Algorithm of designing compound recognition system on the basis of combining classifiers with simultaneous splitting feature space into competence areas

2008 ◽  
Vol 12 (4) ◽  
pp. 415-425 ◽  
Author(s):  
Konrad Jackowski ◽  
Michal Wozniak
Keyword(s):  
Feature Space ◽  
Recognition System ◽  
Combining Classifiers ◽  
Simultaneous Splitting

scholarly journals Recognizing Cursive Typewritten Text Using Segmentation-Free System

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Mohammad S. Khorsheed
Keyword(s):  
Feature Extraction ◽  
Feature Vector ◽  
Feature Space ◽  
Recognition System ◽  
Image Feature ◽  
Text Recognition ◽  
Statistical Features ◽  
Free System ◽  
Novel Method ◽  
Image Pixels

Feature extraction plays an important role in text recognition as it aims to capture essential characteristics of the text image. Feature extraction algorithms widely range between robust and hard to extract features and noise sensitive and easy to extract features. Among those feature types are statistical features which are derived from the statistical distribution of the image pixels. This paper presents a novel method for feature extraction where simple statistical features are extracted from a one-pixel wide window that slides across the text line. The feature set is clustered in the feature space using vector quantization. The feature vector sequence is then injected to a classification engine for training and recognition purposes. The recognition system is applied to a data corpus which includes cursive Arabic text of more than 600 A4-size sheets typewritten in multiple computer-generated fonts. The system performance is compared to a previously published system from the literature with a similar engine but a different feature set.

INVARIANT OBJECT RECOGNITION BASED ON A NEURAL NETWORK OF CASCADED RCE NETS

1993 ◽  
Vol 07 (04) ◽  
pp. 815-829 ◽  
Author(s):  
WEI LI ◽  
NASSER M. NASRABADI
Keyword(s):  
Neural Network ◽  
Learning Strategy ◽  
Feature Vector ◽  
Recognition Rate ◽  
Feature Space ◽  
Recognition System ◽  
Coulomb Energy ◽  
Geometrical Shape ◽  
Shape Information ◽  
Shift Rotation

A neural network of cascaded Restricted Coulomb Energy (RCE) nets is constructed for the recognition of two-dimensional objects. A number of RCE nets are cascaded together to form a classifier where the overlapping decision regions are progressively resolved by a set of cascaded networks. Similarities among objects which have complex decision boundaries in the feature space are resolved by this multi-net approach. The generalization ability of an RCE net recognition system, referring to the ability of the system to correctly recognize a new pattern even when the number of learning exemplars is small, is increased by the proposed coarse-to-fine learning strategy. A feature extraction technique is used to map the geometrical shape information of an object into an ordered feature vector of fixed length. This feature vector is then used as an input to the neural network. The feature vector is invariant to object changes such as positional shift, rotation, scaling, illumination variance, variation of camera setup, perspective distortion, and noise distortion. Experimental results for recognition of several objects are also presented. A correct recognition rate of 100% was achieved for both the training and the testing input patterns.

EMG AMPLITUDE ESTIMATORS BASED ON PROBABILITY DISTRIBUTION FOR MUSCLE–COMPUTER INTERFACE

2013 ◽  
Vol 12 (03) ◽  
pp. 1350016 ◽  
Author(s):  
ANGKOON PHINYOMARK ◽  
FRANCK QUAINE ◽  
YANN LAURILLAU ◽  
SIRINEE THONGPANJA ◽  
CHUSAK LIMSAKUL ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Feature Space ◽  
Recognition System ◽  
Surface Emg ◽  
Classification Performance ◽  
Computer Interface ◽  
Computational Time ◽  
Flexor Muscle ◽  
Emg Amplitude ◽  
Emg Signal

To develop an advanced muscle–computer interface (MCI) based on surface electromyography (EMG) signal, the amplitude estimations of muscle activities, i.e., root mean square (RMS) and mean absolute value (MAV) are widely used as a convenient and accurate input for a recognition system. Their classification performance is comparable to advanced and high computational time-scale methods, i.e., the wavelet transform. However, the signal-to-noise-ratio (SNR) performance of RMS and MAV depends on a probability density function (PDF) of EMG signals, i.e., Gaussian or Laplacian. The PDF of upper-limb motions associated with EMG signals is still not clear, especially for dynamic muscle contraction. In this paper, the EMG PDF is investigated based on surface EMG recorded during finger, hand, wrist and forearm motions. The results show that on average the experimental EMG PDF is closer to a Laplacian density, particularly for male subject and flexor muscle. For the amplitude estimation, MAV has a higher SNR, defined as the mean feature divided by its fluctuation, than RMS. Due to a same discrimination of RMS and MAV in feature space, MAV is recommended to be used as a suitable EMG amplitude estimator for EMG-based MCIs.

scholarly journals Stable descriptors in image recognition tasks

2020 ◽  
Vol 47 (3) ◽  
pp. 93-100
Author(s):  
V. B. Melekhin ◽  
V. M. Khachumov
Keyword(s):  
Image Recognition ◽  
Experimental Studies ◽  
Feature Space ◽  
Recognition System ◽  
Covariance Matrices ◽  
Methodological Basis ◽  
Image Descriptors ◽  
Facies Classification ◽  
Definition Of

Objective. The objective of the study is to determine various stable characteristics of images (semi-invariants and invariants) as descriptors necessary for the formation of a feature space of standards intended for recognizing images of different nature belonging to different classes of objects. Methods. The authors propose metrics for evaluating the proximity of the recognized image to a given standard in the space of covariance matrices, based on the obtained descriptors as a methodological basis for constructing image recognition methods. Results. The content of the main stages of selecting descriptors for a given class of objects is developed, taking into account the different illumination of the recognized images. The effectiveness of the results obtained is confirmed by experimental studies related to the solution of the problem of recognition of special images - facies. Conclusions. The definition of stable image descriptors as invariants or semi-invariants to zoom and brightness transformations allows solving the problems of facies classification in conditions of the unstable shooting of recognized images. The images can be rotated and shifted in any way. In general, the proposed approach allows developing an effective image recognition system in the presence of various types of interference on the recognized images. 

scholarly journals Designing Efficient Feature Space Reduction Schemes for Multi-Algorithmic Iris Recognition System based on Feature Level Fusion of Texture and Phase Features

2019 ◽  
Vol 8 (3) ◽  
pp. 2761-2767
Keyword(s):  
Iris Recognition ◽  
Gabor Filter ◽  
Feature Space ◽  
Recognition System ◽  
Feature Reduction ◽  
High Dimensional ◽  
Reduction Scheme ◽  
Biometric Systems ◽  
Feature Level Fusion ◽  
Level Fusion

Iris recognition system has gained prominent focus because of its uniqueness, stability over time. But the recognition level of single biometric based recognition systems is greatly affected by environmental conditions, physiological deficiency. Multi-biometric systems diminish this problem with the fusion of features collected from various traits or samples of the same trait, a single trait by employing multiple algorithms or multiple instances. To gain the advantages of multi-biometric systems in iris recognition, a Multi-algorithmic iris recognition system has been proposed where Texture features from iris are extracted by using 2D-Log Gabor filter and Phase features are extracted by Haar Wavelet; And these features can be integrated at various levels like Decision, Rank, Score, feature, and pixel. Even though the feature level fusion contains rich information about biometric samples when compared to remaining fusion levels; it involves mapping complexity, high dimensional feature space. To gain advantage of feature level fusion in iris recognition and to overcome the problem of resulted high dimensional feature space, Genetic Algorithm (GA) based reduction scheme, Principal Component Analysis (PCA) reduction strategy and a hybrid reduction scheme which is a combination of PCA and GA have been applied to reduce the resulted feature space. The performance of these reduction strategies have evaluated on CASIA iris database, IIT Delhi iris database using Machine Learning approaches. The results have shown that the feature space has dramatically reduced while keeping recognition accuracy and also revealed that space and time requirements have significantly decreased after employing feature reduction schemes.

Learning Object Representations Using A Priori Constraints Within ORASSYLL

2001 ◽  
Vol 13 (2) ◽  
pp. 389-410 ◽  
Author(s):  
Norbert Krüger
Keyword(s):  
Developmental Psychology ◽  
Statistical Evaluation ◽  
A Priori ◽  
Evaluation Criteria ◽  
Feature Space ◽  
Recognition System ◽  
Object Representations ◽  
Complex Scenes ◽  
Bias Variance

In this article, a biologically plausible and efficient object recognition system (called ORASSYLL) is introduced, based on a set of a priori constraints motivated by findings of developmental psychology and neuro-physiology. These constraints are concerned with the organization of the input in local and corresponding entities, the interpretation of the input by its transformation in a highly structured feature space, and the evaluation of features extracted from an image sequence by statistical evaluation criteria. In the context of the bias-variance dilemma, the functional role of a priori knowledge within ORASSYLL is discussed. In contrast to systems in which object representations are defined manually, the introduced constraints allow an autonomous learning from complex scenes.

Sign Language Recognition System Simulated for Video Captured with Smart Phone Front Camera

2016 ◽  
Vol 6 (5) ◽  
pp. 2176 ◽  
Author(s):  
G. Ananth Rao ◽  
P.V.V. Kishore
Keyword(s):  
Sign Language ◽  
Smart Phone ◽  
Feature Space ◽  
Recognition System ◽  
Frame Rate ◽  
Language Recognition ◽  
Mobile Platforms ◽  
Sign Language Recognition ◽  
Indian Sign Language ◽  
Matching Score

<p>This works objective is to bring sign language closer to real time implementation on mobile platforms. A video database of Indian sign language is created with a mobile front camera in selfie mode. This video is processed on a personal computer by constraining the computing power to that of a smart phone with 2GB ram. Pre-filtering, segmentation and feature extraction on video frames creates a sign language feature space. Minimum distance classification of the sign feature space converts signs to text or speech. ASUS smart phone with 5M pixel front camera captures continuous sign videos containing around 240 frames at a frame rate of 30fps. Sobel edge operator’s power is enhanced with morphology and adaptive thresholding giving a near perfect segmentation of hand and head portions. Word matching score (WMS) estimates performance of the proposed method with an average WMS of around 90.58%.</p>

COLOR IMAGE SEGMENTATION WITH SUPPORT VECTOR MACHINES: APPLICATIONS TO ROAD SIGNS DETECTION

2008 ◽  
Vol 18 (04) ◽  
pp. 339-345 ◽  
Author(s):  
BOGUSŁAW CYGANEK
Keyword(s):  
Input Data ◽  
Color Image ◽  
Feature Space ◽  
Recognition System ◽  
Color Image Segmentation ◽  
Support Vector ◽  
The Road ◽  
Road Signs ◽  
Higher Dimensional ◽  
Vector Machines

In this paper we propose efficient color segmentation method which is based on the Support Vector Machine classifier operating in a one-class mode. The method has been developed especially for the road signs recognition system, although it can be used in other applications. The main advantage of the proposed method comes from the fact that the segmentation of characteristic colors is performed not in the original but in the higher dimensional feature space. By this a better data encapsulation with a linear hypersphere can be usually achieved. Moreover, the classifier does not try to capture the whole distribution of the input data which is often difficult to achieve. Instead, the characteristic data samples, called support vectors, are selected which allow construction of the tightest hypersphere that encloses majority of the input data. Then classification of a test data simply consists in a measurement of its distance to a centre of the found hypersphere. The experimental results show high accuracy and speed of the proposed method.

DYNAMIC HAND MOTION RECOGNITION BASED ON TRANSIENT AND STEADY-STATE EMG SIGNALS

2012 ◽  
Vol 09 (01) ◽  
pp. 1250007 ◽  
Author(s):  
DAPENG YANG ◽  
JINGDONG ZHAO ◽  
LI JIANG ◽  
HONG LIU
Keyword(s):  
Steady State ◽  
Motion Control ◽  
Feature Space ◽  
Recognition System ◽  
Support Vector ◽  
Motion Recognition ◽  
Hand Motion ◽  
Dynamic Motion ◽  
Detection Delay ◽  
The Time Domain

Traditional motion recognition methods developed on the basis of steady-state electromyography (EMG) signals cannot well deal with the transient EMG signals. Thus, a large quantity of incorrect classification outputs would be introduced during the dynamic stages of the motions. In order to achieve a high-accuracy recognition system especially for dynamic motion control, this paper combines the transient EMG and the steady-state EMG signals together for training the recognition system. A threshold decision method is utilized in the time-domain feature space to collect the combined EMG signals. Besides, a statistical classifier named support vector machine is adopted in the online recognition procedure to distinguish the motion types. Experiments are conducted to quantify the classification accuracy and response delay of the recognition systems, and compare these with traditional steady-state EMG-based methods. The results indicate that the recognition accuracy can be greatly improved and the detection delay of the motions can be significantly compressed in the transient stages of the hand motions. The method shows a promising application in the dynamic motion control of dexterous prosthetic hands in the future.

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