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检索条件"任意字段=International Conference on Computer Vision and Image Processing, CVIP 2016"

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Intensity-difference based 3D video stabilization for planetary robots

Intensity-difference based 3D video stabilization for planet...

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international conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

作者： Geovanni Martinez Image Processing and Computer Vision Research Laboratory (IPCV-IAB) Escuela de Ingeniería Eléctrica Universidad de Costa Rica San José Costa Rica

ISBN: (纸本)9781509035120

In this paper, an algorithm to remove the camera jitter from image sequences captured by planetary robots is investigated. First, the frame to frame surface 3D motion with respect to the camera coordinate system is estimated and integrated over time. The estimation is performed by maximizing a likelihood function of the frame to frame intensity differences measured at key observation points. Then, the jitter is determined as the perspective projection of the difference between the integrated surface 3D translation and a smoothed version of it. Finally, the stabilized video is synthesized by moving the entire content of each image with a displacement vector having the same magnitude but opposite direction to the estimated jitter for that image. The experimental results with synthetic data revealed real time operation with low latency and a reduction of the jitter in a factor of 20. Experimental results with real image sequences captured by a rover platform in indoor and outdoor conditions show very reliable and encouraging stabilization results.

关键词： Three-dimensional displays Cameras Robot vision systems Shape Robot kinematics Jitter

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Fungus Detection System

Fungus Detection System

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international conference on Autonomic Computing (ICAC)

作者： Muhammad Waseem Tahir N. A. Zaidi R. Blank P. P. Vinayaka W. Lang Institute of Micro Sensors Actuators and Systems (IMSAS) University of Bremen Germany

ISBN: (纸本)9781509016556

Fungus is the main risk for food logistics and millions of euro lost per annum just due to different kinds of fungus. The main aim of this research is to develop an automated system for the detection of fungus spores in air. We are developing a novel system for fungus detection which is based on computer vision. Air samples are collected on the glass slides and then placed these samples under microscopic camera and images of air samples are obtained. Pre processing techniques, as images have noise regions and spores boundaries are not clear, have been applied and different filters have been used. Which is followed by thresholding and morphologic operation. Then features are extracted and feature vector were formed. After that Support Vector Machine (SVM) was used for the purpose of classification.

关键词： computer vision Feature extraction image segmentation Support vector machines Sensor systems Microscopy Monitoring

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A novel method for robust multi-directional image projection computation

A novel method for robust multi-directional image projection...

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IEEE international conference on Intelligent Engineering Systems (INES)

作者： Gábor Kertész Sándor Szénási Zoltán Vamossy John von Neumann Faculty of Informatics Óbuda University

This paper introduces a novel method to calculate multi-directional projections of squared images, similar to the Radon transformation. image projections are often used as object signatures for detection, matching and tracking techniques in computer vision. The Radon transformation provides a fast solution to calculate these pixel intensity sums. The proposed method is based on trigonometric functions and basic coordinate-geometry. The solution is implemented sequentially and the runtimes of a GPU-based implementation are measured and evaluated. The analysis of the results indicate that further research is applicable, new parallel models should be discussed.

关键词： Graphics processing units Instruction sets Radon Robustness Runtime computers image segmentation

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Evaluation of feature sensitivity to training data inaccuracy in detection of retinal lesions

Evaluation of feature sensitivity to training data inaccurac...

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Workshops on image processing Theory, Tools and Applications, IPTA

作者： Lauri Laaksonen Antti Hannuksela Ela Claridge Pauli Fält Markku Hauta-Kasari Hannu Uusitalo Lasse Lensu Machine Vision and Pattern Recognition Laboratory Lappeenranta university of Technology Lappeenranta Finland School of Computer Science The University of Birmingham United Kingdom School of Computing University of Eastern Finland Finland Department of Ophthalmology University of Tampere Finland TAUH Eye Center Tampere University Hospital Finland

computer aided diagnostic and segmentation tools have become increasingly important in reducing the workload of medical experts performing diagnosis, monitoring and documentation of various eye diseases such as age-related macular degeneration (AMD), diabetic retinopathy (DR) and glaucoma. Supervised methods have been developed for the segmentation and detection of lesions, and the reported performance has been good. The supervised methods, however, need representative data to properly train the classifier. Inaccuracies in the ground truth may have a significant impact on the performance of a supervised method as the training data are not representative. In this study, a quantitative evaluation of the sensitivity of different image features, including colour, texture, edge and higher-level features, to inaccuracy in the ground truth on exudates is presented. A mean decrease of approx. 20% in sensitivity and 13% in specificity was observed when using the most inaccurate training data.

关键词： Training data Sensitivity Retina image color analysis image segmentation Feature extraction Lesions

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Occlusion detection using iterative motion estimation and error evaluation

Occlusion detection using iterative motion estimation and er...

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IEEE international conference on Consumer Electronics-Asia (ICCE-Asia)

作者： Raksha Umesh Bandana Jinbeum Jang Jieun Jo Sangwoo Park Joonki Paik Department of Electronics and Communication The Oxford College of Engineering Banglalore India Department of Image Chung-Ang University Seoul Korea

ISBN: (纸本)9781509027446

This paper presents an iterative motion estimation and error evaluation method for efficient occlusion detection. From a pair of reference and target images, the proposed method detects occlusions using four steps: (i) pre-processing for robust motion estimation, (ii) detection of candidates for the occlusion, (iii) one-dimensional motion estimation, and (iv) motion evaluation and update in the target image. The proposed method can be used in various computer vision areas that need an accurate occlusion detection.

关键词： Motion estimation Iterative methods image color analysis Histograms conferences Robustness Dynamic range

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Automatic grading of potatoes with fuzzy logic

Automatic grading of potatoes with fuzzy logic

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Online international conference on Green Engineering and Technologies (IC-GET)

作者： Nikhil B. Bhagat S. D. Markande Department of E & TC NBN Sinhgad School of Engineering Ambegaon Pune India

ISBN: (纸本)9781509045570

In manual grading, an expert person has to keep in mind each and every characteristic of fruit for grading. More time and cost is required for manual grading of fruits. Most of the existing systems work on individual parameters like color, shape, and size. Captured image and database image gives grading output by comparison. The proposed system is implemented for potato grading by combining different parameters like shape, size, percentage of deficiency and weight. The proposed system is real-time image processing. Potato image is properly captured by a camera and processed by image processing chamber. The system gives 1-D image parameter detection for grading of potatoes. Shape, size, percentage of deficiency and weight are calculated. Knowledge base fuzzy rules are designed. The system is automatic grading of potatoes, to overcome manual grading. The system reduces time and cost required for manual grading. The system gives the better result as compared with manual grading. By using computer vision and fuzzy logic, faster grading of potatoes is achieved.

关键词： Fuzzy logic Manuals Shape image color analysis Belts Microcontrollers

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Advanced low cost clustering system

Advanced low cost clustering system

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Workshops on image processing Theory, Tools and Applications, IPTA

作者： Giuseppe Spampinato Arcangelo Ranieri Bruna Salvatore Curti Viviana D'Alto STMicroelectronics Catania Italy STMicroelectronics Agrate Brianza Italy

ISBN: (纸本)9781467389112

This document describes a system to gather information from a stationary camera to identify moving objects. The proposed solution makes only use of motion vectors between adjacent frames, obtained from any algorithm. Starting from them, the system is able to retrieve clusters of moving objects in a scene acquired by an image sensor device. Since all the system is only based on optical flow, it is really simple and fast, to be easily integrated directly in low cost cameras. The experimental results show fast and robust performance of our method. The computation time is about 800 frame/sec for a VGA sequence on a 2.3GHz processor (ARM Cortex-A15). Moreover, the system has been tested for different applications, cross traffic alert and video surveillance, in different conditions, indoor and outdoor, and with different lens, linear and fish-eye.

关键词： computer vision image motion analysis Optical filters Optical imaging Optical noise Cameras Optical reflection

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HAVE WE UNDERESTIMATED THE POWER OF image UNDISTORTION?

HAVE WE UNDERESTIMATED THE POWER OF IMAGE UNDISTORTION?

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IEEE international conference on image processing

作者： Wei Li Wenting Huang Matthias Breier Dorit Merhof Institute of Imaging and Computer Vision RWTH Aachen University 52056 Aachen Germany

ISBN: (纸本)9781467399623

An indispensable pre-processing step for image-based 3D reconstruction or photogrammetry tasks is undistortion, which is employed for correcting the non-linear projection of the surface points of objects onto the image plane due to lens distortion. To deal with this undesirable artifact, state-of-the-art methods focus on directly recovering the 2D coordinates of image points from distorted images, where a mapping function rather than the real undistortion model is obtained. Contrary to the standard undistortion, we explicitly consider the imaging process of a finite projective camera: the projection of a 3D point onto the image plane is determined not only by the lens, but also by the intrinsic parameters of the camera. After an appropriate transformation of the intrinsic matrix for parameter reduction, it is shown that an exact recovery of the real undistorted coordinates can be achieved for most of the commonly used undistortion functions. Interestingly, a simultaneous estimation of the two intrinsic parameters, aspect ratio a and skew factor s, is also embedded in the undistortion process, which demonstrates the underestimated power of image undistortion in a conventional way. As a benefit of considering the physical model instead of fitting a mapping function, improved undistortion performance can be achieved.

关键词： Cameras Three-dimensional displays Distortion Lenses Estimation Computational modeling three-dimensional displays Cameras Lenses image plane abnormal shapes Computational modeling distortion internal parameter images imagery (Psychotherapy) Energy sources imaging processes lens image

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Supervised Feature Learning Network Based on the Improved LLE for face recognition

Supervised Feature Learning Network Based on the Improved LL...

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international conference on Audio, Language and image processing, ICALIP

作者： Dan Meng Guitao Cao Wenming Cao Zhihai He School of Computer Science and Software Engineering East China Normal University Shanghai China Computer Science Department University of Mossouri USA College of Information Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781509006557

Deep neural networks (DNNs) have been successfully applied in the fields of computer vision and pattern recognition. One drawback of DNNs is that most of existing DNNs models and their variants usually need to learn a very large set of parameters. Another drawback of DNNs is that DNNs does not fully take the class label and local structure into account during the training stage. To address these issues, this paper proposes a novel approach, called Supervised Feature Learning Network Based on the Improved LLE (SFLNet) for face recognition. The goal of SFLNet is to extract features efficiently. Thus SFLNet consists of learning kernels based on the improved Locally Linear Embedding (LLE) and multiscale feature analysis. Instead of taking image pixels as the input of LLE algorithm, the improved LLE uses linear discriminant kernel distance (LDKD). Besides, the outputs of the improved LLE are convolutional kernels, not the dimensional reduction features. Mutiscale feature analysis enhances the insensitive to complex changes caused by large pose, expression, or illumination variations. So SFLNet has better discrimination and is more suitable for face recognition task. Experimental results on Extended Yale B and AR dataset shows the impressive improvement of the proposed method and robustness to occlusion when compared with other state-of-art methods.

关键词： Face recognition Kernel Feature extraction Face Histograms Training Lighting

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De-identifying people in videos using neural art

De-identifying people in videos using neural art

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Workshops on image processing Theory, Tools and Applications, IPTA

作者： Karla Brkić Ivan Sikirić Tomislav Hrkać Zoran Kalafatić Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Mireo d.d. Zagreb Croatia

ISBN: (纸本)9781467389112

We propose a computer vision-based de-identification pipeline that enables automated segmentation of humans in videos and effective protection of their identities. Due to the ubiquity of video surveillance, many jurisdictions implement strict regulations for the protection of personal data in publicly collected video sequences, requiring the data to be de-identified. However, soft biometric and non-biometric features like clothing, hair color, personal items, skin marks, etc., are often overlooked in the process. Assuming a surveillance scenario, we combine GMM-based background subtraction with an improved version of the GrabCut algorithm to find and segment pedestrians. We use the responses of a deep neural network to de-identify soft and non-biometric features through style mixing with images of other pedestrians. Our method produces de-identified versions of the input frames while preserving the naturalness and utility of the de-identified data.

关键词： Art Surveillance image segmentation Videos image color analysis Pipelines Neural networks

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