scholarly journals Active Learning for Recognition of Shipwreck Target in Side-Scan Sonar Image

2019 ◽  
Vol 11 (3) ◽  
pp. 243 ◽  
Author(s):  
Bangyan Zhu ◽  
Xiao Wang ◽  
Zhengwei Chu ◽  
Yi Yang ◽  
Juan Shi
Keyword(s):  
Feature Extraction ◽  
Target Recognition ◽  
Recognition Rate ◽  
Correct Recognition ◽  
Matching Model ◽  
Side Scan Sonar ◽  
Recognition Model ◽  
Adaboost Algorithm ◽  
Recognition Process ◽  
Correct Recognition Rate

In order to realize the automatic and accurate recognition of shipwreck targets in side-scan sonar (SSS) waterfall images, a pipeline that contains feature extraction, selection, and shipwreck recognition, an AdaBoost model was constructed by sample images. Shipwreck targets are detected quickly by a nonlinear matching model, and a shipwreck recognition in SSS waterfall images are given, and according to a wide set of combinations of different types of these individual procedures, the model is able to recognize the shipwrecks accurately. Firstly, two feature-extraction methods suitable for recognizing SSS shipwreck targets from natural sea bottom images were studied. In addition to these two typical features, some commonly used features were extracted and combined as comprehensive features to characterize shipwrecks from various feature spaces. Based on Independent Component Analysis (ICA), the preferred features were selected from the comprehensive features, which avoid dimension disaster and improved the correct recognition rate. Then, the Gentle AdaBoost algorithm was studied and used for constructing the shipwreck target recognition model using sample images. Finally, a shipwreck target recognition process for the SSS waterfall image was given, and the process contains shipwreck target fast detection by a nonlinear matching model and accurate recognition by the Gentle AdaBoost recognition model. The results show that the correct recognition rate of the model for the sample image is 97.44%, while the false positive rate is 3.13% and the missing detection rate is 0. This study of a measured SSS waterfall image confirms the correctness of the recognition process and model.

Feature Extraction by Decryption to Target

2014 ◽  
Vol 1008-1009 ◽  
pp. 1509-1512
Author(s):  
Qing E Wu ◽  
Hong Wang ◽  
Li Fen Ding
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Extraction Method ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Correct Recognition ◽  
Palmprint Recognition ◽  
Feature Extraction Method ◽  
Energy Feature ◽  
Correct Recognition Rate

To carry out an effective classification and recognition for target, this paper studied the target owned characteristics, discussed a decryption algorithm, gave a feature extraction method based on the decryption process, and extracted the feature of palmprint in region of interest. Moreover, this paper used the wavelet transform to extract the energy feature of target, gave an approach on matching and recognition to improve the correctness and efficiency of existing recognition approaches, and compared it with existing approaches of palmprint recognition by experiments. The experiment results show that the correct recognition rate of the approach in this paper is improved averagely by 2.34% than that of the existing recognition approaches.

scholarly journals IDENTIFIKASI IRIS MENGGUNAKAN IMPROVED LEVENBERG-MARQUARDT

2015 ◽  
Vol 1 (1) ◽  
pp. 10
Author(s):  
Rocky Yefrenes Dillak
Keyword(s):  
Hough Transform ◽  
Recognition Rate ◽  
Correct Recognition ◽  
Gray Level ◽  
Maximum Probability ◽  
Circular Hough Transform ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Correct Recognition Rate

Sistem biometrika adalah suatu sistem pengenalan diri menggunakan bagian tubuh atau perilaku manusia seperti sidik jari, telapak tangan, telinga, retina, iris mata, wajah, suhu tubuh, tanda tangan, dll. Iris mata merupakan salah satu biometrika yang sangat stabil, handal, akurat dan merupakan metode autentikasi biometrika tercepat  oleh karena itu merupakan suatu topik penelitian yang sangat diminati oleh banyak peneliti. Penelitian ini bertujuan untuk mengembangkan suatu metode yang dapat digunakan untuk mengidentifikasi secara otomatis seseorang berdasarkan citra iris mata miliknya menggunakan jaringan syaraf tiruan levenberg-marquardt. Penelitian ini menggunakan metode deteksi tepi cany dan circular hough transform untuk segmentasi wilayah iris yang terletak diantara pupil dan sclera serta metode ekstraksi ciri gray level cooccurence matrix (GLCM) yang digunakan untuk ekstraksi ciri. Ciri-ciri tersebut adalah maximum probability, correlation, contrast, energy, homogeneity, dan entropy. Ciri-ciri tersebut kemudian dilatih menggunakan jaringan syaraf tiruan dengan aturan pembelajaran levenberg–marquardt algorithm untuk mengidentifikasi seseorang berdasarkan citra irisnya. Penelitian ini menggunakan 150 data citra iris yang masing-masing terbagi atas 100 data citra iris untuk pelatihan dan 50 data citra iris  untuk pengujian. Berdasarkan hasil pengujian yang dilakukan diperoleh correct recognition rate (CRR) sebesar 99.98%  yang menunjukkan bahwa metode ini dapat digunakan untuk mengidentifikasi secara otomatis seseorang berdasarkan citra iris mata miliknya.

Study on face recognition under unconstrained conditions based on LBP and deep learning

2020 ◽  
pp. 1-12
Author(s):  
Liping Zhou ◽  
Mingwei Gao ◽  
Chun He
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Input Data ◽  
Recognition Rate ◽  
Texture Feature ◽  
Recognition Algorithm ◽  
Correct Recognition ◽  
Layer By Layer ◽  
The Face ◽  
Correct Recognition Rate

At present, the correct recognition rate of face recognition algorithm is limited under unconstrained conditions. To solve this problem, a face recognition algorithm based on deep learning under unconstrained conditions is proposed in this paper. The algorithm takes LBP texture feature as the input data of deep network, and trains the network layer by layer greedily to obtain optimized parameters of network, and then uses the trained network to predict the test samples. Experimental results on the face database LFW show that the proposed algorithm has higher correct recognition rate than some traditional algorithms under unconstrained conditions. In order to further verify its effectiveness and universality, this algorithm was also tested in YALE and YALE-B, and achieved a high correct recognition rate as well, which indicated that the deep learning method using LBP texture feature as input data is effective and robust to face recognition.

Application of ART-2 Neural Network and Invariant Moment in Image Pattern Recognition

2012 ◽  
Vol 433-440 ◽  
pp. 4014-4019 ◽  
Author(s):  
Lei Hao ◽  
Yue Hua Gao ◽  
Rui Jun Jia
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Recognition Rate ◽  
Experimental Results ◽  
Invariant Moment ◽  
Recognition Model ◽  
Image Pattern

This paper mainly uses image pre-processing and feature extraction to calculate the invariant moment of image, and ultimately realizes the image pattern recognition based on ART-2 neural network. Experimental results show that ART-2 neural network has high recognition rate. It also solves the contradiction between network's plasticity and stability, when new recognition model appears.

scholarly journals The Use of Fuzzy BackPropagation Neural Networks for the Early Diagnosis of Hypoxic Ischemic Encephalopathy in Newborns

2011 ◽  
Vol 2011 ◽  
pp. 1-5 ◽  
Author(s):  
Liu Li ◽  
Huo Liqing ◽  
Lu Hongru ◽  
Zhang Feng ◽  
Zheng Chongxun ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Early Diagnosis ◽  
Recognition Rate ◽  
Hypoxic Ischemic Encephalopathy ◽  
Correct Recognition ◽  
Backpropagation Neural Networks ◽  
Artificial Neural ◽  
Correct Recognition Rate ◽  
Ischemic Encephalopathy

Objective. To establish an early diagnostic system for hypoxic ischemic encephalopathy (HIE) in newborns based on artificial neural networks and to determine its feasibility.Methods. Based on published research as well as preliminary studies in our laboratory, multiple noninvasive indicators with high sensitivity and specificity were selected for the early diagnosis of HIE and employed in the present study, which incorporates fuzzy logic with artificial neural networks.Results. The analysis of the diagnostic results from the fuzzy neural network experiments with 140 cases of HIE showed a correct recognition rate of 100% in all training samples and a correct recognition rate of 95% in all the test samples, indicating a misdiagnosis rate of 5%.Conclusion. A preliminary model using fuzzy backpropagation neural networks based on a composite index of clinical indicators was established and its accuracy for the early diagnosis of HIE was validated. Therefore, this method provides a convenient tool for the early clinical diagnosis of HIE.

A Recognition Algorithm for M-QAM Based on Amplitude Distribution

2011 ◽  
Vol 128-129 ◽  
pp. 20-24
Author(s):  
Lu Yuan Tan ◽  
Qian Wang ◽  
Xiao Yan ◽  
Kai Yu Qin
Keyword(s):  
Carrier Frequency ◽  
Carrier Frequency Offset ◽  
Recognition Rate ◽  
Amplitude Distribution ◽  
Frequency Offset ◽  
Recognition Algorithm ◽  
Automatic Recognition ◽  
Correct Recognition ◽  
Nonlinear Amplification ◽  
Correct Recognition Rate

An automatic recognition algorithm for M-QAM based on the amplitude distribution is proposed. This algorithm uses the normalized amplitude distribution to achieve automatic recognition for M-QAM signals, and enhances the correct recognition rate through the nonlinear amplification. Compared with the recognition algorithm based on amplitude moment, this algorithm does not need those prior conditions, such as carrier frequency offset, code element rate, amplitude factor and so on. The simulation confirmed that, when SNR≥16dB the correct recognition rate of this algorithm is greater than 90%.

A Hierarchical Target Recognition Method Based on Image Processing

2017 ◽  
pp. 91-107
Author(s):  
QingE Wu ◽  
Weidong Yang
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Target Recognition ◽  
Wavelet Packet ◽  
Recognition Rate ◽  
Image Decomposition ◽  
Extraction Process ◽  
Practical Significance ◽  
Recognition Method ◽  
Target Feature

In order to provide an accurate and rapid target recognition method for some military affairs, public security, finance and other departments, this paper studies firstly a variety of fuzzy signal, analyzes the uncertainties classification and their influence, eliminates fuzziness processing, presents some methods and algorithms for fuzzy signal processing, and compares with other methods on image processing. Moreover, this paper uses the wavelet packet analysis to carry out feature extraction of target for the first time, extracts the coefficient feature and energy feature of wavelet transformation, gives the matching and recognition methods, compares with the existing target recognition methods by experiment, and presents the hierarchical recognition method. In target feature extraction process, the more detailed and rich texture feature of target can be obtained by wavelet packet to image decomposition to compare with the wavelet decomposition. In the process of matching and recognition, the hierarchical recognition method is presented to improve the recognition speed and accuracy. The wavelet packet transformation is used to carry out the image decomposition. Through experiment results, the proposed recognition method has the high precision, fast speed, and its correct recognition rate is improved by an average 6.13% than that of existing recognition methods. These researches development in this paper can provide an important theoretical reference and practical significance to improve the real-time and accuracy on fuzzy target recognition.

Target Type Recognition Algorithm of Imaging GIF Fuze

2013 ◽  
Vol 416-417 ◽  
pp. 1170-1175
Author(s):  
Bin Liu ◽  
Yang Yu Fan ◽  
Jian Guo
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Edge Detection ◽  
Real Time ◽  
Target Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Target Type ◽  
Time Performance ◽  
Simulation Results

According to the requirement of aerial infrared target recognition, a group of image segmentation, edge detection, feature extraction, type recognition algorithms are put forward in this article after analysis and comparison of many algorithms. The simulation results show that the typical aerial target type recognition rate of this group of algorithms can reach more than 80%, so that the algorithms have higher ability of target type recognition, and its real-time performance can meet the requirement of imaging GIF fuze.

Recognition Memory for Photographs in Homogeneous Sequences

1967 ◽  
Vol 24 (3_suppl) ◽  
pp. 1181-1182 ◽  
Author(s):  
Michael J. A. Howe
Keyword(s):  
Recognition Memory ◽  
Recognition Rate ◽  
Correct Recognition ◽  
Correct Recognition Rate

Ss inspected sequences of photographs belonging to a particular category. Half of the items were presented twice within a sequence, and the task was to recognize the items which had occurred earlier. Correct recognition rate was around 72%, irrespective of whether the number of intervening items was 8, 16, 24, or 32.

Accuracy of Visual Recognition in a Complex Natural Environment

1972 ◽  
Vol 34 (2) ◽  
pp. 445-446 ◽  
Author(s):  
Gerard Ezinga ◽  
Guy L. Rowland
Keyword(s):  
Natural Environment ◽  
Visual Memory ◽  
Visual Recognition ◽  
Recognition Rate ◽  
Suburban Area ◽  
Correct Recognition ◽  
Correct Recognition Rate

The accuracy of long-term visual memory in terms of recognition was examined in a complex natural environment, a car route through a typical suburban area. The correct recognition rate for 8 Ss compared favorably with results obtained in laboratory settings

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