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检索条件"任意字段=1st International Conference on Image Processing Machine Learning and Pattern Recognition"

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learning a visual attention model for adaptive fast-forward in video surveillance

Learning a visual attention model for adaptive fast-forward ...

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1st international conference on pattern recognition Applications and Methods, ICPRAM 2012

作者： Ḧoferlin, Benjamin Pflüger, Hermann Ḧoferlin, Markus Heidemann, Gunther Weiskopf, Daniel Intelligent Systems Group University of Stuttgart Stuttgart Germany Visualization Research Center University of Stuttgart Stuttgart Germany Computer Vision Group Institute of Cognitive Science University of Osnabruck Osnabrück Germany

ISBN: (纸本)9789898425980

The focus of visual attention is guided by salient signals in the peripheral field of view (bottom-up) as well as by the relevance feedback of a semantic model (top-down). As a result, humans are able to evaluate new situations very fast, with only a view numbers of fixations. In this paper, we present a learned model for the fast prediction of visual attention in video. We consider bottom-up and memory-less top-down mechanisms of visual attention guidance, and apply the model to video playback-speed adaption. The presented visual attention model is based on rectangle features that are fast to compute and capable of describing the known mechanisms of bottom-up processing, such as motion, contrast, color, symmetry, and others as well as topdown cues, such as face and person detectors. We show that the visual attention model outperforms other recent methods in adaption of video playback-speed.

关键词： Security systems

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Automatic selection of the training set for semi-supervised land classification and segmentation of satellite images

Automatic selection of the training set for semi-supervised ...

引用

1st international conference on pattern recognition Applications and Methods, ICPRAM 2012

作者： Rajadell, Olga García-Sevilla, Pedro Institute of New Imaging Technologies University Jaume I Castellón de la Plana Spain

ISBN: (纸本)9789898425980

Different scenarios can be found in land classification and segmentation of satellite images. First, when prior knowledge is available, the training data is generally selected by randomly picking samples within classes. When no prior knowledge is available the system can pick samples at random among all unlabeled data, which is highly unreliable, and ask the expert to label them or it can rely on the expert collaboration to improve progressively the training data applying an active learning function. We suggest a scheme to tackle the lack of prior knowledge without actively involving the expert, whose collaboration may be expensive. The proposed scheme uses a clustering technique to analyze the feature space and find the most representative samples for being labeled. In this case the expert is just involved in labeling once a reliable training data set for being representative of the features space. Once the training set is labeled by the expert, different classifiers may be built to process the rest of samples. Three different approaches are presented in this paper: the result of the clustering process, a distance based classifier, and support vector machines (SVM).

关键词： image segmentation

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The mapping algorithm of triangular Vertex Chain Code from thinned binary image

The mapping algorithm of triangular Vertex Chain Code from t...

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1st Joint international conference on Advances in Signal processing and Information Technology, SPIT 2011 and the international conference on Recent Trends in Information processing and Computing, IPC 2011

作者： Wulandhari, Lili A. Haron, Habibollah Sallehuddin, Roselina Department of Modeling and Industrial Computing Faculty of Computer Science and Information Systems Universiti Teknologi Malaysia 81310 UTM Skudai Johor Bahru Malaysia

ISBN: (纸本)9783642325724

image representation has always been an important and interesting topic in image processing and pattern recognition. In 1999, Bribiesca introduced a new two dimensional chain code scheme called Vertex Chain Code (VCC). VCC is composed of three regular cells, namely rectangular, triangular, and hexagonal. This paper presents the mapping algorithm that covers one of the VCC cells, the Triangular VCC cell. The mapping algorithm consists of a cell-representation algorithm that represents a thinned binary image into triangular cells, and a transcribing algorithm that transcribes the cells into Vertex Chain Code. The algorithms have been tested and validated by using three thinned binary images: L-block, hexagon and pentagon. The results show that this algorithm is capable of visualizing and transcribing them into VCC;it can also be improved by testing on more thinned binary images. © 2012 ICst Institute for Computer Science, Social Informatics and Telecommunications Engineering.

关键词： Cytology

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image Based Automated Phenological stage Detection of Cereal Plants

Image Based Automated Phenological Stage Detection of Cereal...

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1st international conference on Agro-Geoinformatics (Agro-Geoinformatics)

作者： Bagis, Serdar Ustundag, B. Berk Istanbul Tech Univ Fac Comp Engn & Informat TR-80626 Istanbul Turkey

ISBN: (纸本)9781467324946

Most of the agricultural loss and yield evaluation methods require separate plant parameters depending on phenological stages. In this study, an automated image processing based phenological stages detection system for cereal plants is explained. TARIT Project (***) in Turkey aims to predict crop yield and loss rates in crop lands over a vast region in South-Eastern Turkey. The image data is acquired in RGB format at 30minutes period while the accompanied meteorological data is sampled at 10 minutes period. We construct a state machine for each field, and states represent the phonological stages. Each state has different transition function for the next stage related logical state. 7 major stages for wheat and barley are considered to determine transition dates although it could be increased in general. The sampled image is enhanced and normalized by using light rate and surface wetness related sensor data. Four different processes are then applied and their outcomes are used to determine the state transition conditions. The first is vegetation area green pixel ratio after RGB to HIS color space conversion. The second is change detection with respect to a virtual reference or recognized pattern for growth rate measurement. The fourth is probabilistic supporting stage transition factors based on agro-meteorological parameters. We set state transition functions depending on combination of these parameters with state dependent thresholds. It has shown that phenological stage transition dates of cereal plants can automatically be assigned less than a week tolerance with respect to expert diagnosis, by using the proposed image based state transition model.

关键词： TARIT project phenological stage detection remote sensing image processing

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Outline matching of the 2D shapes using extracting XML data 1

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5th international conference on image and Signal processing, ICISP 2012

作者： Gherabi, Noreddine Bahaj, Mohamed Hassan 1st University FSTS Department of Mathematics and Computer Science Morocco

ISBN: (数字)9783642312540

ISBN: (纸本)9783642312533

This paper presents an efficient shape matching method based on XML data, we extract the contour of the shape and this one is represented by set of points. Using corner detection method for representing the contour by a sequence of convex and concave segments. After, each segment is described by local and global features, this features are coded in string of symbols and stored in a XML file. Finally, using the dynamic programming, we find the optimal alignment between sequences of symbols. Results are presented and compared with existing methods using MATLAB for KIMIA-25 database and MPEG7 databases. © 2012 Springer-Verlag.

关键词： Dynamic programming

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Classification Oriented Semi-supervised Band Selection for Hyperspectral images

Classification Oriented Semi-supervised Band Selection for H...

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international conference on pattern recognition

作者： Jun Bai Shiming Xiang Chunhong Pan Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781467322164

This paper proposes a new framework of band selection for object classification in hyperspectral images. Different from traditional approaches where the selected bands are shared from all classes, in this work, different subsets of bands are selected for different class pairs. Without prior knowledge of spectral database, we estimate the spectral characteristic of objects with the labeled and unlabeled samples, benefiting from the concept of semi-supervised learning, Under the assumption of Gaussian mixture model (GMM), the vectors of mean values and covariance matrices for each class are estimated. The separabilities for all pairs of classes are thus calculated on each band. The bands with the highest separabilities are then selected. To validate our band selection result, support vector machine (SVM) is employed using a strategy of one against one (OAO). Experiments are conducted on a real data set of hyperspectral image, and the results can validate our algorithm.

关键词： Gaussian processes covariance matrices geophysical image processing hyperspectral imaging image classification learning (artificial intelligence) spectral analysis support vector machines visual databases

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Indian sign language recognition

Indian sign language recognition

引用

international conference on Emerging Technology Trends in Electronics, Communication and Networking (ET2ECN)

作者： Divya Deora Nikesh Bajaj Department of Electronics Engineering Z.H.College of Engineering and Technology Aligarh Muslim University Aligarh India

Understanding human motions can be posed as a pattern recognition problem. Applications of pattern recognition in information processing problems are diverse ranging from Speech, Handwritten character recognition to medical research and astronomy. Humans express time-varying motion patterns (gestures), such as a wave, in order to convey a message to a recipient. If a computer can detect and distinguish these human motion patterns, the desired message can be reconstructed, and the computer can respond appropriately. This paper represents a framework for a human computer interface capable of recognizing gestures from the Indian sign language. The complexity of Indian sign language recognition system increases due to the involvement of both the hands and also the overlapping of the hands. Alphabets and numbers have been recognized successfully. This system can be extended for words and sentences recognition is done with PCA (Principal Component analysis). This paper also proposes recognition with neural networks. Further it is proposed that number of finger tips and the distance of fingertips from the centroid of the hand can be used along with PCA for robustness and efficient results.

关键词： Handicapped aids Gesture recognition Thumb image segmentation Principal component analysis image color analysis

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Comparative performance of state-of-the-art classifiers in computer-aided detection for CT colonography

Comparative performance of state-of-the-art classifiers in c...

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4th international Workshop on Computational and Clinical Applications in Abdominal Imaging, Held in Conjunction with the 15th international conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2012

作者： Lee, Sang Ho Näppi, Janne J. Yoshida, Hiroyuki Department of Radiology Massachusetts General Hospital Harvard Medical School 25 New Chardon St. Boston MA 02114 United States

ISBN: (纸本)9783642336119

Several effective machine learning and pattern recognition schemes have been developed for medical imaging. Although many classifiers have been used with computer-aided detection (CAD) for computed tomographic colonography (CTC), little is known about their relative performance. This pilot study compares the performance of several state-of-the-art classifiers and feature selection methods in the classification of lesion candidates detected by CAD in CTC. There were four classifiers: linear discriminant analysis (LDA), radial basis function support vector machine (RBF-SVM), random forests (RF), and gradient boosting machine (GBM). There were five feature selection methods: sequential forward inclusion (SFI) of principal components (PCs), univariate filtering (UF), UF of PCs, recursive feature elimination (RFE), and RFE of PCs. A strategy of using all available features was tested also. For evaluation, 232,211 detections by a CAD system on 1,211 patients were subsampled randomly to create 10 different populations of 500 true-positive (TP) and 500 false-positive (FP) detections. The classifier performance was evaluated by use of the area under the receiver operating characteristic curve of 3 repeated 10-fold cross-validations. According to the result, the discrimination performance of the RBF-SVM classifier with feature selection by the RFE of PCs compared favorably with other methods, although no single classifier outperformed other classifiers under all conditions and feature selection schemes. © 2012 Springer-Verlag.

关键词： Feature Selection

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pattern recognition and machine Intelligence 2011

引用

丛书名： Lecture Notes in Computer Science

2011年

作者： Sergei O. Kuznetsov Deba P. Mandal Malay K. Kundu Sankar Kumar Pal

ISBN: (数字)9783642217869

ISBN: (纸本)9783642217852

This book constitutes the refereed proceedings of the 4th international conference on pattern recognition and machine Intelligence, PReMI 2011, held in Moscow, Russia in June/July 2011. The 65 revised papers presented together with 5 invited talks were carefully reviewed and selected from 140 submissions. The papers are organized in topical sections on pattern recognition and machine learning; image analysis; image and video information retrieval; natural language processing and text and data mining; watermarking, steganography and biometrics; soft computing and applications; clustering and network analysis; bio and chemo analysis; and document image processing.

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Analysis of Feature recognition of Neural Network Method in the string recognition

Analysis of Feature Recognition of Neural Network Method in ...

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1st international conference on Computer Science, Engineering and Information Technology (CCSEIT 2011)

作者： Gupta, Amit Kumar Singh, Yash Pal KIET MCA Dept Ghaziabad UP India BIET CSE Dept Jhansi Uttar Pradesh India

ISBN: (纸本)9783642240362;9783642240379

This paper aims that analysing neural network method in pattern recognition. A neural network is a processing device, whose design was inspired by the design and functioning of human brain and their components. The proposed solutions focus on applying Feature recognition Neural Network model for pattern recognition. The primary function of which is to retrieve in a pattern stored in memory, when an incomplete or noisy version of that pattern is presented. An associative memory is a storehouse of associated patterns that are encoded in some form. In auto-association, an input pattern is associated with itself and the states of input and output units coincide. When the storehouse is incited with a given distorted or partial pattern, the associated pattern pair stored in its perfect form is recalled. pattern recognition techniques are associated a symbolic identity with the image of the pattern. This problem of replication of patterns by machines (computers) involves the machine printed patterns. There is no idle memory containing data and programmed, but each neuron is programmed and continuously active.

关键词： Neural network machine printed string pattern recognition

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