Learning algorithms can only perform well when the model is trained using sufficient number of training examples with respect to the complexity of the model. To obtain good generalization performance with a limited training data set, it is essential that prior knowledge of the problem is included in the representation of the objects or in the model of the data. Here we will consider image data ...

-This paper proposes a computationally efficient procedure for skew detection and text line position determination in digitized documents, which is based on the cross-correlation between the pixels of vertical lines in a document. The determination of the skew angle in documents is essential in optical character recognition systems. Due to the text skew, each horizontal text line intersects a p...

Image matting aims to extract foreground objects from a given image in a fuzzy mode. One of the major state-of-the-art methods in this field is spectral matting. It automatically computes fuzzy matting components by using the smallest eigenvectors of a defined Laplacian matrix that is generated from affinities computation between adjacent pixels in an image. Results obtained by such approach ar...

We present a novel dedicated hardware system for the extraction of second-order statistical features from high-resolution images. The selected features are based on gray level co-occurrence matrix analysis and are angular second moment, correlation, inverse difference moment and entropy. The proposed system was evaluated using input images with resolutions that range from 512×512 to 2048×2048 p...

Hyperspectral images contain mixed pixels due to low spatial resolution of hyperspectral sensors. Mixed pixels are pixels containing more than one distinct material called endmembers. The presence percentages of endmembers in mixed pixels are called abundance fractions. Spectral unmixing problem refers to decomposing these pixels into a set of endmembers and abundance fractions. Due to nonnegat...

In this paper we present a new method for coin identification. The proposed method adopts a hybrid scheme using Eigenvalues of covariance matrix, Circular Hough Transform (CHT) and Bresenham’s circle algorithm. The statistical and geometrical properties of the small and large Eigenvalues of the covariance matrix of a set of edge pixels over a connected region of support are explored for the pur...

We present a method of ordering pixels (the elements of a rectangular matrix) based on an arithmetic progression with wrap-around (modular arithmetic). For appropriate choices of the progression’s parameters based on a generalization of Fibonacci numbers and the golden mean, we achieve uniformly distributed collections of pixels formed by intervals of the pixel progression or “shuffle.” We illu...

One parametric classify (Maximum likelihood classify, MLC for short) and two non-parametric classifiers (Adaptive resonance theory mappings and this study. Base on the confusion matrix and the pixels fuzzy analysis, the nonparametric classifier may be a more preferable approach than the parametric classifier for some remote sensing applications and deserves further investigation. The ARTMAP cla...

Traditional Fuzzy C-means segmentation algorithm requires to set clustering number in advance, and to calculate image clustering center by the iterative arithmetic. So the traditional algorithm is sensitive to the initial value and the computation complexity is high. In order to improve the traditional Fuzzy Cmeans algorithm, this paper presents an infrared image segmentation method using adapt...