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Detection of infarcted myocardium in the left ventricle is achieved with delayed enhancement magnetic resonance imaging (DE-MRI). However, manual segmentation is tedious and prone to variability. We studied three texture analysis methods (run-length matrix, co-occurrence matrix, and autoregressive model) in combination with histogram features to characterize the infarcted myocardium. We evaluated...
Gradient boosting tree (GBT), a widely used machine learning algorithm, achieves state-of-the-art performance in academia, industry, and data analytics competitions. Although existing scalable systems which implement GBT, such as XGBoost and MLlib, perform well for datasets with medium-dimensional features, they can suffer performance degradation for many industrial applications where the trained...
Recently a new set consisting of six information theory features was proposed for on-line signature verification by Rosso, Ospina and Frery. The proposed features were evaluated on the MCYT-100 on-line signature database resulting in the best performance ever measured on that dataset. In this paper we repeat their measurements and show that their result is erroneous. In addition, we evaluate the performance...
In this paper, we propose a compact image steganalysis method for the LSB-matching steganography, in which a feature vector composed by only 12 elements is extracted from the image. We analyze the statistical artifact occurred in images when a secret data is embedded in it by the LSB-matching steganography. We selected 12 most relevant features based on the probability density function (PDF) of difference...
Automated detection and recognition of human abnormal behavior is the key problem of monitoring systems. We construct a complete system that is able to alert the human operator when a knife in hand is visible in camera views. We use RealSense 3D camera to track hands, modified MPEG-7 EHD as feature vector and none-linear SVM as classifier. In this paper, we improve the feature extraction algorithm...
In this paper, we propose a novel method for global abnormal events detection in crowded scenes. Each video is described as the set of overlapping space-time cubes. The histogram of optical flow orientation and motion magnitude are used as global feature descriptor to capture the motion magnitude and orientation of the normal and abnormal events. The motion-rich space-time cubes are selected to enhance...
Human Activity analysis is one of the most captivating and important open problem in automated video surveillance community. In recent years, most of the analysis of human activities/behavior is carried out using computer vision & pattern recognition techniques and has paved the way for amalgamation of various such fields. This paper gives an assessment of this new development, proposed to analyze...
Plants play an important role in Earth's ecology by providing sustenance, shelter and maintaining a healthy atmosphere. Some of these plants have important medicinal properties. Automatic recognition of plant leaf is a challenging problem in the area of computer vision. An efficient Ayurvedic plant leaf recognition system will beneficial to many sectors of society which include medicinal field botanic...
Here, evaluate the abasement in execution of well known and effective face detector when human captured picture quality is corrupted by additive gaussian noise and blur. It is observed that, inside a specific scope of recognized picture quality, an adequate increase in picture quality can improve face detection performance. These results can be utilized to guide data transfer capacity which regards...
In this paper, we propose a neural network based distance metric learning method for a better discrimination in the sequence-matching based keyword search (KWS). In this technique, we conduct a version of Dynamic Time Warping (DTW) based similarity search on the speaker independent posteriorgram space. With this, we aim to compensate for the scarcity of the resources and overcome the out-of-vocabulary...
In this paper we focus on the characterization of singing styles in world music. We develop a set of contour features capturing pitch structure and melodic embellishments. Using these features we train a binary classifier to distinguish vocal from non-vocal contours and learn a dictionary of singing style elements. Each contour is mapped to the dictionary elements and each recording is summarized...
Human body analysis raises special interest because it enables a wide range of interactive applications. In this paper we present a gesture estimator that discriminates body poses in depth images. A novel collaborative method is proposed to learn 3D features of the human body and, later, to estimate specific gestures. The collaborative estimation framework is inspired by decision forests, where each...
Distributed surveillance systems have become popular in recent years due to security concerns. However, transmitting high dimensional data in bandwidth-limited distributed systems becomes a major challenge. In this paper, we address this issue by proposing a novel probabilistic algorithm based on the divergence between the probability distributions of the visual features in order to reduce their dimensionality...
In this paper, we propose a new texture descriptor, scale selective extended local binary pattern (SSELBP), to characterize texture images with scale variations. We first utilize multi-scale extended local binary patterns (ELBP) with rotation-invariant and uniform mappings to capture robust local microand macro-features. Then, we build a scale space using Gaussian filters and calculate the histogram...
Malicious software, referred to as malware, continues to grow in sophistication. Past proposals for malware detection have primarily focused on software-based detectors which are vulnerable to being compromised. Thus, recent work has proposed hardware-assisted malware detection. In this paper, we introduce a new framework for hardware-assisted malware detection based on monitoring and classifying...
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...
Passive wireless-device fingerprinting - the act of passively and automatically identifying specific types of wireless devices through sequential analysis of wireless traffic - is useful for network monitoring and management. This study presents a novel passive fingerprinting approach for wireless devices, by modeling network traffic with carefully chosen wireless parameters from 802.11 frames, and...
This paper presents a finger-spelling recognition system focusing on Thai finger-spelling sign language, derived from the computer vision, using SVM. In this study, global and local features were extracted from input finger images. In order to develop the recognition system, 15 Thai alphabet characters were collected from five hand signers, totally 375 character pictures, in order to train the system...
Recent advancements in computer vision, multimedia and Internet of Things (IoT) have shown that human detection methods are useful for applications of intelligent transportation system in smart environment. However, detection of a human in real world remains a challenging problem. Histogram of oriented gradients (HOG) based human detection gives an emphasis towards finding an effective solution to...
In advanced semiconductor-process technology, the ability to detect and repair lithography hotspots, which can affect printability, is essential. In this paper, we propose a two-stage cascade classifier for accurate hotspot detection. Our classifier uses a novel layout feature based on the propagation of light passing through a photomask. We performed experiments to evaluate our cascade classifier...
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