A statistical reduced-reference method for color image quality assessment

Although color is a fundamental feature of human visual perception, it has been largely unexplored in the reduced-reference (RR) image quality assessment (IQA) schemes. In this paper, we propose a natural scene statistic (NSS) method, which efficiently uses this information. It is based on the statistical deviation between the steerable pyramid coefficients of the reference color image and the degraded one. We propose and analyze the multivariate generalized Gaussian distribution (MGGD) to model the underlying statistics. In order to quantify the degradation, we develop and evaluate two measures based respectively on the Geodesic distance between two MGGDs and on the closed-form of the Kullback Leibler divergence. We performed an extensive evaluation of both metrics in various color spaces (RGB, HSV, CIELAB and YCrCb) using the TID 2008 benchmark and the FRTV Phase I validation process. Experimental results demonstrate the effectiveness of the proposed framework to achieve a good consistency with human visual perception. Furthermore, the best configuration is obtained with CIELAB color space associated to KLD deviation measure

An Efficient CBIR Technique with YUV Color Space and Texture Features

In areas of government, academia and hospitals, large collections of digital images are being created. These image collections are the product of digitizing existing collections of analogue photographs, diagrams, drawings, paintings, and prints. Retrieving the specified similar image from a large dataset is very difficult. A new image retrieval system is presented in this paper, which used YUV color space and wavelet transform approach for feature extraction. Firstly, the color space is quantified in non-equal intervals, then constructed one dimension feature vector and represented the color feature. Similarly, the texture feature extraction is obtained by using wavelet. Finally, color feature and texture feature are combined based on wavelet transform. The image retrieval experiments specified that visual features were sensitive for different type images. The color features opted to the rich color image with simple variety. Texture feature opted to the complex images. At the same time, experiments reveal that YUV texture feature based on wavelet transform has better effective performance and stability than the RGB and HSV. The same work is performed for the RGB and HSV color space and their results are compared with the proposed system. The result shows that CBIR with the YUV color space retrieves image with more accuracy and reduced retrieval time. Keywords---Content based image retrieval, Wavelet transforms, YUV, HSV, RG

Adaptivna tehnika obrade slike za kontrolu kvalitete u proizvodnji keramičkih pločica

Automation of the visual inspection for quality control in production of materials with textures (tiles, textile, leather, etc.) is not widely implemented. A sophisticated system for image acquisition, as well as a fast and efficient procedure for texture analysis is needed for this purpose. In this paper the Surface Failure Detection (SFD) algorithm for quality control in ceramic tiles production is presented. It is based on Discrete Wavelet Transform (DWT) and Probabilistic Neural Networks (PNN) with radial basis. DWT provides a multi-resolution analysis, which mimics behavior of a human visual system and it extracts from the tile image the features important for failure detection. Neural networks are used for classification of the tiles with respect to presence of defects. Classification efficiency mainly depends on the proper choice of the training vectors for neural networks. For neural networks preparation we propose an automated adaptive technique based on statistics of the tiles defects textures. This technique enables fast adaptation of the SFD algorithm to different textures, which is important for automated visual inspection in the production of a new tile type.Automatizacija vizualne provjere za kontrolu kvalitete u proizvodnji materijala s teksturama (pločice, tekstil, kože, itd.) nije široko primijenjena u praksi. Za ovu namjenu potreban je sofisticirani sustav za snimanje slika, kao i brza i efikasna procedura za analizu tekstura. U ovom je radu predstavljen algoritam za detekciju površinskih oštećenja (SFD) u proizvodnji keramičkih pločica. Temelji se na diskretnoj valićnoj transformaciji (DWT) i probabilističkim neuronskim mrežama (PNN) s radijalnim bazama. DWT omogućava više-rezolucijsku analizu koja oponaša ljudski vizualni sustav i izdvaja iz slike pločice značajne za detekciju oštećenja. Neuronske mreže se koriste za klasifikaciju pločica ovisno o postojanju oštećenja. Efikasnost klasifikacije najviše ovisi o odgovarajućem odabiru vektora za učenje neuronskih mreža. Za pripremu neuronskih mreža predlažemo automatiziranu adaptivnu tehniku koja se temelji na statistici tekstura oštećenja na pločicama. Ova tehnika omogućava brzu adaptaciju SFD algoritma na različite teksture, što je posebno važno za automatiziranu vizualnu provjeru u proizvodnji novog tipa pločica

Adaptivna tehnika obrade slike za kontrolu kvalitete u proizvodnji keramičkih pločica

Automation of the visual inspection for quality control in production of materials with textures (tiles, textile, leather, etc.) is not widely implemented. A sophisticated system for image acquisition, as well as a fast and efficient procedure for texture analysis is needed for this purpose. In this paper the Surface Failure Detection (SFD) algorithm for quality control in ceramic tiles production is presented. It is based on Discrete Wavelet Transform (DWT) and Probabilistic Neural Networks (PNN) with radial basis. DWT provides a multi-resolution analysis, which mimics behavior of a human visual system and it extracts from the tile image the features important for failure detection. Neural networks are used for classification of the tiles with respect to presence of defects. Classification efficiency mainly depends on the proper choice of the training vectors for neural networks. For neural networks preparation we propose an automated adaptive technique based on statistics of the tiles defects textures. This technique enables fast adaptation of the SFD algorithm to different textures, which is important for automated visual inspection in the production of a new tile type.Automatizacija vizualne provjere za kontrolu kvalitete u proizvodnji materijala s teksturama (pločice, tekstil, kože, itd.) nije široko primijenjena u praksi. Za ovu namjenu potreban je sofisticirani sustav za snimanje slika, kao i brza i efikasna procedura za analizu tekstura. U ovom je radu predstavljen algoritam za detekciju površinskih oštećenja (SFD) u proizvodnji keramičkih pločica. Temelji se na diskretnoj valićnoj transformaciji (DWT) i probabilističkim neuronskim mrežama (PNN) s radijalnim bazama. DWT omogućava više-rezolucijsku analizu koja oponaša ljudski vizualni sustav i izdvaja iz slike pločice značajne za detekciju oštećenja. Neuronske mreže se koriste za klasifikaciju pločica ovisno o postojanju oštećenja. Efikasnost klasifikacije najviše ovisi o odgovarajućem odabiru vektora za učenje neuronskih mreža. Za pripremu neuronskih mreža predlažemo automatiziranu adaptivnu tehniku koja se temelji na statistici tekstura oštećenja na pločicama. Ova tehnika omogućava brzu adaptaciju SFD algoritma na različite teksture, što je posebno važno za automatiziranu vizualnu provjeru u proizvodnji novog tipa pločica

Texture Feature Abstraction Based on Assessment of HOG and GLDM Features for Diagnosing Brain Abnormalities in MRI Images

Recognition of vehicles has always been a desired technology for curbing the crimes done with the help of vehicles Number imprinted on plates of cars and motorbikes are consist of numerals and alphabets and these plates can be easily recognized The uniqueness of combination of characters and numbers can be easily utilized for multiple purposes For instance fines can be imposed on people automatically for wrong parking toll fee can be automatically collected just by recognizing the number plate apart from these two there may be several numbers of uses can be accommodated Computer vision is comprehended as a sub space of the computerized reasoning furthermore software engineering fields Alternate ranges most firmly identified with computer vision are picture handling picture examination and machine vision As an exploratory order computer vision is apprehensive with the counterfeit frameworks that concentrate data from pictures and recordings The picture information can take numerous structures for instance segmentations of videos taken from several cameras This thesis presents a training based approach for the recognition of vehicle number plate The whole process has been divided into three stages i e capturing the image plate localization and recognition of digits over the plate The characteristics of HOG have been utilized for training and SVM has been used for adopted for classifying while recognizing This algorithm has been checked for more than 100 picture