scholarly journals A Novel Fast and Robust Binary Affine Invariant Descriptor for Image Matching

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xiujie Qu ◽  
Fei Zhao ◽  
Mengzhe Zhou ◽  
Haili Huo
Keyword(s):  
Image Matching ◽  
Perspective Transformation ◽  
Simple Random Sampling ◽  
Small Scale ◽  
Affine Invariant ◽  
Invariant Region ◽  
Binary Descriptors ◽  
Irregular Pattern ◽  
Matching Performance ◽  
High Computational Complexity

As the current binary descriptors have disadvantages of high computational complexity, no affine invariance, and the high false matching rate with viewpoint changes, a new binary affine invariant descriptor, called BAND, is proposed. Different from other descriptors, BAND has an irregular pattern, which is based on local affine invariant region surrounding a feature point, and it has five orientations, which are obtained by LBP effectively. Ultimately, a 256 bits binary string is computed by simple random sampling pattern. Experimental results demonstrate that BAND has a good matching result in the conditions of rotating, image zooming, noising, lighting, and small-scale perspective transformation. It has better matching performance compared with current mainstream descriptors, while it costs less time.

Within-person variability can improve the identification of unfamiliar faces across changes in viewpoint

2021 ◽  
pp. 174702182110097
Author(s):  
Niamh Hunnisett ◽  
Simone Favelle
Keyword(s):  
Image Matching ◽  
Matching Task ◽  
Target Image ◽  
Front View ◽  
Multiple Image ◽  
Profile View ◽  
Unfamiliar Face ◽  
Matching Performance ◽  
Sequential Matching ◽  
Unfamiliar Faces

Unfamiliar face identification is concerningly error prone, especially across changes in viewing conditions. Within-person variability has been shown to improve matching performance for unfamiliar faces, but this has only been demonstrated using images of a front view. In this study, we test whether the advantage of within-person variability from front views extends to matching to target images of a face rotated in view. Participants completed either a simultaneous matching task (Experiment 1) or a sequential matching task (Experiment 2) in which they were tested on their ability to match the identity of a face shown in an array of either one or three ambient front-view images, with a target image shown in front, three-quarter, or profile view. While the effect was stronger in Experiment 2, we found a consistent pattern in match trials across both experiments in that there was a multiple image matching benefit for front, three-quarter, and profile-view targets. We found multiple image effects for match trials only, indicating that providing observers with multiple ambient images confers an advantage for recognising different images of the same identity but not for discriminating between images of different identities. Signal detection measures also indicate a multiple image advantage despite a more liberal response bias for multiple image trials. Our results show that within-person variability information for unfamiliar faces can be generalised across views and can provide insights into the initial processes involved in the representation of familiar faces.

scholarly journals An affine invariant approach for dense wide baseline image matching

2016 ◽  
Vol 12 (12) ◽  
pp. 155014771668082
Author(s):  
Fanhuai Shi ◽  
Jian Gao ◽  
Xixia Huang
Keyword(s):  
Image Matching ◽  
Intelligent Systems ◽  
Region Growing ◽  
Low Rank ◽  
Affine Invariant ◽  
Affine Transformations ◽  
Visual Sensor ◽  
Dense Matching ◽  
Rank Matrix ◽  
Distributed Intelligent Systems

Visual sensor networks have emerged as an important class of sensor-based distributed intelligent systems, where image matching is one of the key technologies. This article presents an affine invariant method to produce dense correspondences between uncalibrated wide baseline images. Under affine transformations, both point location and its neighborhood texture are changed between views, so dense matching becomes a tough task. The proposed approach tends to solve this problem within a sparse-to-dense framework. The contribution of this article is in threefolds. First, a strategy of reliable sparse matching is proposed, which starts from affine invariant features extraction and matching and then these initial matches are utilized as spatial prior to produce more sparse matches. Second, match propagation from sparse feature points to its neighboring pixels is conducted in the way of region growing in an affine invariant framework. Third, the unmatched points are handled by low-rank matrix recovery technique. Comparison experiments of the proposed method versus existing ones show a significant improvement in the presence of large affine deformations.

scholarly journals Robust Multisensor Image Matching Using Bayesian Estimated Mutual Information

2013 ◽  
Vol 2013 ◽  
pp. 1-11
Author(s):  
Lurong Shen ◽  
Xinsheng Huang ◽  
Yuzhuang Yan ◽  
Yongbin Zheng ◽  
Wanying Xu
Keyword(s):  
Mutual Information ◽  
Cost Saving ◽  
Image Matching ◽  
Prior Information ◽  
Kernel Density ◽  
Density Estimate ◽  
Kernel Density Estimate ◽  
Template Image ◽  
Matching Performance

Mutual information (MI) has been widely used in multisensor image matching, but it may lead to mismatch among images with messy background. However, additional prior information can be of great help in improving the matching performance. In this paper, a robust Bayesian estimated mutual information, named as BMI, for multisensor image matching is proposed. This method has been implemented by utilizing the gradient prior information, in which the prior is estimated by the kernel density estimate (KDE) method, and the likelihood is modeled according to the distance of orientations. To further improve the robustness, we restrict the matching within the regions where the corresponding pixels of template image are salient enough. Experiments on several groups of multisensor images show that the proposed method outperforms the standard MI in robustness and accuracy and is similar with Pluim’s method. However, our computation is far more cost saving.

scholarly journals An Assessment of Entrepreneurial Affinity in Bangladesh: A Comparative Analysis between Small Live Entrepreneurs and Potential Entrepreneurs of University Level Students

2016 ◽  
Vol 10 (1) ◽  
pp. 1-20
Author(s):  
Serena Aktar
Keyword(s):  
Family Business ◽  
Risk Taking ◽  
Random Sampling ◽  
Sampling Technique ◽  
Simple Random Sampling ◽  
Influential Factors ◽  
Influential Factor ◽  
Small Scale ◽  
Factors Influencing ◽  
University Level

This is an empirical and quantitative study conducted on small scale live entrepreneurs and potential entrepreneurs of university level students of Bangladesh. The main purpose of this study is to identify and examine the factors influencing decision of becoming an entrepreneur. For fulfilling the study purpose, by using simple random sampling technique a total of 600 questionnaires were administered; 300 were distributed to the students who were interested to become entrepreneurs and 300 questionnaires were also distributed to small scale live entrepreneurs who formed their business during the last two years and more. Data were analyzed according to objectivity. The results indicated that need for achievement is highly influential factor in picking up decision of becoming an entrepreneur of potential entrepreneurs of university level students and family business background is the main influential factor in taking decision of becoming an entrepreneur of the small scale live entrepreneurs. Parallel factors, e.g., locus of control, risk taking propensity and proactive personality also acted as the influential factors of creating entrepreneurial affinity in both of them.Journal of Business and Technology (Dhaka) Vol.10(1) 2015; 1-20

scholarly journals A 3D Reconstruction Pipeline of Urban Drainage Pipes Based on MultiviewImage Matching Using Low-Cost Panoramic Video Cameras

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2101 ◽  
Author(s):  
Zhang ◽  
Zhao ◽  
Hu ◽  
Wang ◽  
Ai ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
High Precision ◽  
Image Matching ◽  
Low Cost ◽  
Geometric Distortion ◽  
Urban Drainage ◽  
Affine Invariant ◽  
Pipe Inspection ◽  
Video Cameras ◽  
Panoramic Video

Urban drainage pipe networks have complex spatial contributions, andthey are now facing problems such as damage, defects, and aging. A rapid and high-precision pipe inspection strategy is thekey to ensuring thesustainable development of urban water supply and drainage system. In this paper, a three-dimensional (3D) reconstruction pipeline of urban drainage pipes based on multiview image matching using low-cost panoramic video cameras is proposed, which provides an innovative technical approach for pipe inspection. Firstly, we extracted frames from the panoramic video of the pipes andcorrected the geometric distortion using a spherical reprojection to obtain multiview pipe images. Second, the robust feature matching method using support lines and affine-invariant ratios isintroduced to conduct pipe image matching. Finally, the photogrammetric processing, using structure from motion (SfM) and dense reconstruction, wasintroduced to achieve the 3D modeling of drainage pipes. Several typical drainage pipes and shafts of the real scenes were taken for the 3D reconstruction experiments. Theresults show that our strategy can realize high-precision 3D reconstruction of different types of pipes, which can provide effective technical support for rapid and efficient inspection of urban pipes with broad application prospects in the daily management of sustainable urban drainage systems (SUDSs).

scholarly journals Farmers' Attitudes toward Public Support Policy for Sustainable Agriculture in GAP-Şanlıurfa, Turkey

2019 ◽  
Vol 11 (23) ◽  
pp. 6617 ◽  
Author(s):  
Sevinç ◽  
Aydoğdu ◽  
Cançelik ◽  
Sevinç
Keyword(s):  
Sustainable Agriculture ◽  
Public Support ◽  
Simple Random Sampling ◽  
Irrigated Agriculture ◽  
Small Scale ◽  
Factors Affecting ◽  
Property Type ◽  
Support Policy ◽  
Agricultural Support ◽  
Small Scale Farmers

Despite agricultural support in Turkey, agricultural production areas, production quantities, and the number of farmers have gradually decreased. In this research, we aimed to determine farmers’ attitudes toward public agricultural support policy for sustainability in GAP, Şanlıurfa, Turkey, and the factors affecting their attitudes. This research is the first of its type for GAP, Şanlıurfa, Turkey. The data were obtained in 2017 from face-to-face interviews with farmers who were selected using the simple random sampling method. Categorical regression, based on the optimal scaling model, was used in the analysis. The results indicate that although 80% of the farmers believe that support has improved agricultural sustainability, 76.2% find public support policy inadequate. The average land area of those who were in favor of the policy was 18.3 hectares, whereas that of those who stated that support does not provide a significant contribution was 7.17 hectares. The age of the farmer, total cultivated area, settlement area, education level, property type, crop pattern, irrigated agriculture, and income were factors affecting farmers’ attitudes. The support policy should be reviewed for small-scale farmers and farmers who engage in dry farming. The results could be helpful to support policy and decision-makers during sustainable agriculture policy planning.

A New Image Affine-Invariant Region Detector and Descriptor

2011 ◽  
Vol 130-134 ◽  
pp. 2911-2914
Author(s):  
Xiu Xin Chen ◽  
Ke Bin Jia ◽  
Chong Chong Yu ◽  
Shiang Wei
Keyword(s):  
Connected Region ◽  
Affine Invariant ◽  
Invariant Region ◽  
Region Detection ◽  
Invariant Moments ◽  
Invariant Regions

To solve the problems that exist in present affine-invariant region detection and description methods, a new affine-invariant region detector and descriptor are proposed in this paper. First, affine-invariant regions in an image are detected using a connected-region based method. And then a vector composed of a group of affine invariant moments is adopted to descript the regions. Experiments show the effectiveness and robustness of the method. It is also very fast.

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