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Similar to the impact of ageing on human beings, digital image sensors develop ageing effects over time. Since these imager's ageing effects (commonly denoted as pixel defects) leave marks in the captured images, it is not clear whether this affects the accuracy of iris recognition systems. This paper proposes a method to investigate the influence of sensor ageing on iris recognition by simulative...
The problem of blind estimation of the room acoustic clarity index C50 from single-channel reverberant speech signals is presented in this paper. We analyze the performance of several machine learning methods for a regression task using 309 features derived from the speech signal and modeled with a Deep Belief Network (DBN), Classification And Regression Tree (CART) and Linear Regression (LR). These...
Automatic recognition of handwritten texts in video lectures has important applications. In video lectures, the presenter usually writes on white / colored board. The video camera often captures the writing board along with certain other objects possibly including the presenter itself. Recognition of handwritten texts from such a video frame requires prior detection of the region of texts in the frame...
This paper presents the results of the HDSRC 2014 competition on handwritten digit string recognition in challenging datasets organized in conjunction with ICFHR 2014. The general objective of this competition is to identify, evaluate and compare recent developments in Western Arabic digit string recognition with varying length. In addition, this competition introduces two new challenging datasets...
In this paper, we present a non-invasive method of counting fish in their natural habitat using automated analysis of video data. Our approach uses three modular components to preprocess, detect, and track the fish. The preprocessing reduces noise present in the image while enhancing the fish using several different techniques. The fish detection is based on two background subtraction algorithms which...
Identification and authentication is done using various biometric sign like fingerprints. The recognition rate of correct person is depending on quality of fingerprints images. Fingerprints quality also varying from rural and urban population. Rural population having more physical work than urban population. Therefore the ridges, valleys, bifurcation, joints, minutia etc. features are not good quality...
Social scientists who collect large amounts of medical data value the privacy of their survey participants. As they follow participants through longitudinal studies, they develop unique profiles of these individuals. A growing challenge for these researchers is to maintain the privacy of their study participants, while sharing their data to facilitate research. Differential privacy is a new mechanism...
Web databases contain a huge amount of structured data which are easily obtained via their query interfaces only. The query results are presented in dynamically generated web pages, usually in the form of data records, for human use. The automatical web data extraction is critical in web integration. A number of approaches have been proposed. The early work are most based on the source code or the...
This paper proposes a high-performance audio fingerprint extraction method for identifying TV commercial advertisement. In the proposed method, a salient audio peak pair fingerprints based on constant Q transform (CQT) are hashed and stored, to be efficiently compared to one another. Experimental results confirm that the proposed method is quite robust in different noise conditions and improves the...
Robust syllabification of continuous speech is a vital aspect of language and speech processing systems. Syllabification of speech can be done by detecting the syllable nuclei. Syllable is the basic production unit of human speech and syllable nuclei can be attributed to high energy sonarants or resonant sounds which are relatively loud and carry a clear pitch. In this work, high spectral energy at...
Evaluation of respiratory activity during sleep is essential in order to reliably diagnose sleep disorder breathing (SDB); a condition associated with serious cardio-vascular morbidity and mortality. In the current study, we developed and validated a robust automatic breathing-sounds (i.e. inspiratory and expiratory sounds) detection system of audio signals acquired during sleep. Random forest classifier...
This paper explores differences between two methods for Blind Source Separation within frame of ECG de-noising. First method is Joint Approximate Diagonalization of Eigenmatrices, which is based on estimation of fourth order cross-cummulant tensor and its diagonalization. Second one is the statistical method known as Canonical Correlation Analysis, which is based on estimation of correlation matrices...
Snoring is one of the representative phenomena of the sleep disorder and detection of snoring is quite important for improving quality of daily human life. The purpose of this research is to define the noises of the ordinary sleep situation and to find its characteristics as a preliminary research of snoring detection. Differently from previous snoring researches, we use a built-in sound recording...
This paper addresses the challenging problem of recognition and classification of textured surfaces under illumination variation, geometric transformations and noisy sensor measurements. We propose a new texture operator, Adaptive Median Binary Patterns (AMBP) that extends our previous Median Binary Patterns (MBP) texture feature. The principal idea of AMBP is to hash small local image patches into...
Label noise is not uncommon in machine learning applications nowadays and imposes great challenges for many existing classifiers. In this paper we propose a new type of auto-encoder coined label-denoising auto-encoder to learn a representation for robust classification under this situation. For this purpose, we include both the feature and the (noisy) label of a data point in the input layer of the...
In this paper, we focus on developing a novel noise-robust LBP-based texture feature extraction scheme for texture classification. Specifically, two solutions have been proposed to overcome the primary two reasons that cause local binary pattern sensitive to noise. First, a hybrid model is proposed for noise-robust texture description. In this new model, the local primitive micro features are encoded...
This work investigates the effectiveness of features from the spectral envelope such as the frequency and bandwidth of the first peak obtained from a 30th order Linear Predictive Coefficients (LPC) to identify pathological voices. Other spectral features are also investigated and tested to improve the recognition rate. The value of the Relative Power of the Periodic Component is combined with spectral...
The paper proposes an adaptive shock filter to restore noisy blurred image characters. This filter introduces an fuzzy decision mechanism to sharpen image features like edges and singularities while an anisotropic diffusion process is used to remove noise. A useful application of the proposed filter is the improvement of image segmentation and binarization task. Its efficiency on degraded document...
The local feature descriptor called SIFT, is one of the most widely used descriptors. The keypoints found with RSIFT and describe them in a standard way, which makes them invariant to the size changes, rotation, position, scale, and so on. These are quite powerful features and are used in a variety of tasks. This local feature SIFT descriptor gives potential key points, which are extracted from the...
Feature descriptor based methods (e.g. Local Binary Patterns, Local Ternary Patterns) have gained encouraging results in face recognition. However one needs to manually set the threshold in Local Ternary Patterns (LTP). The threshold in LTP is not data adaptive and not robust to noise. In some cases, we may not give a suitable threshold for LTP. Inspired by Weber's Law, here a data adaptive threshold...
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