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Tytuł:
The effect of patterns on image-based modelling of texture-less objects
Autorzy:
Hafeez, J.
Jeon, H.-J.
Hamacher, A.
Kwon, S.-C.
Lee, S.-H.
Powiązania:
https://bibliotekanauki.pl/articles/221814.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
structure-from-motion
feature detection
patterns analysis
3D reconstruction
surface comparison
Opis:
The task of generating fast and accurate three-dimensional (3D) models of objects or scenes through a sequence of non-calibrated images is an active field of research. The recent development in algorithm optimization has resulted in many automatic solutions that can provide an accurate 3D model from texture-full objects. Structure-from-motion (SfM) is an image-based method that uses discriminative point-based feature identifier, such as SIFT, to locate feature points in the images. This method faces difficulties when presented with the objects made of homogenous or texture-less surfaces. To reconstruct such surfaces a well-known technique is to apply an artificial variety by covering the surface with a random texture pattern prior to the image capturing process. In this work, we designed three series of image patterns which are tested based on the contrast and density ratio which increases from the first to the last pattern within the same series. The performance of the patterns is evaluated by reconstructing the surface of a texture-less object and comparing it with the true data. Using the best-found patterns from the experiments, a 3D model of a Moai statue is reconstructed. The experimental results demonstrate that the density and structure of a pattern highly affects the quality of the reconstruction.
Źródło:
Metrology and Measurement Systems; 2018, 25, 4; 755-767
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analysis of operators for detection of corners set in automatic image matching
Autorzy:
Zawieska, D.
Powiązania:
https://bibliotekanauki.pl/articles/129929.pdf
Data publikacji:
2011
Wydawca:
Stowarzyszenie Geodetów Polskich
Tematy:
feature detection
corner detection
3D modelling
image matching
funkcja wykrywania
wykrywanie krawędzi
modelowanie 3D
dopasowanie obrazów
Opis:
Reconstruction of three dimensional models of objects from images has been a long lasting research topic in photogrammetry and computer vision. The demand for 3D models is continuously increasing in such fields as cultural heritage, computer graphics, robotics and many others. The number and types of features of a 3D model are highly dependent on the use of the models, and can be very variable in terms of accuracy and time for their creation. In last years, both computer vision and photogrammetric communities have approached the reconstruction problems by using different methods to solve the same tasks, such as camera calibration, orientation, object reconstruction and modelling. The terminology which is used for addressing the particular task in both disciplines is sometimes diverse. On the other hand, the integration of methods and algorithms coming from them can be used to improve both. The image based modelling of an object has been defined as a complete process that starts with image acquisition and ends with an interactive 3D virtual model. The photogrammetric approach to create 3D models involves the followings steps: image pre-processing, camera calibration, orientation of images network, image scanning for point detection, surface measurement and point triangulation, blunder detection and statistical filtering, mesh generation and texturing, visualization and analysis. Currently there is no single software package available that allows for each of those steps to be executed within the same environment. For high accuracy of 3D objects reconstruction operators are required as a preliminary step in the surface measurement process, to find the features that serve as suitable points when matching across multiple images. Operators are the algorithms which detect the features of interest in an image, such as corners, edges or regions. This paper reports on the first phase of research on the generation of high accuracy 3D model measurement and modelling, focusing upon the application of different operators for accurate feature point extraction. The implementation of those operators is discussed and performance of differen operators is analysed. The optimal operator for high accuracy close range object reconstruction is then highlighted. This research has facilitated a development of the feature extraction and image measurement process that will be central to the development of an automatic procedure for high accuracy point cloud generation in multi image networks where robust orientation and 3D point determination will facilitate surface measurement and modelling within a single software system.
Źródło:
Archiwum Fotogrametrii, Kartografii i Teledetekcji; 2011, 22; 423-436
2083-2214
2391-9477
Pojawia się w:
Archiwum Fotogrametrii, Kartografii i Teledetekcji
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Digital watermarking algorithm based on 4-level discrete wavelet transform and discrete fractional angular transform
Autorzy:
Li, Jing-You
Zhao, Chun-Hui
Zhang, Guang-Da
Powiązania:
https://bibliotekanauki.pl/articles/2033921.pdf
Data publikacji:
2021
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
digital watermarking algorithm
mix optical bistability
Harris feature point detection
discrete wavelet transform
discrete fractional angular transform
singular value decomposition
Opis:
Nowadays, there are many watermarking algorithms based on wavelet transform. The simple one is to insert directly the watermark into the wavelet transform coefficients. However, most of the existing watermarking schemes can only resist traditional signal processing attacks, such as image compression, noise and filtering. When the watermarked image is subject to geometric transformations, especially rotation attack, it is hard to detect the watermark successfully. In this paper, a digital watermarking algorithm is proposed based on 4-level discrete wavelet transform and discrete fractional angular transform. To enhance the security of the algorithm, the watermark is scrambled with the simplicity of Arnold transform and chaos-based mix optical bistability model, since the chaos is pseudorandom and sensitive to the initial values. And the watermark is embedded into the medium frequency sub-band of the 1-level wavelet decomposition according to the Harris feature point detection. Simulation results show that the proposed digital watermarking algorithm by combining 4-level discrete wavelet transform with discrete fractional angular transform could resist rotation attack and other common attacks.
Źródło:
Optica Applicata; 2021, 51, 4; 605-619
0078-5466
1899-7015
Pojawia się w:
Optica Applicata
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Human Detection in Thermal Images Using Low-level Features
Autorzy:
Budzan, S.
Powiązania:
https://bibliotekanauki.pl/articles/114333.pdf
Data publikacji:
2015
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
human detection
infrared
feature extraction
HOG
Opis:
In this work the human detection method in infrared has been presented. The proposed solution focuses on the use low-level features and detecting parts of the human body. Low–level processing is based on modified HOG (Histogram of Oriented Gradients) algorithm. First, the only squared cells have been used, also calculation of the gradient has been improved. Next, the model of the head from the dataset IR (Infra Red) images has been created, also the model of the human body. Finally, the probability matrix has been examined using minimal distance classifier. The novelty of the proposed solution focuses on the combination of the pixel-gradient and body parts processing, also three stage classification process (head modelling, human modelling and classifier), which has been proposed to reduce the false detection. The experiments were performed on self-created IR images database, which contains images with most of the possible difficult situations such as overlapped people, different pose, small and high resolution of the people. The performance of the proposed algorithm was evaluated using Precision and Recall quality measure.
Źródło:
Measurement Automation Monitoring; 2015, 61, 6; 191-194
2450-2855
Pojawia się w:
Measurement Automation Monitoring
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Feature Engineering for Anti-Fraud Models Based on Anomaly Detection
Autorzy:
Przekop, Damian
Powiązania:
https://bibliotekanauki.pl/articles/2075464.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
fraud detection
application fraud
feature engineering
anomaly detection
risk modeling
Opis:
The paper presents two algorithms as a solution to the problem of identifying fraud intentions of a customer. Their purpose is to generate variables that contribute to fraud models’ predictive power improvement. In this article, a novel approach to the feature engineering, based on anomaly detection, is presented. As the choice of statistical model used in the research improves predictive capabilities of a solution to some extent, most of the attention should be paid to the choice of proper predictors. The main finding of the research is that model enrichment with additional predictors leads to the further improvement of predictive power and better interpretability of anti-fraud model. The paper is a contribution to the fraud prediction problem but the method presented may generate variable input to every tool equipped with variable- selection algorithm. The cost is the increased complexity of the models obtained. The approach is illustrated on a dataset from one of the European banks.
Źródło:
Central European Journal of Economic Modelling and Econometrics; 2020, 3; 301-316
2080-0886
2080-119X
Pojawia się w:
Central European Journal of Economic Modelling and Econometrics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Depth-based Descriptor for Matching Keypoints in 3D Scenes
Autorzy:
Matusiak, K.
Skulimowski, P.
Strumillo, P.
Powiązania:
https://bibliotekanauki.pl/articles/226051.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
feature matching
keypoints detection
object recognition
depth map
Opis:
Keypoint detection is a basic step in many computer vision algorithms aimed at recognition of objects, automatic navigation and analysis of biomedical images. Successful implementation of higher level image analysis tasks, however, is conditioned by reliable detection of characteristic image local regions termed keypoints. A large number of keypoint detection algorithms has been proposed and verified. In this paper we discuss the most important keypoint detection algorithms. The main part of this work is devoted to description of a keypoint detection algorithm we propose that incorporates depth information computed from stereovision cameras or other depth sensing devices. It is shown that filtering out keypoints that are context dependent, e.g. located at boundaries of objects can improve the matching performance of the keypoints which is the basis for object recognition tasks. This improvement is shown quantitatively by comparing the proposed algorithm to the widely accepted SIFT keypoint detector algorithm. Our study is motivated by a development of a system aimed at aiding the visually impaired in space perception and object identification.
Źródło:
International Journal of Electronics and Telecommunications; 2018, 64, 3; 299-306
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Double Layered Priority based Gray Wolf Algorithm (PrGWO-SK) for safety management in IoT network through anomaly detection
Autorzy:
Agrawal, Akhileshwar Prasad
Singh, Nanhay
Powiązania:
https://bibliotekanauki.pl/articles/2200943.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
Gray Wolf Optimizer
anomaly detection
feature selection
predictive maintenance
Opis:
For mitigating and managing risk failures due to Internet of Things (IoT) attacks, many Machine Learning (ML) and Deep Learning (DL) solutions have been used to detect attacks but mostly suffer from the problem of high dimensionality. The problem is even more acute for resource starved IoT nodes to work with high dimension data. Motivated by this problem, in the present work a priority based Gray Wolf Optimizer is proposed for effectively reducing the input feature vector of the dataset. At each iteration all the wolves leverage the relative importance of their leader wolves’ position vector for updating their own positions. Also, a new inclusive fitness function is hereby proposed which incorporates all the important quality metrics along with the accuracy measure. In a first, SVM is used to initialize the proposed PrGWO population and kNN is used as the fitness wrapper technique. The proposed approach is tested on NSL-KDD, DS2OS and BoTIoT datasets and the best accuracies are found to be 99.60%, 99.71% and 99.97% with number of features as 12,6 and 9 respectively which are better than most of the existing algorithms.
Źródło:
Eksploatacja i Niezawodność; 2022, 24, 4; 641--654
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Research on stacked ore detection based on improved Mask RCNN under complex background
Badania nad wykrywaniem usypanej (stos) rudy w oparciu o ulepszoną maskę RCNN w złożonym tle
Autorzy:
Zhou, Hehui
Cai, Gaipin
Liu, Shun
Powiązania:
https://bibliotekanauki.pl/articles/2203302.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Instytut Gospodarki Surowcami Mineralnymi i Energią PAN
Tematy:
feature fusion
Mask RCNN
feature pyramid
ore detection
połączenie funkcji
maska RCNN
piramida funkcji
wykrywanie rudy
Opis:
In order to achieve accurate identification and segmentation of ore under complex working conditions, machine vision and neural network technology are used to carry out intelligent detection research on ore, an improved Mask RCNN instance segmentation algorithm is proposed. Aiming at the problem of misidentification of stacked ores caused by the loss of deep feature details during the feature extraction process of ore images, an improved Multipath Feature Pyramid Network (MFPN) was proposed. The network firstly adds a single bottom-up feature fusion path, and then adds with the top-down feature fusion path of the original algorithm, which can enrich the deep feature details and strengthen the fusion of the network to the feature layer, and improve the accuracy of the network to the ore recognition. The experimental results show that the algorithm proposed in this paper has a recognition accuracy of 96.5% for ore under complex working conditions, and the recall rate and recall rate function values reach 97.4% and 97.0% respectively, and the AP75 value is 6.84% higher than the original algorithm. The detection results of the ore in the actual scene show that the mask size segmented by the network is close to the actual size of the ore, indicating that the improved network model proposed in this paper has achieved a good performance in the detection of ore under different illumination, pose and background. Therefore, the method proposed in this paper has a good application prospect for stacked ore identification under complex working conditions.
Aby uzyskać dokładną identyfikację i segmentację rudy w złożonych warunkach pracy, do prowadzenia inteligentnych badań wykrywania rudy wykorzystywane są technologie wizji maszynowej i sieci neuronowych, zaproponowano udoskonalony algorytm segmentacji obrazu Mask RCNN (Region Convolutional Neural Networks). Mając na celu rozwiązanie problemu błędnej identyfikacji ułożonych rud, spowodowanego utratą głębokich szczegółów cech podczas procesu ekstrakcji cech z obrazów rudy, zaproponowano ulepszoną sieć wielościeżkową piramidy cech MFPN (Multipath Feature Pyramid Network). Sieć najpierw dodaje pojedynczą ścieżkę łączenia funkcji od dołu do góry, a następnie dodaje ścieżkę łączenia funkcji od góry do dołu oryginalnego algorytmu, co może wzbogacić głębokie szczegóły funkcji i wzmocnić połączenie sieci z warstwą funkcji (obiektową) i poprawić dokładność sieci do rozpoznawania rudy. Wyniki eksperymentalne pokazują, że algorytm zaproponowany w niniejszej pracy ma dokładność rozpoznawania na poziomie 96,5% dla rudy w złożonych warunkach pracy, a wartości współczynnika czułości i współczynnika czułości funkcji osiągają odpowiednio 97,4 i 97,0%, a wartość AP75 jest wyższa o 6,84% niż oryginalny algorytm. Wyniki wykrywania rudy w rzeczywistej scenie pokazują, że rozmiar maski podzielonej na segmenty przez sieć jest zbliżony do rzeczywistego rozmiaru rudy, co wskazuje, że ulepszony model sieci zaproponowany w tym artykule osiągnął dobrą efektywność w wykrywaniu rudy przy różnym oświetleniu, ułożeniu i tle. Dlatego zaproponowana w pracy metoda ma dobre perspektywy aplikacyjne do identyfikacji usypanych rud w złożonych warunkach pracy.
Źródło:
Gospodarka Surowcami Mineralnymi; 2023, 39, 1; 131--148
0860-0953
Pojawia się w:
Gospodarka Surowcami Mineralnymi
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Negative feature selection algorithm for anomaly detection in real time
Autorzy:
Hryniów, K.
Dzieliński, A.
Powiązania:
https://bibliotekanauki.pl/articles/92969.pdf
Data publikacji:
2011
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
anomaly detection
feature selection
frequent pattern mining
neural networks
rule-based systems
Opis:
Anomaly detection methods are of common use in many fields, including databases and large computer systems. This article presents new algorithm based on negative feature selection, which can be used to find anomalies in real time. Proposed algorithm, called Negative Feature Selection algorithm (NegFS) can be also used as first step for preprocessing data analyzed by neural networks, rule-based systems or other anomaly detection tools, to speed up the process for large and very large datasets of different types.
Źródło:
Studia Informatica : systems and information technology; 2011, 1-2(15); 15-23
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Enhancing Intrusion Detection in Industrial Internet of Things through Automated Preprocessing
Autorzy:
Sezgin, Anıl
Boyacı, Aytuğ
Powiązania:
https://bibliotekanauki.pl/articles/2201911.pdf
Data publikacji:
2023
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
feature selection
intrusion detection
machine learning
industrial internet of things
Internet of things
IIoT
Opis:
Industrial Internet of Things (IIoT) is a rapidly growing field, where interconnected devices and systems are used to improve operational efficiency and productivity. However, the extensive connectivity and data exchange in the IIoT environment make it vulnerable to cyberattacks. Intrusion detection systems (IDS) are used to monitor IIoT networks and identify potential security breaches. Feature selection is an essential step in the IDS process, as it can reduce computational complexity and improve the accuracy of the system. In this research paper, we propose a hybrid feature selection approach for intrusion detection in the IIoT environment using Shapley values and a genetic algorithm-based automated preprocessing technique which has three automated steps including imputation, scaling and feature selection. Shapley values are used to evaluate the importance of features, while the genetic algorithm-based automated preprocessing technique optimizes feature selection. We evaluate the proposed approach on a publicly available dataset and compare its performance with existing state-of-the-art methods. The experimental results demonstrate that the proposed approach outperforms existing methods, achieving high accuracy, precision, recall, and F1-score. The proposed approach has the potential to enhance the performance of IDS in the IIoT environment and improve the overall security of critical industrial systems.
Źródło:
Advances in Science and Technology. Research Journal; 2023, 17, 2; 120--135
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Presenting a technique for registering images and range data using a topological representation of a path within an environment
Autorzy:
Ferreira, F.
Davim, L.
Rocha, R.
Dias, J.
Santos, V.
Powiązania:
https://bibliotekanauki.pl/articles/385035.pdf
Data publikacji:
2007
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
sensor feature integration
binary data
Bernoulli mixture model
dimensionality reduction
robot localisation
change detection
Opis:
This article presents a novel method to the utilize topological representation of a path, thatpath that is created from sequences of images from digital cameras and sensor data from range sensors. A topological representation of the environment is created by leading the robot around the environment during a familiarisation phaseLeading the robot around the environment during a familiarisation phase creates a topological representation of the environment. While moving down the same path, the robot is able to localise itself within the topological representation that is has been previously created. The principal contribution to the state of the art is that, by using a topological representation of the environment, individual 3D data sets acquired from a set of range sensors need not be registered in a single, [Global] Coordinate Reference System. Instead, 3D point clouds for small sections of the environment are indexed to a sequence of multi-sensor views, of images and range data. Such a registration procedure can be useful in the construction of 3D representations of large environments and in the detection of changes that might occur within these environments.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2007, 1, 3; 47-56
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An AI & ML based detection & identification in remote imagery: state-of-the-art
Autorzy:
Hashmi, Hina
Dwivedi, Rakesh
Kumar, Anil
Powiązania:
https://bibliotekanauki.pl/articles/2141786.pdf
Data publikacji:
2021
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
convolutional neural network
remote sensed imagery
object detection
artificial intelligence
feature extraction
deep learning
machine learning
Opis:
Remotely sensed images and their allied areas of application have been the charm for a long time among researchers. Remote imagery has a vast area in which it is serving and achieving milestones. From the past, after the advent of AL, ML, and DL-based computing, remote imagery is related techniques for processing and analyzing are continuously growing and offering countless services like traffic surveillance, earth observation, land surveying, and other agricultural areas. As Artificial intelligence has become the charm of researchers, machine learning and deep learning have been proven as the most commonly used and highly effective techniques for object detection. AI & ML-based object segmentation & detection makes this area hot and fond to the researchers again with the opportunities of enhanced accuracy in the same. Several researchers have been proposed their works in the form of research papers to highlight the effectiveness of using remotely sensed imagery for commercial purposes. In this article, we have discussed the concept of remote imagery with some preprocessing techniques to extract hidden and fruitful information from them. Deep learning techniques applied by various researchers along with object detection, object recognition are also discussed here. This literature survey is also included a chronological review of work done related to detection and recognition using deep learning techniques.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2021, 15, 4; 3-17
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of Two Dimensional Wavelet Transform for Classification of Power Quality Disturbances
Autorzy:
Mollayi, N.
Mokhtari, H.
Powiązania:
https://bibliotekanauki.pl/articles/262752.pdf
Data publikacji:
2014
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie
Tematy:
power quality
event detection and classification
two dimensional wavelet transform
pattern classification
image processing
feature
classifier system
Opis:
Identification of voltage and current disturbances is an important task in power system monitoring and protection. In this paper, the application of two-dimensional wavelet transform for characterization of a wide variety range of power quality disturbances is discussed, and a new algorithm, based on image processing techniques is proposed for this purpose. A matrix is formed based on a specified number of cycles in such a way that the samples of voltage signal in each cycle form one row of that matrix. This matrix can be regarded as a two dimensional image. A two-dimensional wavelet transform is used to decompose the image into approximation and details, which contain low frequency and high frequency components along the rows and columns, respectively. Different disturbances result into different special patterns in detail images. By processing the detail images, specific features are defined which can suitably discriminate various types of disturbances. Combination of the feature generation algorithm and a classifier system leads to a smart system for classification of wide variety range of disturbances.
Źródło:
Electrical Power Quality and Utilisation. Journal; 2014, 17, 2; 1-7
1896-4672
Pojawia się w:
Electrical Power Quality and Utilisation. Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A contemporary multi-objective feature selection model for depression detection using a hybrid pBGSK optimization algorithm
Autorzy:
Kavi Priya, Santhosam
Pon Karthika, Kasirajan
Powiązania:
https://bibliotekanauki.pl/articles/2201021.pdf
Data publikacji:
2023
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
depression detection
text classification
dimensionality reduction
hybrid feature selection
wykrywanie depresji
klasyfikacja tekstu
redukcja wymiarowości
wybór funkcji
Opis:
Depression is one of the primary causes of global mental illnesses and an underlying reason for suicide. The user generated text content available in social media forums offers an opportunity to build automatic and reliable depression detection models. The core objective of this work is to select an optimal set of features that may help in classifying depressive contents posted on social media. To this end, a novel multi-objective feature selection technique (EFS-pBGSK) and machine learning algorithms are employed to train the proposed model. The novel feature selection technique incorporates a binary gaining-sharing knowledge-based optimization algorithm with population reduction (pBGSK) to obtain the optimized features from the original feature space. The extensive feature selector (EFS) is used to filter out the excessive features based on their ranking. Two text depression datasets collected from Twitter and Reddit forums are used for the evaluation of the proposed feature selection model. The experimentation is carried out using naive Bayes (NB) and support vector machine (SVM) classifiers for five different feature subset sizes (10, 50, 100, 300 and 500). The experimental outcome indicates that the proposed model can achieve superior performance scores. The top results are obtained using the SVM classifier for the SDD dataset with 0.962 accuracy, 0.929 F1 score, 0.0809 log-loss and 0.0717 mean absolute error (MAE). As a result, the optimal combination of features selected by the proposed hybrid model significantly improves the performance of the depression detection system.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2023, 33, 1; 117--131
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Music Playlist Generation using Facial Expression Analysis and Task Extraction
Autorzy:
Sen, A.
Popat, D.
Shah, H.
Kuwor, P.
Johri, E.
Powiązania:
https://bibliotekanauki.pl/articles/908868.pdf
Data publikacji:
2016
Wydawca:
Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
Tematy:
facial expression analysis
emotion recognition
feature extraction
viola jones face detection
gabor filter
adaboost
k-NN algorithm
task extraction
music classification
playlist generation
Opis:
In day to day stressful environment of IT Industry, there is a truancy for the appropriate relaxation time for all working professionals. To keep a person stress free, various technical or non-technical stress releasing methods are now being adopted. We can categorize the people working on computers as administrators, programmers, etc. each of whom require varied ways in order to ease themselves. The work pressure and the vexation of any kind for a person can be depicted by their emotions. Facial expressions are the key to analyze the current psychology of the person. In this paper, we discuss a user intuitive smart music player. This player will capture the facial expressions of a person working on the computer and identify the current emotion. Intuitively the music will be played for the user to relax them. The music player will take into account the foreground processes which the person is executing on the computer. Since various sort of music is available to boost one's enthusiasm, taking into consideration the tasks executed on the system by the user and the current emotions they carry, an ideal playlist of songs will be created and played for the person. The person can browse the playlist and modify it to make the system more flexible. This music player will thus allow the working professionals to stay relaxed in spite of their workloads.
Źródło:
Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica; 2016, 16, 2; 1-6
1732-1360
2083-3628
Pojawia się w:
Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 19

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