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Clustering analysis has been widely used in many areas such as astronomy, bioinformatics, and pattern recognition. In 2014, Rodriguez proposed an algorithm based on the idea that cluster centers are characterized by a higher density than their neighbors and by a relatively large distance from points with higher density. But the density relies on cutoff distance, which might be affected by large statistical...
How exactly our brain works is still an open question, but one thing seems to be clear: biological neural systems are computationally powerful, robust and noisy. Using the Reservoir Computing paradigm based on Spiking Neural Networks, also known as Liquid State Machines, we present results from a novel approach where diverse and noisy parallel reservoirs, totalling 3,000 modelled neurons, work together...
The traditional affine iterative closest point (ICP) algorithm is fast and accuracy for affine registration of point sets, but it performs worse when the point sets with large outliers. This paper introduces a novel algorithm based on correntropy for affine registration of point sets with outliers. First, a novel objective function is proposed by introducing the maximum correntropy criterion (MCC)...
We describe for the first time how the Euler number of a 2-D binary image can be obtained by means of Artificial Neural Network (ANN). Calculating the Euler image number is treated as a pattern classification problem. To arrive at the specialized ANN architecture, we perform a partial results analysis provided by a known formulation to compute the Euler image number. We use this analysis for designing...
Person re-identification has become a hot research topic due to its importance in surveillance and forensics applications. The purpose of person re-identification is to find the same person from disjoint camera views at different time. Most of the existing methods try to identify the person by measuring the similarity of two still images from different camera views, which only uses intraimage features...
Owing to their universal approximation capability and online learning manner, kernel adaptive filters have been widely used in nonlinear systems modeling. Under Gaussian assumption, traditional kernel adaptive algorithms utilize the well-known mean square error(MSE) as a cost function to get optimal solutions. For non-Gaussian situations, MSE will not properly represent the statistics of the error,...
The online writer identification is a required component in many applications of Computer vision and Pattern Recognition. The offline writer identification is more developed in literature due to the use of traditional system based on Image Processing. There is a lack of works done in the case of online writer identification. In this paper, we propose a novel method to text independent writer identification...
Dynamic neural field (DNF) is a popular mesoscopic model for cortical column interactions. It is widely studied analytically and successfully applied to physiological modelling, bioinspired computation and robotics. DNF behavior emerges from distributed and decentralized interactions between computing units which makes it an interesting candidate as a cellular building-block for unconventional computations...
A smile is a common human facial expression used as the indicator for positive emotion. The detection of smiling has many applications, for example, controlling camera shutter when a smile is detected and measuring the degree of satisfaction during a video conference. Many feature extraction methods have been proposed for detecting a smile in the unconstrained scenarios. However, the dimensions of...
Due to the rapid increase of different digitized documents, the development of a system to automatically retrieve document images from a large collection of structured and unstructured document images is in high demand. Many techniques have been developed to provide an efficient and effective way for retrieving and organizing these document images in the literature. This paper provides an overview...
Classification of high-dimensional data with imbalanced classes poses problems. Especially such time series classification tasks are problematic, because the ordering of each time step (feature) is important and therefore dimensionality reduction and feature selection cannot be applied. The cascade classification model was developed for such time series classification tasks. The cascade classifier...
An adaptive learning algorithm for Radial Basis Functions Neural Networks, RBFNNs, is provided. In recent years, RBFs have been subject to extensive areas of interests. But the setting up of RBFs in a network architecture can be time consuming, computationally deficient and unstable. Thus we have developed an efficient adaptive algorithm in a feedforward neural architecture in which the hidden neurons...
We present a novel model to represent and match contour lines of closed shapes. This model is based on the mechanism of visual cortex. It extracts orientation features from input images with simple computation units that imitate simple cells in the visual cortex. The contour lines are accurately located by searching adjacent activated simple units. These activated simple units are concatenated in...
In this study, we present a new approach to the problem of face classification, which relies on the linguistic description of the facial features. In this method, face descriptors are represented through the Analytic Hierarchy Process (AHP) and formalized as information granules. Moreover, neural networks are used to construct efficient classifiers. Furthermore, with usage of AHP we realize a transition...
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