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Abundant Semantic Information Module for Object Detection 2

Abundant Semantic Information Module for Object Detection

引用

2nd International Conference on Algorithms, High Performance Computing and Artificial Intelligence, AHPCAI 2022

作者： Wu, Taida Li, Qishen Li, Qiufeng Gao, Jun School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China Jiangxi Nanchang China

ISBN: (纸本)9798350334784

Nowadays, with the high-speed iteration of convolution neural network, the efficient object detector emerges one after another. As an important branch of computer vision, object detection aims to detect where and what the object is. However, nowadays, many detector cannot extract abundant semantic information to discriminate the location and size of the objects, resulting in poor performance of the network. In this paper, a new module is proposed, named Abundant Semantic Information Module (ASIM), to enrich and expand the semantic information of the object with more and larger receptive fields. In ASIM, we blend the extracted feature maps to different degrees with different blending factors and fuse them so that all object information is given full attention. Compared to the baseline method, a wealth of experiments show that our module has achieved a significant performance improvement. © 2022 IEEE.

关键词： Object detection

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Brain MRI Segmentation with Patch-based CNN Approach

Brain MRI Segmentation with Patch-based CNN Approach

引用

第35届中国控制会议

作者： Zhipeng CUI Jie YANG Yu QIAO Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University The Key Laboratory of Ministry of Education for System Control and Information Processing

ISBN: (纸本)9781509009107

Brain Magnetic Resonance image(MRI) plays a non-substitutive role in clinical *** symptom of many diseases corresponds to the structural variants of *** structure segmentation in brain MRI is of great importance in modern medical *** methods were developed for automatic segmenting of brain MRI but failed to achieve desired *** this paper,we proposed a new patch-based approach for automatic segmentation of brain MRI using convolutional neural network(CNN).Each brain MRI acquired from a small portion of public dataset is firstly divided into *** of these patches are then used for training CNN,which is used for automatic segmentation of brain *** results showed that our approach achieved better segmentation accuracy compared with other deep learning methods.

关键词： CNN Deep Learning Brain MRI Segmentation Patch-based

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Generalized tube model: recognizing 3D elongated objects from 2D intensity images

Generalized tube model: recognizing 3D elongated objects fro...

引用

Conference on Computer Vision and pattern recognition (CVPR)

作者： Q. Huang G.C. Stockman Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East Lansing MI USA

The issue of recognizing 3D elongated objects from 2D intensity images is addressed. A tube model, locally similar to generalized cones, is developed for the class of elongated objects. A recognition strategy that combines 2D contour properties and surface shading information is used to exploit the power of the 3D model. Reliable contours provide constraints for localizing the objects of interest. The theory of optimal filters is adopted in verifying the shading of hypothesized objects. Object recognition is achieved through optimizing the signal-to-noise response with respect to model parameters. A sweeping operation is proposed as a further stage of identifying objects so that the overall performance of the system does not heavily rely on the quality of local feature detection.< >

关键词： image recognition Filters Object recognition Object detection Robustness image processing Laboratories Computer science Filtering theory Power system modeling

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Background suppression based-on wavelet transformation to detect infrared target

Background suppression based-on wavelet transformation to de...

引用

International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Sun, Y.U.-Qiu Tian, Jin-Wen Liu, Jian School of Information and Mathematics Yangtze University Jingzhou 434023 China State Education Commission Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China State Education Commission Key Laboratory for Image Processing and Intelligent Control Dept. of Electronic Information and Enginery Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)078039092X

Target detection and location in infrared clutter background is very important to infrared search and track system. Especially for small target detection in infrared image in background of sea and sky, there are no geometric and structure character to use. When targets such as ship and naval vessel sailing at long distance, they always appear around the sea-level line, and it is mixed with cloud and sea-wave clutter. It is difficult to segment and locate precisely. Background suppression based-on wavelet transformation is proposed in the paper. Wavelet decomposition makes it possible to analyze a signal both in time and frequency domains. In the paper infrared sea and key background images are processed. For their SNR is low and background is complex, using wavelet transformation decomposes an original image and extract approximate feature to reconstruct an image, which mainly includes background information. A new image will be obtained using background image subtracted from original image. There are mainly target and noise points left in the new image. By setting proper threshold, the target can be detected perfectly. At last, experiment results are given and show the method is practical. © 2005 IEEE.

关键词： pattern recognition

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A robust cell tracking framework by fusing global and local optimization algorithms

A robust cell tracking framework by fusing global and local ...

引用

第28届中国控制与决策会议

作者： Shu Meng Hong-Bin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (纸本)9781467397155

Automated cell tracking is an important branch of multi-object tracking,which can be used for quantitatively analyzing cell migration,proliferation and *** this paper,we proposed a hierarchical tracking method,fusing the global optimal method in consecutive frames assignment and local optimal approach in spatial trajectory *** the process,the detection errors were recognized and cell moving trajectories were completed *** also introduced the concept of clustering to measure the correlation between established short trajectories and reduce the tracking errors caused by fast *** rare information of cells was used in the linkage,the system can work well with *** experimental results show the effectiveness of our approach with cells having different density and activity.

关键词： Multi-object tracking Bipartite graph Optimization Hierarchical tracking

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CAD-based computer vision: from CAD models to relational graphs

CAD-based computer vision: from CAD models to relational gra...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： P.J. Flynn A.K. Jain Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East Lansing MI USA

The authors outline their approach for automatic translation of geometric entities produced by a CAD system into a relational graph structure. They have developed a system which uses 3-D object descriptions created on a commercial CAD system and expressed in the industry-standard IGES form, and performs geometric inferencing to object a relational graph representation of the object which can be stored in a database of models of object recognition. Details of the IGES standard, the geometric inference engine, and some formal properties of 3-D models are discussed. In addition to the process of translation from one data format to another, the interference engine extracts higher-level information from the CAD model and stores it explicitly in the new data structure. The higher-level features will allow the search space explored during the object recognition stage to be pruned early.< >

关键词： Computer vision Solid modeling Object recognition Engines Space exploration Industrial relations Relational databases Spatial databases Interference Data mining

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Path Integral Based Contour Extraction Method Using Multilevel Metropolis Sampling

Path Integral Based Contour Extraction Method Using Multilev...

引用

7th International Conference on Photonics and Imaging in Biology and Medicine

作者： Yangguang, Sun Tian, Lan Xiaowei, Fu Mingyue, Ding Institute of Pattern Recognition and Artificial Intelligence Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China School of Life Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819475398

In this paper we proposed a novel path integral method using multilevel Metropolis sampling to extract the contours of interested objects of medical images, which is a quantum statistical approach inspired by the essential characteristics of quantum. The implemented multilevel Metropolis sampling is a fast approach to the sampling problem and can be used to sample any distribution function and calculate all actions for the movements of quantum particle towards the position with high probability density likely to be accepted. Comparing with traditional deformable models based on classical mechanics which are sensitive to the initialization of contour and difficult to adapt topology changes of the contour, our work has the capability to break through the limitations of deformable models and makes the boundary extraction in high noise and low contrast of medical images possible. Experiments were performed with synthetic and medical images and the feasibility and potential of our method was demonstrated. © 2009 SPIE.

关键词： Distribution functions

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On-line fingerprint verification

On-line fingerprint verification

引用

International Conference on pattern recognition

作者： A. Jain Lin Hong Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East Lansing MI USA

We describe the design and implementation of an online fingerprint verification system which operates in two stages: (i) minutia extraction and (ii) minutia matching. An improved minutia extraction algorithm that is much faster and more accurate than our earlier algorithm has been implemented. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between input minutiae and the stored template without resorting to exhaustive search and has the ability to adaptively compensate for the nonlinear deformations and inexact pose transformations between finger prints. The system has been tested on two sets of finger print images captured with inkless scanners. The verification accuracy is found to be over 99% with a 15% reject rate. Typically, a complete fingerprint verification procedure takes, on an average, about 8 seconds on a SPARC 20 workstation. It meets the response time requirements of on-line verification with high accuracy.

关键词： Fingerprint recognition Access control Feature extraction Flowcharts pattern recognition image processing Laboratories Computer science pattern matching System testing

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Page segmentation using texture discrimination masks

Page segmentation using texture discrimination masks

引用

IEEE International Conference on image processing

作者： A.K. Jain Y. Zhong Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East Lansing MI USA

We propose a new texture-based page segmentation algorithm which automatically extracts the text, halftone, and line-drawing regions from input greyscale document images. This approach utilizes a neural network to train a set of masks which is optimal for discriminating the three main texture classes in the page segmentation problem: halftone, background, and text and line-drawing regions. The test and line-drawing regions are further discriminated based on connectivity analysis. We have applied the algorithm to successfully segment English and Chinese document images. We also demonstrate that the masks can perform language separation (English/Chinese) when appropriately trained.

关键词： image segmentation Neural networks Gabor filters Natural languages image analysis Multi-layer neural network image texture analysis pattern recognition image processing Laboratories

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Room Layout Estimation based on Planar Semantic Segmentation

Room Layout Estimation based on Planar Semantic Segmentation

引用

2022 IEEE World Conference on Applied Intelligence and Computing, AIC 2022

作者： Huang, Shizhong Miao, Jun Yao, Hui Zheng, Yilin Chu, Jun Zhang, Guoxiang Nanchang Hangkong University School of Aeronautical Manufacturing Engineering Nanchang330063 China Nanchang Hangkong University Department of Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang330063 China University of California MercedCA95343 United States

ISBN: (数字)9781665479882

ISBN: (纸本)9781509050017

Layout estimation is one of fundamental tasks for understanding indoor scenes. In a single image of indoor scenes, key information, such as key points and boundaries for inferring the layout is often severely occluded or completely invisible. In this paper, a novel end-to-end deep learning network, which is extended from Xception module, is constructed for segmentation of semantic planes to infer the room layout structure. Moreover, the network contains an optimized depth wise separable convolution (DSC) in Xception module and a reinforced spatial attention module (SAM), so that it obtained good learning ability in severely occluded areas. Tests on public datasets show that the proposed network has a lower pixel error (PE) and higher generalization compared with existing algorithms. Ablation experiments prove that optimized DSC and SAM in the network are effective in improving the accuracy of layout estimation. © 2022 IEEE.

关键词： Semantic Segmentation

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