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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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The Extraction Algorithm of Crop Rows Line Based on Machine vision 1

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9th IFIP WG 5.14 international conference on computer and Computing Technologies in Agriculture (CCTA)

作者： Diao, Zhihua Wu, Beibei Wei, Yuquan Wu, Yuanyuan Zhengzhou Univ Light Ind Inst Elect & Informat Engn Zhengzhou 450002 Peoples R China Henan Key Lab Informat Based Elect Appliances Zhengzhou 450002 Peoples R China

ISBN: (数字)9783319483573

ISBN: (纸本)9783319483573;9783319483566

The accuracy of crop rows line extraction is the key to the automatic navigation of agricultural machinery. In the paper, an improved algorithm is proposed to solve the problem of poor connectivity, single pixel and redundant pixels. Firstly, image pre-processing operations is used in order to obtain a binary image, then the binary image is thinned. In the refinement process, the connectivity of the skeleton is maintained by the introduction of Euclidean distance. Experimental results show that the proposed method has good adaptability to the row crop, and the skeleton lines that are extracted is more accurate. Compared with the traditional algorithms, the error of the navigation line is relatively small by using this algorithm, which could meet the needs of the practical application.

关键词： Super green algorithm Crop rows Skeleton point Redundant pixels

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How to reach top accuracy for a visual pedestrian warning system from a car?

How to reach top accuracy for a visual pedestrian warning sy...

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Workshops on image processing Theory, Tools and Applications, IPTA

作者： Floris De Smedt Steven Puttemans Toon Goedemé Eavise KULeuven

ISBN: (纸本)9781467389112

Due to the wide applicability of pedestrian detection in surveillance and safety, this research topic has received much attention in computer vision literature. However, the focus of this research mainly lies in detecting and locating pedestrians individually as accurate as possible. In recent years, a number of datasets are captured using a forward looking camera from a car, which imposes the application of warning the driver when pedestrians are in front of the car. For such applications, it is not required to detect each pedestrian independently, but to generate an alarm when necessary. In this paper we explore techniques to boost the accuracy of recent channel-based algorithms in this application: algorithmic refinements as well as the inclusion of an LWIR image channel. We use the KAIST dataset which is constructed from image-pairs of both the visual and the LWIR spectrum, in day and night conditions. We study the influence of techniques that have shown success in literature.

关键词： Detectors Training Automobiles image color analysis Feature extraction Visualization Alarm systems

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Controlling an embedded robot through image processing based object tracking using MATLAB

Controlling an embedded robot through image processing based...

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international conference on (ISCO) Intelligent Systems and Control

作者： Sk. Zeeshan Murshed Anwesha Dutta Sombuddha Chatterjee Ishani Mondal Aroni Saha Souradeep Saha Debasish Kundu Sudipta Ghosh Sauvik Das Gupta West Bengal University of Technology Kolkata West Bengal India Gobindapur Saphali Memorial Polytechnic Burdwan West Bengal India Bengal Institute of Technology and Management Birbhum West Bengal India School of Electrical and Computer Engineering Oklahoma State University Stillwater OK USA

ISBN: (纸本)9781467378086

Object detection is a well-researched technology related to computer vision(CV) and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. It has got some real-world applications such as smart-home automation, etc. Detection of a particular object by the computer's camera is an important aspect. In this paper, we have proposed a method by which a particular coloured object is detected by the computer and according to the movement of the object, a robot, which is being interfaced with the computer using a wired medium is also moved. The arduino controlled robot is interfaced with the computer which acts as a slave. The object movement is the determining factor of the movement of the robot. The experimental results and analysis shown in the paper illustrates the performance of the proposed methodology in real-time.

关键词： Robots DC motors Synchronous motors image color analysis Pins Induction motors

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FORMAL REPRESENTATION OF EVENTS IN A SURVEILLANCE DOMAIN ONTOLOGY

FORMAL REPRESENTATION OF EVENTS IN A SURVEILLANCE DOMAIN ONT...

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IEEE international conference on image processing

作者： F. Sobhani K. Chandramouli Q. Zhang E. Izquierdo Multimedia and Vision Research Group School of Electronic Engineering and Computer Science Queen Mary University of London

ISBN: (纸本)9781467399623

Following the exponential deployment of surveillance systems across a wide-spread region of geographic locations, detection and representation of events has become a critical element in automated surveillance systems. In this paper, we present an extensive ontology framework for representing complex semantic events. The proposed ontology builds on DOLCE ontology and relies on the linguistic and cognitive modelling of philosophical knowledge to achieve interoperability between proprietary surveillance systems. The explicit definition of event vocabulary presented in the paper is aimed at aiding forensic analysts to objectively identify and represent complex events. The expressiveness of the proposed ontology framework is described in the context of London Riots which took place in 2011.

关键词： Surveillance ontology Forensic analyst Event Detection DOLCE

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vision and Language Integration Meets Multimedia Fusion: Proceedings of ACM Multimedia 2016 Workshop 16

Vision and Language Integration Meets Multimedia Fusion: Pro...

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Proceedings of the 24th ACM international conference on Multimedia

作者： Marie-Francine Moens Katerina Pastra Kate Saenko Tinne Tuytelaars KU Leuven Heverlee Belgium CSRI / ILSP / ATHENA Athens Greece UMass Lowell Lowell MA USA

ISBN: (纸本)9781450336031

Multimodal information fusion both at the signal and the semantics levels is a core part in most multimedia applications, including multimedia indexing, retrieval, summarization and others. Early or late fusion of modality-specific processing results has been addressed in multimedia prototypes since their very early days, through various methodologies including rule-based approaches, information-theoretic models and machine learning. vision and Language are two of the predominant modalities that are being fused and which have attracted special attention in international challenges with a long history of results, such as TRECVid, imageClef and others. During the last decade, vision-language semantic integration has attracted attention from traditionally non-interdisciplinary research communities, such as computer vision and Natural Language processing. This is due to the fact that one modality can greatly assist the processing of another providing cues for disambiguation, complementary information and noise/error filtering. The latest boom of deep learning methods has opened up new directions in joint modelling of visual and co-occurring verbal information in multimedia discourse. The workshop on vision and Language Integration Meets Multimedia Fusion has been held during the workshop weekend of the ACM Multimedia 2016 conference and the European conference on computer vision (ECCV 2016) on October 16, 2016 in Amsterdam, the Netherlands. The proceedings contain seven selected long papers, which have been orally presented at the workshop, and three abstracts of the invited keynote speeches. The papers and abstracts discuss data collection, representation learning, deep learning approaches, matrix and tensor factorization methods and graph based clustering with regard to the fusion of multimedia data. A variety of applications is presented including image captioning, summarization of news, video hyperlinking, sub-shot segmentation of user generated video, cross-modal c

关键词： combining computer vision and natural language processing cross-modal and multimodal processing of visual and language data

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A fast texture synthesis using Gene Expression Programming

A fast texture synthesis using Gene Expression Programming

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international conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Jifeng Guo Na Zhang Lin Wang Bo Yang Xiuyang Zhao Jin Zhou Shuangrong Liu Shandong Provincial Key Laboratory of Network based Intelligent Computing University of Jinan Jinan China Information Department China United Network Communications Co. Ltd. Jinan China School of Informatics Linyi University Linyi China

ISBN: (纸本)9781509033683

In computer graphics, vision, and image processing, the texture synthesis occupies an important position. However, most of the existing methods are relatively inefficient. Thus, it is necessary to design a fast texture synthesis algorithm. This article describes an efficient algorithm for texture synthesis. The texture model of this algorithm is derived from the Markov random field. It uses Gene Expression Programming (GEP) to find the best function and the value of each pixel in the synthesized image determined by this function. In this way, this algorithm can avoid scanning all of the pixels, so as to improve the speed of texture synthesis. This algorithm is faster than the previous synthesis algorithm.

关键词： Gene expression Head Algorithm design and analysis Markov processes Programming computer graphics image processing

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OBLIQUE RANDOM FOREST BASED ON PARTIAL LEAST SQUARES APPLIED TO PEDESTRIAN DETECTION

OBLIQUE RANDOM FOREST BASED ON PARTIAL LEAST SQUARES APPLIED...

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IEEE international conference on image processing

作者： Artur Jordao Lima Correia William Robson Schwartz Smart Surveillance Interest Group Computer Science Department Universidade Federal de Minas Gerais

ISBN: (纸本)9781467399623

The increasing popularity of approaches based on random forest in computer vision tasks is due to its simplicity and flexibility with complex data. Random forest is a set of decision trees that can be divided in two subsets according to the view of the feature descriptors provided as input: orthogonal and oblique. In the former, the feature space is separated orthogonally (axis-aligned) by a single feature at a time. In the latter, it separates the space by oriented hyperplanes, which usually provides better data modeling. This work proposes a novel oblique random forest associated with Partial Least Squares to perform the oblique split. We validate the proposed approach, referred to as oRF-PLS, on the challenge INRIA Person dataset. Experimental results demonstrate that the proposed method outperforms traditional state-of-the-art detectors. In addition, we demonstrate that PLS is a more suitable choice to build oblique random forest than SVM, being faster and producing more accurate forests.

关键词： Pedestrian detection Random forest Oblique random forest Partial least squares Machine learning

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MAX-MIN CONVOLUTIONAL NEURAL NETWORKS FOR image CLASSIFICATION

MAX-MIN CONVOLUTIONAL NEURAL NETWORKS FOR IMAGE CLASSIFICATI...

引用

IEEE international conference on image processing

作者： Michael Blot Matthieu Cord Nicolas Thome Sorbonne Universites UPMC Univ Paris 06 CNRS LIP6 UMR 7606

ISBN: (纸本)9781467399623

Convolutional neural networks (CNN) are widely used in computer vision, especially in image classification. However, the way in which information and invariance properties are encoded through in deep CNN architectures is still an open question. In this paper, we propose to modify the standard convolutional block of CNN in order to transfer more information layer after layer while keeping some invariance within the network. Our main idea is to exploit both positive and negative high scores obtained in the convolution maps. This behavior is obtained by modifying the traditional activation function step before pooling. We are doubling the maps with specific activations functions, called MaxMin strategy, in order to achieve our pipeline. Extensive experiments on two classical datasets, MNIST and CIFAR-10, show that our deep MaxMin convolutional net outperforms standard CNN.

关键词： Convolutional neural network Pooling Activation function Invariance image representation Classification invariance image representation Taxonomy image classification pooling Activating function images Neural network maps Convolution

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FAST DEPTH ESTIMATION FROM SINGLE image USING STRUCTURED FOREST

FAST DEPTH ESTIMATION FROM SINGLE IMAGE USING STRUCTURED FOR...

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IEEE international conference on image processing

作者： Shuai Fang Ren Jin Yang Cao School of Computer and Information Hefei University of Technology Department of Automation University of Science and Technology of China

ISBN: (纸本)9781467399623

Depth estimation from single image is an important component of many vision systems, including robot navigation, motion capture and video surveillance. In this paper, we propose to apply a structure forest framework to infer depth information from single RGB image. The core idea of our approach is to exploit the structure properties exhibit in local patches of depth map to learn the depth level for each pixel. We formulate the problem of depth estimation in a structured learning framework based on random decision forests. Each trained forest infers a patch of structured labels that are accumulated across the image to obtain the final depth map. Moreover, we systematically investigate a variety of depth-relevant features and the regression forest framework automatically determines the best feature combination and uses as input of structure forest. Our approach achieves quasi real-time performance that is orders of magnitude faster than state-of-the-art approaches, while also achieving state-of-the-art depth estimation results on the Make3D dataset.

关键词： Depth estimation Structured forests Monocular image

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Real-time image distortion correction: Analysis and evaluation of FPGA-compatible algorithms

Real-time image distortion correction: Analysis and evaluati...

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international conference on Reconfigurable Computing and FPGAs (ReConFig)

作者： Paolo Di Febbo Stefano Mattoccia Carlo Dal Mutto DISI University of Bologna Italy Aquifi Inc

image distortion correction is a critical preprocessing step for a variety of computer vision and image processing algorithms. Standard real-time software implementations are generally not suited for direct hardware porting, so appropriated versions need to be designed in order to obtain implementations deployable on FPGAs. In this paper, hardware-compatible techniques for image distortion correction are introduced and analyzed in details. The considered solutions are compared in terms of output quality by using a geometrical-error-based approach, with particular emphasis on robustness with respect to increasing lens distortion. The required amount of hardware resources is also estimated for each considered approach.

关键词： Distortion Field programmable gate arrays Hardware Software Software algorithms Cameras Algorithm design and analysis

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