scholarly journals Hough Transform-Based Angular Features for Learning-Free Handwritten Keyword Spotting

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4648
Author(s):  
Subhranil Kundu ◽  
Samir Malakar ◽  
Zong Woo Geem ◽  
Yoon Young Moon ◽  
Pawan Kumar Singh ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Hough Transform ◽  
Feature Matching ◽  
State Of The Art ◽  
Document Image ◽  
Keyword Spotting ◽  
Vertical Zone ◽  
Word Images ◽  
Query Word ◽  
Zone Division

Handwritten keyword spotting (KWS) is of great interest to the document image research community. In this work, we propose a learning-free keyword spotting method following query by example (QBE) setting for handwritten documents. It consists of four key processes: pre-processing, vertical zone division, feature extraction, and feature matching. The pre-processing step deals with the noise found in the word images, and the skewness of the handwritings caused by the varied writing styles of the individuals. Next, the vertical zone division splits the word image into several zones. The number of vertical zones is guided by the number of letters in the query word image. To obtain this information (i.e., number of letters in a query word image) during experimentation, we use the text encoding of the query word image. The user provides the information to the system. The feature extraction process involves the use of the Hough transform. The last step is feature matching, which first compares the features extracted from the word images and then generates a similarity score. The performance of this algorithm has been tested on three publicly available datasets: IAM, QUWI, and ICDAR KWS 2015. It is noticed that the proposed method outperforms state-of-the-art learning-free KWS methods considered here for comparison while evaluated on the present datasets. We also evaluate the performance of the present KWS model using state-of-the-art deep features and it is found that the features used in the present work perform better than the deep features extracted using InceptionV3, VGG19, and DenseNet121 models.

scholarly journals Local Feature-Aware Siamese Matching Model for Vehicle Re-Identification

2020 ◽  
Vol 10 (7) ◽  
pp. 2474
Author(s):  
Honglie Wang ◽  
Shouqian Sun ◽  
Lunan Zhou ◽  
Lilin Guo ◽  
Xin Min ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Large Scale ◽  
Feature Matching ◽  
State Of The Art ◽  
Intelligent Transportation ◽  
The State ◽  
Local Feature ◽  
Public Security ◽  
Matching Model ◽  
Image Deformation

Vehicle re-identification is attracting an increasing amount of attention in intelligent transportation and is widely used in public security. In comparison to person re-identification, vehicle re-identification is more challenging because vehicles with different IDs are generated by a unified pipeline and cannot only be distinguished based on the subtle differences in their features such as lights, ornaments, and decorations. In this paper, we propose a local feature-aware Siamese matching model for vehicle re-identification. A local feature-aware Siamese matching model focuses on the informative parts in an image and these are the parts most likely to differ among vehicles with different IDs. In addition, we utilize Siamese feature matching to better supervise our attention. Furthermore, a perspective transformer network, which can eliminate image deformation, has been designed for feature extraction. We have conducted extensive experiments on three large-scale vehicle re-ID datasets, i.e., VeRi-776, VehicleID, and PKU-VD, and the results show that our method is superior to the state-of-the-art methods.

scholarly journals Image Classification for the Automatic Feature Extraction in Human Worn Fashion Data

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 624
Author(s):  
Stefan Rohrmanstorfer ◽  
Mikhail Komarov ◽  
Felix Mödritscher
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Image Data ◽  
Classification Model ◽  
Upper Body ◽  
Automatic Feature Extraction

With the always increasing amount of image data, it has become a necessity to automatically look for and process information in these images. As fashion is captured in images, the fashion sector provides the perfect foundation to be supported by the integration of a service or application that is built on an image classification model. In this article, the state of the art for image classification is analyzed and discussed. Based on the elaborated knowledge, four different approaches will be implemented to successfully extract features out of fashion data. For this purpose, a human-worn fashion dataset with 2567 images was created, but it was significantly enlarged by the performed image operations. The results show that convolutional neural networks are the undisputed standard for classifying images, and that TensorFlow is the best library to build them. Moreover, through the introduction of dropout layers, data augmentation and transfer learning, model overfitting was successfully prevented, and it was possible to incrementally improve the validation accuracy of the created dataset from an initial 69% to a final validation accuracy of 84%. More distinct apparel like trousers, shoes and hats were better classified than other upper body clothes.

scholarly journals A Multi-Branch Feature Fusion Strategy Based on an Attention Mechanism for Remote Sensing Image Scene Classification

2021 ◽  
Vol 13 (10) ◽  
pp. 1950
Author(s):  
Cuiping Shi ◽  
Xin Zhao ◽  
Liguo Wang
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Feature Fusion ◽  
State Of The Art ◽  
Rapid Development ◽  
Remote Sensing Image ◽  
Classification Performance ◽  
Attention Mechanism ◽  
Scene Classification

In recent years, with the rapid development of computer vision, increasing attention has been paid to remote sensing image scene classification. To improve the classification performance, many studies have increased the depth of convolutional neural networks (CNNs) and expanded the width of the network to extract more deep features, thereby increasing the complexity of the model. To solve this problem, in this paper, we propose a lightweight convolutional neural network based on attention-oriented multi-branch feature fusion (AMB-CNN) for remote sensing image scene classification. Firstly, we propose two convolution combination modules for feature extraction, through which the deep features of images can be fully extracted with multi convolution cooperation. Then, the weights of the feature are calculated, and the extracted deep features are sent to the attention mechanism for further feature extraction. Next, all of the extracted features are fused by multiple branches. Finally, depth separable convolution and asymmetric convolution are implemented to greatly reduce the number of parameters. The experimental results show that, compared with some state-of-the-art methods, the proposed method still has a great advantage in classification accuracy with very few parameters.

Identification of the Hook on Investment Casting Shell Line Based on Machine Vision

2012 ◽  
Vol 220-223 ◽  
pp. 1356-1361
Author(s):  
Xi Jie Tian ◽  
Jing Yu ◽  
Chang Chun Li
Keyword(s):  
Machine Vision ◽  
Investment Casting ◽  
Hough Transform ◽  
Image Matching ◽  
Feature Matching ◽  
Image Acquisition ◽  
Target Range ◽  
Target Area ◽  
Matching Method ◽  
Vertical Projection

In this paper, the idea identify the hook on investment casting shell line based on machine vision has been proposed. According to the characteristic of the hook, we do the image acquisition and preprocessing, we adopt Hough transform to narrow the target range, and find the target area based on the method combining the level projection and vertical projection, use feature matching method SIFT to do the image matching. Finally, we get the space information of the target area of the hook.

scholarly journals The State-of-the-Art Technology of Currency Identification

2017 ◽  
Vol 9 (3) ◽  
pp. 58-72 ◽  
Author(s):  
Guangyu Wang ◽  
Xiaotian Wu ◽  
WeiQi Yan
Keyword(s):  
Feature Extraction ◽  
New Zealand ◽  
Comparative Study ◽  
State Of The Art ◽  
The State ◽  
Classification Algorithms ◽  
Security Issue ◽  
The Public ◽  
Us Dollar ◽  
Chinese Yuan

The security issue of currency has attracted awareness from the public. De-spite the development of applying various anti-counterfeit methods on currency notes, cheaters are able to produce illegal copies and circulate them in market without being detected. By reviewing related work in currency security, the focus of this paper is on conducting a comparative study of feature extraction and classification algorithms of currency notes authentication. We extract various computational features from the dataset consisting of US dollar (USD), Chinese Yuan (CNY) and New Zealand Dollar (NZD) and apply the classification algorithms to currency identification. Our contributions are to find and implement various algorithms from the existing literatures and choose the best approaches for use.

scholarly journals The Role and Utilization of CNN in Automatic Logo Based Document Image Retrieval Methods

2018 ◽  
Vol 7 (3.1) ◽  
pp. 13
Author(s):  
Raveendra K ◽  
R Vinoth Kanna
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Document Image ◽  
Retrieval Process ◽  
Pictorial Representations

Automatic logo based document image retrieval process is an essential and mostly used method in the feature extraction applications. In this paper the architecture of Convolutional Neural Network (CNN) was elaborately explained with pictorial representations in order to understand the complex Convolutional Neural Networks process in a simplified way. The main objective of this paper is to effectively utilize the CNN in the process of automatic logo based document image retrieval methods.  

Feature Matching-based Approaches to Improve the Robustness of Android Visual GUI Testing

2022 ◽  
Vol 31 (2) ◽  
pp. 1-32
Author(s):  
Luca Ardito ◽  
Andrea Bottino ◽  
Riccardo Coppola ◽  
Fabrizio Lamberti ◽  
Francesco Manigrasso ◽  
...  
Keyword(s):  
Computer Vision ◽  
Feature Matching ◽  
State Of The Art ◽  
Design Of Algorithms ◽  
Computational Burden ◽  
Domain Specific ◽  
Gui Testing ◽  
Wide Range ◽  
Full Screen ◽  
Feature Based

In automated Visual GUI Testing (VGT) for Android devices, the available tools often suffer from low robustness to mobile fragmentation, leading to incorrect results when running the same tests on different devices. To soften these issues, we evaluate two feature matching-based approaches for widget detection in VGT scripts, which use, respectively, the complete full-screen snapshot of the application ( Fullscreen ) and the cropped images of its widgets ( Cropped ) as visual locators to match on emulated devices. Our analysis includes validating the portability of different feature-based visual locators over various apps and devices and evaluating their robustness in terms of cross-device portability and correctly executed interactions. We assessed our results through a comparison with two state-of-the-art tools, EyeAutomate and Sikuli. Despite a limited increase in the computational burden, our Fullscreen approach outperformed state-of-the-art tools in terms of correctly identified locators across a wide range of devices and led to a 30% increase in passing tests. Our work shows that VGT tools’ dependability can be improved by bridging the testing and computer vision communities. This connection enables the design of algorithms targeted to domain-specific needs and thus inherently more usable and robust.

The State-of-the-Art Technology of Currency Identification

2018 ◽  
pp. 252-269
Author(s):  
Guangyu Wang ◽  
Xiaotian Wu ◽  
WeiQi Yan
Keyword(s):  
Feature Extraction ◽  
New Zealand ◽  
Comparative Study ◽  
State Of The Art ◽  
The State ◽  
Classification Algorithms ◽  
Security Issue ◽  
The Public ◽  
Us Dollar ◽  
Chinese Yuan

The security issue of currency has attracted awareness from the public. De-spite the development of applying various anti-counterfeit methods on currency notes, cheaters are able to produce illegal copies and circulate them in market without being detected. By reviewing related work in currency security, the focus of this paper is on conducting a comparative study of feature extraction and classification algorithms of currency notes authentication. We extract various computational features from the dataset consisting of US dollar (USD), Chinese Yuan (CNY) and New Zealand Dollar (NZD) and apply the classification algorithms to currency identification. Our contributions are to find and implement various algorithms from the existing literatures and choose the best approaches for use.

scholarly journals Using control charts for on-line video summarisation

2019 ◽  
Vol 277 ◽  
pp. 01012 ◽  
Author(s):  
Clare E. Matthews ◽  
Paria Yousefi ◽  
Ludmila I. Kuncheva
Keyword(s):  
Feature Extraction ◽  
Control Chart ◽  
Control Charts ◽  
State Of The Art ◽  
Synthetic Data ◽  
New Method ◽  
Data Sets ◽  
On Line ◽  
Memory Constraints ◽  
Video Summarisation

Many existing methods for video summarisation are not suitable for on-line applications, where computational and memory constraints mean that feature extraction and frame selection must be simple and efficient. Our proposed method uses RGB moments to represent frames, and a control-chart procedure to identify shots from which keyframes are then selected. The new method produces summaries of higher quality than two state-of-the-art on-line video summarisation methods identified as the best among nine such methods in our previous study. The summary quality is measured against an objective ideal for synthetic data sets, and compared to user-generated summaries of real videos.

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