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International Symposium on Innovations in Intelligent systems and Applications (INISTA)

作者： Canayaz, Murat Hanbay, Kazim Yuzuncu Yil Univ Dept Comp Engn Bardakci Van Merkez Van Turkey Bingol Univ Dept Informat Bingol Merkez Bingol Turkey

ISBN: (纸本)9781467399104

image segmentation is important part of image processing applications. A given image is separated the different regions with homogeneous characteristics at image segmentation process. This paper will introduce an image segmentation approach that can be used in image processing applications. Recently Neutrosophic Set (NS) that use to evulate indeterminacy information, and metaheuristic algorithms are frequently used in image segmentation process. Our study contain both these methods. At first, an image is transformed to NS domain that has T, I, F subset, and then, features of image are extracted. Then, according to Shannon entopy model, threshold values that correspond to the values maximizing the function in the entropy, is found on the image. Finally, image is thresholded with this value. This search of maximum entropy values is made using the Cricket Algorithm, a new metaheuristic algorithm inspired behaviour of cricket, minimizing the complexity of operation and search time. To summarize, this study aims not only to represent image segmentation technique but also introduce the Cricket Algorithm. At the end of study, the performance of this approach on test images will be shown.

关键词： image segmentation

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On the efficiency of OSEM algorithm for tomographic lung CT images reconstruction 2

On the efficiency of OSEM algorithm for tomographic lung CT ...

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2nd International image processing, Applications and systems Conference, IPAS 2016

作者： Romdhane, Hamida Cherni, Mohamed Ali Sallem, Dorra Ben ISTMT Tunisia Université de Tunis LR13 ES03 SIME ENSIT Montfleury1008 Tunisia Université de Tunis El Manar Département de Biophysique et Médecine Nucléaire Institut Salah Azaiez Tunisia

ISBN: (纸本)9781509016457

Sometimes, computed tomography (CT) examinations need to be repeated. This may generate adverse effects on patients. To avoid it, an efficient reconstruction technique should be applied. This paper presents a qualitative and quantitative comparative study of four iterative algorithms (Algebraic Reconstruction Technique (ART), Maximum Likelihood Expectation Maximization (MLEM), Ordered-subsets expectation maximization (OSEM) and Simultaneous algebraic reconstruction technique (SART)). The four techniques are applied on a 'dicom' lung computed tomography image. Qualitatively, we can not differentiate between the reconstructed images. They are almost the same for all the methods. But, qualitatively, the best performance was observed with OSEM algorithm which provides the best quality image according to practically all the computed evaluation criteria. Moreover, OSEM insures this best performance in shorter processing time ranging from two to three times less compared to the other methods. © 2016 IEEE.

关键词： image reconstruction

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SmartPhoto: A Resource-Aware Crowdsourcing Approach for image Sensing with Smartphones

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2016年第5期15卷 1249-1263页

作者： Wu, Yibo Wang, Yi Hu, Wenjie Cao, Guohong Penn State Univ Dept Comp Sci & Engn University Pk PA 16802 USA Google Inc Mountain View CA 94043 USA

Photos obtained via crowdsourcing can be used in many critical applications. Due to the limitations of communication bandwidth, storage, and processing capability, it is a challenge to transfer the huge amount of crowdsourced photos. To address this problem, we propose a framework, called SmartPhoto, to quantify the quality (utility) of crowdsourced photos based on the accessible geographical and geometrical information (called metadata) including the smartphone's orientation, position, and all related parameters of the built-in camera. From the metadata, we can infer where and how the photo is taken, and then only transmit the most useful photos. Four optimization problems regarding the tradeoffs between photo utility and resource constraints, namely Max-Utility, online Max-Utility, Min-Selection, and Min-Selection with k-coverage, are studied. Efficient algorithms are proposed and their performance bounds are theoretically proved. We have implemented SmartPhoto in a testbed using Android based smartphones, and proposed techniques to improve the accuracy of the collected metadata by reducing sensor reading errors and solving object occlusion issues. Results based on real implementations and extensive simulations demonstrate the effectiveness of the proposed algorithms.

关键词： Crowdsourcing image sensing photo sharing camera sensor smartphone

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Sparse Representation Based Binary Hypothesis Model for Hyperspectral image Classification

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MATHEMATICAL PROBLEMS IN ENGINEERING 2016年第1期2016卷 1-10页

作者： Tang, Yidong Huang, Shucai Xue, Aijun Air Force Engn Univ Sch Air & Missile Def Xian 710051 Peoples R China

The sparse representation based classifier (SRC) and its kernel version (KSRC) have been employed for hyperspectral image (HSI) classification. However, the state-of-the-art SRC often aims at extended surface objects with linear mixture in smooth scene and assumes that the number of classes is given. Considering the small target with complex background, a sparse representation based binary hypothesis (SRBBH) model is established in this paper. In this model, a query pixel is represented in two ways, which are, respectively, by background dictionary and by union dictionary. The background dictionary is composed of samples selected from the local dual concentric window centered at the query pixel. Thus, for each pixel the classification issue becomes an adaptive multiclass classification problem, where only the number of desired classes is required. Furthermore, the kernel method is employed to improve the interclass separability. In kernel space, the coding vector is obtained by using kernel-based orthogonal matching pursuit (KOMP) algorithm. Then the query pixel can be labeled by the characteristics of the coding vectors. Instead of directly using the reconstruction residuals, the different impacts the background dictionary and union dictionary have on reconstruction are used for validation and classification. It enhances the discrimination and hence improves the performance.

关键词： SPARSE approximations BINARY system (Mathematics) HYPERSPECTRAL imaging systems image processing SEARCH algorithms

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Multi-scale superpixel spectral-spatial classification of hyperspectral images

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2016年第20期37卷 4905-4922页

作者： Li, Shanshan Ni, Li Jia, Xiuping Gao, Lianru Zhang, Bing Peng, Man Chinese Acad Sci Inst Remote Sensing & Digital Earth 9 Dengzhuang South Rd Beijing 100094 Peoples R China Univ New South Wales China Remote Sensing Satellite Ground Stn Sydney NSW Australia

A novel multi-scale superpixel-based spectral-spatial classification (MS-SSC) approach is proposed for hyperspectral images in this study. Superpixels are considered as the basic processing units for spectral-spatial-based classification. The use of multiple scales allows the capturing of local spatial structures of various sizes. The proposed technique consists of three steps. In the first step, hierarchical superpixel segmentations are performed from fine to coarse scales for the original hyperspectral image and the spectral information of each superpixel is used for classification at each scale. In the second step, each single scale superpixel-based classification is improved by combining with the segmentations at a higher level. Finally, the multi-scale classification is achieved via decision fusion. Experimental results are presented for two hyperspectral images and compared with recently advanced pixel-wise and pixel-based spectral-spatial classification approaches. The experiments demonstrate that the proposed method works effectively on the homogeneous regions and is also able to preserve the small local spatial structures in the image.

关键词： HYPERSPECTRAL imaging systems PIXELS CLASSIFICATION algorithms SEGMENTATION (image processing) POLARIMETRIC remote sensing

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Smart motion detection sensor based on video processing using self-organizing maps

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EXPERT systems WITH APPLICATIONS 2016年 64卷 476-489页

作者： Ortega-Zamorano, Francisco Molina-Cabello, Miguel A. Lopez-Rubio, Ezequiel Palomo, Esteban J. Univ Malaga Dept Comp Languages & Comp Sci Malaga Spain Univ Yachay Tech Sch Math & Comp Sci San Miguel De Urcuqui Ecuador

Most current approaches to computer vision are based on expensive, high performance hardware to meet the heavy computational requirements of the employed algorithms. These system architectures are severely limited in their practical application due to financial and technical limitations. In this work a different strategy is used, namely the development of an inexpensive and easy to deploy computer vision system for motion detection. This is achieved by three means. First of all, an affordable and flexible hardware platform is employed. Secondly, the motion detection algorithm is specifically tailored to involve a very small computational load. Thirdly, a fixed point programming paradigm is followed in implementing the system so as to further reduce the computational requirements. The proposed system is experimentally compared to the standard motion detector for a wide range of benchmark videos. The reported results indicate that our proposal attains substantially better performance, while it remains affordable and easy to install in practice. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Self-organizing map Microcontroller Arduino image processing Block processing

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Holonic Multi Agent System for Data Fusion in Vehicle Classification 10th

Holonic Multi Agent System for Data Fusion in Vehicle Classi...

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10th KES Conference on Agent and Multi-Agent systems - Technologies and Applications (KES-AMSTA)

作者： Seric, Ljiljana Krstinic, Damir Braovic, Maja Milatic, Ivan Mircevski, Aljosa Stipanicev, Darko Univ Split Fac Elect Engn Mech Engn & Naval Architecture Rudera Boskovica 32 Split 210000 Croatia

ISBN: (纸本)9783319398839;9783319398822

In this paper we describe holonic organization of a multi agent system for automatic vehicle classification in a road toll system. Classification of vehicles in road toll systems is based on physical vehicle features and in this paper we focus on axle counting as the first discriminant feature for class determination. Our system relies on two main sensors-video camera and depth sensor. Video image and depth image processing is performed in several holons. The results from individual holons are fused into the final decision on a number of axles of a passing vehicle. We show that fusion of results from individual holons givesmore precise results than individual holons. Holonic organization of the system aids scalability and simplifies inclusion of new sensors and new algorithms.

关键词： Holonic MAS Vehicle classification image processing Data fusion

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Optical Signal processing of Video Surveillance for Recognizing and Measurement Location Railway Infrastructure Elements 14

Optical Signal Processing of Video Surveillance for Recogniz...

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14th International Conference on Optical Technologies for Telecommunications

作者： Diyazitdinov, Rinat R. Vasin, Nikolay N. PSUTI Dept Commun Syst 23 Tolstoy St Samara 443010 Russia

ISBN: (纸本)9781510600485

processing of optical signals, which are received from CCD sensors of video cameras, allows to extend the functionality of video surveillance systems. Traditional video surveillance systems are used for saving, transmitting and preprocessing of the video content from the controlled objects. Video signal processing by analytics systems allows to get more information about object's location and movement, the flow of technological processes and to measure other parameters. For example, the signal processing of video surveillance systems, installed on carriage-laboratories, are used for getting information about certain parameters of the railways. Two algorithms for video processing, allowing recognition of pedestrian crossings of the railways, as well as location measurement of the so-called "Anchor Marks" used to control the mechanical stresses of continuous welded rail track are described in this article. The algorithms are based on the principle of determining the region of interest (ROI), and then the analysis of the fragments inside this ROI.

关键词： video surveillance systems optical signal processing diagnostic systems pedestrian crossing railway tracks railways Anchor Marks RANSAC Canny Gaussian impulse the image histogram the region of interest

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The Development of Markov Random Field Theory and Applications on image Segmentation Algorithm

The Development of Markov Random Field Theory and Applicatio...

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International Conference on Communication and Electronics systems (ICCES)

作者： Zhang, Renqian Huang, Yuru Fu, Sirui Fudan Univ Renqian Zhang Elect Engn Informat Dept Shanghai Peoples R China Beijing Univ Technol Beijing Peoples R China Lanzhou Univ Lanzhou Peoples R China

ISBN: (纸本)9781509010660

image segmentation algorithm is to divide the images into several regions with specific and unique characteristics, and is an important technology to extract the interested target. image segmentation is the key step to realize the research from general image processing into image analysis, and is vital preprocessing method of image recognition and computer vision. We cannot obtain correct recognition if we do not have correct segmentation. Nevertheless, the only basis of segmentation process is brightness or color of pixels in an image. In the processing of computer automatic segmentation, we experience several problems, such as uneven illumination, effect of noise, indistinct part in image, and shadow, and these factors may cause false segmentation. In order to overcome the disadvantages of the traditional segmentation algorithm, in this paper, we propose a novel segmentation algorithm based on Markov Random Field. The segmentation algorithm proposed in this paper is based on Markov Random Field Mode and Bayesian theory, and we determine the objective function in image segmentation problem on the basis of optimality criterion of statistical decision and estimation theory. Some optimization algorithms are used to obtain the maximum possible distribution of Markov Random Field which satisfy these conditions. The experimental result reflects the effectiveness and robustness of our algorithm. As a supplement, we analyze the development trend of the Markov Random Field theory.

关键词： Markov Random Field image segmentation Bayesian theory mathematical optimization algorithm design

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Evolution of Active Categorical image Classification via Saccadic Eye Movement

Evolution of Active Categorical Image Classification via Sac...

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14th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Olson, Randal S. Moore, Jason H. Adami, Christoph Univ Penn Inst Biomed Informat Philadelphia PA 19104 USA Michigan State Univ Dept Microbiol & Mol Genet E Lansing MI 48824 USA

ISBN: (纸本)9783319458236;9783319458229

Pattern recognition and classification is a central concern for modern information processing systems. In particular, one key challenge to image and video classification has been that the computational cost of image processing scales linearly with the number of pixels in the image or video. Here we present an intelligent machine (the "active categorical classifier," or ACC) that is inspired by the saccadic movements of the eye, and is capable of classifying images by selectively scanning only a portion of the image. We harness evolutionary computation to optimize the ACC on the MNIST hand-written digit classification task, and provide a proof-of-concept that the ACC works on noisy multi-class data. We further analyze the ACC and demonstrate its ability to classify images after viewing only a fraction of the pixels, and provide insight on future research paths to further improve upon the ACC presented here.

关键词： Active categorical perception Attention-based processing Evolutionary computation Machine learning Supervised classification

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