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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VI"

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Pattern Recognition Framework for Histological Slide Segmentation 1

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17th International conference on Computer Information systems and Industrial Management (CISIM)

作者： Jelen, Lukasz Kulus, Michal Jurek, Tomasz Wroclaw Univ Sci & Technol Dept Comp Engn Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland Wroclaw Med Univ Dept Human Morphol & Embryol Histol & Embryol Div Ul Chalubinskiego 6a PL-50368 Wroclaw Poland Wroclaw Med Univ Dept Forens Med Ul J Mikulicza Radeckiego 4 PL-50345 Wroclaw Poland

ISBN: (数字)9783319999548

ISBN: (纸本)9783319999548;9783319999531

The venous system is similar in all people, but there are some individual variations. The system is well described and compared in the literature but it seems that there is a lack of comparison on the microscopic level. In this paper we present a segmentation framework for histological image segmentation that uses a clustering approach. The described framework can be used for further comparison of the venous system at the microscopic level. For that purpose we adopted a k-means and fuzzy c-means algorithms to classify image pixels to obtain vein segmentation. The presented results are promising and achieved partitioning can further be utilized for quantitative vein comparison.

关键词： Movat FCM Segmentation KNN segmentation Fuzzy c-means k-means image processing Computer aided diagnosis Pattern Recognition Histological slide

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A Real-time video Fusion processing System Based On SIFT Algorithm and FPGA

A Real-time Video Fusion Processing System Based On SIFT Alg...

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IEEE International conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Chaodong Ling Hongkai Qin Qing Tao Ruimin Huang Huipeng Duan Wencai Lin Huaqiao University Xiamen China

ISBN: (数字)9781728150307

ISBN: (纸本)9781728150314

In order to combine multi-views into one ultra-wide single-view for present surveillance desire, a video fusion scheme based on FPGA is proposed in this paper. The geometric transformation and image fusion algorithms used in our scheme are simulated by Matlab for verification and optimization. Then, a combination of two 640*480 video images captured by cameras is implemented in a Altera FPGA platform DE2-115. Moreover, the weighted fusion method is used to realize video fusion for better performance; and larger size of 1280*768@60Hz video images' combination are also realized in DE2-115. Experiments show that the proposed approach can realize the mosaic of two video images with a delay of 16 clock cycles, i.e. a total of 0.16us. The error induced by the fusion process is less than 1 pixel. The implementation in DE2-115 for two RGB 1280*768@60 pixel size image combination can be 1743 times faster than software implementation with a good splicing performance.

关键词： Streaming media Field programmable gate arrays Real-time systems Splicing Software Hardware image fusion

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Secure UAV-Based System to Detect and Filter Sea Objects Using image processing 16th

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16th International conference on Computer Aided systems Theory (EUROCAST)

作者： Lodeiro-Santiago, Moises Caballero-Gil, Candido Aguasca-Colomo, Ricardo Munilla-Fajardo, Jorge Ramio-Aguirre, Jorge Univ La Laguna Dept Ingn Informat & Sistemas Tenerife Spain Univ Las Palmas Gran Canaria Inst Univ SIANI Gran Canaria Spain Univ Malaga Dept Ingn Comunicac Malaga Spain Univ Politecn Madrid Dept Sistemas Informat Madrid Spain

ISBN: (纸本)9783319747187;9783319747170

In the last years irregular immigration has aroused again due to the fact of war in some regions like middle east and in continents like Africa. Because of that, in this paper we presented a novel solution based in new emerging technology to face off the immigration problem focused on the case of sea-type immigration. The presented system is composed by unmanned aerial vehicles (UAV), also known as RPAs or drones, used to fly autonomously over an area in order to detect unallocated potentials immigration boats. The method used is the image processing using combined algorithms and software like OpenCV2 to detect certain patterns in images to determinate whether it is a possible immigration boat or not. Besides, it has been included some prof of concepts to show the effectiveness of the presented system. As main results we was able to determine and detect using aerial pictures if a boat it is a boat with an 82.3% of accuracy (according to the F1 score accuracy test).

关键词： UAV image processing Patera boat Ship detection Security

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Identification of Acute Lymphoblastic Leukemia in Microscopic Blood image Using image processing and Machine Learning algorithms 7

Identification of Acute Lymphoblastic Leukemia in Microscopi...

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7th International conference on Computing, Communications and Informatics (ICACCI)

作者： Rajpurohit, Subhash Patil, Sanket Choudhary, Nitu Gavasane, Shreya Kosamkar, Pranali MIT Dept Comp Engn Pune Maharashtra India

ISBN: (纸本)9781538653142

Cancer has been plaguing the society for a long time and still there is no certain treatment;especially if detected in later stages. That is why early detection and treatment of cancer is of utmost importance. Acute lymphoblastic leukemia is a type of blood cancer which is known to progress very rapidly and prove fatal if there is a delay in detection. Detection of this type of cancer is carried out manually by observing the blood samples of patient under microscope and conducting various other tests. This process may produce undesirable drawbacks: slowness, non-standardized accuracy since it depends on examiner's / pathologist's capabilities and fatigue due to work overload can cause human errors in detection. A few automated systems for detection of Acute Lymphoblastic Leukemia (ALL) have been proposed which involve extracting features from blood images using MATLAB and implementing different classifiers to produce results, which gave remarkable accuracies though not enough for practical usage. Our proposed system is further improving the classification accuracy. It uses openCV and skimage for image processing to extract relevant features from blood image and not just sheer number of features and further classification is carried out using various classifiers: CNN, FNN, SVM and KNN of which CNN gives the highest accuracy of 98.33%. CNN and FNN are written using TensorFlow framework. The accuracies obtained by other classifiers: FNN, SVM, and KNN are 95.40%, 91.40% and 93.30% respectively.

关键词： Leukemia Lymphoblast Neural Network Support Vector Machine TensorFlow

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Real-time Depth Map Processor for Offset Aperture based Single Camera System 18

Real-time Depth Map Processor for Offset Aperture based Sing...

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23rd Asia and South Pacific Design Automation conference (ASP-DAC)

作者： Kim, Hyeji Lim, Jinyeon Lee, Yeongmin Yun, Woojin Kim, Young-Gyu Choi, Wonseok Khan, Asim Khan, Muhammad Umar Karim Homidov, Said Park, Hyun Sang Kyung, Chong-Min Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea Ctr Integrated Smart Sensors Daejeon South Korea Kongju Natl Univ Div Elect Engn Cheonan South Korea

ISBN: (纸本)9781509006021

This paper presents a Offset Aperture (OA) based single camera system and proposes a optimized vision processor, a new hardware architecture for fast, low-energy, and low-complexity depth extraction. The proposed design was fabricated in 110nm CMOS image sensor technology and supports 32-level depth resolution on 1920 x 1080 full HD image with 30fps, consuming 280.53mW from 1.5V supply and a mere 2.8% of bad classification. The low-complexity algorithms are employed to eliminate the DRAM access, thereby the proposed OA architecture can be directly embedded with the CMOS image sensor and commercial image processing chip.

关键词： Cameras Apertures Hardware High definition video Real-time systems Computer architecture Power demand

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Retinal Vessel Extraction and Fovea Detection Using Morphological processing

Retinal Vessel Extraction and Fovea Detection Using Morpholo...

引用

International conference on Computing, Analytics and Networking (ICCAN)

作者： Banerjee, Avik Bhattacharjee, Soumyadeep Latib, Sk. St Thomas Coll Engn & Technol Dept Comp Sci & Engn Kolkata India

ISBN: (纸本)9789811078712;9789811078705

Extraction of blood vessels from retinal fundus images is a primary phase in the diagnosis of several eye disorders including diabetic retinopathy, a leading cause of vision impairment among working-age adults globally. Since manual detection of blood vessels by ophthalmologists gets progressively difficult with increasing scale, automated vessel detection algorithms provide an efficient and cost-effective alternative to manual methods. This paper aims to provide an efficient and highly accurate algorithm for the extraction of retinal blood vessels. The proposed algorithm uses morphological processing, background elimination, neighborhood comparison for preliminary detection of the vessels. Detection and removal of fovea, and bottom-hat filtering are performed subsequently to improve the accuracy, which is then calculated as a percentage with respect to ground truth images.

关键词： Diabetic retinopathy Vessel extraction Fovea detection Fundus image Bottom-hat filter Neighborhood comparison

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Discrimination of Satellite Signals from Opencast Mining of Mineral Ores of Hematite and Uranium Using Digital image processing and Geostatistical algorithms 3rd

Discrimination of Satellite Signals from Opencast Mining of ...

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3rd International conference on Advanced Machine Learning Technologies and Applications (AMLTA)

作者： Sharma, Richa N. K. Bhatnagar, Roheet Ojha, Abhishek Birla Inst Technol Dept Remote Sensing Ranchi 835215 Bihar India Manipal Univ Dept Comp Sci & Engn Jaipur 303007 Rajasthan India

ISBN: (纸本)9783319746906;9783319746890

This study has been conducted on mining areas of Gua and Banduhurang in Jharkhand State, India, Barbil in Orissa State, India and Mines d'Arlit, Niger. Data from the corresponding sensors of EO-I, LANDSAT and IRS (i.e., Hyperion, ETM+ and LISS III respectively) were used to discriminate satellite signals of Hematite and Uranium ores from these locations. For the data of Hyperion being hyper-spectral, correction mechanism were performed through relevant algorithms: Fast Line-of-sight Atmospheric Analysis of Hypercube (FLAASH) to remove atmospheric aerosol effects, Minimum Noise Fraction (MNF) to remove noise, Pixel Purity Index (PPI) to get spectrally the most pure pixel and Spectral Angle Mapper (SAM) in order to match the spectral similarity between an image pixel spectrum and a referenced spectrum. The data achieved from ETM+ had line-stripping, and thus were restored. On the LISS III data, vegetation had to be, virtually, removed from the images of the Indian sites, using Normalized Difference Vegetation Index (NDvi), in order to equate them with that from the Niger site. The processed data was put to a common platform statistically. Segregation of Uranium, a radioactive ore, from Hematite, a non-radioactive iron ore, could be achieved up to 82.35% using TOPSIS and 90% using pair-wise Student's t-Test. The technique of Band Ratio was also carried out and an index was generated to isolate these mines from their surroundings.

关键词： FLAASH Pixel PPI SAM NDvi Ground-truth TOPSIS Pair-wise Student's t-Test Graphic screen Band Band ratio Index image

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Training classifiers with limited data using the Radon cumulative distribution transform

Training classifiers with limited data using the Radon cumul...

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conference on Medical Imaging - image processing

作者： Fitzgerald, Cailey E. Cattell, Liam Rohde, Gustavo K. Univ Virginia Dept Biomed Engn Charlottesville VA 22908 USA Univ Virginia Dept Elect & Comp Engn Charlottesville VA USA

ISBN: (数字)9781510616387

ISBN: (纸本)9781510616387

Our purpose in this study is to investigate whether a recently introduced image transform, denoted as the Radon cumulative distribution transform (R-CDT), can be used as a viable preprocessing step for augmenting the robustness of training end-to-end systems with fewer training samples. In order to assess the ability of the R-CDT to perform this aim, we identified a standard machine learning dataset, MNIST, and a preliminary dataset comprised of liver cell nuclei images derived from one of two tissue types: benign or malignant tumor lesions. We separated the data into training and testing sets with 20% of the total data used for testing across all training set size conditions. To simulate a range of limited size of training examples, we randomly generated data subsets ranging in size from 80% to 0.8% of the total dataset size to be used for training. Linear classification algorithms were implemented via logistic regression and a support vector machine model with a linear kernel on both the raw images and images transformed via the R-CDT. Additionaly, non-linear classification accuracies were assessed via comparing the R-CDT paired with a shallow CNN and using a deep CNN to classify images. Results indicate that classification in Radon cumulative distribution transform space outperforms classification in image space in conditions of limited data, as one is likely to see in medical imaging.

关键词： R-CDT image processing classification biomedical

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Banana (Musa sp.) Maturity Prediction System based on Chlorophyll Content using visible-NIR Imaging 2

Banana (<i>Musa</i> sp.) Maturity Prediction System based on...

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International conference on Signals and systems (ICSigSys)

作者： Saputro, Adhi Harmoko Juansyah, Syifa Dzulhijjah Handayani, Windri Univ Indonesia Dept Phys Depok Indonesia Univ Indonesia Dept Biol Depok Indonesia

ISBN: (纸本)9781538656891

The chlorophyll content is one of the parameters to predict the maturity level of banana fruit. Nevertheless, the measurement of the chlorophyll is commonly destructive and performed manually through the biological test. In this paper, a banana maturity prediction system was introduced using visible-Near Infrared (V-NIR) imaging based on the chlorophyll characteristic to estimate the maturity and the chlorophyll content non-destructively. The hardware of the measurement system consists of a set of sliders including controllable motor, Teflon table, halogen light source and a hyperspectral camera that connected directly to PC through Camera Link. The hypercube processing algorithms consist of reflectance image profile computation, spatial segmentation, spectral feature extraction, feature reduction, regression, and classification algorithm. The reflectance of the current image of the banana surface was corrected by the intensity value of the white and dark image. The spectral feature sets were computed using a principal component analysis on the full wavelength range of the camera spectra. The chlorophyll content was estimated using principal component regression. Thus, the maturity stage of banana was classified using support vector machine into three classes i.e. immature, mature and very mature based on the chlorophyll profile characteristic. The proposed system was evaluated using 45 Ambon bananas (Musa acuminata colla) samples which consist of 15 samples for each maturity stage. The correlation coefficient is 0.89 and RMSE value is 5.98 x 10(-4) %. The maturity classification error using five folding of cross-validation is 2.1%. The results show that the proposed system can predict the banana maturity stage perfectly and suitable in an industrial sorting system for banana fruit quality.

关键词： banana chlorophyll content visible-NIR Principal Component Analysis

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A Finger-vein Biometric System Based on Textural Features

A Finger-vein Biometric System Based on Textural Features

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International conference on Information Technology and systems (ICITS)

作者： Carrera, Enrique V. Izurieta, Santiago Carrera, Ricardo Univ Fuerzas Armadas ESPE Dept Elect & Elect Sangolqui 171103 Ecuador Univ San Francisco Quito Colegio Ciencias & Ingn Cumbaya 170901 Ecuador

ISBN: (纸本)9783319734507;9783319734491

Biometric systems are being widely used today for automated authentication purposes. In particular, vascular biometrics or vein recognition is receiving a large amount of attention because of its several advantages related to security and convenience. However, images containing vein patterns normally include more information than just those structural arrangements. Thus, we propose a finger-vein biometric system based exclusively on textural features to evaluate the usefulness of the remaining information around vein patters. Textural features are obtained through gray-level co-occurrence matrices from the wavelet detail coefficients belonging to finger-vein images. The evaluation of the proposed biometric system is based on a standardized finger-vein database and its results show favorable improvements on the finger-vein authentication accuracy when textural features are incorporated in the biometric process.

关键词： Finger-vein biometry Digital image processing Machine learning algorithms

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