Alphabet Recognition Based on Computer Vision

2014 ◽  
Vol 543-547 ◽  
pp. 2354-2357
Author(s):  
Hui Zhou
Keyword(s):  
Computer Vision ◽  
Experimental Results ◽  
Correct Match ◽  
Test Image ◽  
Recognition Method

In order to realize rapid alphabet recognition, the paper proposes an alphabet recognition method based on computer vision optimization technical which can also extract the classification features. Experimental results show that the obtained variance value of the test image and the standard image obtained by the proposed method is the minimum which indicating the method can achieve correct match, effective classification, and provide a great method of identification.

A New Method for Railway Fastener Detection Using the Symmetrical Image and Its EA-HOG Feature

2019 ◽  
Vol 34 (02) ◽  
pp. 2055006
Author(s):  
Jiajia Liu ◽  
Jianying Yuan ◽  
Yongfang Jia
Keyword(s):  
Computer Vision ◽  
Sparse Representation ◽  
Efficient Method ◽  
Important Task ◽  
New Method ◽  
Automatic Inspection ◽  
Test Image ◽  
Recognition Result ◽  
The Status ◽  
Railway Operation

Railway fastener recognition and detection is an important task for railway operation safety. However, the current automatic inspection methods based on computer vision can effectively detect the intact or completely missing fasteners, but they have weaker ability to recognize the partially worn ones. In our method, we exploit the EA-HOG feature fastener image, generate two symmetrical images of original test image and turn the detection of the original test image into the detection of two symmetrical images, then integrate the two recognition results of symmetrical image to reach exact recognition of original test image. The potential advantages of the proposed method are as follows: First, we propose a simple yet efficient method to extract the fastener edge, as well as the EA-HOG feature of the fastener image. Second, the symmetry images indeed reflect some possible appearance of the fastener image which are not shown in the original images, these changes are helpful for us to judge the status of the symmetry samples based on the improved sparse representation algorithm and then obtain an exact judgment of the original test image by combining the two corresponding judgments of its symmetry images. The experiment results show that the proposed approach achieves a rather high recognition result and meets the demand of railway fastener detection.

scholarly journals Random Forest with Adaptive Local Template for Pedestrian Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Xiang ◽  
Tao Li ◽  
Mao Ye ◽  
Zijian Liu
Keyword(s):  
Computer Vision ◽  
Random Forest ◽  
Classification Accuracy ◽  
Template Matching ◽  
Detection Method ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Sliding Window ◽  
Experimental Results ◽  
Training Samples

Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well-known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance.

Multi-Resolution-Based Contour Corner Extraction Algorithm for Computer Vision-Based Measurement

2018 ◽  
Author(s):  
Jianhua Li ◽  
Lin Liao
Keyword(s):  
Computer Vision ◽  
Experimental Results ◽  
Original Algorithm ◽  
Industry Standard ◽  
Extraction Algorithm ◽  
Corner Points ◽  
Multiple Resolutions ◽  
Key Steps

Corner-based registration of the industry standard contour and the actual product contour is one of the key steps in industrial computer vision-based measurement. However, existing corner extraction algorithms do not achieve satisfactory results in the extraction of the standard contour and the deformed contour of the actual product. This paper proposes a multi-resolution-based contour corner extraction algorithm for computer vision-based measurement. The algorithm first obtains different corners in multiple resolutions, then sums up the weighted corner values, and finally chooses the corner points with the appropriate corner values as the final contour corners. The experimental results show that the proposed algorithm, based on multi-resolution, outperforms the original algorithm in the aspect of the corner matching situation and helps in subsequent product measurements.

Combining Block DCV and Support Vector Machine for Ear Recognition

2016 ◽  
Vol 8 (2) ◽  
pp. 36-44 ◽  
Author(s):  
Zhao Hailong ◽  
Yi Junyan
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Construction Method ◽  
Experimental Results ◽  
Support Vector ◽  
Recognition Method ◽  
Ear Recognition ◽  
Human Ear ◽  
Better Than

In recent years, automatic ear recognition has become a popular research. Effective feature extraction is one of the most important steps in Content-based ear image retrieval applications. In this paper, the authors proposed a new vectors construction method for ear retrieval based on Block Discriminative Common Vector. According to this method, the ear image is divided into 16 blocks firstly and the features are extracted by applying DCV to the sub-images. Furthermore, Support Vector Machine is used as classifier to make decision. The experimental results show that the proposed method performs better than classical PCA+LDA, so it is an effective human ear recognition method.

scholarly journals Image Retrieval Based on Fractal Dictionary Parameters

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Yuanyuan Sun ◽  
Rudan Xu ◽  
Lina Chen ◽  
Xiaopeng Hu
Keyword(s):  
Computer Vision ◽  
Image Compression ◽  
Image Retrieval ◽  
Experimental Results ◽  
Content Based Image Retrieval ◽  
Efficient Management ◽  
Retrieval Rate ◽  
Statistical Indices ◽  
Visual Database

Content-based image retrieval is a branch of computer vision. It is important for efficient management of a visual database. In most cases, image retrieval is based on image compression. In this paper, we use a fractal dictionary to encode images. Based on this technique, we propose a set of statistical indices for efficient image retrieval. Experimental results on a database of 416 texture images indicate that the proposed method provides a competitive retrieval rate, compared to the existing methods.

scholarly journals NICE: Superpixel Segmentation Using Non-Iterative Clustering with Efficiency

2020 ◽  
Vol 10 (12) ◽  
pp. 4415 ◽  
Author(s):  
Cheng Li ◽  
Baolong Guo ◽  
Geng Wang ◽  
Yan Zheng ◽  
Yang Liu ◽  
...  
Keyword(s):  
Computer Vision ◽  
Computational Efficiency ◽  
Batch Processing ◽  
Experimental Results ◽  
Processing Strategy ◽  
Trade Off ◽  
Quantitative Metrics ◽  
Visual Tasks ◽  
Pixel Correlation ◽  
Region Description

Superpixels intuitively over-segment an image into small compact regions with homogeneity. Owing to its outstanding performance on region description, superpixels have been widely used in various computer vision tasks as the substitution for pixels. Therefore, efficient algorithms for generating superpixels are still important for advanced visual tasks. In this work, two strategies are presented on conventional simple non-iterative clustering (SNIC) framework, aiming to improve the computational efficiency as well as segmentation performance. Firstly, inter-pixel correlation is introduced to eliminate the redundant inspection of neighboring elements. In addition, it strengthens the color identity in complicated texture regions, thus providing a desirable trade-off between runtime and accuracy. As a result, superpixel centroids are evolved more efficiently and accurately. For further accelerating the framework, a recursive batch processing strategy is proposed to eliminate unnecessary sorting operations. Therefore, a large number of neighboring elements can be assigned directly. Finally, the two strategies result in a novel synergetic non-iterative clustering with efficiency (NICE) method based on SNIC. Experimental results verify that it works 40% faster than conventional framework, while generating comparable superpixels for several quantitative metrics—sometimes even better.

Traffic Sign Recognition Based on SIFT Features

2010 ◽  
Vol 121-122 ◽  
pp. 596-599 ◽  
Author(s):  
Ni An Cai ◽  
Wen Zhao Liang ◽  
Shao Qiu Xu ◽  
Fang Zhen Li
Keyword(s):  
Traditional Method ◽  
Euclidean Distance ◽  
Experimental Results ◽  
Traffic Sign Recognition ◽  
Recognition Method ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Recognition ◽  
Good Ability ◽  
Sift Features

A recognition method for traffic signs based on an SIFT features is proposed to solve the problems of distortion and occlusion. SIFT features are first extracted from traffic signs and matched by using the Euclidean distance. Then the recognition is implemented based on the similarity. Experimental results show that the proposed method, superior to traditional method, can excellently recognize traffic signs with the transformation of scale, rotation, and distortion and has a good ability of anti-noise and anti-occlusion.

Phased Array Radar Signal Recognition Method Based on Ant Colony Optimization and SVM

2012 ◽  
Vol 263-266 ◽  
pp. 2566-2569
Author(s):  
Jian Feng Pu ◽  
Jun Lin ◽  
Yan Zhi Li ◽  
Wei Quan
Keyword(s):  
Ant Colony Optimization ◽  
Phased Array ◽  
Separation Efficiency ◽  
Experimental Results ◽  
Ant Colony ◽  
Radar Signal ◽  
Access Time ◽  
Signal Recognition ◽  
Recognition Method ◽  
Phased Array Radar

In order to improve the efficiency for phased array radar's ESM, an ACO and SVM conjoint method is used in this paper to solve the problem of phased array radar signal recognition. By introducing ACO to supervise SVM parametric selection, the method is able to quickly discover seemly parameter value and improve SVM separation efficiency. Experimental results show that textual algorithm possess upper exactness rate to phased array radar that the whole pulse signals sorting can be identified. With normal-SVM and RST-SVM means to compare, the algorithm SVM parameter access time is short, thereby shorten the monolithic hour.

Face Recognition Method Based on the Fuzzy Neural Network

2011 ◽  
Vol 128-129 ◽  
pp. 134-137
Author(s):  
Xiang Pan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Face Recognition ◽  
Fuzzy Neural Network ◽  
Experimental Results ◽  
Fuzzy Rules ◽  
Good Effect ◽  
Recognition Method ◽  
Fuzzy Neural ◽  
Artificial Neural

This paper discusses a face recognition method based on the fuzzy neural network (FNN). The fuzzy neural network has more advantages than artificial neural network alone. The paper firstly introduces the structure of the FNN. Than proposed the fuzzy rules and the study algorithm. Thirdly it researches on the process of face recognition. The experimental results prove that this method can achieve good location performance and good effect of extraction.

Recognition of Offline Handwritten Chinese Characters of Amount in Words Based on Integrated Features and HMM

2012 ◽  
Vol 263-266 ◽  
pp. 2639-2642
Author(s):  
Cai Feng Liu ◽  
Xue Dong Tian ◽  
Fang Yang
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Recognition Rate ◽  
Experimental Results ◽  
Chinese Characters ◽  
Recognition Method ◽  
Voting Rule

A recognition method of offline handwritten Chinese characters of amount in words is presented. The method uses elastic mesh strategy to divide character images written by special men into meshes, and extracts directional element and key point features in every mesh to produce a vector. Based on independent Hidden Markov Model classifiers, this paper uses voting rule to integrate the Hidden Markov Model classifiers. The experimental results show that this method has a relative high recognition rate.

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