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检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

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CVBL IRIS Gender Classification Database image processing and Biometric Research, computer vision and Biometric Laboratory (CVBL)

CVBL IRIS Gender Classification Database Image Processing an...

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international conference on image, vision and Computing (ICIVC)

作者： Saeed Aryanmehr Mohsen Karimi Farsad Zamani Boroujeni Computer Department Islamic Azad University Isfahan Iran Electric Department Islamic Azad University Isfahan Iran

ISBN: (纸本)9781538649923

Iris recognition has been an interesting subject for many research studies in the last two decades and has raised many challenges for the researchers. One new and interesting challenge in the iris studies is gender recognition using iris images. Gender classification can be applied to reduce processing time of the identification process. On the other hand, it can be used in applications such as access control systems, and gender-based marketing and so on. To the best of our knowledge, only a few numbers of studies are conducted on gender recognition through analysis of iris images. Considering the importance of this research area and its commercial applications, it is highly essential for researchers to make use of efficient color features in their algorithms which necessitates the production of color iris image databases. The present study introduces an iris image database for gender classification and proposes a new gender classification algorithm for its evaluation. The database consists of iris images taken from 720 subjects including 370 females and 350 males in university students. For each student, more than 6 images were taken from his/her both left and right eyes. After examining the images, 3 images from the left eye and 3 images from the right eye were selected among the most appropriate images and were included in the database. All 4320 images from this database were taken under the same condition and by the same color camera. Finally, the quality and the efficiency of the introduced database are evaluated using a new method that extract Zernike moments on spectral features and two well-known classifiers, namely, SVM and KNN. The results revealed that there is a significant improvement in gender classification compared with the similar databases.

关键词： Iris recognition Databases Feature extraction image recognition image segmentation image color analysis Classification algorithms

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Automated Brain Tumor Segmentation on Magnetic Resonance images and Patient's Overall Survival Prediction Using Support Vector Machines 3rd

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3rd international Workshop on Brain-Lesion (BrainLes) held jointly at the conference on Medical image Computing for computer Assisted Intervention (MICCAI)

作者： Osman, Alexander F. I. Amer Univ Beirut Med Ctr Radiat Oncol Dept Beirut 11072020 Riad El Solh Lebanon

ISBN: (纸本)9783319752389;9783319752372

This study is aimed to develop two algorithms for glioma tumor segmentation and patient's overall survival (OS) prediction with machine learning approaches. The segmentation algorithm is fully automated to accurately and efficiently delineate the whole tumor on a magnetic resonance imaging (MRI) scan for radiotherapy treatment planning. The survival algorithm predicts the OS for glioblastoma multiforme (GBM) patients based on regression and classification principles. Multi-institutional BRATS' 2017 data of MRI scans from 477 patients with high-grade and lower-grade glioma (HGG/LGG) used in this study. Clinical patient survival data of 291 glioblastoma multiforme (GBM) were available in the provided data. Support vector machines (SVMs) were used to develop both algorithms. The segmentation chain comprises pre-processing with a goal of noise removal, feature extraction of the image intensity, segmentation process using a non-linear classifier with 'Gaussian' kernel, and post-processing to enhance the segmentation morphology. The OS prediction algorithm sequence involves two steps;extraction of patient's age, and segmented tumor's size and its location features;prediction process using a non-linear classifier and a linear regression model with 'Gaussian' kernels. The algorithms were trained, validated and tested on BRATS' 2017' s training, validation, and testing datasets. Average Dice for the whole tumor segmentation obtained on the validation and testing datasets is 0.53 +/- 0.31 (median 0.60) which indicates the consistency of the proposed algorithm on the new "unseen" data. For OS prediction, the mean accuracy is 0.49 for the validation dataset and 0.35 for the testing dataset based on regression principle;whereas an overall accuracy of 1.00 achieved in classification into short, medium, and long-survivor classes for a designed validation dataset. The computational time for the automated segmentation algorithm took approximately 3 min. In its present form, the segm

关键词： Brain tumors MRI image segmentation SVM Glioma overall survival

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Human action detection and recognition using SIFT and SVM 3rd

Human action detection and recognition using SIFT and SVM

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3rd international conference on Cognitive Computing and Information processing, CCIP 2017

作者： Dhulavvagol, Praveen M. Kundur, Niranjan C. KLE Technological University Hubballi India JSS Academy of Technical Education Bangalore India

ISBN: (纸本)9789811090585

Human action detection and recognition is the most trending research topic in applications like surveillance of videos, analysis of sports videos and many applications which involve human computer interaction. Many researchers are working on different algorithms to improve the accuracy of human detection. Identifying the actions of human from the given video is a challenging task. In the proposed paper combination of two different techniques is applied i.e. SVM and SIFT techniques are used to identify and recognize the human actions in a given video or image. To extract local features of the given video SIFT based technique is used. In this techniques initially we extract features based on the interest points at a particular point or frames, Mainly SIFT techniques involves 4 basic steps Scale-space extreme detection, Key-point localization, Orientation assignment and Key-point descriptor. Once the key features are extracted they are further classified using SVM classifier. In the results and discussion we perform the comparative analysis of these two techniques on a standard KTH dataset with running and hand clapping actions. The experimental results determine the overall accuracy of 82% for the actions: running and hand clapping actions. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Human computer interaction

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Despeckling filter evaluation using image quality metrics and coefficient of variation 3rd

Despeckling filter evaluation using image quality metrics an...

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3rd international conference on Next Generation Computing Technologies, NGCT 2017

作者： Hemalatha, R.J. Vijayabaskar, V. Department of Biomedical Engineering Sathyabama University Chennai India Department of Telecommunication and Engineering Sathyabama University Chennai India

ISBN: (纸本)9789811086595

In the current trend of imaging ultrasound is one of the most preferred non-invasive tools for diagnosis. Generally medical images are corrupted by noise during acquisition. Speckle noise which is inherent in ultrasound images highly degrades the quality of the image there by decreases the diagnostic accuracy. Thus speckle reduction is significant and primary step in pre-processing of ultrasound images. The objective of this study is to evaluate the performance of ten different despeckle filters based on the image quality metrics and coefficient of variation, in ultrasound image of arthritis after filtering. The performance analysis of DsFlsminsc, DsFlsmv, DsFsrad, DsFmedian, DsFwiener, DsFhomog, DsFlecasort, DsFgf, DsFad, DsFls filters on ultrasound image was analysed by comparing the established seven image quality metrics like PSNR, SRMSE, SSI, LMSE, UQI and SNR for the filtered image. In addition the coefficient of variation (CV) was computed for the seven image quality metrics parameters and for the filtered images based on the statistical parameters like mean and standard deviation. Further the correlation coefficient value ρ was calculated for the CV value which is positively correlated, thus it provides a validation measure for the filter evaluation process using CV value. The result of the study shows that DsFlsmv and DsFmedian filters gave best results and can be used for despeckling the ultrasound arthritis image. © Springer Nature Singapore Pte Ltd. 2018.

关键词： image quality

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Detection of palm oil bud rot employing artificial vision 3

Detection of palm oil bud rot employing artificial vision

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3rd international Congress of Mechanical Engineering and Agricultural Science, CIIMCA 2017

作者： Montero, D. Rueda, C. Universidad Pontificia Bolivariana Autopista Piedecuesta km 7 Bucaramanga Colombia

This paper describes the development of a system capable of capturing, processing, and analysing images of palm oil plantations in order to detect and identify bud rot disease. The process begins with the capturing of images using the DJI Phantom 4 UAV, which is first configured for a flight plan of the desired area and altitude. The resulting images are processed by photogrammetry software to create orthomosaics. The developed algorithm uses the grayscale of the generated orthomosaic and identifies the palms affected by bud rot. This is accomplished using the sliding-window method, by which smaller samples of the image are used and evaluated independently. Each sample is then extracted with a ULBP feature vector that numerically represents the texture of the image and is classified by means of a previously trained logistic regression model, allowing the recognition of positive cases of the disease. Possible positive cases are further distinguished using the non-maximum suppression algorithm. The system was tested with different images than the images used for training and for establishing the set point. As a result, the system showed a 92% precision and a 96% of sensitivity for bud rot disease detection. These results are satisfactory in terms of detecting bud rot disease using a low-cost system. © Published under licence by IOP Publishing Ltd.

关键词： Palm oil

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An Extension of Temporal Mechanism for Graph Grammar

An Extension of Temporal Mechanism for Graph Grammar

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international conference on image, vision and Computing (ICIVC)

作者： Zhan Shi Jiande Zhang Tingting Zhang Xiaoqin Zeng Dawei Li School of Economics & Management Nanjing University of Science and Technology Nanjing China School of Computer Engineering Nanjing Institute of Technology Nanjing China College of Command Information Systems PLA Army Engineering University Nanjing China Institute of Intelligence Science and Technology Hohai University Nanjing China

ISBN: (纸本)9781538649923

Graph grammars are a rigorous but intuitive way to define and handle graph languages. To tackle time-related issues, this paper proposes a new extension of temporal mechanism based on the existing Edge-based Graph Grammar (EGG), which includes grammatical specifications, productions, operations and so on. In the paper, formal definitions of temporal mechanism are provided first. Then, a new parsing algorithm is presented to check the correctness of a given graph's structure, and to analyze operations' timing when needed.

关键词： Grammar Production Business Semantics Visualization Time complexity Solids

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Incorporating Multiscale Contextual Loss for image Style Transfer

Incorporating Multiscale Contextual Loss for Image Style Tra...

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international conference on image, vision and Computing (ICIVC)

作者： Pan Li Lei Zhao Duanqing Xu Dongming Lu Department of Computer Science and Technology Zhejiang University

ISBN: (纸本)9781538649923

In this paper, we propose to impose a multiscale contextual loss for image style transfer based on Convolutional Neural Networks (CNN). In the traditional optimization framework, a new stylized image is synthesized by constraining the high-level CNN features similar to a content image and the lower-level CNN features similar to a style image, which, however, appears to lost many details of the content image, presenting unpleasing and inconsistent distortions or artifacts. The proposed multiscale contextual loss, named Haar loss, is responsible for preserving the lost details by dint of matching the features derived from the content image and the synthesized image via wavelet transform. It endows the synthesized image with the characteristic to better retain the semantic information of the content image. More specifically, the unpleasant distortions can be effectively alleviated while the style can be well preserved. In the experiments, we show the visually more consistent and simultaneously well-stylized images generated by incorporating the multiscale contextual loss.

关键词： image resolution Semantics Wavelet coefficients Optimization Distortion

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A Foreground Extraction Approach Using Convolutional Neural Network with Graph Cut

A Foreground Extraction Approach Using Convolutional Neural ...

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international conference on image, vision and Computing (ICIVC)

作者： Matee Ullah Adnan Iltaf Qiujun Hou Farman Ali Chuancai Liu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Automation Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781538649923

In the literature of computer vision, many techniques have demonstrated their potentials for interactive image segmentation. However, most of these state-of-the-art algorithms are unable to produce accurate boundaries without more user interaction, as they are highly sensitive to the seed's quantity and quality. These techniques frequently depend on more user interaction to refine the boundaries. In order to solve the problem and get accurate boundaries via less user interaction, in this work, a robust interactive image segmentation method is proposed based on generic multiscale oriented contours via single forward pass Convolutional Neural Networks (CNNs) and graph cut framework. We first utilize CNN to construct the boundary-level information and then combine this boundary-level information with the boundary energy term of graph cut framework. The proposed method exhibits a significant leap in robustness to user interaction, smooth boundaries, accurate segmentation and the ability to handle the changes. We show that the sensitivity to the seeds can be controlled and accuracy can be improved via boundary-level information. We further conduct both qualitative and quantitative experiments on benchmark datasets, showing that our proposed method outperforms the state-of-the-art interactive image segmentation techniques.

关键词： image segmentation Convolutional neural networks Benchmark testing Labeling Partitioning algorithms image edge detection computer vision

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Four-Directional Total Variation Denoising Using Fast Fourier Transform and ADMM

Four-Directional Total Variation Denoising Using Fast Fourie...

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international conference on image, vision and Computing (ICIVC)

作者： Zhuyuan Cheng Yuqun Chen Lingzhi Wang Fan Lin Haiguang Wang Yingpin Chen Zhangzhou Branch of Fujian Electric Power Supply Company of State Grid Zhangzhou China School of Physics and Information Engineering Minnan Normal University Zhangzhou China

ISBN: (纸本)9781538649923

Noise removal is a fundamental problem in image processing. Among many approaches, the total variation has attracted great attention because of its nice mathematical interpretation. Traditional total variation explores the gradient information of the vertical and the horizontal directions. Thus, the number of directions can be increased to further improve denoising performance. The resulting challenge is higher computation since multiple constraints are introduced in denoising model. This work first transforms the quaternion total variation constraints problem in the spatial domain into a problem in the frequency domain by using the fast Fourier transform and the convolution theorem. Then, it incorporates the alternating direction method of multipliers (ADMM) to enable fast image denoising. This fast computation is verified by the comparisons with other total variation based methods including state-of-the-art methods.

关键词： TV Noise reduction Convolution Mathematical model Convex functions Fast Fourier transforms image processing

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ORB-SLAM2 with 6DOF Motion

ORB-SLAM2 with 6DOF Motion

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international conference on image, vision and Computing (ICIVC)

作者： Fanyu Zeng Wenchao Zeng Yan Gan Center for Robotics University of Electronic Science and Technology of China Chengdu China Center for Robotics University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781538649923

ORB-SLAM2 is an excellent keyframe-based SLAM with six degrees of freedom(6DOF), it can only triangulate 6DOF image frames for localization and mapping, and has no ability to process 3DOF(degrees of freedom) image frames which are not satisfied with the requirement of triangulation. However, ORB-SLAM2 both receive 3DOF and 6DOF image frames and can't distinguish them, that it wrongly triangulates 3DOF keyframes, resulting processing more useless and redundant keyframes. To address this problem, we add a new 6DOF keyframe selection module into the local thread of ORB-SLAM2 to filter out the 3DOF keyframe candidates in this paper. Experiments demonstrate the improved ORB-SLAM2 can process less keyframes, while keeping good location ability and very low absolute camera trajectory RMSE according to qualitative and quantitative evaluations.

关键词： Cameras Simultaneous localization and mapping Instruction sets Trajectory Robot vision systems conferences

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