A Rapid Vote-Counting System Based on Image Understanding

2012 ◽  
Vol 490-495 ◽  
pp. 76-80
Author(s):  
Gang Xiao ◽  
Jing Jing Zhang ◽  
Yuan Ming Zhang ◽  
Jia Wei Lu ◽  
Zhi Ye
Keyword(s):  
Geometric Structure ◽  
High Speed ◽  
Recognition Accuracy ◽  
Logical Structure ◽  
Image Understanding ◽  
Counting System ◽  
Wide Applicability ◽  
High Recognition Accuracy ◽  
Vote Counting

This paper proposes a system, which can rapidly count votes in traditional elections based on image understanding. Firstly, the system gets ballot images through high-speed scanner and preprocesses the images. Then, it recognizes the geometric structure and layout of ballot image through detecting table lines. In addition, it also recognizes the logical structure of ballot image through analyzing the relative positions of candidates and vote symbols. Thirdly, it locates candidates and symbols on the ballot table, and recognizes the specific symbols based on run features. The system has been implemented, which shows high counting speed, high recognition accuracy with wide applicability.

Machete

2021 ◽  
Vol 28 (1) ◽  
pp. 1-46
Author(s):  
Eugene M. Taranta II ◽  
Corey R. Pittman ◽  
Mehran Maghoumi ◽  
Mykola Maslych ◽  
Yasmine M. Moolenaar ◽  
...  
Keyword(s):  
Video Game ◽  
Recognition Accuracy ◽  
Training Sample ◽  
Low Latency ◽  
Computationally Efficient ◽  
Extensive Evaluation ◽  
Segmentation Accuracy ◽  
Alternative Techniques ◽  
High Recognition Accuracy ◽  
Continuous Dynamic

We present Machete, a straightforward segmenter one can use to isolate custom gestures in continuous input. Machete uses traditional continuous dynamic programming with a novel dissimilarity measure to align incoming data with gesture class templates in real time. Advantages of Machete over alternative techniques is that our segmenter is computationally efficient, accurate, device-agnostic, and works with a single training sample. We demonstrate Machete’s effectiveness through an extensive evaluation using four new high-activity datasets that combine puppeteering, direct manipulation, and gestures. We find that Machete outperforms three alternative techniques in segmentation accuracy and latency, making Machete the most performant segmenter. We further show that when combined with a custom gesture recognizer, Machete is the only option that achieves both high recognition accuracy and low latency in a video game application.

Ion-gating synaptic transistors with long-term synaptic weight modulation

2021 ◽  
Vol 9 (16) ◽  
pp. 5396-5402
Author(s):  
Youngjun Park ◽  
Min-Kyu Kim ◽  
Jang-Sik Lee
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Synaptic Weight ◽  
Recognition Accuracy ◽  
Artificial Neural ◽  
High Recognition Accuracy

This paper presents synaptic transistors that show long-term synaptic weight modulation via injection of ions. Linear and symmetric weight update is achieved, which enables high recognition accuracy in artificial neural networks.

scholarly journals Simulation and Recognition of Concrete Lining Infiltration Degree via an Indoor Experiment

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Dongsheng Wang ◽  
Jun Feng ◽  
Xinpeng Zhao ◽  
Yeping Bai ◽  
Yujie Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cement Mortar ◽  
Recognition Accuracy ◽  
Image Data ◽  
Concrete Lining ◽  
Learning Method ◽  
Recognition Method ◽  
Data Set ◽  
Recognition Model ◽  
High Recognition Accuracy

It is difficult to form a method for recognizing the degree of infiltration of a tunnel lining. To solve this problem, we propose a recognition method by using a deep convolutional neural network. We carry out laboratory tests, prepare cement mortar specimens with different saturation levels, simulate different degrees of infiltration of tunnel concrete linings, and establish an infrared thermal image data set with different degrees of infiltration. Then, based on a deep learning method, the data set is trained using the Faster R-CNN+ResNet101 network, and a recognition model is established. The experiments show that the recognition model established by the deep learning method can be used to select cement mortar specimens with different degrees of infiltration by using an accurately minimized rectangular outer frame. This model shows that the classification recognition model for tunnel concrete lining infiltration established by the indoor experimental method has high recognition accuracy.

scholarly journals Isolated Word Recognition Based on Combination of Multiple Noise-Robust Techniques

1970 ◽  
Vol 6 (1) ◽  
pp. 81-88
Author(s):  
Noboru Hayasaka
Keyword(s):  
Recognition Accuracy ◽  
Recognition System ◽  
Temporal Filtering ◽  
Low Snr ◽  
Robust Techniques ◽  
Noise Robust Speech Recognition ◽  
Isolated Word ◽  
High Recognition Accuracy ◽  
Noise Robust ◽  
Mean Variance

Although many noise-robust techniques have been presented, the improvement under low SNR condition is still insufficient. The purpose of this paper is to achieve the high recognition accuracy under low SNR condition with low calculation costs. Therefore, this paper proposes a novel noise-robust speech recognition system that makes full use of spectral subtraction (SS), mean variance normalization (MVN), temporal filtering (TF), and multi-condition HMMs (MC-HMMs). First, from the results of SS with clean HMMs, we obtained the improvement from 46.61% to 65.71% under 0 dB SNR condition. Then, SS+ MVN+TF with clean HMMs improved the recognition accuracy from 65.71% to 80.97% under the same SNR condition. Finally, we achieved the further improvement from 80.97% to 92.23% by employing SS+MVN+TF with MC-HMMs.

scholarly journals Design and Image Research of Tennis Line Examination Based on Machine Vision Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liu Yan ◽  
Sun Xin
Keyword(s):  
Machine Vision ◽  
Camera Calibration ◽  
Recognition Accuracy ◽  
Calibration Method ◽  
Recognition Method ◽  
Systematic Analysis ◽  
High Recognition Accuracy ◽  
Intelligent Recognition ◽  
Region Growth ◽  
Design Experiments

In view of the intelligent demand of tennis line examination, this paper performs a systematic analysis on the intelligent recognition of tennis line examination. Then, a tennis line recognition method based on machine vision is proposed. In this paper, the color region of the image recognition region is divided based on the region growth, and the rough estimation of the court boundary is realized. In order to achieve the effect of camera calibration, a fast camera calibration method which can be used for a variety of court types is proposed. On the basis of camera calibration, a tennis line examination and segmentation system based on machine vision analysis is constructed, and the experimental results are verified by design experiments. The results show that the machine vision analysis-based intelligent segmentation system of tennis line examination has high recognition accuracy and can meet the actual needs of tennis line examination.

scholarly journals Recognition of Crop Diseases Based on Depthwise Separable Convolution in Edge Computing

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4091
Author(s):  
Musong Gu ◽  
Kuan-Ching Li ◽  
Zhongwen Li ◽  
Qiyi Han ◽  
Wenjie Fan
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Recognition Accuracy ◽  
Recognition Algorithm ◽  
Edge Computing ◽  
Recognition Model ◽  
Fast Recognition ◽  
Proposed Model ◽  
Crop Diseases ◽  
High Recognition Accuracy

The original pattern recognition and classification of crop diseases needs to collect a large amount of data in the field and send them next to a computer server through the network for recognition and classification. This method usually takes a long time, is expensive, and is difficult to carry out for timely monitoring of crop diseases, causing delays to diagnosis and treatment. With the emergence of edge computing, one can attempt to deploy the pattern recognition algorithm to the farmland environment and monitor the growth of crops promptly. However, due to the limited resources of the edge device, the original deep recognition model is challenging to apply. Due to this, in this article, a recognition model based on a depthwise separable convolutional neural network (DSCNN) is proposed, which operation particularities include a significant reduction in the number of parameters and the amount of computation, making the proposed design well suited for the edge. To show its effectiveness, simulation results are compared with the main convolution neural network (CNN) models LeNet and Visual Geometry Group Network (VGGNet) and show that, based on high recognition accuracy, the recognition time of the proposed model is reduced by 80.9% and 94.4%, respectively. Given its fast recognition speed and high recognition accuracy, the model is suitable for the real-time monitoring and recognition of crop diseases by provisioning remote embedded equipment and deploying the proposed model using edge computing.

A Novel High-Speed Photon Counting System with Programmed Dead Time

2019 ◽  
Author(s):  
Fei Yuan ◽  
Xiao-Long Lu ◽  
Li Jing ◽  
Yun-Xiu Yang ◽  
Fan-Lin Kong ◽  
...  
Keyword(s):  
High Speed ◽  
Dead Time ◽  
Photon Counting ◽  
Counting System

A Research on HMM based Speech Recognition in Spoken English

2021 ◽  
Vol 14 ◽  
Author(s):  
Na Wang ◽  
Xiaohong Zhang ◽  
Ashutosh Sharma
Keyword(s):  
Speech Recognition ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Learning System ◽  
Computer Assisted ◽  
Speech Signals ◽  
English Learning ◽  
Speech Recognition System ◽  
Spoken English ◽  
High Recognition Accuracy

: The computer assisted speech recognition system enabling voice recognition for understanding the spoken words using sound digitization is extensively being used in the field of education, scientific research, industry, etc. This article unveils the technological perspective of automated speech recognition system in order to realize the spoken English speech recognition system based on MATLAB. A speech recognition technology has been designed and implemented in this work which can collect the speech signals of the spoken English learning system and then filter those speech signals. This paper mainly adopts the preprocessing module for the processing of the raw speech data collected utilizing the MATLAB commands. The method of feature extraction is based on HMM model, codebook generation and template training. The research results show that the recognition accuracy of 98% is achieved by the spoken English speech recognition system studied in this paper. It can be seen that the spoken English speech recognition system based on MATLAB has high recognition accuracy and fast speed. This work addresses the current research issued needed to be tackled in the speech recognition field. This approach is able to provide the technical support and interface for the spoken English learning system.

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