92 research outputs found
Fuzzy binary patterns for uncertainty-aware texture representation
No full textThe Local Binary Pattern (LBP) representation of textures has been proved useful for a wide range of pattern recognition applications, including texture segmentation, face detection, and biomedical image analysis. The interest of the research community in the LBP texture representation gave rise to plenty of LBP and other binary pattern (BP)-based variations. However, noise sensitivity is still a major concern to their applicability on the analysis of real world images. To cope with this problem we propose a generic, uncertainty-aware methodology for the derivation of Fuzzy BP (FBP) texture models. The proposed methodology assumes that a local neighbourhood can be partially characterized by more than one binary patterns due to noise-originated uncertainty in the pixel values. The texture discrimination capability of four representative FBP-based approaches has been evaluated on the basis of comprehensive classification experiments on three reference datasets of natural textures under various types and levels of additive noise. The results reveal that the FBP-based approaches lead to consistent improvement in texture classification as compared with the original BP-based approaches for various degrees of uncertainty. This improved performance is also validated by illustrative unsupervised segmentation experiments on natural scenes
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Language learning strategies in EFL
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M<sup>3</sup>G: Maximum Margin Microarray Gridding
No full textAbstract Background Complementary DNA (cDNA) microarrays are a well established technology for studying gene expression. A microarray image is obtained by laser scanning a hybridized cDNA microarray, which consists of thousands of spots representing chains of cDNA sequences, arranged in a two-dimensional array. The separation of the spots into distinct cells is widely known as microarray image gridding. Methods In this paper we propose M3G, a novel method for automatic gridding of cDNA microarray images based on the maximization of the margin between the rows and the columns of the spots. Initially the microarray image rotation is estimated and then a pre-processing algorithm is applied for a rough spot detection. In order to diminish the effect of artefacts, only a subset of the detected spots is selected by matching the distribution of the spot sizes to the normal distribution. Then, a set of grid lines is placed on the image in order to separate each pair of consecutive rows and columns of the selected spots. The optimal positioning of the lines is determined by maximizing the margin between these rows and columns by using a maximum margin linear classifier, effectively facilitating the localization of the spots. Results The experimental evaluation was based on a reference set of microarray images containing more than two million spots in total. The results show that M3G outperforms state of the art methods, demonstrating robustness in the presence of noise and artefacts. More than 98% of the spots reside completely inside their respective grid cells, whereas the mean distance between the spot center and the grid cell center is 1.2 pixels. Conclusions The proposed method performs highly accurate gridding in the presence of noise and artefacts, while taking into account the input image rotation. Thus, it provides the potential of achieving perfect gridding for the vast majority of the spots.</p
Parallel computing techniques for performance enhancement of a cDNA microarray gridding algorithm
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Dedicated Hardware for Real-Time Computation of Second-Order Statistical Features for High Resolution Images
No full textAbstract. 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 pixels. Each image is divided into blocks of userdefined size and a feature vector is extracted for each block. The system is implemented on a Xilinx VirtexE-2000 FPGA and uses integer arithmetic, a sparse co-occurrence matrix representation and a fast logarithm approximation to improve efficiency. It allows the parallel calculation of sixteen co-occurrence matrices and four feature vectors on the same FPGA core. The experimental results illustrate the feasibility of real-time feature extraction for input images of dimensions up to 2048×2048 pixels, where a performance of 32 images per second is achieved.
Dedicated Hardware for Real-Time Computation of Second-Order Statistical Features for High Resolution Images
No full textA Precise and Automatic Gridding Approach to Noise-Affected and Distorted Microarray Images
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A precise and automatic gridding approach to noise-affected and distorted microarray images
No full textIn this paper, a precise and fully-automatic approach to the determination of the grid alignment (Gridding) on microarray images is presented. The proposed approach is compared to state-of-the-art software programs and techniques. The conducted experiments demonstrate that it is very effective even when it is applied to noisy or distorted images as well as to images containing spots with various intensities
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