scholarly journals The Improvement of DS Evidence Theory and Its Application in IR/MMW Target Recognition

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Yibing Li ◽  
Jie Chen ◽  
Fang Ye ◽  
Dandan Liu
Keyword(s):  
Target Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Evidence Theory ◽  
Single Mode ◽  
Recognition System ◽  
Battlefield Environment ◽  
Compound Decision ◽  
The Military ◽  
Decision Making Rule

ATR system has a broad application prospect in the military field, especially in the field of modern defense technology. When paradoxes are in existence in ATR system due to adverse battlefield environment, integration cannot be effectively and reliably carried out only by traditional DS evidence theory. In this paper, a modified DS evidence theory is presented and applied in IR/MMW target recognition system. The improvement of DS evidence theory is realized by three parts: the introduction of sensor priority and evidence credibility to realize the discount processing of evidences, the modification of DS combination rule to enhance the accuracy of synthesis results, and the compound decision-making rule. The application of the modified algorithm in IR/MMW system is designed to deal with paradoxes, improve the target recognition rate, and ensure the reliability of target recognition system. Experiments are given to illustrate that the introduction of the modified DS evidence theory in IR/MMW system is better able to realize satisfactory target recognition performance through multisensor information fusion than any single-mode system.

Investigation of ANFIS and FFBNN Recognition Methods Performance in Tamil Speech Word Recognition

2014 ◽  
Vol 2 (2) ◽  
pp. 43-53 ◽  
Author(s):  
S. Rojathai ◽  
M. Venkatesulu
Keyword(s):  
Feature Extraction ◽  
Word Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Inference System ◽  
Feed Forward Back Propagation ◽  
Statistical Measures ◽  
Recognition Systems

In speech word recognition systems, feature extraction and recognition plays a most significant role. More number of feature extraction and recognition methods are available in the existing speech word recognition systems. In most recent Tamil speech word recognition system has given high speech word recognition performance with PAC-ANFIS compared to the earlier Tamil speech word recognition systems. So the investigation of speech word recognition by various recognition methods is needed to prove their performance in the speech word recognition. This paper presents the investigation process with well known Artificial Intelligence method as Feed Forward Back Propagation Neural Network (FFBNN) and Adaptive Neuro Fuzzy Inference System (ANFIS). The Tamil speech word recognition system with PAC-FFBNN performance is analyzed in terms of statistical measures and Word Recognition Rate (WRR) and compared with PAC-ANFIS and other existing Tamil speech word recognition systems.

scholarly journals TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1724
Author(s):  
Zilu Ying ◽  
Chen Xuan ◽  
Yikui Zhai ◽  
Bing Sun ◽  
Jingwen Li ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Network Architecture ◽  
Learning Strategy ◽  
Target Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Speckle Noise ◽  
Small Sample ◽  
Operating Conditions ◽  
Small Samples

Since Synthetic Aperture Radar (SAR) targets are full of coherent speckle noise, the traditional deep learning models are difficult to effectively extract key features of the targets and share high computational complexity. To solve the problem, an effective lightweight Convolutional Neural Network (CNN) model incorporating transfer learning is proposed for better handling SAR targets recognition tasks. In this work, firstly we propose the Atrous-Inception module, which combines both atrous convolution and inception module to obtain rich global receptive fields, while strictly controlling the parameter amount and realizing lightweight network architecture. Secondly, the transfer learning strategy is used to effectively transfer the prior knowledge of the optical, non-optical, hybrid optical and non-optical domains to the SAR target recognition tasks, thereby improving the model’s recognition performance on small sample SAR target datasets. Finally, the model constructed in this paper is verified to be 97.97% on ten types of MSTAR datasets under standard operating conditions, reaching a mainstream target recognition rate. Meanwhile, the method presented in this paper shows strong robustness and generalization performance on a small number of randomly sampled SAR target datasets.

Computer-aided teaching mode of oral English intelligent learning based on speech recognition and network assistance

2020 ◽  
Vol 39 (4) ◽  
pp. 5749-5760
Author(s):  
Yanfei Hai
Keyword(s):  
Recognition Performance ◽  
Recognition Rate ◽  
Recognition System ◽  
Prosodic Features ◽  
Simulation Experiments ◽  
Teaching Mode ◽  
Speech Detection ◽  
Rate Intensity ◽  
Recognition Systems ◽  
Better Than

The purpose of this paper is to use English specific syllables and prosodic features in spoken speech data to carry out English spoken recognition, and to explore effective methods for the design and application of English speech detection and automatic recognition systems. The method proposed by this study is a combination of SVM_FF based classifier, SVM_IER based classifier and syllable classifier. Compared with the method based on the combination of other phonological characteristics such as phonological rate, intensity, formant and energy statistics and pronunciation rate, and the syllable-based classifier based on specific syllable training, a better recognition rate is obtained. In addition, this study conducts simulation experiments on the proposed English recognition and identification method based on specific syllables and prosodic features and analyzes the experimental results. The result found that the recognition performance of the English spoken recognition system constructed by this study is significantly better than the traditional model.

A Reliability Design Methodology for Chinese Character Recognition

1998 ◽  
Vol 12 (02) ◽  
pp. 159-172 ◽  
Author(s):  
Y. S. Huang ◽  
K. Liu ◽  
C. Y. Suen ◽  
Y. Y. Tang
Keyword(s):  
Character Recognition ◽  
Chinese Character ◽  
Nearest Neighbor ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Recognition System ◽  
Reliability Design ◽  
Chinese Character Recognition ◽  
Nearest Neighbor Rule ◽  
Pattern Distribution

This paper proposes a novel method which enables a Chinese character recognition system to obtain reliable recognition. In this method, two thresholds, i.e. class region thresholdRk and disambiguity thresholdAk, are used by each Chinese character k when the classifier is designed based on the nearest neighbor rule, where Rk defines the pattern distribution region of character k, and Ak prevents the samples not belonging to character k from being ambiguously recognized as character k. A novel algorithm to derive the appropriate thresholds Ak and Rk is developed so that a better recognition reliability can be obtained through iterative learning. Experiments performed on the ITRI printed Chinese character database have achieved highly reliable recognition performance (such as 0.999 reliability with a 95.14% recognition rate), which shows the feasibility and effectiveness of the proposed method.

scholarly journals Research on the Enhancement of Laser Radar Range Image Recognition Using a Super-Resolution Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5185
Author(s):  
Yu Zhai ◽  
Jieyu Lei ◽  
Wenze Xia ◽  
Shaokun Han ◽  
Fei Liu ◽  
...  
Keyword(s):  
Recognition Performance ◽  
Recognition Rate ◽  
Super Resolution ◽  
Recognition System ◽  
Scene Recognition ◽  
Image Texture ◽  
Laser Radar ◽  
Range Image ◽  
Range Images ◽  
Model Library

This work introduces a super-resolution (SR) algorithm for range images on the basis of self-guided joint filtering (SGJF), adding the range information of the range image as a coefficient of the filter to reduce the influence of the intensity image texture on the super-resolved image. A range image SR recognition system is constructed to study the effect of four SR algorithms including the SGJF algorithm on the recognition of the laser radar (ladar) range image. The effects of different model library sizes, SR algorithms, SR factors and noise conditions on the recognition are tested via experiments. Results demonstrate that all tested SR algorithms can improve the recognition rate of low-resolution (low-res) range images to varying degrees and the proposed SGJF algorithm has a very good comprehensive recognition performance. Finally, suggestions for the use of SR algorithms in actual scene recognition are proposed on the basis of the experimental results.

AN EFFICIENT SPEECH RECOGNITION SYSTEM FOR THE INITIALS OF MANDARIN SYLLABLES

1990 ◽  
Vol 04 (04) ◽  
pp. 687-704 ◽  
Author(s):  
PEI-YIH TING ◽  
CHIU-YU TSENG ◽  
LIN-SHAN LEE
Keyword(s):  
Recognition Performance ◽  
Recognition Rate ◽  
Recognition System ◽  
Two Phase ◽  
Efficient System ◽  
System A ◽  
Finite State ◽  
Phase Recognition ◽  
Characteristic Interval

In a long-term research project, the recognition of Mandarin speech for very large vocabulary and unlimited text is considered. Its first stage goal is to recognize the Mandarin syllables. In a previous paper, an initial/final two-phase recognition approach to recognize these very confusing syllables was proposed, in which each syllable is divided into initial and final parts and recognized separately, and efficient recognition techniques for the finals were proposed and discussed. This paper serves as a continuation and proposes an efficient system to recognize the Mandarin initials. In this system, a classification procedure is first used to categorize the unknown initials into two groups C1 and C2; different approaches are then separately applied and independently optimized to recognize C1 and C2. It is found that Finite State Vector Quantization (FSVQ) is very useful, whose two modified versions, Modified FSVQ (MFSVQ) and the Second Order FSVQ (SOFSVQ), can provide the best recognition performance for C1 and C2 by carefully adjusting a design parameter called characteristic interval. Experimental results show that a recognition rate of 94.1% to 94.7% can be achieved using this system. Such a design is accomplished by carefully considering the special characteristics of Mandarin syllables and initials.

scholarly journals An Integrated Counterfactual Sample Generation and Filtering Approach for SAR Automatic Target Recognition with a Small Sample Set

2021 ◽  
Vol 13 (19) ◽  
pp. 3864
Author(s):  
Changjie Cao ◽  
Zongyong Cui ◽  
Zongjie Cao ◽  
Liying Wang ◽  
Jianyu Yang
Keyword(s):  
Target Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Automatic Target Recognition ◽  
Small Sample ◽  
Generative Adversarial Networks ◽  
Training Set ◽  
Stationary Target ◽  
Sample Set ◽  
Filtering Approach

Although automatic target recognition (ATR) models based on data-driven algorithms have achieved excellent performance in recent years, the synthetic aperture radar (SAR) ATR model often suffered from performance degradation when it encountered a small sample set. In this paper, an integrated counterfactual sample generation and filtering approach is proposed to alleviate the negative influence of a small sample set. The proposed method consists of a generation component and a filtering component. First, the proposed generation component utilizes the overfitting characteristics of generative adversarial networks (GANs), which ensures the generation of counterfactual target samples. Second, the proposed filtering component is built by learning different recognition functions. In the proposed filtering component, multiple SVMs trained by different SAR target sample sets provide pseudo-labels to the other SVMs to improve the recognition rate. Then, the proposed approach improves the performance of the recognition model dynamically while it continuously generates counterfactual target samples. At the same time, counterfactual target samples that are beneficial to the ATR model are also filtered. Moreover, ablation experiments demonstrate the effectiveness of the various components of the proposed method. Experimental results based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) and OpenSARship dataset also show the advantages of the proposed approach. Even though the size of the constructed training set was 14.5% of the original training set, the recognition performance of the ATR model reached 91.27% with the proposed approach.

scholarly journals Biometric Recognition of Finger Knuckle Print Based on the Fusion of Global Features and Local Features

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Wenwen Li
Keyword(s):  
Recognition Rate ◽  
Principal Component ◽  
Texture Features ◽  
Single Mode ◽  
Recognition System ◽  
Local Features ◽  
Identification System ◽  
Biometric Recognition ◽  
Global Features ◽  
Hand Shape

Compared with the most traditional fingerprint identification, knuckle print and hand shape are more stable, not easy to abrase, forge, and pilfer; in aspect of image acquisition, the requirement of acquisition equipment and environment are not high; and the noncontact acquisition method also greatly improves the users’ satisfaction; therefore, finger knuckle print and hand shape of single-mode identification system have attracted extensive attention both at home and abroad. A large number of studies show that multibiometric fusion can greatly improve the recognition rate, antiattack, and robustness of the biometric recognition system. A method combining global features and local features was designed for the recognition of finger knuckle print images. On the one hand, principal component analysis (PCA) was used as the global feature for rapid recognition. On the other hand, the local binary pattern (LBP) operator was taken as the local feature in order to extract the texture features that can reflect details. A two-layer serial fusion strategy is proposed in the combination of global and local features. Firstly, the sample library scope was narrowed according to the global matching result. Secondly, the matching result was further determined by fine matching. By combining the fast speed of global coarse matching and the high accuracy of local refined matching, the designed method can improve the recognition rate and the recognition speed.

Learning to Recognize 3D Objects using Sparse Depth and Intensity Information

1997 ◽  
Vol 11 (06) ◽  
pp. 909-931 ◽  
Author(s):  
Brendan MccCane ◽  
Terry Caelli ◽  
Olivier de Vel
Keyword(s):  
Recognition Performance ◽  
Recognition Rate ◽  
Synthetic Data ◽  
Recognition System ◽  
Rule Generation ◽  
3D Objects ◽  
Depth Data ◽  
Intensity Information ◽  
Intensity Images ◽  
Object Parts

In this paper we further explore the use of machine learning (ML) for the recognition of 3D objects in isolation or embedded in scenes. Of particular interest is the use of a recent ML technique (specifically CRG — Conditional Rule Generation) which generates descriptions of objects in terms of object parts and part-relational attribute bounds. We show how this technique can be combined with intensity-based model and scene–views to locate objects and their pose. The major contributions of this paper are: the extension of the CRG classifier to incorporate fuzzy decisions (FCRG), the application of the FCRG classifier to the problem of learning 3D objects from 2D intensity images, the study of the usefulness of sparse depth data in regards to recognition performance, and the implementation of a complete object recognition system that does not rely on perfect or synthetic data. We report a recognition rate of 80% for unseen single object scenes in a database of 18 non-trivial objects.

The Robot Target Recognition Based on Support Vector Machine and D-S Evidence Theory

2011 ◽  
Vol 308-310 ◽  
pp. 1215-1219
Author(s):  
Meng Si Zhu ◽  
Xiang Jun Zou ◽  
Li Juan Chen ◽  
Hai Xin Zou ◽  
Ke Yin Chen
Keyword(s):  
Support Vector Machine ◽  
Target Recognition ◽  
Recognition Rate ◽  
Evidence Theory ◽  
Support Vector ◽  
Complex Environments ◽  
Color Features ◽  
Basic Probability ◽  
Geometry Feature ◽  
Feature Information

Aiming at the problem of low recognition rate and easy affected by environment during the process of robot target recognition in complex environments, the target recognition method combining support vector machine (SVM) with D-S evidence theory was proposed. Taking citrus recognition as an example, SVM was used by the method to local classification according to citrus color and geometry feature information respectively, and the results of SVM were transformed to probability outputs through Platt model, and treated them as the basic probability assignment (BPA) of D-S evidence theory to reason and fuse local recognition results, and then realized the combination of SVM and D-S evidence theory in citrus recognition, finally improved the recognition rate. The experimental results showed that: the recognition rate of the method combining SVM with D-S evidence theory and integrating color features and geometry features was higher than SVM method with only color or geometry features.

Sign in / Sign up

Export Citation Format

Share Document