1,902 research outputs found
A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets
The term "outlier" can generally be defined as an observation that is significantly different from
the other values in a data set. The outliers may be instances of error or indicate events. The
task of outlier detection aims at identifying such outliers in order to improve the analysis of
data and further discover interesting and useful knowledge about unusual events within numerous
applications domains. In this paper, we report on contemporary unsupervised outlier detection
techniques for multiple types of data sets and provide a comprehensive taxonomy framework and
two decision trees to select the most suitable technique based on data set. Furthermore, we
highlight the advantages, disadvantages and performance issues of each class of outlier detection
techniques under this taxonomy framework
Streaming Algorithms for Subspace Analysis: Comparative Review and Implementation on IoT Devices
Subspace analysis is a widely used technique for coping with high-dimensional data and is becoming a fundamental step in the early treatment of many signal processing tasks. However, traditional subspace analysis often requires a large amount of memory and computational resources, as it is equivalent to eigenspace determination. To address this issue, specialized streaming algorithms have been developed, allowing subspace analysis to be run on low-power devices such as sensors or edge devices. Here, we present a classification and a comparison of these methods by providing a consistent description and highlighting their features and similarities. We also evaluate their performance in the task of subspace identification with a focus on computational complexity and memory footprint for different signal dimensions. Additionally, we test the implementation of these algorithms on common hardware platforms typically employed for sensors and edge devices
Valvekaameratel põhineva inimseire täiustamine pildi resolutsiooni parandamise ning näotuvastuse abil
Due to importance of security in the society, monitoring activities and recognizing specific
people through surveillance video camera is playing an important role. One of
the main issues in such activity rises from the fact that cameras do not meet the resolution
requirement for many face recognition algorithms. In order to solve this issue,
in this work we are proposing a new system which super resolve the image. First,
we are using sparse representation with the specific dictionary involving many natural
and facial images to super resolve images. As a second method, we are using deep
learning convulutional network. Image super resolution is followed by Hidden Markov
Model and Singular Value Decomposition based face recognition. The proposed system
has been tested on many well-known face databases such as FERET, HeadPose, and
Essex University databases as well as our recently introduced iCV Face Recognition
database (iCV-F). The experimental results shows that the recognition rate is increasing
considerably after applying the super resolution by using facial and natural image
dictionary. In addition, we are also proposing a system for analysing people movement
on surveillance video. People including faces are detected by using Histogram of Oriented
Gradient features and Viola-jones algorithm. Multi-target tracking system with
discrete-continuouos energy minimization tracking system is then used to track people.
The tracking data is then in turn used to get information about visited and passed
locations and face recognition results for tracked people
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