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Salient region detection and segmentation for general object recognition and image understanding

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Science China(Information Sciences) 2011年第12期54卷 2481-2490页

作者： HUANG TieJun 1 , TIAN YongHong 1 , LI Jia 2 & YU HaoNan 11 National Engineering Laboratory for Video Technology, School of Electrical Engineering and Computer Science, Peking University, Beijing 100871, China 2 Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China National Engineering Laboratory for Video Technology School of Electrical Engineering and Computer Science Peking University Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

General object recognition and image understanding is recognized as a dramatic goal for computer vision and multimedia retrieval. In spite of the great efforts devoted in the last two decades, it still remains an open problem. In this paper, we propose a selective attention-driven model for general image understanding, named GORIUM (general object recognition and image understanding model). The key idea of our model is to discover recurring visual objects by selective attention modeling and pairwise local invariant features matching on a large image set in an unsupervised manner. Towards this end, it can be formulated as a four-layer bottom-up model, i.e., salient region detection, object segmentation, automatic object discovering and visual dictionary construction. By exploiting multi-task learning methods to model visual saliency simultaneously with the bottom-up and top-down factors, the lowest layer can effectively detect salient objects in an image. The second layer exploits a simple yet effective learning approach to generate two complementary maps from several raw saliency maps, which then can be utilized to segment the salient objects precisely from a complex scene. For the third layer, we have also implemented an unsupervised approach to automatically discover general objects from large image set by pairwise matching with local invariant features. Afterwards, visual dictionary construction can be implemented by using many state-of-the-art algorithms and tools available nowadays.

关键词： object recognition image understanding visual saliency salient object segmentation visual dictionary

Multi-Modality Medical image Fusion Based on Wavelet Analysis and Quality Evaluation

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Journal of Systems Engineering and Electronics 2001年第1期12卷 42-48页

作者： Yu Lifeng & Zu Donglin Institute of Heavy Ion Physics, Peking University, 100871, P. R. China Wang Weidong General Hospital of PLA, Beijing 100853, P. R. China Bao Shanglian Institute of Heavy Ion Physics, Peking University, 100871, P. R. China Institute of Heavy Ion Physics Peking University 100871 P. R. China General Hospital of PLA Beijing 10085. P. R. China

Multi-modality medical image fusion has more and more important applications in medical image analysis and understanding. In this paper, we develop and apply a multi-resolution method based on wavelet pyramid to fuse medical images from different modalities such as PET-MRI and CT-MRI. In particular, we evaluate the different fusion results when applying different selection rules and obtain optimum combination of fusion parameters.

关键词： Computer simulation Computerized tomography image analysis image quality image understanding Magnetic resonance imaging Optical resolving power

A review of object representation based on local features

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Journal of Zhejiang University-Science C(Computers and Electronics) 2013年第7期14卷 495-504页

作者： Jian CAO Dian-hui MAO Qiang CAI Hai-sheng LI Jun-ping DU College of Computer and Information Engineering Beijing Technology and Business University School of Computer Science Beijing University of Posts and Telecommunications

Object representation based on local features is a topical subject in the domain of image understanding and computer vision. We discuss the defects of global features in present methods and the advantages of local features in object recognition, and briefly explore state-of-the-art recognition methods using local features, especially the main approaches of local feature extraction and object representation. To clearly explain these methods, the problem of local feature extraction is divided into feature region detection, feature region description, and feature space optimization. The main components and merits of these steps are presented. Technologies for object presentation are classified into three types: vector space, sliding window, and structure relationship models. Future development trends are discussed briefly.

关键词： Object presentation Local feature image understanding Object recognition Visual words

Encoder-Decoder Based Multi-Feature Fusion Model for image Caption Generation

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Journal on Big Data 2021年第2期3卷 77-83页

作者： Mingyang Duan Jin Liu Shiqi Lv Shanghai Maritime University Shanghai201306China

image caption generation is an essential task in computer vision and image *** image caption generation models usually use the encoder-decoder model as the underlying network ***,in the traditional Encoder-Decoder architectures,only the global features of the images are extracted,while the local information of the images is not well *** paper proposed an Encoder-Decoder model based on fused features and a novel mechanism for correcting the generated caption *** use VGG16 and Faster R-CNN to extract global and local features in the encoder ***,we train the bidirectional LSTM network with the fused features in the ***,the local features extracted is used to correct the caption *** experiment results prove that the effectiveness of the proposed method.

关键词： image understanding image captioning deep learning fused features

A Method for 3D Scene Description and Segmentation in an Object Record

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The Journal of China Universities of Posts and Telecommunications 1996年第1期3卷 37-42页

作者： Chen Tingbiao(Department of Radio Engineering,Naming University of Posts and Telecommunications,Naming 210003,P.R.China) Department of Radio Engineering Nanjing University of Posts and Telecommunications Nanjing 210003 P.R.China

in this poper a novel data-and rule-driven system for 3D scene description and segmentation inan unknown environment is *** system generatss hierachies of features that correspond tostructural elements such as boundaries and shape classes of individual object as well as relationshipsbetween *** is implemented as an added high-level component to an existing low-level binocularvision system[1]. Based on a pair of matched stereo images produced by that system,3D segmentation is firstperformed to group object boundary data into several edge-sets,each of which is believed to belong to aparticular *** gross features of each object are extracted and stored in an object *** finalstructural description of the scene is accomplished with information in the object record,a set of rules and arule implementor. The System is designed to handle partially occluded objects of different shapes and sizeson the 2D *** results have shown its success in computing both object and structurallevel descriptions of common man-made objects.

关键词： s:image segmentation 3D scene description object record image understanding

Labeling Line Drawings with Hidden-Part-Drawn of Planar Object with Trihedral Vertices

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Computer Aided Drafting,Design and Manufacturing 2015年第2期25卷 1-13页

作者： GAO Man-tun CAI Xu-peng School of Mechanical Engineering Northwestern Polytechnical University

Although line drawings consist of only line segments on a plane, they convey much information about the three-dimensional object structures. For a computer interpreting line drawings, some intelligent mechanism is required to extract three-dimensional information from the two-dimensional line drawings. In this paper, a new labeling theory and method are proposed for the two-dimensional line drawing with hidden-part-draw of a three-dimensional planar object with trihedral vertices. Some rules for labeling line drawing are established. There are 24 kinds of possible junctions for line drawing with hidden-part-draw, in which there are 8 possible Y and 16 W junctions. The three problems are solved that Sugihara＇s line drawing labeling technique exists. By analyzing the projections of the holes in manifold planar object, we have put forward a labeling method for the line drawing. Our labeling theory and method can discriminate between correct and incorrect hidden-part-draw natural line drawings. The hidden-part-draw natural line drawings can be labeled correctly by our labeling theory and method, whereas the labeling theory of Sugihara can only label the hidden-part-draw unnatural line drawings in which some visible lines must be drawn as hidden lines, and some invisible lines must be drawn as continuous lines.

关键词： image understanding line drawing labeling junction line drawing with hidden-part-drawn planar object