This paper proposes a novel method using graph based features for detecting copy move forgery done in digital images. For extracting the features of the image patches the graph Fourier transform are used. A graph can ...
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ISBN:
(数字)9781728188935
ISBN:
(纸本)9781728188942
This paper proposes a novel method using graph based features for detecting copy move forgery done in digital images. For extracting the features of the image patches the graph Fourier transform are used. A graph can represent data in different applications with a generic data structure. A graph different from classical transforms like DCT and DWT in the sense that it can represent signals on irregular and high dimensional domain. A graph can fit to local characteristics through edges for regular signals like images. Graph Fourier transform extracts the frequency interpretation for signals on graph. To reduce the processing time of feature matching patch match algorithm is used which is a fast approximate nearest neighbor search algorithm. The experiments conducted on the GRIP database shows that the proposed method is highly accurate and are robust to rotation and scaling.
In order to automate the coil delivery for the cold rolling process and reduce manual operation error, this paper presents a method to locate and track the steel coil using monocular camera and imageprocessing, and t...
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ISBN:
(数字)9781728158556
ISBN:
(纸本)9781728158563
In order to automate the coil delivery for the cold rolling process and reduce manual operation error, this paper presents a method to locate and track the steel coil using monocular camera and imageprocessing, and the core circle of coil is used as the tracking target. This proposed method firstly determines the initial target location information by RHT, then configure the qualification conditions of detection for the next time step based on the target parameters from the last time step. Canny edge detection is used to detect initial contour candidates, limited domain method, contour separation method, quadrant method are adopted to separate contour candidates and filter interference, then fit the target, and track the target by dynamic window method. Fitting the theoretical motion trajectory of the target by process a large number of video data, accurately mark the falling point and alignment point of the coil delivery process. It has been verified in the industrial field that this method has high accuracy, good tracking stability and real-time performance in the case of sufficient light. Compare with the traditional key-points detection methods based on sensor array, this system requires only a monocular camera, and realize the real-time location tracking, with higher accuracy and controllability, convenient for later maintenance.
In the field of imageprocessing, color correction is usually used when there are significant differences in tone or brightness in the original images. This method is also often used as an image preprocessing method t...
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作者:
Li, GaoheXu, BoZhang, YanliSchool of Economic Management
Xi'an Shiyou University Xi'an 710065 China School of Petroleum Engineering Xi'an Shiyou University Xi'an 710065 China International Business School Shaanxi Normal University Xi'an 710119 China
Using OpenCV imageprocessing library and VC++ programming language, this paper realizes the virtual simulation experiment measurement of core permeability of 'Reservoir Physics'. The permeability measurements...
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Nowadays, digital image forgery detection is one of important topics in research world. In this paper, we propose a novel forgery detection algorithm using the logarithmic basis of Benford's law which states the m...
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ISBN:
(数字)9781728186290
ISBN:
(纸本)9781728186306
Nowadays, digital image forgery detection is one of important topics in research world. In this paper, we propose a novel forgery detection algorithm using the logarithmic basis of Benford's law which states the mantissa of the logarithm of all practical numbers should be uniformly distributed. Based on this fact, the proposed method uses extracted features from mantissa distribution of discrete cosine transform (DCT) coefficients in JPEG images. Support vector machine (SVM) is used for classification to detect authentic and forged images based on these features. Results show that our proposed algorithm has the highest mean accuracy (99.78%), sensitivity (99.77%) and specificity (99.79%) in comparison with previous works on CASIA V1.0 dataset.
This study presents an image analysis framework coupled with machine learning algorithms for the classification of microscopy pollen grain images. Pollen grain classification has received notable attention concerning ...
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ISBN:
(纸本)9781728138688
This study presents an image analysis framework coupled with machine learning algorithms for the classification of microscopy pollen grain images. Pollen grain classification has received notable attention concerning a wide range of applications such as paleontology and honey certification, forecasting of allergies caused of airborne pollen and food technology. It requires an extensive qualitative process that is mostly performed manually by an expert. Although manual classification shows satisfactory performance, it may suffer from intra and inter-observer variability and it is time consuming. This study benefits from the advances of imageprocessing and machine learning and proposes a fully-automated analysis pipeline aiming to: a) calculate morphological characteristics from the images using a cost-effective microscope, and b) classify images into 6 pollen classes. A private dataset from the Department of Agriculture of the Hellenic Mediterranean University in Crete containing 564 images was used in this study. A Random Forest (RF) classifier was utilized to classify images. A repeated nested cross-validation (nested-CV) schema was used to estimate the generalization performance and prevent overfitting. image preprocessing, extraction of geometric and textural characteristics and feature selection were implemented prior to the assessment of the classification performance and a mean accuracy of 88.24% was reported.
A longwave infrared (LWIR) handheld surveillance camera has been modified through the addition of a second sensor which provides both visible (RGB) and near-infrared (NIR) image streams. The challenges and constraints...
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ISBN:
(数字)9781510630222
ISBN:
(纸本)9781510630222
A longwave infrared (LWIR) handheld surveillance camera has been modified through the addition of a second sensor which provides both visible (RGB) and near-infrared (NIR) image streams. The challenges and constraints imposed on the development process of this Handheld Fusion Camera (HHFC) are described, and the approach to the dual and tri-band image fusion processing schemes is presented. The physical characteristics of the existing camera acted as a major constraint on the HHFC design with the Size, Weight, and Power (SWaP) requirements restricting the choice of both the additional sensor as well as the processor engine available within the camera. The primary use of the HHFC is in ground-based security and surveillance operations which is challenging in terms of variability in the scene content. Establishing an effective processing architecture is critical to both image interpretability by the user, and operational effectiveness. The HHFC allows the user to view different image streams including enhanced single-band image data as well as both dual and tri-band fused imagery. Such flexibility allows the user to select the best imagery for their immediate requirements. Power consumption and latency figures have been minimised by the use of relatively simple arithmetical fusion algorithms combined with an Adaptive Weight Map (AWM) for regional-based optimisation. In practice, the potential performance gain achieved is necessarily limited by the required performance robustness, and this trade-off was critical to the HHFC design and the final imageprocessing solution.
This paper presents new algorithms to solve the feature-sparsity constrained PCA problem (FSPCA), which performs feature selection and PCA simultaneously. Existing optimization methods for FSPCA require data distribut...
ISBN:
(纸本)9781713829546
This paper presents new algorithms to solve the feature-sparsity constrained PCA problem (FSPCA), which performs feature selection and PCA simultaneously. Existing optimization methods for FSPCA require data distribution assumptions and lack of global convergence guarantee. Though the general FSPCA problem is NP-hard, we show that, for a low-rank covariance, FSPCA can be solved globally (Algorithm 1). Then, we propose another strategy (Algorithm 2) to solve FSPCA for the general covariance by iteratively building a carefully designed proxy. We prove (data-dependent) approximation bound and convergence guarantees for the new algorithms. For the spectrum of covariance with exponential/Zipf's distribution, we provide exponential/posynomial approximation bound. Experimental results show the promising performance and efficiency of the new algorithms compared with the state-of-the-arts on both synthetic and real-world datasets.
Pairwise comparison data arise in many domains with subjective assessment experiments, for example in image and video quality assessment. In these experiments observers are asked to express a preference between two co...
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Pairwise comparison data arise in many domains with subjective assessment experiments, for example in image and video quality assessment. In these experiments observers are asked to express a preference between two conditions. However, many pairwise comparison protocols require a large number of comparisons to infer accurate scores, which may be unfeasible when each comparison is time-consuming (e.g. videos) or expensive (e.g. medical imaging). This motivates the use of an active sampling algorithm that chooses only the most informative pairs for comparison. In this paper we propose ASAP, an active sampling algorithm based on approximate message passing and expected information gain maximization. Unlike most existing methods, which rely on partial updates of the posterior distribution, we are able to perform full updates and therefore much improve the accuracy of the inferred scores. The algorithm relies on three techniques for reducing computational cost: inference based on approximate message passing, selective evaluations of the information gain, and selecting pairs in a batch that forms a minimum spanning tree of the inverse of information gain. We demonstrate, with real and synthetic data, that ASAP offers the highest accuracy of inferred scores compared to the existing methods. We also provide an open-source GPU implementation of ASAP for large-scale experiments.
Robotic grasp should be carried out in a real-time manner by proper accuracy. Perception is the first and significant step in this procedure. This paper proposes an improved pip line model trying to detect grasp as a ...
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ISBN:
(数字)9781728186290
ISBN:
(纸本)9781728186306
Robotic grasp should be carried out in a real-time manner by proper accuracy. Perception is the first and significant step in this procedure. This paper proposes an improved pip line model trying to detect grasp as a rectangle representation for different seen or unseen objects. It helps the robot to start control procedures from nearer to the proper part of the object. The main idea consists in the pre-processing, output normalization, and data augmentation to improve accuracy by 4.3 percent without making the system slow. Also, a comparison has been conducted over different pre-trained models like AlexNet, ResNet, Vgg19, which are the most famous feature extractors for imageprocessing in object detection. Although AlexNet has less complexity than other ones, it outperformed them, which helps the real-time property.
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