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Adaptive Polar based Filtering Method for image Copy-Move Forgery Detection

Adaptive Polar based Filtering Method for Image Copy-Move Fo...

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IEEE International conference on Trust, Security and Privacy in Computing and Communications

作者： Xiuli Bi Chi-Man Pun Xiao-Chen Yuan Dept. of Comput. & Inf. Sci. Univ. of Macau Macau China

ISBN: (纸本)9781509032068

In this paper, an Adaptive Polar based Filtering Method is proposed for image copy-move forgery detection. In order to improve the performance of detection method, the post-processing of the matching results is worth being focused on. To filter out the redundant pixels from the initially matched pixels, two pixels sets-Symmetrical Matched Pixels set and Unsymmetrical Matched Pixels set, are extracted from the matched pixel pairs;furthermore, the polar distributions of the two sets are calculated respectively. Then, the filtering thresholds can be adaptively calculated according to the polar distribution, thus the redundant pixels can be filtered out accordingly. Finally, some morphological operations are applied to the remained pixels to generate the detected forged regions. Experimental results show that the proposed scheme can achieve much better detection results compared with the existing state-of-the-art copy-move forgery detection methods.

关键词： Copy-Move Forgery Polar Distribution Adaptive Polar based Filtering Method

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Expert and machine discrimination of marine flora: a comparison of recognition accuracy of field-collected phytoplankton

Expert and machine discrimination of marine flora: a compari...

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2003 International conference on visual Information Engineering vIE 2003

作者： P.F. Culverhouse R. Williams B. Reguera v. Herryl S. Gonzalez-Gil University of Plymouth Plymouth UK Plymouth Marine Laboratory Plymouth UK Instituto Español de Oceanografía Vigo Spain

The categorisation of labelling of phytoplankton specimens is carried out manually using microscopes by marine ecologists and taxonomists. Research to automate the task has been on going for many years. Although many systems have been shown to work in small-scale laboratory conditions with cultured populations, few have succeeded when applied to field collected specimens. The reasons are diverse, but are principally due to severely degraded performance of the chosen processing algorithms in the presence of noise and natural morphological variability of the organisms. The application of statistical and neural network pattern learning methods have allowed progress to be made in this difficult area. The machine learning system DiCANN was trained on 128 of the 310 image data set and tested on the 182 samples. This study has highlighted the difficulties facing human ecologists and has shown that automation methods can perform as well as humans on complex categorisations.

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Identification of Medicinal Plants by visual Characteristics of Leaves and Flowers

Identification of Medicinal Plants by Visual Characteristics...

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International conference on Industrial and Information Systems, ICIIS

作者： A.D.A.D.S. Jayalath T.G.A.G.D Amarawanshaline D. P. Nawinna P.v.D. Nadeeshan H.P Jayasuriya Faculty of Computing SLIIT Malabe Sri Lanka

ISBN: (数字)9781728137063

ISBN: (纸本)9781728137070

In Ayurveda medicine, correct identification of medicinal plants is of great importance. Plants are identified by human experts using their visual features and aroma. Incorrect identification of medicinal plants may lead to adverse results. Plant identification can be automated using visual morphological characteristics such as the shape, color, and texture of the leaves and flowers. This paper presents how rare medicinal plants were identified with high accuracy by applying image processing and machine learning capabilities. For this study, a database was created from scanned images of leaves and flowers of rare medicinal plants used in Sri Lankan Ayurveda medicine. Both the front and back sides of leaves and flowers were captured. The leaves are classified based on the unique feature combination. Identification rates up to 98% have been obtained when tested over 10 plants.

关键词： Plant identification Feature extraction morphological features Classification Indigenous medicine Ayurveda Neural network Plants, Medicinal plant identification Ayurvedic medicine Indigenous Medicine Flowers foliage Feature extraction Neural network Taxonomy scanned imagery

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Computational method for automatic recovery and extraction of weather information

Computational method for automatic recovery and extraction o...

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Computer Science Society (SCCC) International conference Chilean FLAGGED

作者： Claudia Palacios Bianca Benegas Horacio Legal Ayala José Luis vázquez Noguera Facultad Politécnica Universidad Nacional de Asunción San Lorenzo Paraguay

ISBN: (纸本)9781509033409

Some meteorological centers stored in their facilities a lot of paper sheets containing measurements of atmospheric pressure, temperature, humidity and precipitation. The meteorological measurements are shown as a continuous line drawn on a graph paper divided into periods of time. The paper sheets are in danger of being lost either by the deterioration suffered because of the passage of time, factors such as humidity or any calamity. Reading and recording of information is performed manually and therefore a considerable amount of time and effort is employed. The recovery of paper sheets will allow to have a database of images and the automatic extraction will allow to get more frequent and accurate measurement data. This paper proposes a computational method that involves digital processing and analysis of images scanned using a mathematical model for calculating the measurements. Binarization methods, morphological operators and filters were applied according to their performance metrics (accuracy, precision and error rate). The application of these procedures generated a representative curve that was introduced to the mathematical model and allowed to calculate the measurement values in the corresponding schedules. The results showed an average error of ±0,1676 hPa (hectopascal) of pressure and the average processing time per sheet is 1,03 seconds.

关键词： Biomedical imaging Instruments image segmentation Atmospheric measurements Temperature measurement Mathematical model Zinc

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vlsi Realization For Decomposition Of Dynamic Texture Using Efficient Motion Estimation Algorithm

Vlsi Realization For Decomposition Of Dynamic Texture Using ...

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International conference on Information Communication and Embedded Systems

作者： v. Aruljyothi A. Nithya P.G. Student Department of Electronics and Communication Engineering Arunai College of Engineering CollegeThiruvanamalai Tamilnadu India Assistant professor Department of Electronics and Communication Engineering Arunai College of Engineering College Thiruvanamalai Tamilnadu.

ISBN: (纸本)9781467357869

Dynamic textures are sequences of images of moving scenes that exhibit certain stationary properties in time, these includes sea-waves, smoke, foliage, whirlwind, crowd, etc. This proposal is made to present a novel characterization of dynamic textures. That poses the problems of modeling, learning, recognizing, classifying, synthesizing, and editing dynamic textures on a firm analytical footing. Dynamic textures are decomposed using diamond search motion estimation methodology;Diamond search motion estimation algorithm which could divides the frames into 16x16 pixels that can make the decomposition faster when compared to existing MCA method. The RGB frames are converted into gray scale image using MATLAB tool and resulted MIF (memory initialization file) is applied to diamond search logic which is designed using MATLAB. Our proposed system increases the processing speed of decomposite textures. The morphological Component Analysis (MCA), and Diamond Search (DS) algorithm are used calculate the PSNR values.

关键词： Dynamic Texture Diamond Search algorithm

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Axonal Transport velocity Estimation from Kymographs Based on Curvilinear Feature Extraction and Spline Fitting

Axonal Transport Velocity Estimation from Kymographs Based o...

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Annual International conference of the IEEE Engineering in Medicine and Biology Society

作者： Alka Nair Sriprabha Ramanarayanan Shikha Ahlawat Sandhya Koushika Niranjan Joshi Mohanasankar Sivaprakasam Department of Electrical Engineering IIT Madras Healthcare Technology Innovation Centre IITM Research Park National Center for Biological Sciences Dept. of Biologial Sciences TIFR

ISBN: (纸本)9781424479276

Axonal transport velocities are obtained from spatio-temporal maps called kymographs developed from time-lapse confocal microscopy movies of neurons. The kymographs of axonal transport of *** worms are much noisier due to in vivo nature of imaging. Existing methodologies for velocity measurement include laborious manual delineation of axonal movement ridges on the kymographs and thereby determining particle velocities from the slopes of ridges marked. Manual kymograph analysis is not only time consuming but also prone to human errors in marking the ridges. An automated algorithm to extract all the ridges and determine the velocities without significant manual efforts is highly preferred. Not many methods are currently available for such biological studies. We present an almost automated method based on information fusion using LDA classifier, morphological image processing and spline fitting for determining axonal transport velocities. Experimental analysis of 50 kymographs shows considerable reduction of 89% in time taken with manual intervention of 10.83%. Comparitive study with the results of two of the previous literatures shows that our algorithm performs better.

关键词： Manuals Algorithm design and analysis Splines (mathematics) image edge detection Feature extraction Motion pictures

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Design and Implementation of a Tilapia (Oreochromis niloticus) Fingerling Counter Using Otsu Thresholding and morphological Transformation Techniques

Design and Implementation of a Tilapia (Oreochromis niloticu...

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International conference on Electrical Engineering, Computer Science and Informatics (EECSI)

作者： Tom Jairus v. Portea Maricris I. De La Fuente Mae Z. Medrano Farah Faye P. Felices Ian Jake A. Naldo Rizza T. Loquias Keith Marlon R. Tabal College of Engineering & Architecture Camarines Sur Polytechnic Colleges Camarines Sur Philippines

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

Many different image processing techniques have been developed and implemented in counting systems for livestock and fisheries applications. These existing systems often require sophisticated hardware and computational resources, leading to an increase in the financial costs associated with their production. In this paper, an innovative Tilapia (Oreochromis niloticus) fingerling counting system is described. The system is comprised of low-cost computer vision-based hardware and software that integrates Otsu thresholding and morphological transformation techniques. These techniques derive contours from segmented regions generated by the formulated algorithms. It uses a Raspberry Pi 4B single-board computer platform in conjunction with a Raspberry Pi Camera Module version 1.3 to capture images. The performance of the manual process and the proposed method were evaluated by determining its mean squared error using a ground truth as a reference. Based on the tests performed, it was revealed that the proposed automatic counting system resulted in a better performance than the manual counting method.

关键词： Performance evaluation Thresholding (Imaging) Accuracy Software algorithms Prototypes Manuals Production Hardware Software Python

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Recent advances in scientific computing and partial differential equations : international conferenc

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2003年

作者： S.Y. Cheng C.-W. Shu T. Tang editors.

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A spatiotemporal approach to tornado prediction

A spatiotemporal approach to tornado prediction

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International Joint conference on Neural Networks (IJCNN)

作者： v. Lakshmanan I. Adrianto T. Smith G. Stumpf University of Oklahoma and National Servere Storms Laboratory USA Sch. of Industrial Engineering University of Oklahama USA University of Oklahoma and National Weather Service USA

Automated tornado detection or prediction techniques in the literature have all been based on analyzing signatures of tornadoes that appear in Doppler radar velocity data. Attributes of these signatures are derived from radar data, as well as the near-storm environment, and associated with observed tornadoes. This associated database has been used to train neural networks and support vector machines to automatically classify radar signatures. In this paper, we formulate the tornado prediction problem differently. Instead of devising a machine intelligence approach to classify detections, we formulate the problem as a spatiotemporal one: of estimating the probability of a tornado event at a particular spatial location within a given time window. In this paper, we also describe our initial approach to addressing this differently formulated problem. We use a least-squares methodology to estimate shear, morphological image processing to estimate gradients, fuzzy logic to generate compact measures of tornado possibility and a classification neural network to generate the final spatio-temporal probability field.

关键词： Spatiotemporal phenomena Tornadoes Doppler radar Radar detection Neural networks Databases Support vector machines Support vector machine classification Machine intelligence Event detection

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Plant Disease Detection Using Machine Learning

Plant Disease Detection Using Machine Learning

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International conference on Futuristic Technologies (INCOFT)

作者： Shifa Aiman Bagban Ravi Hosur Sahebgouda Patil Dept. of Computer Science & Engineering BLDEA's V.P.Dr. P. G Halakatti College of Engineering & Technology Vijaypura Karnataka (INIDA) Dept. of CSE(AI & ML) BLDEA's V.P.Dr. P. G Halakatti College of Engineering & Technology Vijaypura Karnataka (INIDA)

Agricultural productivity is the factor on which many countries' economies heavily rely on. Identifying plant diseases is extremely crucial in the agricultural sector as they can hamper the plant's robustness and health which is important in agricultural productivity. These obstacles are regular in plant production ,and if adequate procedures of prevention are not followed it may inversely affect the cultivation. Farmers are confronting financial loss due to various crop diseases. The traditional method of disease detection is achieved by the opinion of an expert and physical inspection, which takes much time and is expensive in the practical life. And when the cultivated area is big in acres, it becomes very tough for the cultivators to track the crops periodically. That's why an automated computer based disease detection system has become essential. The system developed in this project involves using image processing techniques such as opencv2 that are designed using python to segment the disease part from the leaf while using machine learning techniques like SvM and CNN to categorize the leaves of the plant as healthy or infected. For classification purposes, morphological characteristics and properties such as colour, strength and measurements of the plant leaves are taken into account.

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