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Detection level of mango based on neural network and digital image

Journal of Chemical and Pharmaceutical Research 2014年第2期6卷 129-132页

作者： Li, Xiaoling Yuan, Jimin School of Computer Science and Technology of Chengdu University China University Key Laboratory of Pattern Recognition and Intelligent Information Processing China Department Computer Engineering of Chengdu Aeronautic Vocational and Technical College China

In view of the draw backs of mango grade identification in China, which still relies on photoelectric sorting and manual separation, this paper presents a processing method on the basis of the technology of computer vision and digital image. Utilizing image processing technology, the researcher calculated the length of the long-short-axis, marked the location of it and calculated the 7 parameters, chroma, length, width and etc.,4 of which are chosen as the key characteristics of the BP input of network to build a network and identify the level of mango through analysis of the external characteristics of mango. The method is based on traditional characteristics detection, using boundary tracking algorithm and the length of the new long-short-axis detection algorithm. The result of experiment indicates that the calculating method and judging of the level of mango are precise and accurate, with an average recognition rate of 92%. Therefore, the method has a great practical value, which can be applied to other agricultural products classification.

关键词： Fruits

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A Robust Method for High-Accuracy Microbe Identification Based on image processing

A Robust Method for High-Accuracy Microbe Identification Bas...

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2014 IEEE International Conference on Progress in Informatics and Computing

作者： Mingqi Cao Yuncai Liu Shanghai JiaoTong University Institute of Image Processing and Pattern Recognition

We present a novel fast method based on computer vision toidentify microbe. The proposed method is simple but absolutely effective. It combines approximate parallel light source and industrial camera, toautomatically accomplish the bacteria identification and monitor the growing states of bacteria during the progress of a drug sensitive test. Based on this method, the color information and turbidity information, which reflect the primary information of drug sensitive tests, can be obtained fast, while processing efficiency can be as high as hundreds of milliseconds per frame. The performance of our method is significantly accurate and robust.

关键词： microbe identification drug sensitive test localization color feature turbidity feature

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Accurate Computation of Fingerprint Intrinsic images with PDE-Based Regularization

Accurate Computation of Fingerprint Intrinsic Images with PD...

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7th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2012

作者： Li, Mingyan Chen, Xiaoguang College of Sciences Beijing Forestry University 100083 Beijing China Institute of Image Processing and Pattern Recognition College of Sciences North China University of Technology 100083 Beijing China

The intrinsic images of fingerprint, such as orientation field and frequency map, represent the particular and basic characteristics of fingerprint ridge/valley patterns, and play a key role in feature extraction and matching of the fingerprint recognition system. In this paper, a novel algorithm is presented for accurate computation of fingerprint intrinsic images with PDE-based regularization technique. First, the coarse orientation field is estimated using general gradient-based method, and the frequency map is estimated by Fourier spectrum analysis of the projected curve of local window. Then, to measure the reliability of the intrinsic images, the quality map is computed based on the gray-scale intensity and local structural information. Finally, accurate orientation field and frequency map are reconstructed with PDE-based regularization of nonlinear diffusion filtering which is controlled by the quality-based diffusivity. Experimental results illustrated the efficiency of the proposed approach. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Spectrum analysis

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Joint probabilistic constrained robust beamforming and antenna selection

Joint probabilistic constrained robust beamforming and anten...

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IEEE International Conference on Signal processing, Communications and Computing (ICSPCC)

作者： Jinwang Yue Yong Jin Zhentao Hu Xianxing Liu Institute of Image Processing & Pattern Recognition Henan University Kaifeng China

We propose a novel method for joint probabilistic constrained robust beamforming and antenna selection used in cognitive radio (CR) networks. Assuming complex Gaussian distributed channel state information (CSI) errors, the Bernstein-type inequalities are used to transform the no closed-form probabilistic constrained into the deterministic forms. Moreover, l1-norm is introduced as the closest convex approximation of ℓ0-norm. So, the original NP-hard optimal problem can be relaxed to as a tractable convex optimization problem. A computationally efficient and near-optimal solution is obtained by a iteratively re-weighted algorithm. Simulations show that the proposed algorithm meet prescribed service levels at a relatively small excess transmission power in a number of transmitter reduction scenarios.

关键词： Array signal processing Interference Probabilistic logic Antennas Signal to noise ratio Robustness Vectors

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SALIENCY DETECTION VIA FOREGROUND RENDERING AND BACKGROUND EXCLUSION

SALIENCY DETECTION VIA FOREGROUND RENDERING AND BACKGROUND E...

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IEEE International Conference on image processing

作者： Yijun Li Keren Fu Lei Zhou Yu Qiao Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University

ISBN: (纸本)9781479957521

In this paper, a novel approach for image visual saliency detection is proposed from both the salient object (foreground) and the background perspective. To better highlight the salient object, we start from what is a salient object and adopt priors including contrast prior and center prior to measure the dissimilarity between different image elements. To better suppress the background, we focus on what is the background and measure the pixel-wise saliency by the minimum seam cost where the seam is an optimal 8-connected path from the pixel to some boundary pixel. The final saliency map is obtained by the combination of two measure systems which leads to the goal of both highlighting the salient object and suppressing the background. Both qualitative and quantitative experiments conducted on a benchmark dataset show that our approach outperforms seven state-of-the-art methods.

关键词： Saliency detection SODM map BDM map Saliency map

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recognition by detection: Perceiving human motion through part-configured feature maps

Recognition by detection: Perceiving human motion through pa...

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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Lei Wang Jun Wu Zhimin Zhou Yuncai Liu Xu Zhao Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition

Visually perceiving human motion at semantic level is an important however challenging problem in multimedia area. In this work, we propose a novel approach to map the low-level responses from visual detection to semantically sensitive description to human actions. The feature map is triggered by the output of deformable part model detection, in which the critical information about body parts configuration is contained implicitly under the specific human actions. We map the filter responses of the detectors to an effective feature description, which encodes the position and appearance information of the root and every body parts simultaneously. Statistically, the obtained feature map captures the significance of relative configuration of body parts, therefore is robust to the false detections occurred in the individual part detectors. We conduct comprehensive experiments and the results show that the method generates discriminative action features and achieves remarkable performance in most of the cases.

关键词： Accuracy Feature extraction YouTube Vectors Detectors Visualization Deformable models

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Repetitive shapes detection using affine invariant constraints

Repetitive shapes detection using affine invariant constrain...

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2014 International Conference on Communication Technology and Application(CTA2014)

作者： Z.H.Yang P.Dong Institute of Image Processing & Pattern Recognition North China University of Technology

ISBN: (纸本)9781845649302

This paper presents a novel approach for finding more accurate feature pairs which is not only invariant to affine transformation, but also deals with images with repetitive shapes. First, the more accurate and robust homographic transformation between different views could be calculated through bi-directional matching and appropriate selection of threshold. Second, an affine geometric property, ratios of areas of corresponding triangles are affine invariant, is used to obtain more feature pairs based on the first step. Experiments demonstrate that the proposed method outperforms the state-of-art ASIFT in the number of correct feature pairs and matching accuracy among images with repetitive patterns.

关键词： image matching repetitive shapes feature points affine invariance ASIFT

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SALIENCY DRIVEN CLUSTERING FOR SALIENT OBJECT DETECTION

SALIENCY DRIVEN CLUSTERING FOR SALIENT OBJECT DETECTION

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Lei ZHOU YiJun LI YiPeng SONG Yu QIAO Jie YANG Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China

ISBN: (纸本)9781479928941

In this work, a novel salient object detection method is proposed based on the saliency driven clustering. To capture visual patterns of an image, the color contrast prior and boundary prior are utilized to generate the image clusters automatically. Then, a simple operation like regional saliency computation is applied to refine the saliency maps generated by two priors. The final saliency map are obtained by combining the refined contrast prior saliency and boundary prior saliency. Extensive experiments show that our proposed model achieves better performance on salient region detection against the state-of-the-art methods.

关键词： image color analysis Visualization Computational modeling Conferences pattern recognition Computer vision Object detection

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pattern matching with flexible wildcard gaps

Pattern matching with flexible wildcard gaps

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5th International Symposium on IT in Medicine and Education, ITME 2013

作者： Junyan, Zhang Chenhui, Yang Information Science and Technology College Chengdu University Chengdu China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Chengdu China

ISBN: (纸本)9789400776173

pattern matching is a fundamental application in biomedicine and biological sequence analysis. A wildcard can match any one character in a sequence. Multiple wildcards form a gap. A flexible wildcard gap can match any characters with specific length which is specified by users. Therefore, the effective algorithm performing this kind of matching is in great need. In this paper, we design PMFG algorithm and achieve it by dividing a pattern into multiple sub-patterns with different length based on gap segmentation. After computing the starting positions and ending positions of each subpattern, the effective intervals and effective starting positions can be determined one by one. The number of the elements in the last effective position set equals to the number of the matching. A comparison experiments are done based on three DNA sequences. The results show that PMFG algorithm has better performance in the same fields. © Springer Science+Business Media Dordrecht 2014.

关键词： pattern matching

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Adaptive multi-level region merging for salient object detection 25

Adaptive multi-level region merging for salient object detec...

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25th British Machine Vision Conference, BMVC 2014

作者： Fu, Keren Gong, Chen Yun, Yixiao Li, Yijun Gu, Irene Yu-Hua Yang, Jie Yu, Jingyi Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Signals and Systems Chalmers University of Technology Gothenburg Sweden University of Delaware Newark United States

Most existing salient object detection algorithms face the problem of either under-or over-segmenting an image. More recent methods address the problem via multi-level segmentation. However, the number of segmentation levels is manually predetermined and only works well on specific class of images. In this paper, a new salient object detection scheme is presented based on adaptive multi-level region merging. A graph-based merging scheme is developed to reassemble regions based on their shared contour strength. This merging process is adaptive to complete contours of salient objects that can then be used for global perceptual analysis, e.g., foreground/ground separation. Such contour completion is enhanced by graph-based spectral decomposition. We show that even though simple region saliency measurements are adopted for each region, encouraging performance can be obtained after across-level integration. Experiments by comparing with 13 existing methods on three benchmark datasets including MSRA-1000, SOD and SED show the proposed method results in uniform object enhancement and achieves state-of-the-art performance. © 2014. The copyright of this document resides with its authors.

关键词： Merging

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