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Computer vision for automatic quality inspection of dried figs (Ficus carica L.) in real-time

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2016年 120卷 17-25页

作者： Benalia, Souraya Cubero, Sergio Manuel Prats-Montalban, Jose Bernardi, Bruno Zimbalatti, Giuseppe Blasco, Jose Univ Mediterranea Reggio Calabria Dipartimento Agr I-89122 Reggio Di Calabria Italy Inst Valenciano Invest Agr Ctr Agroingn Cra Moncada Naquera Km 5 E-46113 Moncada Spain Univ Politecn Valencia Dept Estadist & Invest Operat Camino Vera S-N E-46022 Valencia Spain

This work reports the development of automated systems based on computer vision to improve the quality control and sorting of dried figs of Cosenza (protected denomination of origin) focusing on two research issues. The first was based on qualitative discrimination of figs through colour assessment comparing the analysis of colour images obtained using a digital camera with those obtained according to conventional instrumental methods, i.e. colourimetry currently done in laboratories. Data were expressed in terms of CIE XYZ, CIELAB and HunterLab colour spaces, as well as the browning index measurement of each fruit, and then, analysed using PCA and PLS-DA based methods. The results showed that both chroma meter and image analysis allowed a complete distinction between high quality and deteriorated figs, according to colour attributes. The second research issue had the purpose of developing image processing algorithms to achieve real-time sorting of figs using an experimental prototype based on machine vision, simulating an industrial application. An extremely high 99.5% of deteriorated figs were classified correctly as well as 89.0% of light coloured good quality figs A lower percentage was obtained for dark good quality figs but results were acceptable since the most of the confusion was among the two classes of good product. (c) 2015 Elsevier B.V. All rights reserved.

关键词： Fig image analysis Computer vision Quality Colour Post-harvest processing

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Shape classification of wear particles by image boundary analysis using machine learning algorithms

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MECHANICAL systems AND SIGNAL processing 2016年 72-73卷 346-358页

作者： Yuan, Wei Chin, K. S. Hua, Meng Dong, Guangneng Wang, Chunhui Xi An Jiao Tong Univ Key Lab Modern Design & Rotor Bearing Syst Educ Minist Xian 710049 Peoples R China City Univ Hong Kong Dept Syst Engn & Engn Management 83 Tat Chee Ave Kowloon Tong Hong Kong Peoples R China City Univ Hong Kong Dept Mech & Biomed Engn 83 Tat Chee Ave Kowloon Tong Hong Kong Peoples R China

The shape features of wear particles generated from wear track usually contain plenty of information about the wear states of a machinery operational condition. Techniques to quickly identify types of wear particles quickly to respond to the machine operation and prolong the machine's life appear to be lacking and are yet to be established. To bridge rapid off-line feature recognition with on-line wear mode identification, this paper presents a new radial concave deviation (RCD) method that mainly involves the use of the particle boundary signal to analyze wear particle features. Signal output from the RCDs subsequently facilitates the determination of several other feature parameters, typically relevant to the shape and size of the wear particle. Debris feature and type are identified through the use of various classification methods, such as linear discriminant analysis, quadratic discriminant analysis, naive Bayesian method, and classification and regression tree method (CART). The average errors of the training and test via ten-fold cross validation suggest CART is a highly suitable approach for classifying and analyzing particle features. Furthermore, the results of the wear debris analysis enable the maintenance team to diagnose faults appropriately. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Wear particles image processing Radial concave deviation Particle classification Machine learning

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Local motion compensation in image sequences degraded by atmospheric turbulence: a comparative analysis of optical flow vs. block matching methods 19

Local motion compensation in image sequences degraded by atm...

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Conference on Optics in Atmospheric Propagation and Adaptive systems XIX

作者： Huebner, Claudia S. Fraunhofer IOSB Gutleuthausstr 1 D-76275 Ettlingen Germany

ISBN: (数字)9781510604094

ISBN: (纸本)9781510604087;9781510604094

As a consequence of fluctuations in the index of refraction of the air, atmospheric turbulence causes scintillation, spatial and temporal blurring as well as global and local image motion creating geometric distortions. To mitigate these effects many different methods have been proposed. Global as well as local motion compensation in some form or other constitutes an integral part of many software-based approaches. For the estimation of motion vectors between consecutive frames simple methods like block matching are preferable to more complex algorithms like optical flow, at least when challenged with near real-time requirements. However, the processing power of commercially available computers continues to increase rapidly and the more powerful optical flow methods have the potential to outperform standard block matching methods. Therefore, in this paper three standard optical flow algorithms, namely Horn-Schunck (HS), Lucas-Kanade (LK) and Farneback (FB), are tested for their suitability to be employed for local motion compensation as part of a turbulence mitigation system. Their qualitative performance is evaluated and compared with that of three standard block matching methods, namely Exhaustive Search (ES), Adaptive Rood Pattern Search (ARPS) and Correlation based Search (CS).

关键词： atmospheric turbulence turbulence mitigation optical flow block matching motion estimation local motion compensation image restoration

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Bio-inspired computer vision: Towards a synergistic approach of artificial and biological vision

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COMPUTER VISION AND image UNDERSTANDING 2016年 150卷 1-30页

作者： Medathati, N. V. Kartheek Neumann, Heiko Masson, Guillaume S. Kornprobst, Pierre Inria Sophia Antipolis Mediterranee Biovis Team 2004 Route LuciolesBP 93 F-06902 Sophia Antipolis France Univ Ulm Inst Neural Informat Proc D-89081 Ulm Germany CNRS UMR7289 Inst Neurosci Timone 27 Blvd Jean Moulin F-13005 Marseille France Aix Marseille Univ 27 Blvd Jean Moulin F-13005 Marseille France

Studies in biological vision have always been a great source of inspiration for design of computer vision algorithms. In the past, several successful methods were designed with varying degrees of correspondence with biological vision studies, ranging from purely functional inspiration to methods that utilise models that were primarily developed for explaining biological observations. Even though it seems well recognised that computational models of biological vision can help in design of computer vision algorithms, it is a non-trivial exercise for a computer vision researcher to mine relevant information from biological vision literature as very few studies in biology are organised at a task level. In this paper we aim to bridge this gap by providing a computer vision task centric presentation of models primarily originating in biological vision studies. Not only do we revisit some of the main features of biological vision and discuss the foundations of existing computational studies modelling biological vision, but also we consider three classical computer vision tasks from a biological perspective: image sensing, segmentation and optical flow. Using this task-centric approach, we discuss well-known biological functional principles and compare them with approaches taken by computer vision. Based on this comparative analysis of computer and biological vision, we present some recent models in biological vision and highlight a few models that we think are promising for future investigations in computer vision. To this extent, this paper provides new insights and a starting point for investigators interested in the design of biology-based computer vision algorithms and pave a way for much needed interaction between the two communities leading to the development of synergistic models of artificial and biological vision. (C) 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license.

关键词： Canonical computations Event based processing Dynamic sensors Multiplexed representation Population coding Soft selectivity Feedback Lateral interactions Form-motion interactions

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Application of the method of pyramid for synthesis of parallel algorithm for difference solution of the two-dimensional partial differentials equation

Application of the method of pyramid for synthesis of parall...

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2016 International Conference Information Technology and Nanotechnology, ITNT 2016

作者： Golovashkin, D.L. Yablokova, L.V. Belova, E.V. Samara National Research University Samara Russia Image Processing Systems Institute Branch of the Federal Scientific Research Centre Crystallography and Photonics Russian Academy of Sciences Samara Russia

The work is devoted to the synthesis and investigation of parallel algorithm for a finite difference solution of the Poisson equation using the Jacobi method. For example, two-dimensional case demonstrates the efficac... 详细信息

关键词： Parallel algorithms

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Enhancement of dental digital X-ray images based on the image quality

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International Symposium on Intelligent systems Technologies and Applications, ISTA 2016

作者： Menon, Hema P. Rajeshwari, B. Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Amrita University Coimbatore India

ISBN: (纸本)9783319479514

Medical image Enhancement has made revolution in medical field, in improving the image quality helping doctors in their analysis. Among the various modalities available, the Digital X-rays have been extensively utilized in the medical world of imaging, especially in Dentistry, as it is reliable and affordable. The output scan pictures are examined by practitioners for scrutiny and clarification of tiny setbacks. A technology which is automated with the help of computers to examine the X-Ray images would be of great help to practitioners in their diagnosis. Enhancing the visual quality of the image becomes the prerequisite for such an automation process. The image quality being a subjective measure, the choice of the methods used for enhancement depends on the image under concern and the related application. This work aims at developing a system that automates the process of image enhancement using methods like Histogram Equalization(HE), Gamma Correction(GC),and Log Transform(LT). The decision of the enhancement parameters and the method used is chosen, with the help of the image statistics (like mean, variance, and standard deviation). This proposed system also ranks the algorithms in the order of their visual quality and thus the best possible enhanced output image can be used for further processing. Such an approach would give the practitioners flexibility in choosing the enhanced output of their choice. © Springer International Publishing AG 2016.

关键词： image enhancement

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Oil Spill Detection Using Hyperspectral Infrared Camera 4

Oil Spill Detection Using Hyperspectral Infrared Camera

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Conference on Infrared, Millimeter-Wave, and Terahertz Technologies IV

作者： Yu Hui Wang Qun Zhang Zhen Zhang Zhi-jie Tang wei Tang Xin Yue Song Wang Chen-sheng Huazhong Inst Electropt Wuhan Natl Lab Optoelect 717 Yangguang Rd Wuhan 430074 Peoples R China State Ocean Adm North China Sea Marine Tech Support Ctr 22 Fushun Rd Qingdao Peoples R China

ISBN: (纸本)9781510604797;9781510604803

Oil spill pollution is a severe environmental problem that persists in the marine environment and in inland water systems around the world. Remote sensing is an important part of oil spill response. The hyperspectral images can not only provide the space information but also the spectral information. Pixels of interests generally incorporate information from disparate component that requires quantitative decomposition of these pixels to extract desired information. Oil spill detection can be implemented by applying hyperspectral camera which can collect the hyperspectral data of the oil. By extracting desired spectral signature from hundreds of band information, one can detect and identify oil spill area in vast geographical regions. There are now numerous hyperspectral image processing algorithms developed for target detection. In this paper, we investigate several most widely used target detection algorithm for the identification of surface oil spills in ocean environment. In the experiments, we applied a hyperspectral camera to collect the real life oil spill. The experimental results shows the feasibility of oil spill detection using hyperspectral imaging and the performance of hyperspectral image processing algorithms were also validated.

关键词： oil spill hyperspectral image processing classification feature extraction

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NASA Technical Reports Server (Ntrs) 19940018010: Telescope image Modelling Software in Stsdas

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2016年

NASA Technical Reports Server (Ntrs) 19940018010: Telescope image Modelling Software in Stsdas by NASA Technical Reports Server (Ntrs); NASA Technical Reports Server (Ntrs); published by

关键词： (ntrs) 19940018010: algorithms eisenhamer, j. d. hodge, p. e. image image processing modelling nasa technical reports server (ntrs) operating systems (computers) point spread functions ray tracing reports shaw, r. a. subroutines telescopes vax computers williamson, r. l., ii

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Improving stereo reconstruction by sub-pixel correction using histogram matching

Improving stereo reconstruction by sub-pixel correction usin...

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IEEE Intelligent Vehicles Symposium (IV)

作者： Vancea, Cristian-Cosmin Miclea, Vlad-Cristian Nedevschi, Sergiu Tech Univ Cluj Napoca Dept Comp Sci Cluj Napoca Romania

ISBN: (纸本)9781509018215

The accuracy of stereo matching algorithms is one of the key aspects in autonomous driving nowadays. In case of large distances, sub-pixel accurate solutions are required, especially for algorithms in discrete settings. It has been previously shown that a strong correlation between the matching algorithm and the sub-pixel interpolation method exists, and there are ways to determine it. Unfortunately all methodologies presented so far are laborious and time-consuming. We present here a novel sub-pixel disparity correction technique based on applying histogram matching through the use of generated Look-up Tables (LUTs). Our method is flexible, fast and produces more accurate results than previous solutions in the discrete domain. Although we show the improvements over the Semi-Global matching algorithm, it can be adapted to other matching algorithms that preserve constant misalignment for any kind of 3D scenarios. The proposed method was tested on multiple systems and datasets (Synthetic images, Traffic scenes, Middlebury images, KITTI images) and we show that we can find LUTs that outperform the accuracy of previous solutions on all these sets. The histogram matching procedure lacks in complexity and results indicate a strict dependency of a particular LUT to the underlying stereo matching and the stereo vision system, but not on the image composition.

关键词： Stereo image processing

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Rough Surface Wear Analysis Using image processing Techniques

Rough Surface Wear Analysis Using Image Processing Technique...

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12th IFAC Workshop on Intelligent Manufacturing systems (IMS)

作者： Takimoto, Rogerio Yugo Guerra Tsuzuki, Marcos de Sales Ueda, Edson Kenji Sato, Andre Kubagawa Martins, Thiago de Castro Cousseau, Tiago Tanaka, Deniol Sinatora, Amilton Univ Sao Paulo Escola Politecn Mechatron & Mech Syst Engn Dept Computat Geometry Lab Sao Paulo Brazil Univ Sao Paulo Escola Politecn Mechatron & Mech Syst Engn Dept Surface Phenomena Lab Sao Paulo Brazil Inst Vale Tecnol Minera Ao ITV Belem Para Brazil

Due to the great interest of the automotive industry to quickly and systematically evaluate low wear quantities a new approach based on image processing is presented. By comparing the captured rough surface point cloud using optical interferometry before and after wear, the proposed algorithm was able to estimate the amount of volume wear and mass loss. The proposed algorithm has two main steps. In the first step, the 3D point clouds obtained before and after wear are mapped to 2D images, and conventional edge detection algorithms are applied. Keypoints are determined using the SIFT algorithm on both images (before and after the wear), followed by the use of the RANSAC algorithm to create a good 2D registration between both 2D images. In the second step an enhanced ICP algorithm is used to perform a 3D registration. The main advantage of this method is the visualization of the wear geometry provided by the wear topography. The proposed algorithm was tested and the results are presented. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Point Cloud Registration image processing Rigid Transform ICP SIFT

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