A Flexible Method for Reordering Feature Points on a Planar Target

2014 ◽  
Vol 543-547 ◽  
pp. 1179-1183
Author(s):  
Feng Jiao Li ◽  
Xiao Jing Li ◽  
Zhen Liu
Keyword(s):  
Machine Vision ◽  
Image Plane ◽  
Cross Ratio ◽  
Experimental Results ◽  
Feature Points ◽  
Foreign Objects ◽  
Novel Method ◽  
Planar Target

Planar targets have been widely used in the field of machine vision, and reordering the feature points on a planar target is of great difficulties and importance. As the current methods for that are of poor robustness, and are easily interfered by foreign objects or the image background, a novel method, which is of high versatility and is not easily affected by the interferences, is proposed in the paper. Cross ratio invariance and homographic relationship between the target plane and the image plane are utilized in the method. Experimental results show that the method is viable and robust to realize precise reordering of the feature points on planar targets in complex site environment.

Feature Points Extraction of Chinese Characters Based on Blob Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 4214-4219
Author(s):  
Xiao Yin Huang ◽  
Ning Fang
Keyword(s):  
Essential Role ◽  
Chinese Character ◽  
Experimental Results ◽  
Chinese Characters ◽  
Feature Points ◽  
Structural Elements ◽  
End Points ◽  
Novel Method ◽  
The Cross ◽  
Thinning Process

The feature points extraction plays an essential role in modeling structure of Chinese characters for accurate recognition. The distortions caused by the thinning process make the thinning-based stroke segmentation difficult for extracting feature points and handle the ambiguous intersection regions. This paper proposes a novel method that overcomes the distortions to extract feature points from the thinned Chinese characters. Firstly, we use eight structural elements to thin a Chinese character in a series combined way, and then segment different kinds of strokes according to their properties after thinning preprocessing. Finally we draw the end points of every stroke based on the Blob algorithm and obtain the cross information at the same time. The feature points will be abstracted fast and exactly. The experimental results show that the proposed method is effective for Chinese characters recognition (CCR).

A novel method based on machine vision system and deep learning to detect fraud in turmeric powder

2021 ◽  
pp. 104728
Author(s):  
Ahmad Jahanbakhshi ◽  
Yousef Abbaspour-Gilandeh ◽  
Kobra Heidarbeigi ◽  
Mohammad Momeny
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Vision System ◽  
Machine Vision System ◽  
Novel Method

scholarly journals Obstacle Detection in Cluttered Traffic Environment Based on Candidate Generation and Classification

2006 ◽  
Vol 1 (4) ◽  
pp. 93 ◽  
Author(s):  
Qing Li ◽  
F.C. Sun
Keyword(s):  
Vehicle Detection ◽  
Obstacle Detection ◽  
Experimental Results ◽  
Geometrical Features ◽  
Traffic Environment ◽  
Novel Method ◽  
Shadow Cast

A novel method to detect vehicles is presented in the paper. Assumption of the vehicle is made using the geometrical features of the vehicle rear by the statistical histogram. Then hypothesis is verified using the property of the shadow cast by the car according to a prior acknowledgement of traffic scene. Finally, the vehicle detection is realized by hypothesis and verification of objects. The experimental results show the efficiency and feasibility of the method.

scholarly journals Handwritten tifinagh character recognition using simple geometric shapes and graphs

2019 ◽  
Vol 13 (2) ◽  
pp. 598
Author(s):  
Youssef Ouadid ◽  
Abderrahmane Elbalaoui ◽  
Mehdi Boutaounte ◽  
Mohamed Fakir ◽  
Brahim Minaoui
Keyword(s):  
Character Recognition ◽  
Graph Matching ◽  
Recognition Rate ◽  
Recognition System ◽  
Features Extraction ◽  
Experimental Results ◽  
Feature Points ◽  
Matching Method ◽  
Extraction Algorithm ◽  
Novel Algorithm

<p>In this paper, a graph based handwritten Tifinagh character recognition system is presented. In preprocessing Zhang Suen algorithm is enhanced. In features extraction, a novel key point extraction algorithm is presented. Images are then represented by adjacency matrices defining graphs where nodes represent feature points extracted by a novel algorithm. These graphs are classified using a graph matching method. Experimental results are obtained using two databases to test the effectiveness. The system shows good results in terms of recognition rate.</p>

Distributions of projective invariants and model-based machine vision

1996 ◽  
Vol 28 (03) ◽  
pp. 641-661 ◽  
Author(s):  
K. V. Mardia ◽  
Colin Goodall ◽  
Alistair Walder
Keyword(s):  
Machine Vision ◽  
Projective Transformation ◽  
Likelihood Estimation ◽  
Cauchy Distribution ◽  
Projective Line ◽  
Cross Ratio ◽  
Projective Invariants ◽  
Inference Problems ◽  
The Cross ◽  
Insight Into

In machine vision, objects are observed subject to an unknown projective transformation, and it is usual to use projective invariants for either testing for a false alarm or for classifying an object. For four collinear points, the cross-ratio is the simplest statistic which is invariant under projective transformations. We obtain the distribution of the cross-ratio under the Gaussian error model with different means. The case of identical means, which has appeared previously in the literature, is derived as a particular case. Various alternative forms of the cross-ratio density are obtained, e.g. under the Casey arccos transformation, and under an arctan transformation from the real projective line of cross-ratios to the unit circle. The cross-ratio distributions are novel to the probability literature; surprisingly various types of Cauchy distribution appear. To gain some analytical insight into the distribution, a simple linear-ratio is also introduced. We also give some results for the projective invariants of five coplanar points. We discuss the general moment properties of the cross-ratio, and consider some inference problems, including maximum likelihood estimation of the parameters.

Novel Algorithmic Approach to Deciphering Rovash Inscriptions

2015 ◽  
pp. 7222-7233 ◽  
Author(s):  
Loránd Lehel Tóth ◽  
Raymond Pardede ◽  
Gábor Hosszú
Keyword(s):  
Experimental Results ◽  
The Novel ◽  
Statistical Probability ◽  
Algorithmic Approach ◽  
Topological Parameters ◽  
Novel Method ◽  
Novel Algorithm

The article presents a method to decipher Rovash inscriptions made by the Szekelys in the 15th-18th centuries. The difficulty of the deciphering work is that a large portion of the Rovash inscriptions contains incomplete words, calligraphic glyphs or grapheme errors. Based on the topological parameters of the undeciphered symbols registered in the database, the presented novel algorithm estimates the meaning of the inscriptions by the matching accuracies of the recognized graphemes and gives a statistical probability for deciphering. The developed algorithm was implemented in software, which also contains a built-in dictionary. Based on the dictionary, the novel method takes into account the context in identifying the meaning of the inscription. The proposed algorithm offers one or more words in a different random values as a result, from which users can select the relevant one. The article also presents experimental results, which demonstrate the efficiency of method.

scholarly journals Hierarchical Classification Based on Label Distribution Learning

2019 ◽  
Vol 33 ◽  
pp. 5533-5540 ◽  
Author(s):  
Changdong Xu ◽  
Xin Geng
Keyword(s):  
Hierarchical Classification ◽  
Experimental Results ◽  
Challenging Problem ◽  
Training Set ◽  
True Label ◽  
Major Bottleneck ◽  
Novel Method ◽  
Class Labels ◽  
Label Distribution ◽  
The Given

Hierarchical classification is a challenging problem where the class labels are organized in a predefined hierarchy. One primary challenge in hierarchical classification is the small training set issue of the local module. The local classifiers in the previous hierarchical classification approaches are prone to over-fitting, which becomes a major bottleneck of hierarchical classification. Fortunately, the labels in the local module are correlated, and the siblings of the true label can provide additional supervision information for the instance. This paper proposes a novel method to deal with the small training set issue. The key idea of the method is to represent the correlation among the labels by the label distribution. It generates a label distribution that contains the supervision information of each label for the given instance, and then learns a mapping from the instance to the label distribution. Experimental results on several hierarchical classification datasets show that our method significantly outperforms other state-of-theart hierarchical classification approaches.

scholarly journals Personalized Clothing Recommendation Based on User Emotional Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xueping Su ◽  
Meng Gao ◽  
Jie Ren ◽  
Yunhong Li ◽  
Matthias Rätsch
Keyword(s):  
Facial Expression ◽  
Recommendation System ◽  
Experimental Results ◽  
Personalized Recommendation ◽  
Continuous Development ◽  
Expression Intensity ◽  
Novel Method ◽  
Multiclass Svm

With the continuous development of economy, consumers pay more attention to the demand for personalization clothing. However, the recommendation quality of the existing clothing recommendation system is not enough to meet the user’s needs. When browsing online clothing, facial expression is the salient information to understand the user’s preference. In this paper, we propose a novel method to automatically personalize clothing recommendation based on user emotional analysis. Firstly, the facial expression is classified by multiclass SVM. Next, the user’s multi-interest value is calculated using expression intensity that is obtained by hybrid RCNN. Finally, the multi-interest value is fused to carry out personalized recommendation. The experimental results show that the proposed method achieves a significant improvement over other algorithms.

Efficient Multi-Scale Registration of 3D Reconstructions Based on Camera Center Constraint

2014 ◽  
Vol 998-999 ◽  
pp. 1018-1023
Author(s):  
Rui Bin Guo ◽  
Tao Guan ◽  
Dong Xiang Zhou ◽  
Ke Ju Peng ◽  
Wei Hong Fan
Keyword(s):  
Experimental Results ◽  
Cayley Transform ◽  
3D Reconstructions ◽  
Multi Scale ◽  
The Matrix ◽  
Novel Method ◽  
3D Scene ◽  
Coordinates Transformation

Recent approaches for reconstructing 3D scenes from image collections only produce single scene models. To build a unified scene model that contains multiple subsets, we present a novel method for registration of 3D scene reconstructions in different scales. It first normalizes the scales of the models building on similarity reconstruction by the constraint of the 3D position of shared cameras. Then we use Cayley transform to fit the matrix of coordinates transformation for the models in normalization scales. The experimental results show the effectiveness and scalability of the proposed approach.

The Appraisal of Velcro’s Surface Quality Based on Machine Vision Systems

2011 ◽  
Vol 201-203 ◽  
pp. 2045-2048
Author(s):  
Da Xing Zhao ◽  
Qing Lin
Keyword(s):  
Machine Vision ◽  
Product Quality ◽  
Surface Quality ◽  
Detection Method ◽  
Experimental Results ◽  
Vision Systems ◽  
Examination Method ◽  
The Common ◽  
Develop Environment ◽  
Common Defect

The most important problem of the Velcro Manufacturer face is to control the surface quality, and how to improve the product quality has become the key of the enterprise. Therefore, this paper take the research on the examination method of the Velcro’s surface flaw, and propose a simply and effectively detection method on the marginal check and the flaw extraction of the buckle in the considering of the system’s real-team and the effectiveness. The experiments have been carried on the results been analyzed under the Visual c + + develop environment. Experimental results show that the system can detect the common defect of the fastening surface accurately and classify them.

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