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We propose a stable and fast reconstruction technique for parallel-beam (PB) tomographic X-Ray imaging, relying on the discrete pseudo-polar (PP) Radon and PP Fourier transforms. Our main contribution is a resampling method, based on modern sampling theory, that transforms the PB measurements to a PP grid. The resampling process is both fast and accurate, and in addition, simultaneously denoises the...
The aim of this paper is to propose a transformation algorithm for multi-granularity linguistic information assessed in different unbalanced linguistic term sets together with its application in linguistic group decision making (LGDM) problem. Assuming that the linguistic information given to the alternatives by different decision makers distribute in different granularity and/or semantic term sets...
Soft computing in the field of agriculture science is being employed with computer vision techniques in order to detect the diseases in crops to increase the overall yield. A Modified Rotation Kernel Transformation(MRKT) based directional feature extraction scheme is presents to resolve the issues occurring due to shape, color or other deceptive features during plant disease recognition. The MRKT...
In Automatic Fingerprint Identification System (AFIS), the performance highly depends on features extraction. In this paper, a new minutiae detection method based on wave atoms transform is presented. The proposed approach is based on analyzing fingerprints by specific representation highlighting the oscillatory structure. Thereby, a fine analysis from local characteristics via the block wave atoms...
A Wiener filtering scheme in graph Fourier domain is proposed for improving image denoising performance achieved by various spectral graph based denoising methods. The proposed Wiener filter is estimated by using graph Fourier coefficients of the noisy image after they are processed for denoising, to further improve the already achieved denoising accuracy as a post-processing step. It can be estimated...
Ordinal regression which aims to classify instances into ordinal categories has numerous applications. As a supervised learning problem, a large number of labeled data is needed to train an accurate model, in particular when the number of categories is large. Learning an effective ordinal classifier from a small dataset is a challenging task. This paper proposes a framework to transform the ordinal...
Robust stereo matching under radiometric changes is a necessary method to use stereo vision in a real situation. This paper proposes a fast and robust stereo matching under various radiometric changes by using Census transform. Experimental result shows that proposed method has a better performance in terms of computational time and bad pixel error.
This paper presents a study on the classification of consolidations in chest radiographs, namely the infection and fluid regions, using a block-based approach with Naïve Bayes classifier. The experiment is performed on infection and fluid regions within the lung, which are divided into 32-by-32 sub-blocks. Several feature extraction techniques are used to capture the block's low level features, and...
This paper proposes a new approach to recognize iris from distantly acquired facial images by utilizing multiple feature descriptors and classifiers. Firstly, Log-Gabor (LG), Contourlet Transform (CT), Gradient Local Auto-Correlation (GLAC) and Convolutional Neural Network (CNN) descriptors are employed on segmented normalized iris image and contextual eye image to extract features. Then, K-Nearest...
Modern video standards such as H.264 and HEVC introduce new simplified transform functions that allow for simple hardware implementation, different block sizes and enhanced coding efficiency. However, the number of different transforms to implement has increased, leading to the need of shared architectures able to process several transforms with minimum hardware overhead. This trend started with H...
Segmentation of cell nuclei is an important step towards automatic analysis of microscopic images. This paper presents an automated technique for nuclear segmentation in skin histopathological images. The proposed technique first detects nuclear seeds using a bank of generalized Laplacian of Gaussian (gLoG) kernels. Based on the detected nuclear seeds, a multi-scale radial line scanning (mRLS) method...
Fusing multiple features within one biometric modality has attracted increasing attention and interest among researchers during recent decades because the concept is useful in addressing a wide range of real world problems. In this paper, we propose a novel fusion approach that combines two feature extraction algorithms: Local Binary Pattern Histogram Fourier Features (LBP-HF) and Gabor filter technique...
Conventional echo hiding methods have simple encoding and decoding process, Robustness to MP3 compression, but the correct rate of extracted information need be improved. To copy with this problem, we propose a time-spread echo with random intervals. The encoding process generates an interval sequence to dominate the intervals of echoes. The echo is added into the audio according to the interval sequence,...
This paper presents a new lane tracking algorithm for the lane departure warning system without using Kalman filter. The system is capable of extracting the true lane boundaries from all detected lines including noise in the frame and estimates its future position. The new algorithm uses the score mechanism to trace the appearance of lines in previous frames using a score variable which indicates...
The kernel trick becomes a burden for some machine learning tasks such as dictionary learning, where a huge amount of training samples are needed, making the kernel matrix gigantic and infeasible to store or process. In this work, we propose to alleviate this problem and achieve Gaussian RBF kernel expansion explicitly for dictionary learning using Fastfood transform, which is an approximation of...
The Adapteva Epiphany MIMD architecture is a scalable 2D array of RISC cores with a fast network-on-chip (NoC) for parallel processing. The work presented here discusses the suitability of the architecture to handle software defined radio (SDR) applications such as Finite Impulse Response (FIR) filters. This paper discusses implementation of the Hilbert filter through using the COPRTHR 2.0 SDK which...
Gaussian process quadrature is a promising alternative Bayesian approach to numerical integration, which offers attractive advantages over its well-known classical counterparts. We show how Gaussian process quadrature can naturally incorporate gradient information about the integrand. These results are applied for the design of transformation of means and covariances of Gaussian random variables....
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Human action recognition is a challenging vision task due to the complex action patterns in the real-world videos. In this work, we propose a DeepAction Kernel Gaussian Process, which takes advantage of Gaussian process (GP) and deep learning, to capture the distinctive action characteristics. Specifically, we design a unified, deep and non-adjacent kernel structure within Gaussian process to classify...
During the last years, a Generalized Signals and Systems Theory (GSST) is been developed by our research group. The latest version of the GSST includes important concepts concerning the generalization of the (i) study of physical systems by means of infinite dimensional signal and linear-invariant and non invariant-operator spaces; (ii) concepts associated to sets of impulse responses rigorously explained...
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