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29th SIBGRAPI Conference on Graphics, Patterns and images (SIBGRAPI)

作者： de Oliveira, Hugo N. dos Santos, Jefersson A. de Melo, Matheus C. do Rego, Thais G. Batista, Leonardo v. Univ Fed Minas Gerais Dept Ciencia Computacao Belo Horizonte MG Brazil Univ Fed Paraiba UFPB Ctr Informat Joao Pessoa Paraiba Brazil

ISBN: (纸本)9781509035687

Mammographic Computer-Aided Diagnosis systems are applications designed to assist radiologists in diagnosis of malignancy in mammographic findings. Most methods described in the literature do not perform a proper preprocessing step in mammographic images prior to classification, which can generate inconsistent results due to the potentially large amount of noise in medical images. This paper proposes a new method based on Information Theory and Data Compression for detection of random noise in image bit planes. In order to validate the efficiency of the proposed noise removal method, we used Machine Learning algorithms to classify mammographic findings from the Digital Database for Screening Mammography. Results using texture features indicate that a reduction in the radiometric resolution of 4 or 5 bit planes in digitized screen film mammographic images result in a better classification performance.

关键词： noise detection mammogram classification information theory data compression

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Contour smoothing algorithm based on contour extremes

Contour smoothing algorithm based on contour extremes

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2016 International Conference on Interfaces and Human Computer Interaction, IHCI 2016, Game and Entertainment Technologies, GET 2016 and Computer Graphics, visualization, Computer vision and image processing, CGvCvIP 2016

作者： Koriukalov, Oleksandr v. Tereshchenko, vasyl M. Taras Shevchenko National University of Kyiv Glushkova st. 4d Kyiv83000 Ukraine

ISBN: (纸本)9789898533524

In this article we propose linear time algorithm for contour smoothing, based on finding extremes. First we find vertexes where contour convexity changes, than obtain local minimums, maximums and points of support, which should be used in resulting contour. The main goal of proposed approach is to compute accurate interior area of a bounding contour, it was successfully applied for recovering object contour after segmentation algorithms or human annotations, for contours noise reduction after jpeg compression.

关键词： Noise abatement

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Review of MRI-based brain tumor image segmentation using deep learning methods 12th

Review of MRI-based brain tumor image segmentation using dee...

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12th International Conference on Application of Fuzzy systems and Soft Computing (ICAFS)

作者： Isin, Ali Direkoglu, Cem Sah, Melike Near East Univ Dept Biomed Engn POB 99138 TR-10 Mersin Turkey Middle East Tech Univ Dept Elect & Elect Engn Northern Cyprus Campus TR-10 Mersin Turkey Near East Univ Dept Comp Engn POB 99138 TR-10 Mersin Turkey

Brain tumor segmentation is an important task in medical image processing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumors for cancer diagnosis, from large amount of MRI images generated in clinical routine, is a difficult and time consuming task. There is a need for automatic brain tumor image segmentation. The purpose of this paper is to provide a review of MRI-based brain tumor segmentation methods. Recently, automatic segmentation using deep learning methods proved popular since these methods achieve the state-of-the-art results and can address this problem better than other methods. Deep learning methods can also enable efficient processing and objective evaluation of the large amounts of MRI-based image data. There are number of existing review papers, focusing on traditional methods for MRI-based brain tumor image segmentation. Different than others, in this paper, we focus on the recent trend of deep learning methods in this field. First, an introduction to brain tumors and methods for brain tumor segmentation is given. Then, the state-of-the-art algorithms with a focus on recent trend of deep learning methods are discussed. Finally, an assessment of the current state is presented and future developments to standardize MRI-based brain tumor segmentation methods into daily clinical routine are addressed. (C) 2016 The Authors. Published by Elsevier B.v.

关键词： Review image processing deep learning brain tumor segmentation convolutional neural networks mri

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Hyperspectral image analysis of Raman maps of plant cell walls for blind spectra characterization by nonnegative matrix factorization algorithm

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CHEMOMETRICS AND INTELLIGENT LABORATORY systems 2016年 151卷 136-145页

作者： Szymanska-Chargot, Monika Pieczywek, Piotr M. Chylinska, Monika Zdunek, Artur Polish Acad Sci Inst Agrophys Doswiadczalna 4 PL-20290 Lublin 27 Poland

The aim of this contribution was to develop methods of Raman spectral data analysis with respect to its spatial distribution, produced by a signal deriving complex biological substance. A novel approach based on nonnegative matrix factorization (NMF) combined with the clustering algorithms was introduced for analysis of plant tissue chemical composition. The multivariate approach was tested on the Raman maps of two different tissues of carrot root (Daucus carota L. subsp. Sativus) - xylem and cambium were captured and analyzed. The initial step of analysis involved pre-processing of individual spectra on two interconnected information levels spatial and spectral. The proposed approach allowed successful removal of unwanted and corrupted sections of data and replace it with new interpolated values using the nearest neighborhood. The NMF algorithm was tested on refined experimental datasets and showed great performance at reducing the dimensionality of large quantities of spectral information. It also allowed to obtain the pure spectra of individual data components and their concentration profiles which were easily interpretable and had high resemblance to the original data. The output of the NMF analysis was used as a starting point for two clustering algorithms k-means clustering and hierarchical clustering methods. Both methods converged with similar results providing precise spatial separation of spectral data according to the most predominant component (pectins, cellulose and lignins) in specific area of studied tissues. Obtained clusters distribution showed good match not only with chemical component distribution but also with structural features of tissue samples. Moreover, the proposed method of Raman images analysis allowed to blind spectral separation resulting in rapid and robust analysis of cell wall chemical composition with respect to its spatial distribution. (C) 2015 Elsevier B.v. All rights reserved.

关键词： Chemometry Raman images Plant cell wall K-means Hierarchical clustering Nonnegative matrix factorization

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OPTIMIZATION OF image processing TECHNIQUES TO DETECT AND RECONSTRUCT THE image OF CONCEALED BLADE FOR MMW IMAGING SYSTEM 36

OPTIMIZATION OF IMAGE PROCESSING TECHNIQUES TO DETECT AND RE...

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36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Kumar, Bambam Sharma, Prabhat Upadhyay, Rohit Singh, Dharmendra Singh, K. P. Indian Inst Technol Roorkee Dept Elect & Commun Engn Roorkee Uttar Pradesh India Instrument Res & Dev Estab Dehra Dun Uttarakhand India Indian Inst Technol BHU Dept Elect Engn Varanasi Uttar Pradesh India

ISBN: (纸本)9781509033324

The concealed weapon, like blade, detection and identification is one of the most puzzling task faces by security agency. Researchers have demonstrated MMW imaging systems to detect concealed targets like gun, knife and scissors but detection of small size target like blade with different orientation is still challenging due to resolution limitation of MMW imaging system. The success of small size concealed target detection depends upon scanning step size of imaging system and dielectric property of covering cloths and hidden object. Therefore, resolution enhancement techniques may play a very important role for small size concealed target detection. To perceive such challenges, active v-band MMW radar conjunction with image processing techniques has been demonstrated for detection and identification of concealed blade and obtained two dimensional good quality of images of concealed blade under different cloths at various angle. For this purpose, a critical analysis of various signal and image processing has been carried out and integrated following algorithms like singular value decomposition (SvD) for clutter reduction, discrete wavelet transform (DWT) for resolution enhancement, thresholding for target detection and in last artificial neural network (ANN) based algorithm for rotation invariant target identification. An image processing based methodology has been proposed by which the concealed target like blade can be successfully detected.

关键词： MMW imaging DWT SvD ANN

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Robust extensions to guided image filtering 14

Robust extensions to guided image filtering

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image processing: algorithms and systems XIv 2016

作者： Michailovich, Oleg v. University of Waterloo WaterlooON Canada

image denoising is commonly regarded as a problem of fundamental importance in imaging sciences. The last few decades have witnessed the advent of a wide spectrum of denoising algorithms, capable of dealing with noises and images of various types and statistical natures. It is usually the case, however, that the effectiveness of a given denoising procedure and the complexity of its numerical implementation increase pro rata, which is often the reason why more advanced solutions are avoided in situations when data images have relatively large sizes and/or acquired at high frame rates. As a result, substantial efforts have been recently extended to develop efficient means of image denoising, the computational complexity of which would be comparable to that of standard linear filtering. One of such solutions is Guided image Filtering (GIF) - a recently proposed denoising technique, which combines outstanding performance characteristics with real-time implementability. Unfortunately, the standard implementation of GIF is known to perform poorly in situations when noise statistics deviate from that of additive Gaussian noise. To overcome this de-ficiently, in this note, we propose a number of modifications to the filter, which allow it to achieve stable and accurate results in the case of impulse and Poisson noises. © 2016 Society for Imaging Science and Technology.

关键词： image denoising

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Robust object tracking techniques for vision-based 3D motion analysis applications 4

Robust object tracking techniques for vision-based 3D motion...

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Conference on Optics, Photonics and Digital Technologies for Imaging Applications Iv

作者： Knyaz, vladimir A. Zheltov, Sergey Yu. vishnyakov, Boris v. State Res Inst Aviat Syst 7 Victorenko Str Moscow Russia

ISBN: (纸本)9781510601413

Automated and accurate spatial motion capturing of an object is necessary for a wide variety of applications including industry and science, virtual reality and movie, medicine and sports. For the most part of applications a reliability and an accuracy of the data obtained as well as convenience for a user are the main characteristics defining the quality of the motion capture system. Among the existing systems for 3D data acquisition, based on different physical principles (accelerometry, magnetometry, time-of-flight, vision-based), optical motion capture systems have a set of advantages such as high speed of acquisition, potential for high accuracy and automation based on advanced image processing algorithms. For vision-based motion capture accurate and robust object features detecting and tracking through the video sequence are the key elements along with a level of automation of capturing process. So for providing high accuracy of obtained spatial data the developed vision-based motion capture system "Mosca" is based on photogrammetric principles of 3D measurements and supports high speed image acquisition in synchronized mode. It includes from 2 to 4 technical vision cameras for capturing video sequences of object motion. The original camera calibration and external orientation procedures provide the basis for high accuracy of 3D measurements. A set of algorithms as for detecting, identifying and tracking of similar targets, so for marker-less object motion capture is developed and tested. The results of algorithms' evaluation show high robustness and high reliability for various motion analysis tasks in technical and biomechanics applications.

关键词： object tracking vision-based identification calibration motion capture accuracy robustness

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Non photorealistic rendering in frequency domain 14

Non photorealistic rendering in frequency domain

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image processing: algorithms and systems XIv 2016

作者： Mangiatordi, F. Pallotti, E. Baroncini, v. Capodiferro, L. Fondazione Ugo Bordoni Viale del Policlinico 147 Roma00161 Italy

In this work the authors present a novel image abstraction and stylization framework based on the analysis of natural scene in the multi-resolution Laguerre Gauss (LG) domain. The extraction of complex LG image sketches at different resolutions and the corresponding non linear adjustment ensure the preservation of the shapes and the deleting of the unimportant edge details. The reduction of the color range within the region interiors is obtained applying the image LG synthesis formula to the smoothed simplified image sketches. The proposed method is computationally easy to implement and provides the possibility to perform a fine tune regulation of the stylization parameters, customizing the NPR process. Subjective assessment of the proposed method were designed to compare it with two low level"anchors";the complete set of subjective tests are in progress;preliminary expert viewing test show some effectiveness of the proposed NPR technique. © 2016 Society for Imaging Science and Technology.

关键词： Rendering (computer graphics)

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Raindrop Detection Algorithm for ADAS

Raindrop Detection Algorithm for ADAS

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IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology

作者： Pavan Kumar Asundi K S Geetha ECE Dept. R V College of Engineering Professor and PG Dean ECE Dept. R V College of Engineering

ISBN: (纸本)9781509037056

vision based advance driver assistance system (ADAS) completely rely on the images captured by the vehicle bound camera. vision based ADAS also referred as computer vision based ADAS depends on clear images from surroundings for its algorithm implementation. If the images are hampered from external weather conditions such as rain or fog, the ADAS functionalities are affected. In general, checking the image, whether it is free from raindrop occlusions is one of the steps in the pre-processing, as clear, noise free images are required for ADAS algorithm deployments, which would enhance the ADAS functionalities. In ADAS, generically raindrop occlusions is identified by comparing two successive images then sorting analysis using displacement formulae or by identifying them typically based on photometric or geometric properties of raindrop. The computation time and the accuracy for these approaches are trade off factors. In this paper, we propose an algorithm to detect the raindrop, where detection is performed using thresholding and feature transform only on single frame. The approach reduces the computation time and memory resource, executing only on current frame. Once the raindrop occlusion is affirmed, an interrupt is sent to the concurrent algorithm about the occlusions and abort the same. To retrieve the region from occlusion we follow one of the generic method employing Gauss filter.

关键词： ADAS computer vision raindrop detection thresholding feature transform advanced driver assistance systems (ADAS) images Raindrops computer vision Occlusion computational time Parallel algorithms Threshold

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image stitching by means of adaptive normalization 14

Image stitching by means of adaptive normalization

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image processing: algorithms and systems XIv 2016

作者： Michailovich, Oleg v. University of Waterloo WaterlooON Canada

image stitching – the process of amalgamation of separate image fragments to form a complete representation of the entire scene – might become quite a challenging problem in the presence of non-additive noises and/or brightness variability artifacts. An additional degree of complication may further be inflicted in situations when one is dealing with large size data, as it is usually the case in tiling microscopy. To overcome such difficulties, a novel approach to the problem of image stitching is proposed here. In the heart of the proposed solution is Wallis filtering, which is a standard tool of image processing used for adaptive contrast adjustment and local image normalization. More importantly, Wallis filtering allows representing a given image in terms of its normalized version and associated local statistics. Subsequently, we show that stitching the output of the Wallis filter is a much simpler and much more stable task as compared to stitching the images in their original domain. The proposed method has an additional advantage of being computational efficient, which is particularly important in tiling microscopy, where a typical height/width of data images is on the order of tens of thousands of pixels. © 2016 Society for Imaging Science and Technology.

关键词： Additive noise

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