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Whenever a video is being digitized, compressed and transmitted across the network, some degradation might be introduced that could affect the quality of the video received. Thus, it is essential to provide feedback system to the provider which could allow them the freedom to feedback to the system, if the quality of video being transmitted could be improved, in terms of video quality. This is an...
Reduced-reference Image Quality Metric for Contrast-changed images (RIQMC) and No-Reference Quality metric for Contrast-Distorted Images (NR-IQACDI) are the state-of-the-art IQA for Contrast-Distorted Images (CDI). Nevertheless, there is room for improvement especially for the assessment results using image database called TID2013 and CSIQ. Most of the existing No-Reference Image Quality Assessment...
Web mapping services have been offered by many companies. To choose an appropriate online mapping platform, it is important to be able to quantify and compare the quality of the different platforms, in different geographic areas. Most previous research comparing the quality of online maps has used a manual approach. Manual quality evaluation is labor-intensive and difficult to replicate in new geographic...
Many operators are working in jobs that require stressful mental tasks such as transportation supervision, vehicle driving, banking and others. Prevention of fatigued-based human error, that has been a standing challenge in such work areas, can be detected and quantified using human performance level. This paper proposes an enhanced method for operator fatigue detection based on computer-keyboard...
Zero resource spoken term discovery in continuous speech is the discovery of repeated patterns in acoustic signals without any higher level linguistic information. These patterns are then combined to define the compositional units of that speech. We describe and implement an algorithm that tags similar subsequences among sequences of acoustic features. We then discuss the use of this algorithm as...
Person re-identification is an important topic in visual surveillance, which aims at recognizing an individual over disjoint camera views. As a major aspect of person re-identification, distance metric learning has been widely studied to seek a discriminative matching metric. However, most existing distance metric learning methods learn an identical projection matrix for all camera views, while ignoring...
Person re-identification is a critical yet challenging task in video surveillance which intends to match people over non-overlapping cameras. Most metric learning algorithms for person re-identification use symmetric matrix to project feature vectors into the same subspace to compute the similarity while ignoring the discrepancy between views. To solve this problem, we proposed an asymmetric cross-view...
Objective assessment of pathological speech is an important part of existing systems for automatic diagnosis and treatment of various speech disorders. In this paper, we propose a new regression method for this application. Rather than treating speech samples from each speaker as individual data instances, we treat each speaker's data as a probability distribution. We propose a simple non-parametric...
In this paper, we propose a speaker segmentation method for meeting audio based on i-vector. The motivation is to utilize the Total Variability (TV) framework as a feature extractor and to exploit the potential of modeling the speaker and channel variabilities for speaker segmentation in meetings. A distance-based segmentation method is designed with the cosine distance. A sliding window with variable...
Statistical methods for Spoken Dialogue Systems have been shown to reduce the cost of development, while successfully handling a variety of applications. However, such systems are usually trained with simulated users or paid subjects in controlled settings. While this may be sufficient to jump-start learning in the various sub-components, learning is very much dependent on the complete knowledge that...
Metric learning for music is an important problem for many music information retrieval (MIR) applications such as music generation, analysis, retrieval, classification and recommendation. Traditional music metrics are mostly defined on linear transformations of handcrafted audio features, and may be improper in many situations given the large variety of music styles and instrumentations. In this paper,...
In applications involving matching of image sets, the information from multiple images must be effectively exploited to represent each set. State-of-the-art methods use probabilistic distribution or subspace to model a set and use specific distance measure to compare two sets. These methods are slow to compute and not compact to use in a large scale scenario. Learning-based hashing is often used in...
Appearance based person re-identification in real-world video surveillance systems is a challenging problem for many reasons, including ineptness of existing low level features under significant viewpoint, illumination, or camera characteristic changes to robustly describe a person's appearance. One approach to handle appearance variability is to learn similarity metrics or ranking functions to implicitly...
This paper presents a novel person re-identification framework based on data fusion. The pipeline of the proposed method is composed of two stages. First, a metric learning paradigm is applied on a bunch of distinct feature extractors to produce an ensemble of estimated distance measures, which are subsequently penalized according to their confidence in evidencing the correct matches from the false...
In this paper, we propose a pose-robust metric learning framework for unconstrained face verification by jointly optimizing face and pose verification tasks. We learn a joint model for these two tasks and explicitly discourage the information sharing between pose and identity verification metrics so as to mitigate the information contained in the pose verification task leading to making the identity...
In day today life, health insurance data collection plays major role for employers. In several countries misbehavior in health insurance is a major problem. Health insurance data fraud is an intentional act of misleading, hiding or misrepresenting information that makes profit to a single or group of members. These kind of violation leads to major loss for health insurance providing organisation....
A Convolutional Neural Networks (CNNs) approach is proposed to automate the method of Diabetic Retinopathy(DR) screening using color fundus retinal photography as input. Our network uses CNN along with denoising to identify features like micro-aneurysms and haemorrhages on the retina. Our models were developed leveraging Theano, an open source numerical computation library for Python. We trained this...
Biometric template protection techniques like biometric cryptosystems and cancelable biometrics are most widely used in many large-scale biometric systems. Though generic biometric cryptosystems differ from other conventional cryptosystems, still it is insufficient to overcome the challenges ahead of identity frauds and vulnerabilities to major attacks. In recent years it's been used as promising...
Developers often rely on various online resources, such as blogs, to keep themselves up-to-date with the fast pace at which software technologies are evolving. Singer et al. found that developers tend to use channels such as Twitter to keep themselves updated and support learning, often in an undirected or serendipitous way, coming across things that they may not apply presently, but which should...
We propose, in this paper, a lightweight refactoring recommendation tool, namely c-JRefRec, to identify Move Method refactoring opportunities based on four heuristics using static and semantic program analysis. Our tool aims at identiying refactoring opportunities before a code change is committed to the codebase based on current code changes whenever the developer saves/compiles his code. We evaluate...
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