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

scholarly journals License plate identification and recognition in a non-standard environment using neural pattern matching

2021 ◽  
Author(s):  
Imran Shafi ◽  
Imtiaz Hussain ◽  
Jamil Ahmad ◽  
Pyoung Won Kim ◽  
Gyu Sang Choi ◽  
...  
Keyword(s):  
Character Recognition ◽  
Detection Algorithm ◽  
Input Image ◽  
Recognition Algorithm ◽  
Detection Accuracy ◽  
License Plate ◽  
Recognition Mechanism ◽  
Robust Detection ◽  
Feature Representations ◽  
Recognition Efficiency

AbstractNon-standard license plates are a part of current traffic trends in Pakistan. Private number plates should be recognized and, monitored for several purposes including security as well as a well-developed traffic system. There is a challenging task for the authorities to recognize and trace the locations for the certain number plate vehicle. In a developing country like Pakistan, it is tough to have higher constraints on the efficiency of any license plate identification and recognition algorithm. Character recognition efficiency should be a route map for the achievement of the desired results within the specified constraints. The main goal of this study is to devise a robust detection and recognition mechanism for non-standard, transitional vehicle license plates generally found in developing countries. Improvement in the character recognition efficiency of drawn and printed plates in different styles and fonts using single using multiple state-of-the-art technologies including machine-learning (ML) models. For the mentioned study, 53-layer deep convolutional neural network (CNN) architecture based on the latest variant of object detection algorithm-You Only Look Once (YOLOv3) is employed. The proposed approach can learn the rich feature representations from the data of diversified license plates. The input image is first pre-processed for quality improvement, followed by dividing it into suitable-sized grid cells to find the correct location of the license plate. For training the CNN, license plate characters are segmented. Lastly, the results are post-processed and the accuracy of the proposed model is determined through standard benchmarks. The proposed method is successfully tested on a large image dataset consisting of eight different types of license plates from different provinces in Pakistan. The proposed system is expected to play an important role in implementing vehicle tracking, payment for parking fees, detection of vehicle over-speed limits, reducing road accidents, and identification of unauthorized vehicles. The outcome shows that the proposed approach achieves a plate detection accuracy of 97.82% and the character recognition accuracy of 96%.

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 Hyperspectral longwave infrared reflectance spectra of naturally dried algae, anthropogenic plastics, sands and shells

2020 ◽  
Vol 12 (4) ◽  
pp. 2665-2678
Author(s):  
Shungudzemwoyo P. Garaba ◽  
Tomás Acuña-Ruz ◽  
Cristian B. Mattar
Keyword(s):  
Remote Sensing ◽  
Thermal Infrared ◽  
Detection Algorithm ◽  
Natural Environments ◽  
Plastic Pollution ◽  
Related Field ◽  
Robust Detection ◽  
Additional Information ◽  
Hemispherical Reflectance ◽  
Spectrum Region

Abstract. Remote sensing of litter is foreseen to become an important source of additional information relevant to scientific awareness about plastic pollution. Here, we document directional hemispherical reflectance measurements of anthropogenic and natural materials gathered along the shorelines of the Chiloé Archipelago, Chile. These spectral observations were completed in a Chilean laboratory using a state-of-the-art hyperspectral HyLogger-3™ thermal infrared (TIR) spectrometer starting from the medium-wave infrared spectrum (6 µm) and going to the longwave infrared (14.5 µm) spectrum at 0.025 µm intervals. The samples we investigated included sands, shells, algae, nautical ropes, Styrofoam®, gunny sacks and several fragments of plastic-based items. The apparent visible colours of these samples included shades of black, blue, brown, green, orange, white and yellow. We grouped the samples using robust statistical approaches (derivatives, peak-seeking technique) and visual analyses of the derived hyperspectral reflectances. In each group we derived an average or TIR end-member signal and determined diagnostic wavebands. Most of the diagnostic wavebands picked were found to be inside the atmospheric window of the TIR spectrum region. Furthermore, this laboratory reference dataset and findings might become useful in related field observations using similar thermal infrared technologies, especially in identifying anomalies resulting from environmental and meteorological perturbations. Validation and verification of proposed diagnostic wavebands would be part of a continuing effort to advance TIR remote sensing knowledge as well as support robust detection algorithm development to potentially distinguish plastics in litter throughout the natural environments. Data are available in open-access form via the online repository PANGAEA, database of the World Data Center for Marine Environmental Sciences: https://doi.org/10.1594/PANGAEA.919536 (Acuña-Ruz and Mattar, 2020).

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