied. In this paper, we present a generic approach based on Jacobi-Fourier moments for scale and rotation invariant...
Clustering has been used widely in pattern recognition, image processing, data mining and so on. Many clustering algorithms are sensitive to outlier faults in noisy environments. In this paper, we propose a new algorithm called sample weighted possibilistic fuzzy c-means clustering (SWPFCM). Based on combination sample weighting and a suitable for noise environment of initialization clustering center...
In this work we present a scalable feature set which is obtained by fitting orthogonal polynomials to the normalized modulation spectrum of cepstral coefficients and which can be easily adapted to different classification tasks. The performance of the feature set is investigated in a hierarchically structured audio signal classification experiment and compared with other approaches reported in the...
Radial Tchebichef moments as a discrete orthogonal moment in the polar coordinate have been successfully used in the field of pattern recognition. However, the scaling invariant property of these moments has not been studied due to the complexity of the problem. In this paper, we present a new method to construct a complete set of scaling and rotation invariants extract from radial Tchebichef moments,...
A new method for image registration has been previously proposed by the authors, which the registration is based on physical forces. The registration parameters are translation and rotation. This method assumes images like charged materials that attract each other. In this case, one of the images moves in the same direction as the applied force while the other one is still. The movement of the image...
Image registration is an important task in the field of computer vision and pattern recognition. In this paper, we propose a robust sub-pixel registration algorithm which is based on multi-resolution and new edge detection interpolation method. After applying truncated window function to the images to be registration, the low-pass bands of the wavelet decomposition are applied to build the image pyramid...
In this paper, an approach called orthogonal projection transform (OPT) is proposed for invariant shape description. It is inspired by the construction of orthogonal Fourier-Mellin moments (OFMMs), but integral is only performed along lines with different polar angles in the proposed approach. By performing OPT, any object can be converted to a set of closed curves. In comparison with moment based...
Opto-electronic hybrid optical pattern recognition technology has many advantages, but must to recognize the correlation-peak from lots of noise. This paper analyzes the deference of correlation-peak image and the noise, and researches on the contour distribution of the image of the correlation-peak and the noise. So, bring forward using the BP network of the ANN to distinguish the signal and noise...
Ensemble methods represent an approach to combine a set of models, each capable of solving a given task, but which together produce a composite global model whose accuracy and robustness exceeds that of the individual models. Ensembles of neural networks have traditionally been applied to machine learning and pattern recognition but more recently have been applied to forecasting of time series data...
This paper presents a combination of intelligent learning algorithm, the Support Vector Machine, and the recognition of star pattern in Celestial Navigation. Considering the star pattern recognition's character, noticing the advantages of SVM in learning competence, the paper proposes a solution to star pattern recognition with multi-kernel SVM. A multi-kernel algorithm bases on Genetic Programming...
The completeness property of the invariant descriptors, which is of fundamental importance from the theoretical as well as the practical points of views, has been investigated by several research groups. In this paper, we propose a new approach to derive a complete set of pseudo-Zernike moment invariants. We first establish a relationship between the pseudo-Zernike moments of the original image and...
The key of objectivity of traditional Chinese Medicine (TCM) pulse condition lies on the objective test and correct recognition for all kinds of pulse conditions. In view of the ambiguity, variety and complexity of TCM pulse conditions, and the shortcomings of the traditional recognition methods and back propagation (BP) neural network method, a kind of TCM pulse-condition recognition method based...
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