A Multiplicative Watermark Detection Algorithm for Digital Images in the DCT Domains

2005 ◽  
Vol 16 (10) ◽  
pp. 1798 ◽  
Author(s):  
Zhong-Wei SUN
Keyword(s):  
Digital Images ◽  
Detection Algorithm

A robust detection algorithm for copy-move forgery in digital images

2012 ◽  
Vol 214 (1-3) ◽  
pp. 33-43 ◽  
Author(s):  
Yanjun Cao ◽  
Tiegang Gao ◽  
Li Fan ◽  
Qunting Yang
Keyword(s):  
Digital Images ◽  
Detection Algorithm ◽  
Robust Detection

Use of Local Characteristics of Self-Similarity of Digital Images for Solving the Problems of Crack Detection

2015 ◽  
Vol 756 ◽  
pp. 704-708
Author(s):  
A.L. Zhiznyakov ◽  
D.G. Privezentsev
Keyword(s):  
Crack Detection ◽  
Digital Images ◽  
Detection Algorithm ◽  
The Self ◽  
Self Similarity ◽  
Local Characteristics ◽  
Fractal Characteristics

The task of analyzing digital images on the basis of local characteristics of self-similarity is considered in this article. The algorithm of forming fractal characteristics of images and the detection algorithm, which can be used to solve the problems of task detection, are described. The results of studying the possibility of distributing the self-similarity in the problems of crack-detection are given

scholarly journals ANÁLISE DE MÉTODOS DE DETECÇÃO E RECONHECIMENTO DE FACES UTILIZANDO VISÃO COMPUTACIONAL E ALGORITMOS DE APRENDIZADO DE MÁQUINA

2021 ◽  
Vol 13 (2) ◽  
pp. 01-11
Author(s):  
Lucas José da Costa ◽  
Thiago Luz de Sousa ◽  
Francisco Assis da Silva ◽  
Leandro Luiz de Almeida ◽  
Danillo Roberto Pereira ◽  
...  
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Comparative Analysis ◽  
Execution Time ◽  
Digital Images ◽  
Facial Recognition ◽  
Detection Algorithm ◽  
Ambient Lighting ◽  
The Face ◽  
Haar Cascade

The advancement in technology in recent decades has provided many facilities for humanity in various applications, and facial recognition technology is one of them. There are several problemsto be solved to perform face recognition from digital images, such as varying ambient lighting, changing the face physical characteristics and resolution of the images used. This work aimed to perform a comparative analysis between some of thedetection and facial recognition methods, as well as their execution time. We use the Eigenface, Fisherface and LBPH facial recognition algorithms in conjunction with the Haar Cascade facedetection algorithm, all from the OpenCV library. We also explored the use of CNN neural network for facial recognition in conjunction with the HOG facial detection algorithm, these from the Dlib library. The work aimed, besides analyzing the algorithms in relation to hit rates, factors such as reliability and execution time were also considered

scholarly journals Exploration of Face Detection Methods in Digital Images

2019 ◽  
Vol 8 (4) ◽  
pp. 12130-12136
Keyword(s):  
Face Detection ◽  
Digital Images ◽  
Recognition System ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Surveillance Systems ◽  
Human Beings ◽  
Survey Paper ◽  
Wide Range ◽  
The Face

Face detection is a challenging computer vision task that identifies and localizes the faces of human beings from digital images or video streams. It is predominantly the first phase in the process of developing a wide range of face applications such as face recognition, emotion recognition, authentication, surveillance systems etc. The process of face detection is easy from the human perspective but, a complex task for computers that involves searching of the face in variable circumstances of pose, colour, size, occlusion, illumination etc. If the outcome of face detection is intended to be input for another algorithm, an accurate, well informed selection of an appropriate face detection technique is essential because the overall performance of face application is dependent on face detection algorithm’s precision. The survey paper presents a review of three commonly used face detection algorithms available in literature namely Viola Jones, Neural networks (NN) and Local Binary Pattern (LBP) for the purpose of ascertaining the most suitable face detection algorithm to implement for our future work in developing an ‘Online student concentration level recognition system’.

scholarly journals Weighted Quasi-Interpolant Spline Approximations of Planar Curvilinear Profiles in Digital Images

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3084
Author(s):  
Andrea Raffo ◽  
Silvia Biasotti
Keyword(s):  
Digital Images ◽  
Spline Approximation ◽  
Detection Algorithm ◽  
Polynomial Spline ◽  
Automatic Method ◽  
Data Perturbation ◽  
Spline Curves ◽  
Curve Approximation ◽  
Local Approximations ◽  
Edge Points

The approximation of curvilinear profiles is very popular for processing digital images and leads to numerous applications such as image segmentation, compression and recognition. In this paper, we develop a novel semi-automatic method based on quasi-interpolation. The method consists of three steps: a preprocessing step exploiting an edge detection algorithm; a splitting procedure to break the just-obtained set of edge points into smaller subsets; and a final step involving the use of a local curve approximation, the Weighted Quasi Interpolant Spline Approximation (wQISA), chosen for its robustness to data perturbation. The proposed method builds a sequence of polynomial spline curves, connected C0 in correspondence of cusps, G1 otherwise. To curb underfitting and overfitting, the computation of local approximations exploits the supervised learning paradigm. The effectiveness of the method is shown with simulation on real images from various application domains.

Read, Understand, Learn, & Excel: Development and Testing of an Automated Reading Strategy Detection Algorithm for Postsecondary Students

2019 ◽  
Vol 28 (3) ◽  
pp. 1257-1267 ◽  
Author(s):  
Priya Kucheria ◽  
McKay Moore Sohlberg ◽  
Jason Prideaux ◽  
Stephen Fickas
Keyword(s):  
Academic Success ◽  
Undergraduate Students ◽  
Expository Text ◽  
Reading Strategy ◽  
Detection Algorithm ◽  
Expository Texts ◽  
Human Observer ◽  
Behavioral Strategies ◽  
Periodic Review ◽  
Wide Range

PurposeAn important predictor of postsecondary academic success is an individual's reading comprehension skills. Postsecondary readers apply a wide range of behavioral strategies to process text for learning purposes. Currently, no tools exist to detect a reader's use of strategies. The primary aim of this study was to develop Read, Understand, Learn, & Excel, an automated tool designed to detect reading strategy use and explore its accuracy in detecting strategies when students read digital, expository text.MethodAn iterative design was used to develop the computer algorithm for detecting 9 reading strategies. Twelve undergraduate students read 2 expository texts that were equated for length and complexity. A human observer documented the strategies employed by each reader, whereas the computer used digital sequences to detect the same strategies. Data were then coded and analyzed to determine agreement between the 2 sources of strategy detection (i.e., the computer and the observer).ResultsAgreement between the computer- and human-coded strategies was 75% or higher for 6 out of the 9 strategies. Only 3 out of the 9 strategies–previewing content, evaluating amount of remaining text, and periodic review and/or iterative summarizing–had less than 60% agreement.ConclusionRead, Understand, Learn, & Excel provides proof of concept that a reader's approach to engaging with academic text can be objectively and automatically captured. Clinical implications and suggestions to improve the sensitivity of the code are discussed.Supplemental Materialhttps://doi.org/10.23641/asha.8204786

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