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Conflagration is one of the major disasters that threatens human life and property. If the proper action is not taken in detecting the symptom of conflagration events ahead of time, the number of such disasters will keep increasing. An effective solution in this context will alleviate many fire-related global problems to a great extent. Although fire detectors are not available in many places, WiFi...
Occlusion handling is one of the most challenging issues for pedestrian detection, and no satisfactory achievement has been found in this issue yet. Using human body parts has been considered as a reasonable way to overcome such an issue. In this paper, we propose a brand new approach based on the fusion of Mid-level body part mining and Convolutional Neural Network (CNN) to solve this problem, named...
Scene text information extraction plays an important role in many computer vision applications. Unlike most existing text extraction algorithms for English texts, in this paper, we focus on Chinese texts, which are more complex in stroke and structure. To tackle this challenging problem, we propose a novel convolutional neural network (CNN) based text structure feature extractor for Chinese texts...
Proper feature selection for unsupervised outlier detection can improve detection performance but is very challenging due to complex feature interactions, the mixture of relevant features with noisy/redundant features in imbalanced data, and the unavailability of class labels. Little work has been done on this challenge. This paper proposes a novel Coupled Unsupervised Feature Selection framework...
Even today, a large number of organizations collect data using paper forms. However, it can be difficult to aggregate, and analyze the data collected using paper forms. Better management and processing of forms and applications is indispensable to improving customer experience. But, typing the form data into a spreadsheet is time-consuming, mundane and may result in errors. Various attempts have been...
Nowadays, people purchase a lot of products from online shopping sites. To support customers in decision making, some sites collect and provide user reviews on products. However, contents of the user reviews are too abundant for customers to analyze them in a short period of time. The automatic analysis of reviews is important to provide users with valuable information about goods of any category...
Discovering anomalies at sea is one of the critical tasks of Maritime Situational Awareness (MSA) activities and an important enabler for maritime security operations. This paper proposes a data-driven approach to anomaly detection, highlighting challenges specific to the maritime domain. This work builds on unsupervised learning techniques which provide models for normal traffic behaviour. A methodology...
Within our approach to big data, we reduce the number of images in video footage by applying a shot detection with a keyframe extraction of single frames. This can be followed by duplicate removal and face detection processes yielding to a further data reduction. Nevertheless, additional reductions steps are necessary in order to make the data manageable (searchable) for the end user in a meaningful...
Anomaly detection in a network is important for diagnosing attacks or failures that affect the performance and security of a network. Lately, many anomaly detection techniques have been proposed for detecting attacks whose nature is strange. A process for extracting useful features is implemented in the anomaly detection framework. Standard matrices are applied for measuring the operation of the anomaly...
The palmprint recognition has become a focus in biological recognition and image processing fields. In this process, the features extraction (with particular attention to palmprint principal line extraction) is especially important. Although a lot of work has been reported, the representation of palmprint is still an open issue. In this paper we propose a simple, efficient, and accurate palmprint...
Anomaly detection techniques are used to detect the abnormal behavior. It is also used to identify security attack. The proposed system finds flows those are anomalous from the state of flows. Apriori algorithm is uses to identify anomalous flows which are present in network traffic. In this proposed method, first preprocessing of input traffic flow is done; Apriori algorithm is applied over network...
This paper proposes a novel stroke extraction method for the Chinese character. In our method, a Chinese character is represented as a set of triangular mesh that is generated by using the canny contour detector and the constraint Delaunay triangulation (CDT). Based on the representation, the singular regions and the sub-strokes are firstly determined by the properties of triangular mesh. The point-to-boundary...
The research of palm print features has attracted a lot of attention, and the principal lines which is one of the stable and important features in palm print images can provide effective information for application of palm print technology. Aimed to accurate and natural extraction, in this paper, an associated extraction method of palm print principal lines is presented based on the own properties...
Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Android...
Event Extraction is a complex and interesting topic in Information Extraction that includes event extraction methods from free text or web data. The result of event extraction systems can be used in several fields such as risk analysis systems, online monitoring systems or decide support tools. In this paper, we introduce a method that combines lexico -- semantic and machine learning to extract event...
Automatic coronary extraction has great clinical importance in the effective handling and visualization of large amounts of 3D data. Despite tremendous previous research, coronary extraction remains difficult. Two such difficulties are extraction of both normal and abnormal vessels and reconstruction of exact tree structures based on anatomical knowledge. To solve the first difficulty, we propose...
This paper proposes a generic approach combining a bottom-up (low-level) visual detector with a top-down (high-level) fuzzy first-order logic (FOL) reasoning framework in order to detect pedestrians from a moving vehicle. Detections from the low-level visual corner based detector are fed into the logical reasoning framework as logical facts. A set of FOL clauses utilising fuzzy predicates with piecewise...
Some special natural and social events like natural disasters, terrorism attacks, and traffic accidents may have remarkable effect on people's collective behaviors, which means the behaviors of a large number of people viewed as a whole. Analysis of the patterns of collective behaviors in the sense of data mining is an important topic. Solving this problem is crucial and practical in that online detection...
Focusing on the deficiencies of conventional intrusion detection model, Wenke Lee, Salvatore J. Stolfo et al. propose the intrusion detection system based on data mining technology. It solves the problem that self-adaptability of the system is poor, and the conditions of misreport or omission are also further improved. However, as far as mass data are concerned, more and more resources need to be...
In this paper, we present novel approach for text extraction. In computer vision r esearch area, text is very important in images. Here we use edge based extraction of text using ISEF (infinit e symmetrical edge filter). ISEF is optimal edge det ector which gives accurate results for text in imag es. Text extraction involves detection, localization, tracking and enhancement. Large numbers of te chnique...
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