Feature learning in feature-sample networks using multi-objective optimization

Data and knowledge representation are fundamental concepts in machine learning. The quality of the representation impacts the performance of the learning model directly. Feature learning transforms or enhances raw data to structures that are effectively exploited by those models. In recent years, several works have been using complex networks for data representation and analysis. However, no feature learning method has been proposed for such category of techniques. Here, we present an unsupervised feature learning mechanism that works on datasets with binary features. First, the dataset is mapped into a feature--sample network. Then, a multi-objective optimization process selects a set of new vertices to produce an enhanced version of the network. The new features depend on a nonlinear function of a combination of preexisting features. Effectively, the process projects the input data into a higher-dimensional space. To solve the optimization problem, we design two metaheuristics based on the lexicographic genetic algorithm and the improved strength Pareto evolutionary algorithm (SPEA2). We show that the enhanced network contains more information and can be exploited to improve the performance of machine learning methods. The advantages and disadvantages of each optimization strategy are discussed.Comment: 7 pages, 4 figure

A. Eye Detection Using Varients of Hough Transform B. Off-Line Signature Verification

PART (A): EYE DETECTION USING VARIANTS OF HOUGH TRANSFORM: Broadly eye detection is the process of tracking the location of human eye in a face image. Previous approaches use complex techniques like neural network, Radial Basis Function networks, Multi-Layer Perceptrons etc. In the developed project human eye is modeled as a circle (iris; the black circular region of eye) enclosed inside an ellipse (eye-lashes). Due to the sudden intensity variations in the iris with respect the inner region of eye-lashes the probability of false acceptance is very less. Since the image taken is a face image the probability of false acceptance further reduces. Hough transform is used for circle (iris) and ellipse (eye-lash) detection. Hough transform was the obvious choice because of its resistance towards the holes in the boundary and noise present in the image. Image smoothing is done to reduce the presence of noise in the image further it makes the image better for further processing like edge detection (Prewitt method). Compared to the aforementioned models the proposed model is simple and efficient. The proposed model can further be improved by including various features like orientation angle of eye-lashes (which is assumed constant in the proposed model), and by making the parameters adaptive. PART (B): OFF-LINE SIGNATURE VERIFICATION: Hand-written signature is widely used for authentication and identification of individual. It has been the target for fraudulence ever since. A novel off-line signature verification algorithm has been developed and tested successfully. Since the hand-written signature can be random, because of presence of various curves and features, techniques like character recognition cannot be applied for signature verification. The proposed algorithm incorporates a soft-computing technique “CLUSTERING” for extraction of feature points from the image of the signature. These feature points or centers are updated using the clustering update equations for required number of times, then these acts as extracted feature points of the signature image. To avoid interpersonal variation 6 to 8 signature images of the same person are taken and feature points are trained. These trained feature points are compared with the test signature images and based on a specific threshold, the signature is declared original or forgery. This approach works well if there is a high variation in the original signature, but for signatures with low variation, it produces incorrect results

Result Oriented Based Face Recognition using Neural Network with Erosion and Dilation Technique

It has been observed that many face recognition algorithms fail to recognize faces after plastic surgery and wearing the spec/glasses which are the new challenge to automatic face recognition. Face detection is one of the challenging problems in the image processing. This seminar, introduce a face detection and recognition system to detect (finds) faces from database of known people. To detect the face before trying to recognize it saves a lot of work, as only a restricted region of the image is analyzed, opposite to many algorithms which work considering the whole image. In This , we gives study on Face Recognition After Plastic Surgery (FRAPS )and after wearing the spec/glasses with careful analysis of the effects on face appearance and its challenges to face recognition. To address FRAPS and wearing the spec/glasses problem, an ensemble of An Optimize Wait Selection By Genetic Algorithm For Training Artificial Neural Network Based On Image Erosion and Dilution Technology. Furthermore, with our impressive results, we suggest that face detection should be paid more attend to. To address this problem, we also used Edge detection method to detect i/p image properly or effectively. With this Edge Detection also used genetic algorithm to optimize weight using artificial neural network (ANN)and save that ANN file to database .And use that ANN file to compare face recognition in future DOI: 10.17762/ijritcc2321-8169.16041

Inteligentni sustav strojnog vida za automatiziranu kontrolu kvalitete keramičkih pločica

U članku je prikazan automatizirani sustav za vizualnu kontrolu kvalitete keramičkih pločica uporabom strojnog računalnog vida. Proces proizvodnje keramičkih pločica u gotovo svim svojim fazama zadovoljavajuće je automatiziran, osim u fazi kontrole kvalitete, na kraju procesa. Kvaliteta keramičkih pločica provjerava se i ocjenjuje postupcima vizualne provjere kvalitete, gdje se ljudski čimbenik nastoji zamijeniti sustavom strojnog računalnog vida u funkciji povećanja kvalitete i povećanja efikasnosti proizvodnje. Kvaliteta keramičkih pločica definirana je dimenzijama i površinskim značajkama. Predstavljeni sustav strojnog vida analizira geometrijske i površinske značajke te odlučuje o kvaliteti keramičkih pločica na temelju navedenih značajki uporabom klasifikatora s neuronskom mrežom. Predstavljene su također i metode koje poboljšavaju izdvajanje geometrijskih i površinskih svojstava. Potvrđena je efikasnost obradnih algoritama i primjena neuronskog klasifikatora kao zamjene za vizualnu kontrolu kvalitete ljudskim vidom

Inteligentni sustav strojnog vida za automatiziranu kontrolu kvalitete keramičkih pločica

Intelligent system for automated visual quality control of ceramic tiles based on machine vision is presented in this paper. The ceramic tiles production process is almost fully and well automated in almost all production stages with exception of quality control stage at the end. The ceramic tiles quality is checked by using visual quality control principles where main goal is to successfully replace man as part of production chain with an automated machine vision system to increase production yield and decrease the production costs. The quality of ceramic tiles depends on dimensions and surface features. Presented automated machine vision system analyzes those geometric and surface features and decides about tile quality by utilizing neural network classifier. Refined methods for geometric and surface features extraction are presented also. The efficiency of processing algorithms and the usage of neural networks classifier as a substitution for human visual quality control are confirmed.U članku je prikazan automatizirani sustav za vizualnu kontrolu kvalitete keramičkih pločica uporabom strojnog računalnog vida. Proces proizvodnje keramičkih pločica u gotovo svim svojim fazama zadovoljavajuće je automatiziran, osim u fazi kontrole kvalitete, na kraju procesa. Kvaliteta keramičkih pločica provjerava se i ocjenjuje postupcima vizualne provjere kvalitete, gdje se ljudski čimbenik nastoji zamijeniti sustavom strojnog računalnog vida u funkciji povećanja kvalitete i povećanja efikasnosti proizvodnje. Kvaliteta keramičkih pločica definirana je dimenzijama i površinskim značajkama. Predstavljeni sustav strojnog vida analizira geometrijske i površinske značajke te odlučuje o kvaliteti keramičkih pločica na temelju navedenih značajki uporabom klasifikatora s neuronskom mrežom. Predstavljene su također i metode koje poboljšavaju izdvajanje geometrijskih i površinskih svojstava. Potvrđena je efikasnost obradnih algoritama i primjena neuronskog klasifikatora kao zamjene za vizualnu kontrolu kvalitete ljudskim vidom