Vascular Orientation Detection and Feature Point Recognition of Coronary Arterial Angiogram Based on Multi-Scale Gabor Filter

2008 ◽  
Author(s):  
Qin Li ◽  
Ting Wang ◽  
Haomiao Shui ◽  
Xiaoming Hu ◽  
Yue Liu ◽  
...  
Keyword(s):  
Gabor Filter ◽  
Feature Point ◽  
Multi Scale ◽  
Orientation Detection

Iris Recognition Method Based on Feature Discrimination and Category Correlation

2014 ◽  
Vol 602-605 ◽  
pp. 1610-1613
Author(s):  
Ming Hai Yao ◽  
Na Wang ◽  
Jin Song Li
Keyword(s):  
Iris Recognition ◽  
Predictive Accuracy ◽  
Gabor Filter ◽  
Recognition Algorithm ◽  
Recognition Method ◽  
Texture Characteristic ◽  
Multi Scale ◽  
Laplacian Score ◽  
Circular Gabor Filter ◽  
Academy Of Sciences

With the increasing number of internet user, the authentication technology is more and more important. Iris recognition as an important method for identification, which has been attention by researchers. In order to improve the predictive accuracy of iris recognition algorithm, the iris recognition method is proposed based feature discrimination and category correlation. The feature discrimination and category correlation are calculated by laplacian score and mutual information. The formula about feature discrimination and category correlation are built. Aiming at texture characteristic of iris image, the multi-scale circular Gabor filter is used to feature extraction. The computational efficiency of algorithm is improved. In order to verify the validity of the algorithm, the CASIA iris database of Chinese Academy of Sciences is used to do the experiment. The experimental results show that our method has high predictive accuracy.

scholarly journals An improved pix2pix model based on Gabor filter for robust color image rendering

2021 ◽  
Vol 19 (1) ◽  
pp. 86-101
Author(s):  
Hong-an Li ◽  
◽  
Min Zhang ◽  
Zhenhua Yu ◽  
Zhanli Li ◽  
...  
Keyword(s):  
Color Image ◽  
Gabor Filter ◽  
Texture Feature ◽  
Generative Adversarial Networks ◽  
Feature Maps ◽  
Penalty Term ◽  
Multi Scale ◽  
Adversarial Networks ◽  
Robust Image ◽  
Color Rendering

<abstract><p>In recent years, with the development of deep learning, image color rendering method has become a research hotspot once again. To overcome the detail problems of color overstepping and boundary blurring in the robust image color rendering method, as well as the problems of unstable training based on generative adversarial networks, we propose an color rendering method using Gabor filter based improved pix2pix for robust image. Firstly, the multi-direction/multi-scale selection characteristic of Gabor filter is used to preprocess the image to be rendered, which can retain the detailed features of the image while preprocessing to avoid the loss of features. Moreover, among the Gabor texture feature maps with 6 scales and 4 directions, the texture map with the scale of 7 and the direction of 0° has the comparable rendering performance. Finally, by improving the loss function of pix2pix model and adding the penalty term, not only the training can be stabilized, but also the ideal color image can be obtained. To reflect image color rendering quality of different models more objectively, PSNR and SSIM indexes are adopted to evaluate the rendered images. The experimental results of the proposed method show that the robust image rendered by this method has better visual performance and reduces the influence of light and noise on the image to a certain extent.</p></abstract>

scholarly journals Object-Based Change Detection Using Multiple Classifiers and Multi-Scale Uncertainty Analysis

2019 ◽  
Vol 11 (3) ◽  
pp. 359 ◽  
Author(s):  
Kun Tan ◽  
Yusha Zhang ◽  
Xue Wang ◽  
Yu Chen
Keyword(s):  
Change Detection ◽  
Uncertainty Analysis ◽  
Spatial Information ◽  
Gabor Filter ◽  
Evidence Theory ◽  
Support Vector ◽  
Multi Scale ◽  
Multiple Classifiers ◽  
Scale Uncertainty ◽  
Object Based

The drawback of pixel-based change detection is that it neglects the spatial correlation with neighboring pixels and has a high commission ratio. In contrast, object-based change detection (OBCD) depends on the accuracy of the segmentation scale, which is of great significance in image analysis. Accordingly, an object-based approach for automatic change detection using multiple classifiers and multi-scale uncertainty analysis (OB-MMUA) in high-resolution (HR) remote sensing images is proposed in this paper. In this algorithm, the gray-level co-occurrence matrix (GLCM), morphological, and Gabor filter texture features are extracted to construct the input data, along with the spectral features, to utilize the respective advantages of the features and to compensate for the insufficient spectral information. In addition, random forest is used to select the features and determine the optimal feature vectors for the change detection. Change vector analysis (CVA) based on uncertainty analysis is then implemented to select the initial training samples. According to the diversity, support vector machine (SVM), k-nearest neighbor (KNN), and extra-trees (ExT) classifiers are then chosen as the base classifiers for Dempster-Shafer (D-S) evidence theory fusion, and unlabeled samples are selected using an active learning method with spatial information. Finally, multi-scale object-based D-S evidence theory fusion and uncertainty analysis is used to classify the difference image. To validate the proposed approach, we conducted experiments using multispectral images collected by the ZY-3 and GF-2 satellites. The experimental results confirmed the effectiveness and superiority of the proposed approach, which integrates the respective advantages of the pixel-based and object-based methods.

scholarly journals An Automatic Extraction Method for Hatched Residential Areas in Raster Maps Based on Multi-Scale Feature Fusion

2021 ◽  
Vol 10 (12) ◽  
pp. 831
Author(s):  
Jianhua Wu ◽  
Jiaqi Xiong ◽  
Yu Zhao ◽  
Xiang Hu
Keyword(s):  
Random Fields ◽  
Conditional Random Fields ◽  
Feature Fusion ◽  
Gabor Filter ◽  
Residential Areas ◽  
Change Analysis ◽  
Land Quality ◽  
Multi Scale ◽  
Raster Maps ◽  
Fully Connected

Extracting the residential areas from digital raster maps is beneficial for research on land use change analysis and land quality assessment. In traditional methods for extracting residential areas in raster maps, parameters must be set manually; these methods also suffer from low extraction accuracy and inefficiency. Therefore, we have proposed an automatic method for extracting the hatched residential areas from raster maps based on a multi-scale U-Net and fully connected conditional random fields. The experimental results showed that the model that was based on a multi-scale U-Net with fully connected conditional random fields achieved scores of 97.05% in Dice, 94.26% in Intersection over Union, 94.92% in recall, 93.52% in precision and 99.52% in accuracy. Compared to the FCN-8s, the five metrics increased by 1.47%, 2.72%, 1.07%, 4.56% and 0.26%, respectively and compared to the U-Net, they increased by 0.84%, 1.56%, 3.00%, 0.65% and 0.13%, respectively. Our method also outperformed the Gabor filter-based algorithm in the number of identified objects and the accuracy of object contour locations. Furthermore, we were able to extract all of the hatched residential areas from a sheet of raster map. These results demonstrate that our method has high accuracy in object recognition and contour position, thereby providing a new method with strong potential for the extraction of hatched residential areas.

Retinal Blood Vessel Segmentation Using Extreme Learning Machine

2017 ◽  
Vol 21 (7) ◽  
pp. 1280-1290
Author(s):  
Fan Guo ◽  
◽  
Da Xiang ◽  
Beiji Zou ◽  
Chengzhang Zhu ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Gabor Filter ◽  
Hessian Matrix ◽  
Vessel Segmentation ◽  
Comparative Performance ◽  
Multi Scale ◽  
Learning Technique ◽  
Learning Machine ◽  
Vessel Extraction ◽  
Elm Classifier

Extreme learning machine (ELM) is an effective machine learning technique that widely used in image processing. In this paper, a new supervised method for segmenting blood vessels in retinal images is proposed based on the ELM classifier. The proposed algorithm first constructs a 7-D feature vector using multi-scale Gabor filter, Hessian matrix and bottom-hat transformation. Then, an ELM classifier is trained on gold standard examples of vessel segmentation images to classify previous unseen images. The algorithm was tested on the publicly available DRIVE database – a digital image database for vessel extraction. Experimental results on both real-captured images and public database images demonstrate that our method shows comparative performance against other methods, which make the proposed algorithm a suitable tool for automated retinal image analysis.

scholarly journals Enhanced biologically inspired model for image recognition based on a novel patch selection method with moment

2019 ◽  
Vol 17 (02) ◽  
pp. 1940007
Author(s):  
Yanfeng Lu ◽  
Lihao Jia ◽  
Hong Qiao ◽  
Yi Li ◽  
Zongshuai Qi
Keyword(s):  
Image Recognition ◽  
Gabor Filter ◽  
Layer Structure ◽  
Experimental Studies ◽  
Selection Method ◽  
Feature Representation ◽  
Patch Selection ◽  
Biologically Inspired ◽  
Multi Scale ◽  
Random Method

Biologically inspired model (BIM) for image recognition is a robust computational architecture, which has attracted widespread attention. BIM can be described as a four-layer structure based on the mechanisms of the visual cortex. Although the performance of BIM for image recognition is robust, it takes the randomly selected ways for the patch selection, which is sightless, and results in heavy computing burden. To address this issue, we propose a novel patch selection method with oriented Gaussian–Hermite moment (PSGHM), and we enhanced the BIM based on the proposed PSGHM, named as PBIM. In contrast to the conventional BIM which adopts the random method to select patches within the feature representation layers processed by multi-scale Gabor filter banks, the proposed PBIM takes the PSGHM way to extract a small number of representation features while offering promising distinctiveness. To show the effectiveness of the proposed PBIM, experimental studies on object categorization are conducted on the CalTech05, TU Darmstadt (TUD) and GRAZ01 databases. Experimental results demonstrate that the performance of PBIM is a significant improvement on that of the conventional BIM.

Paper Currency Image Features Extraction and Recognition Based on Gabor Filter

2014 ◽  
Vol 635-637 ◽  
pp. 1030-1034 ◽  
Author(s):  
Xi Wen Liu ◽  
Chao Ying Liu
Keyword(s):  
Gabor Filter ◽  
Recognition Rate ◽  
Image Features ◽  
Recognition Method ◽  
Match Method ◽  
Template Match ◽  
Multi Scale ◽  
Paper Currency ◽  
Texture Characteristics ◽  
Directional Texture

The paper currency image recognition method based on Gabor filter set is discussed in this paper. According to the paper currency image features, the suitable parameters of Gabor filter set are selected for the extraction of paper currency characteristics, the multi-scale and multi-directional texture characteristics of paper currency image are gotten; then the texture images are meshed, and the row and column projection sum of grid pixels' average grey are calculated, finally, the template match method based on grid projection characteristics is used for paper currency recognition. Experiments show that, this method has strong anti-interference ability, it can raise the recognition rate of old or dirty paper currency greatly, and it costs little time.

Multi-scale sparse feature point correspondence by graph cuts

2010 ◽  
Vol 53 (6) ◽  
pp. 1224-1232 ◽  
Author(s):  
Hong Zhang ◽  
Ying Mu ◽  
YuHu You ◽  
JunWei Li
Keyword(s):  
Graph Cuts ◽  
Feature Point ◽  
Point Correspondence ◽  
Multi Scale

An improved fast segmentation algorithm for text and graphics data

2020 ◽  
Vol 39 (4) ◽  
pp. 5273-5281
Author(s):  
Zhancang Li
Keyword(s):  
Gabor Filter ◽  
Deep Level ◽  
A Priori ◽  
Video Information ◽  
Multi Scale ◽  
Extraction Step ◽  
Set Up ◽  
Edge Features ◽  
Text Location ◽  
Important Basis

The application of video and image segmentation is carried out from the aspects of improving the accuracy of segmentation and reducing the calculation time, but the segmentation result is affected by the initial curve position, so this paper proposes a new method. As an important part of the Internet, pictures are usually used to help visitors understand. The image contains a lot of deep-level video information, which is an important basis for video content retrieval and data analysis. In this paper, combining the texture and edge features of the image in the process of text location, a multi-scale Gabor filter bank is proposed to transform the original image, and a priori knowledge of the text region is used to process the non-text object in the transform result. In the part of extracting text from pictures, and improved TF-IDF algorithm, BC-TF-IDF algorithm, is proposed to extract text from pictures. To ensure the integrity of the extracted image, the Sobel algorithm is used to process the image in the edge extraction step. Finally, the above method is applied to the Weibo network, and a system of collecting and recognizing the character content of the Weibo image is set up, which completes the function of collecting and gradually recognizing the Weibo image, and verifies the proposed localization method.

Sign in / Sign up

Export Citation Format

Share Document