scholarly journals Recognition of Power Equipment Based on Multitask Sparse Representation

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Lei Lei ◽  
Jian Wu ◽  
Shuhai Zheng ◽  
Xinyi Zhang ◽  
Liang Wang ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Recognition Rate ◽  
Random Projection ◽  
Projection Algorithm ◽  
Practical Significance ◽  
Power Equipment ◽  
Noise Interference ◽  
Multiple Feature ◽  
Inspection And Maintenance ◽  
Correct Recognition Rate

Image analysis of power equipment has important practical significance for power-line inspection and maintenance. This paper proposes an image recognition method for power equipment based on multitask sparse representation. In the feature extraction stage, based on the two-dimensional (2D) random projection algorithm, multiple projection matrices are constructed to obtain the multilevel features of the image. In the classification process, considering that the image acquisition process will inevitably be affected by factors such as light conditions and noise interference, the proposed method uses the multitask compressive sensing algorithm (MtCS) to jointly represent multiple feature vectors to improve the accuracy and robustness of reconstruction. In the experiment, the images of three types of typical power equipment of insulators, transformers, and circuit breakers are classified. The correct recognition rate of the proposed method reaches 94.32%. In addition, the proposed method can maintain strong robustness under the conditions of noise interference and partial occlusion, which further verifies its effectiveness.

Human action recognition using simple geometric features and a finite state machine

2013 ◽  
Vol 18 (2-3) ◽  
pp. 49-60 ◽  
Author(s):  
Damian Dudzńiski ◽  
Tomasz Kryjak ◽  
Zbigniew Mikrut
Keyword(s):  
Action Recognition ◽  
Finite State Machine ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Stream ◽  
State Machine ◽  
Recognition Algorithm ◽  
Finite State ◽  
Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

scholarly journals Applying a random projection algorithm to optimize machine learning model for breast lesion classification

2021 ◽  
pp. 1-1
Author(s):  
Morteza Heidari ◽  
Sivaramakrishnan Lakshmivarahan ◽  
Seyedehnafiseh Mirniaharikandehei ◽  
Gopichandh Danala ◽  
Sai Kiran R. Maryada ◽  
...  
Keyword(s):  
Machine Learning ◽  
Breast Lesion ◽  
Random Projection ◽  
Learning Model ◽  
Projection Algorithm ◽  
Machine Learning Model ◽  
Lesion Classification

Online Biological Literature MRI Image Classification Study

2013 ◽  
Vol 694-697 ◽  
pp. 2336-2340
Author(s):  
Yun Feng Yang ◽  
Feng Xian Tang
Keyword(s):  
Recognition Performance ◽  
Recognition Rate ◽  
Image Features ◽  
Feature Identification ◽  
Identification Method ◽  
Image Library ◽  
Text Information ◽  
Correct Recognition Rate ◽  
Biological Literature ◽  
Mri Image

In order to construct a certain standard structure MRI (Magnetic resonance imaging) image library by extracting and collating unstructured literature data information, an identification method of the image and text information fusion is proposed. The method makes use of PHOW (Pyramid Histogram Of Words) to represent image features, combines with the word frequency characteristics of the embedded icon note (text), and then uses posterior multiplication fusion method to complete the classification and identification of the online biological literature MRI image. The experimental results show that this method has better correct recognition rate and better recognition performance than feature identification method only based on PHOW or text. The study can offer use for reference to construct other structured professional database from online literature.

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.

scholarly journals Randomized Projection Learning Method for Dynamic Mode Decomposition

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2803
Author(s):  
Sudam Surasinghe ◽  
Erik Bollt
Keyword(s):  
Dimensional Space ◽  
Low Cost ◽  
Random Projection ◽  
Dynamic Mode Decomposition ◽  
Projection Algorithm ◽  
Dynamic Mode ◽  
Operator Matrix ◽  
Full Spectrum ◽  
Practical Applications ◽  
Mode Decomposition

A data-driven analysis method known as dynamic mode decomposition (DMD) approximates the linear Koopman operator on a projected space. In the spirit of Johnson–Lindenstrauss lemma, we will use a random projection to estimate the DMD modes in a reduced dimensional space. In practical applications, snapshots are in a high-dimensional observable space and the DMD operator matrix is massive. Hence, computing DMD with the full spectrum is expensive, so our main computational goal is to estimate the eigenvalue and eigenvectors of the DMD operator in a projected domain. We generalize the current algorithm to estimate a projected DMD operator. We focus on a powerful and simple random projection algorithm that will reduce the computational and storage costs. While, clearly, a random projection simplifies the algorithmic complexity of a detailed optimal projection, as we will show, the results can generally be excellent, nonetheless, and the quality could be understood through a well-developed theory of random projections. We will demonstrate that modes could be calculated for a low cost by the projected data with sufficient dimension.

The Recognition of Handwritten Digital Based on BP Neural Network

2013 ◽  
Vol 416-417 ◽  
pp. 1239-1243
Author(s):  
Shan Gao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Practical Significance ◽  
Test Results ◽  
Recognition Method ◽  
Number Recognition ◽  
Recognition Ability ◽  
Fuzzy Recognition ◽  
Zip Code

The article put forward to new recognition method of handwritten digital based on BP neural network. Its recognition process mainly includes ten aspect: incline correction of handwritten number, edge detection and separation of a set number, binarization, denoising, extraction of numerals, window scaling, location standardization, thinning, extraction of numeral feature and fuzzy recognition based on BP neural network. The test results show that the recognition rate of this method can be over 92 percent. The recognition time of characters for character is less than 1.1 second, which means that the method is more effective recognition ability and can better satisfy the real system requirements.It should be widely applied practical significance for Book Number Recognition, zip code recognition sorting.

Hyperspectral Image Classification via Multiple-Feature-Based Improved Sparse Representation

2019 ◽  
Vol 39 (5) ◽  
pp. 0528004
Author(s):  
李非燕 Li Feiyan ◽  
霍宏涛 Huo Hongtao ◽  
李静 Li Jing ◽  
白杰 Bai Jie
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Hyperspectral Image Classification ◽  
Multiple Feature ◽  
Feature Based

Feature and Rank Level Fusion for Privacy Preserved Multi-Biometric System

2015 ◽  
Vol 7 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Padma Polash Paul ◽  
Marina Gavrilova
Keyword(s):  
Main Idea ◽  
Random Projection ◽  
Decision Fusion ◽  
Projection Algorithm ◽  
Human Cognition ◽  
Biometric System ◽  
Biometric Technology ◽  
Template Security ◽  
Multi Level ◽  
Level Fusion

Privacy protection in biometric system is a newly emerging biometric technology that can provide the protection against various attacks by intruders. In this paper, the authors have presented a multi-level of random projection method based on face and ear biometric traits. Privacy preserved templates are used in the proposed system. The main idea behind the privacy preserve computation is the random projection algorithm. Multiple random projection matrixes are used to generate multiple templates for biometric authentication. Newly introduced random fusion method is used in the proposed system; therefore, proposed method can provide better template security, privacy and feature quality. Multiple randomly fused templates are used for recognition purpose and finally decision fusion is applied to generate the final classification result. The proposed method works in a similar way human cognition for face recognition works, furthermore it preserve privacy and multimodality of the system.

A HYBRID SCHEME FOR HANDPRINTED NUMERAL RECOGNITION BASED ON A SELF-ORGANIZING NETWORK AND MLP ClASSIFIERS

2002 ◽  
Vol 16 (07) ◽  
pp. 845-864 ◽  
Author(s):  
UJJWAL BHATTACHARYA ◽  
TANMOY KANTI DAS ◽  
AMITAVA DATTA ◽  
SWAPAN KUMAR PARUI ◽  
BIDYUT BARAN CHAUDHURI
Keyword(s):  
Recognition Rate ◽  
Structural Features ◽  
Data Set ◽  
Novel Approach ◽  
Tree Classifier ◽  
Sample Data ◽  
Validation Set ◽  
Numeral Recognition ◽  
Correct Recognition Rate ◽  
Self Organizing

This paper proposes a novel approach to automatic recognition of handprinted Bangla (an Indian script) numerals. A modified Topology Adaptive Self-Organizing Neural Network is proposed to extract a vector skeleton from a binary numeral image. Simple heuristics are considered to prune artifacts, if any, in such a skeletal shape. Certain topological and structural features like loops, junctions, positions of terminal nodes, etc. are used along with a hierarchical tree classifier to classify handwritten numerals into smaller subgroups. Multilayer perceptron (MLP) networks are then employed to uniquely classify the numerals belonging to each subgroup. The system is trained using a sample data set of 1800 numerals and we have obtained 93.26% correct recognition rate and 1.71% rejection on a separate test set of another 7760 samples. In addition, a validation set consisting of 1440 samples has been used to determine the termination of the training algorithm of the MLP networks. The proposed scheme is sufficiently robust with respect to considerable object noise.

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