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检索条件"任意字段=18th Image Processing: Algorithms and Systems Conference, IPAS 2020"

共 258 条记录，以下是21-30 订阅

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COLOR QUANTIZATION USING C-MEANS CLUSTERING algorithms

COLOR QUANTIZATION USING C-MEANS CLUSTERING ALGORITHMS

引用

18th IEEE International conference on image processing (ICIP)

作者： Celebi, M. Emre Wen, Quan Chen, Juan Louisiana State Univ Dept Comp Sci Shreveport LA 71105 USA Univ Elec Sci & Tech Sch Comp Sci & Eng Chengdu Peoples R China

ISBN: (纸本)9781457713033

Color quantization is an important operation with many applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms. Recent studies have demonstrated the effectiveness of hard c-means (k-means) clustering algorithm in this domain. Other studies reported similar findings pertaining to the fuzzy c-means algorithm. Interestingly, none of these studies directly compared the two types of c-means algorithms. In this study, we implement fast and exact variants of the hard and fuzzy c-means algorithms with several initialization schemes and then compare the resulting quantizers on a diverse set of images. the results demonstrate that fuzzy c-means is significantly slower than hard c-means, and that with respect to output quality the former algorithm is neither objectively nor subjectively superior to the latter.

关键词： Clustering algorithms Color Graphics image color analysis Quantization Wide area networks

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Improving 3D Medical image Compression Efficiency Using Spatiotemporal Coherence 18

Improving 3D Medical Image Compression Efficiency Using Spat...

引用

18th image processing: algorithms and systems conference, ipas 2020

作者： Zerva, Matina Ch. Vrigkas, Michalis Kondi, Lisimachos P. Nikou, Christophoros Department of Computer Science and Engineering University of Ioannina Ioannina Greece

Advanced methodologies for transmitting compressed images, within acceptable ranges of transmission rate and loss of information, make it possible to transmit a medical image through a communication channel. Most prior works on 3D medical image compression consider volumetric images as a whole but fail to account for the spatial and temporal coherence of adjacent slices. In this paper, we set out to develop a 3D medical image compression method that extends the 3D wavelet difference reduction algorithm by computing the similarity of the pixels in adjacent slices and progressively compress only the similar slices. the proposed method achieves high-efficiency performance on publicly available datasets of MRI scans by achieving compression down to one bit per voxel with PSNR and SSIM up to 52.3 dB and 0.7578, respectively. © 2020, Society for Imaging Science and Technology

关键词： Efficiency

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EFFICIENT image CORRESPONDENCE MEASUREMENTS IN AIRBORNE APPLICATIONS USING INERTIAL NAVIGATION SENSORS

EFFICIENT IMAGE CORRESPONDENCE MEASUREMENTS IN AIRBORNE APPL...

引用

18th European Signal processing conference (EUSIPCO)

作者： Woods, Matthew Katsaggelos, Aggelos Northwestern Univ Robert R McCormick Sch Engn & Appl Sci Dept Elect Engn & Comp Sci Evanston IL 60208 USA

this paper presents a computationally efficient method for the measurement of a dense image correspondence vector field using supplementary data from an inertial navigation sensor. the application is suited to airborne imaging systems (such as on a UAV) where size, weight, and power restrictions limit the amount of onboard processing available. the limited processing will typically exclude the use of traditional, but expensive, optical flow algorithms such as Lucas-Kanade. Alternately, the measurements from an inertial navigation sensor lead to a closed-form solution to the correspondence field. Airborne platforms are also well suited to this application because they already possess inertial navigation sensors and global positioning systems (GPS) as part of their existing avionics package. We derive the closed form solution for the image correspondence vector field based on the inertial navigation sensor data. We then show experimentally that the inertial sensor solution outperforms traditional optical flow methods both in processing speed and accuracy.

关键词： Global positioning system

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Bacterial Blight and Cotton Leaf Curl Virus Detection Using Inception V4 Based CNN Model for Cotton Crops 5

Bacterial Blight and Cotton Leaf Curl Virus Detection Using ...

引用

5th IEEE International conference on image processing Applications and systems (ipas)

作者： Anwar, Sohail Kolachi, Abdul Rahim Baloch, Shadi Khan Soomro, Shoaib R. Mehran Univ Engn & Technol Dept Elect Engn Jamshoro Pakistan Mehran Univ Engn & Technol Dept Mechatron Engn Jamshoro Pakistan

ISBN: (纸本)9781665462198

Agriculture sector is an important pillar of the global economy. the cotton crop is considered one of the prominent agricultural resources. It is widely cultivated in India, China, Pakistan, USA, Brazil, and other countries of the world. the worldwide cotton crop production is severely affected by numerous diseases such as cotton leaf curl virus (CLCV/CLCuV), bacterial blight, and ball rot. image processing techniques together with machine learning algorithms are successfully employed in numerous fields and have also used for crop disease detection. In this study, we present a deep learning-based method for classifying diseases of the cotton crop, including bacterial blight and cotton leaf curl virus (CLCV). the dataset of cotton leaves showing disease symptoms is collected from various locations in Sindh, Pakistan. We employ the Inception v4 architecture as a convolutional neural network to identify diseased plant leaves in particular bacterial blight and CLCV. the accuracy of the designed model is 98.26% which shows prominent improvement compared to the existing models and systems.

关键词： cotton crop disease detection CNN CLCV Bacterial Blight detection image processing

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LOW-COMPLEXITY image CODER/DECODER WIth AN APPROACHING-ENTROPY QUAD-TREE SEARCH CODE FOR EMBEDDED COMPUTING PLATFORMS

LOW-COMPLEXITY IMAGE CODER/DECODER WITH AN APPROACHING-ENTRO...

引用

18th IEEE International conference on image processing (ICIP)

作者： Ma, Tao Shrestha, Pradhumna Hempel, Michael Peng, Dongming Sharif, Hamid Univ Nebraska Dept Comp & Elect Engn Lincoln NE 68583 USA

ISBN: (纸本)9781457713033

In this paper, we propose a fast, simple and efficient image codec applicable for embedded processing systems. Among the existing image coding methods, wavelet quad-tree is a foundation leading to an efficient structure to encode images. By searching significant coefficients along quad-trees, an embedded efficient code can be obtained. In this work, we exploit hierarchical relations of the quad-tree structure in terms of searching entropy and present a quad-tree searching model that is very close to the searching entropy. By applying this model, our codec surpasses SPIHT [1] by 0.2-0.4 db over wide code rates, and its performance is comparable to SPIHT with arithmetic coding and JPEG2000 [2]. With no additional overhead of arithmetic coding, our code is much faster and simpler than SPIHT with adaptive arithmetic coding and the more complicated JPEG2000 algorithms. this is a critical factor sought in embedded processing in communication systems where energy consumption and speed are priority concerns. Our simulation results demonstrate that the proposed codec is about twice as fast with very low computational overheads and comparable coding performances than existing algorithms.

关键词： image coding quad-tree search model embedded communication system

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image processing: algorithms and systems XVIII

Image Processing: Algorithms and Systems XVIII

引用

18th image processing: algorithms and systems conference, ipas 2020

the proceedings contain 283 papers. the topics discussed include: a new training model for object detection in aerial images;managing crops across spatial and temporal scales - the roles of UAS and satellite remote sensing;a gaze-contingent system for foveated multiresolution visualization of vector and volumetric data;capturing and 3D rendering of optical behavior: the physical approach to realism;tereoscopic 3D optic flow distortions caused by mismatches between image acquisition and display parameters (JIST-first);active shooter response training environment for a building evacuation in a collaborative virtual environment;learning a CNN on multiple sclerosis lesion segmentation with self-supervision;reducing invertible embedding distortion using graph matching model;creating high-resolution 360-degree single-line 25k video content for modern conference rooms using film compositing techniques;spectral reproduction: drivers, use cases and workflow;SPECTRANET: a deep model for skin oxygenation measurement from multi-spectral data;automotive image quality concepts for the next SAE levels: color separation and contrast detection probability;a 4-tap global shutter pixel with enhanced IR sensitivity for VGA time-of-flight CMOS image sensors;small object bird detection in infrared drone videos using mask R-CNN deep learning;real-world fence removal from a single-image via deep neural network;and perceptual quality assessment of enhanced images using a crowd-sourcing framework.

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Color Restoration of Multispectral images: Near-Infrared (NIR) filter-to-Color (RGB) image 18

Color Restoration of Multispectral Images: Near-Infrared (NI...

引用

18th image processing: algorithms and systems conference, ipas 2020

作者： Trongtirakul, thaweesak Chiracharit, Werapon Agaian, Sos Dept. Electronics and Telecommunication Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Dept. Electronics and Telecommunication Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Dept. Computer Sciences CUNY Graduate Center College of Staten Island New York United States

Multispectral images captured by near-infrared (NIR) filtered devices have an attractive potential for numerous applications in computer vision, robot vision, and an artificial intelligence system. Although near-infrared (NIR) filtered devices display some unseen details, but their colors contain false colors affected main color components. therefore, the color restoration of near-infrared (NIR) filtered images is a challenge for human perception. To overcome the challenge, the proposed method has four main steps: i) color balance with chromatic adaptation;ii) color component switching;iii) Hue-Saturation-Lightness adjustment;and iv) image fusion using a proportional-weighing metric. the proposed results achieve to: i) restore RGB color component, ii) remove the color-overlapped effect and generate structure details close to the ground truth images;and iii) preserve overall brightness. © 2020, Society for Imaging Science and Technology

关键词： Color

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Coconut Disease Prediction System Using image processing and Deep Learning Techniques 4

Coconut Disease Prediction System Using Image Processing and...

引用

4th IEEE International conference on image processing, Applications and systems, ipas 2020

作者： Nesarajan, Dhapitha Kunalan, Lokini Logeswaran, Mithun Kasthuriarachchi, Sanvitha Lungalage, Dilani Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9781728175744

Coconut production is the most important and one of the main sources of income in the Sri Lankan economy. the recent time it has been observed that most of the coconut trees are affected by the diseases which gradually reduces the strength and production of coconut. Most of the tree leaves are affected by pest diseases and nutrient deficiency. Our main intensive is to enhance the livelihood of coconut leaves and identify the diseases at the early stage so that farmers get more benefits from coconut production. this paper proposes the detection of pest attack and nutrient deficiency in the coconut leaves and analysis of the diseases. Coconut leaves monitorization has been taken place after the use of pesticides and fertilizer with the help of the finest machine learning and image processing techniques. Rather than human experts, automatic recognition will be beneficial and the fastest approach to identify the diseases in the coconut leaves very efficiently. thus, in this project, we developed an android mobile application to identify the pests by their food behaviors, pest diseases and the nutrition deficiencies in the coconut trees. As an initial step, all datasets for image processing technology met pre-processing steps such as converting RGB to greyscale, filtering, resizing, horizontal flip and vertical flip. After completing the above steps, the classification was performed by analyzing several algorithms in the literature review. SVM and CNN were chosen as the best and appropriate classifier with 93.54% and 93.72% of accuracy respectively. the outcome of this project will help the farmers to increase the coconut production and undoubtedly will make a revolution in the agriculture sector. © 2020 IEEE.

关键词： image processing

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Machine Vision Based Weight Prediction for Flatfish 18

Machine Vision Based Weight Prediction for Flatfish

引用

18th International conference on Control, Automation and systems (ICCAS)

作者： Hwang, K. H. Choi, J. W. Pusan Natl Univ Sch Mech Engn Busan 46241 South Korea

ISBN: (纸本)9788993215151

In fisheries industry, flatfish accounts for about half of the domestic fish production and is a specie with active aquaculture activities. As flatfish grows, classification according to the size should be periodically carried out for efficient growth. In this paper, the correlation between area and weight of flatfish required for classification algorithms using machine vision is derived. 120 simulations are performed using flatfish models and the area is obtained through image processing. In image processing process, the area of flatfish model is calculated using a reference square known in size. the weight of actual fish corresponding to the length of the model is obtained using the reference equation. then, regression analysis is performed to derive the interpolation of linear and power equations which show correlation between area and weight of the flatfish.

关键词： Machine vision Regression analysis Parameter estimation image processing

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A TRANSLATIONAL AND ROTATIONAL INVARIANT DESCRIPTOR FOR AUTOMATIC FOOTWEAR RETRIEVAL OF REAL CASES SHOE MARKS

A TRANSLATIONAL AND ROTATIONAL INVARIANT DESCRIPTOR FOR AUTO...

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18th European Signal processing conference (EUSIPCO)

作者： Cervelli, Federico Dardi, Francesca Carrato, Sergio Univ Trieste Dept Elettrotecn Elettron & Informat I-34100 Trieste Italy

Shoe marks found on the crime scene are invaluable for the identification of the culprit when no other piece of evidence is available. thus semi-automatic and automatic systems have been recently proposed to find the make and model of the footwear that left the shoe marks. the systems proposed up to now have two main drawbacks, as they (i) are generally not based on rotation and translation invariant descriptions, and (ii) are tested on synthetic shoe marks, i.e. on shoeprints with added synthetic noise. Here we show the results of a translation and rotation invariant description based on the Fourier transform properties: the test is made on both synthetic and real shoe marks and a comparison with algorithms proposed by others is presented.

关键词： Signal processing

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