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Article
Publication date: 21 March 2016

Tao Liu, Zhixiang Fang, Qingzhou Mao, Qingquan Li and Xing Zhang

The spatial feature is important for scene saliency detection. Scene-based visual saliency detection methods fail to incorporate 3D scene spatial aspects. This paper aims…

Abstract

Purpose

The spatial feature is important for scene saliency detection. Scene-based visual saliency detection methods fail to incorporate 3D scene spatial aspects. This paper aims to propose a cube-based method to improve saliency detection through integrating visual and spatial features in 3D scenes.

Design/methodology/approach

In the presented approach, a multiscale cube pyramid is used to organize the 3D image scene and mesh model. Each 3D cube in this pyramid represents a space unit similar to a pixel in the image saliency model multiscale image pyramid. In each 3D cube color, intensity and orientation features are extracted from the image and a quantitative concave–convex descriptor is extracted from the 3D space. A Gaussian filter is then used on this pyramid of cubes with an extended center-surround difference introduced to compute the cube-based 3D scene saliency.

Findings

The precision-recall rate and receiver operating characteristic curve is used to evaluate the method and other state-of-art methods. The results show that the method used is better than traditional image-based methods, especially for 3D scenes.

Originality/value

This paper presents a method that improves the image-based visual saliency model.

Details

Sensor Review, vol. 36 no. 2
Type: Research Article
ISSN: 0260-2288

Keywords

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Article
Publication date: 1 August 2016

Chunlei Li, Ruimin Yang, Zhoufeng Liu, Guangshuai Gao and Qiuli Liu

Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm using learned…

Abstract

Purpose

Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm using learned dictionary-based visual saliency.

Design/methodology/approach

First, the test fabric image is splitted into image blocks, and the learned dictionary with normal samples and defective sample is constructed by selecting the image block local binary pattern features with highest or lowest similarity comparing with the average feature vector; second, the first L largest correlation coefficients between each test image block and the dictionary are calculated, and other correlation coefficients are set to zeros; third, the sum of the non-zeros coefficients corresponding to defective samples is used to generate saliency map; finally, an improve valley-emphasis method can efficiently segment the defect region.

Findings

Experimental results demonstrate that the generated saliency map by the proposed method can efficiently outstand defect region comparing with the state-of-the-art, and segment results can precisely localize defect region.

Originality/value

In this paper, a novel fabric defect detection scheme is proposed via learned dictionary-based visual saliency.

Details

International Journal of Clothing Science and Technology, vol. 28 no. 4
Type: Research Article
ISSN: 0955-6222

Keywords

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Article
Publication date: 7 September 2015

Zhoufeng Liu, Chunlei Li, Quanjun Zhao, Liang Liao and Yan Dong

Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm via context-based…

Abstract

Purpose

Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm via context-based local texture saliency analysis.

Design/methodology/approach

In the proposed algorithm, a target image is first divided into blocks, then the Local Binary Pattern (LBP) technique is used to extract the texture features of blocks. Second, for a given image block, several other blocks are randomly chosen for calculating the LBP contrast between a given block and the randomly chosen blocks. Based on the obtained contrast information, a saliency map is produced. Finally, saliency map is segmented by using an optimal threshold, which is obtained by an iterative approach.

Findings

The experimental results show that the proposed algorithm, integrating local texture features and global image texture information, can detect texture defects effectively.

Originality/value

In this paper, a novel fabric defect detection algorithm via context-based local texture saliency analysis is proposed.

Details

International Journal of Clothing Science and Technology, vol. 27 no. 5
Type: Research Article
ISSN: 0955-6222

Keywords

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Article
Publication date: 15 April 2020

Xiaoliang Qian, Jing Li, Jianwei Zhang, Wenhao Zhang, Weichao Yue, Qing-E Wu, Huanlong Zhang, Yuanyuan Wu and Wei Wang

An effective machine vision-based method for micro-crack detection of solar cell can economically improve the qualified rate of solar cells. However, how to extract…

Abstract

Purpose

An effective machine vision-based method for micro-crack detection of solar cell can economically improve the qualified rate of solar cells. However, how to extract features which have strong generalization and data representation ability at the same time is still an open problem for machine vision-based methods.

Design/methodology/approach

A micro-crack detection method based on adaptive deep features and visual saliency is proposed in this paper. The proposed method can adaptively extract deep features from the input image without any supervised training. Furthermore, considering the fact that micro-cracks can obviously attract visual attention when people look at the solar cell’s surface, the visual saliency is also introduced for the micro-crack detection.

Findings

Comprehensive evaluations are implemented on two existing data sets, where subjective experimental results show that most of the micro-cracks can be detected, and the objective experimental results show that the method proposed in this study has better performance in detecting precision.

Originality/value

First, an adaptive deep features extraction scheme without any supervised training is proposed for micro-crack detection. Second, the visual saliency is introduced for micro-crack detection.

Details

Sensor Review, vol. 40 no. 4
Type: Research Article
ISSN: 0260-2288

Keywords

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Article
Publication date: 13 November 2018

Guoqing Li, Yunhai Geng and Wenzheng Zhang

This paper aims to introduce an efficient active-simultaneous localization and mapping (SLAM) approach for rover navigation, future planetary rover exploration mission…

Abstract

Purpose

This paper aims to introduce an efficient active-simultaneous localization and mapping (SLAM) approach for rover navigation, future planetary rover exploration mission requires the rover to automatically localize itself with high accuracy.

Design/methodology/approach

A three-dimensional (3D) feature detection method is first proposed to extract salient features from the observed point cloud, after that, the salient features are employed as the candidate destinations for re-visiting under SLAM structure, followed by a path planning algorithm integrated with SLAM, wherein the path length and map utility are leveraged to reduce the growth rate of state estimation uncertainty.

Findings

The proposed approach is able to extract distinguishable 3D landmarks for feature re-visiting, and can be naturally integrated with any SLAM algorithms in an efficient manner to improve the navigation accuracy.

Originality/value

This paper proposes a novel active-SLAM structure for planetary rover exploration mission, the salient feature extraction method and active revisit patch planning method are validated to improve the accuracy of pose estimation.

Details

Aircraft Engineering and Aerospace Technology, vol. 91 no. 1
Type: Research Article
ISSN: 1748-8842

Keywords

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Article
Publication date: 16 March 2020

Chunlei Li, Chaodie Liu, Zhoufeng Liu, Ruimin Yang and Yun Huang

The purpose of this paper is to focus on the design of automated fabric defect detection based on cascaded low-rank decomposition and to maintain high quality control in…

Abstract

Purpose

The purpose of this paper is to focus on the design of automated fabric defect detection based on cascaded low-rank decomposition and to maintain high quality control in textile manufacturing.

Design/methodology/approach

This paper proposed a fabric defect detection algorithm based on cascaded low-rank decomposition. First, the constructed Gabor feature matrix is divided into a low-rank matrix and sparse matrix using low-rank decomposition technique, and the sparse matrix is used as priori matrix where higher values indicate a higher probability of abnormality. Second, we conducted the second low-rank decomposition for the constructed texton feature matrix under the guidance of the priori matrix. Finally, an improved adaptive threshold segmentation algorithm was adopted to segment the saliency map generated by the final sparse matrix to locate the defect regions.

Findings

The proposed method was evaluated on the public fabric image databases. By comparing with the ground-truth, the average detection rate of 98.26% was obtained and is superior to the state-of-the-art.

Originality/value

The cascaded low-rank decomposition was first proposed and applied into the fabric defect detection. The quantitative value shows the effectiveness of the detection method. Hence, the proposed method can be used for accurate defect detection and automated analysis system.

Details

International Journal of Clothing Science and Technology, vol. 32 no. 4
Type: Research Article
ISSN: 0955-6222

Keywords

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Article
Publication date: 14 August 2017

Ning Xian

The purpose of this paper is to propose a new algorithm chaotic pigeon-inspired optimization (CPIO), which can effectively improve the computing efficiency of the basic…

Abstract

Purpose

The purpose of this paper is to propose a new algorithm chaotic pigeon-inspired optimization (CPIO), which can effectively improve the computing efficiency of the basic Itti’s model for saliency-based detection. The CPIO algorithm and relevant applications are aimed at air surveillance for target detection.

Design/methodology/approach

To compare the improvements of the performance on Itti’s model, three bio-inspired algorithms including particle swarm optimization (PSO), brain storm optimization (BSO) and CPIO are applied to optimize the weight coefficients of each feature map in the saliency computation.

Findings

According to the experimental results in optimized Itti’s model, CPIO outperforms PSO in terms of computing efficiency and is superior to BSO in terms of searching ability. Therefore, CPIO provides the best overall properties among the three algorithms.

Practical implications

The algorithm proposed in this paper can be extensively applied for fast, accurate and multi-target detections in aerial images.

Originality/value

CPIO algorithm is originally proposed, which is very promising in solving complicated optimization problems.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 10 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

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Article
Publication date: 15 May 2017

Dong Liu, Ming Cong, Yu Du, Qiang Zou and Yingxue Cui

This paper aims to focus on the autonomous behavior selection issue of robotics from the perspective of episodic memory in cognitive neuroscience with biology-inspired…

Abstract

Purpose

This paper aims to focus on the autonomous behavior selection issue of robotics from the perspective of episodic memory in cognitive neuroscience with biology-inspired attention system. It instructs a robot to follow a sequence of behaviors. This is similar to human travel to a target location by guidance.

Design/methodology/approach

The episodic memory-driving Markov decision process is proposed to simulate the organization of episodic memory by introducing neuron stimulation mechanism. Based on the learned episodic memory, the robotic global planning method is proposed for efficient behaviors sequence prediction using bottom-up attention. Local behavior planning based on risk function and feasible paths is used for behavior reasoning under imperfect memory. Aiming at the problem of whole target selection under redundant environmental information, a top-down attention servo control method is proposed to effectively detect the target containing multi-parts and distractors which share same features with the target.

Findings

Based on the proposed method, the robot is able to accumulate experience through memory, and achieve adaptive behavior planning, prediction and reasoning between tasks, environment and threats. Experimental results show that the method can balance the task objectives, select the suitable behavior according to current environment.

Originality/value

The behavior selection method is integrated with cognitive levels to generate optimal behavioral sequence. The challenges in robotic planning under uncertainty and the issue of target selection under redundant environment are addressed.

Details

Industrial Robot: An International Journal, vol. 44 no. 3
Type: Research Article
ISSN: 0143-991X

Keywords

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Article
Publication date: 1 April 2008

Charles A. Pierce, Ivan S. Muslin, Chantay M. Dudley and Herman Aguinis

We reviewed U.S. federal and state sexual harassment court cases involving a prior workplace romance between the plaintiff and alleged harasser. Results of our content…

Abstract

We reviewed U.S. federal and state sexual harassment court cases involving a prior workplace romance between the plaintiff and alleged harasser. Results of our content analysis show that, unlike employees’ decisions, judges’ decisions can be predicted from legal but not ethically salient extralegal case features. Hence, when compared to prior research, our study reveals the following discrepancy: judges follow a traditional legal model, whereas employees follow an ethical model when making decisions about romance‐harassment cases. Our study also reveals that the mere presence (versus absence) of a prior romance reduces the likelihood of a plaintiff’s success in a harassment case. We discuss implications for management practice and research from the perspective of legal and ethical decision making.

Details

Management Research: Journal of the Iberoamerican Academy of Management, vol. 6 no. 1
Type: Research Article
ISSN: 1536-5433

Keywords

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Article
Publication date: 1 May 1996

Malcolm Smith and Richard Taffler

There has been limited study to date of the effectiveness of alternative methods of presenting accounting information for financial decision purposes. Explores the…

Abstract

There has been limited study to date of the effectiveness of alternative methods of presenting accounting information for financial decision purposes. Explores the relative usefulness of the schematic face, compared with conventional presentation formats, for communicating the multivariate information set conveyed in a set of financial statements. Compares the ability of accounting statement users of different levels of sophistication to “predict” bankruptcy and financial health of companies on the basis of cartoon faces, accounting statements and financial ratios. Presents evidence that the schematic representations are processed more quickly than either of the more traditional methods of information presentation, and with no loss of accuracy, by all three different types of user examined. Concludes by arguing the potential generalizability of the cartoon graphic approach to more complex financial decision applications, such as those in banking and financial analysis.

Details

Accounting, Auditing & Accountability Journal, vol. 9 no. 2
Type: Research Article
ISSN: 0951-3574

Keywords

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