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Feature extraction and matching are two crucial components in person Re-Identification (ReID). The large pose deformations and the complex view variations exhibited by the captured person images significantly increase the difficulty of learning and matching of the features from person images. To overcome these difficulties, in this work we propose a Pose-driven Deep Convolutional (PDC) model to learn...
We present a novel deep learning architecture for fusing static multi-exposure images. Current multi-exposure fusion (MEF) approaches use hand-crafted features to fuse input sequence. However, the weak hand-crafted representations are not robust to varying input conditions. Moreover, they perform poorly for extreme exposure image pairs. Thus, it is highly desirable to have a method that is robust...
This study focuses on multimodal artifact metrics and proposes a technique based on multimodal biometric systems that are a type of biometric identification systems. It is expected that this technique can aid in verifying the authenticity of each artifact in a more accurate manner and in increasing the level of difficulty involved in counterfeiting when compared to those of existing artifact metric...
Over a decade of continual expansion in networking and cloud computing has naturally created an increased demand for cybersecurity solutions. Due to the large number of communication devices and content, it is ideal that these cybersecurity solutions are automated. Unfortunately, malicious content and/or activity is often designed to “look” normal and new malicious attacks are repeatedly being developed...
Resource usage data, collected using tools such as TACC_Stats, capture the resource utilization by nodes within a high performance computing system. We present methods to analyze the resource usage data to understand the system performance and identify performance anomalies. The core idea is to model the data as a three-way tensor corresponding to the compute nodes, usage metrics, and time. Using...
Effort-aware just-in-time (JIT) defect prediction aims at finding more defective software changes with limited code inspection cost. Traditionally, supervised models have been used; however, they require sufficient labelled training data, which is difficult to obtain, especially for new projects. Recently, Yang et al. proposed an unsupervised model (LT) and applied it to projects with rich historical...
Software vulnerabilities pose significant security risks to the host computing system. Faced with continuous disclosure of software vulnerabilities, system administrators must prioritize their efforts, triaging the most critical vulnerabilities to address first. Many vulnerability scoring systems have been proposed, but they all require expert knowledge to determine intricate vulnerability metrics...
Recent works on crowd counting have achieved promising performance by employing the Convolutional Neurol Network (CNN) based features. These works usually design a deep network to detect pedestrian heads, and then count them. In this paper, we propose a novel approach to count pedestrians effectively based on the statistical CNN features. In particular, our approach only uses the first layer features...
In real-life environment, the speech of interest is often correlated with different kinds of perturbation. Perturbation can be caused by speaking or non-speaking noise, or even by reverberation. This could make the speech signal auditable but not intelligible. In this case, speech cannot be exploited by other automated applications such as voice-command or speech/speaker identification and identification...
Previous research has shown how developers "selfadmit" technical debt introduced in the source code, commenting why such code represents a workaround or a temporary, incomplete solution. This paper investigates the extent to which previously self-admitted technical debt can be used to provide recommendations to developers when they write new source code, suggesting them when to "self-admit"...
Due to the high spectral resolution and the similarity of some spectrums between different classes, hyperspectral image classification turns out to be an important but challenging task. Researches show the powerful ability of deep learning for hyperspectral image classification. However, the lack of training samples makes it difficult to extract discriminative features and achieve performance as expected...
Emotion recognition system using electrocardiogram (ECG) has received considerable attention recently in the area of human computer interaction (HCI). Our work in this paper is an attempt towards developing an emotion recognition system that would classify emotions effectively into four emotional states: joy, anger, sadness and pleasure. The contributions of this paper is summarized in three fold:...
Previous models based on Deep Convolutional Neural Networks (DCNN) for face verification focused on learning face representations. The face features extracted from the models are applied to additional metric learning to improve a verification accuracy. The models extract high-dimensional face features to solve a multi-class classification. This results in a dependency of a model on specific training...
Person re-identification has received considerable attention in the image processing, computer vision and pattern recognition communities because of its huge potential for video-based surveillance applications and the challenges it presents due to illumination, pose and viewpoint changes among non-overlapping cameras. Being different from the widely used low-level descriptors, visual attributes (e...
Automated, efficient and accurate classification of skin diseases using digital images of skin is very important for bio-medical image analysis. Various techniques have already been developed by many researchers. In this work, a technique based on meta-heuristic supported artificial neural network has been proposed to classify images. Here 3 common skin diseases have been considered namely angioma,...
The widespread penetration of counterfeit integrated circuits (ICs) is not only a major threat to the electronic goods supply chain, but also constitute a great threat to national security. Image processing based counterfeit IC design techniques are promising, but currently often suffer from high computational complexity and requirement of expensive image acquisition infrastructure. We describe two...
Monitoring mental fatigue has become important for improving cognitive performance and health outcomes especially for older adults. Previous models using eye-tracking data allow inference of fatigue during cognitive tasks, such as driving, but they require us to engage in specific cognitive tasks. A model capable of inferring fatigue in natural-viewing situations when individuals are not performing...
In this work we propose a new architecture for person re-identification. As the task of re-identification is inherently associated with embedding learning and non-rigid appearance description, our architecture is based on the deep bilinear convolutional network (Bilinear-CNN) that has been proposed recently for fine-grained classification of highly non-rigid objects. While the last stages of the original...
Re-identification refers to the task of finding the same subject across a network of surveillance cameras. This task must deal with appearance changes caused by variations in illumination, a person's pose, camera viewing angle and background clutter. State-of-the-art approaches usually focus either on feature modeling — designing image descriptors that are robust to changes in imaging conditions,...
Person re-identification is one of the widely studied research topic in the fields of computer vision and pattern recognition. In this paper, we present a deep multi-instance learning approach for person re-identification. Since most publicly available databases for pedestrian re-identification are not enough big, over-fitting problems occur in deep learning architectures. To tackle this problem,...
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