A Pattern Classification Based approach for Blur Classification

Blur type identification is one of the most crucial step of image restoration. In case of blind restoration of such images, it is generally assumed that the blur type is known prior to restoration of such images. However, it is not practical in real applications. So, blur type identification is extremely desirable before application of blind restoration technique to restore a blurred image. An approach to categorize blur in three classes namely motion, defocus, and combined blur is presented in this paper. Curvelet transform based energy features are utilized as features of blur patterns and a neural network is designed for classification. The simulation results show preciseness of proposed approach

Blur Classification Using Segmentation Based Fractal Texture Analysis

The objective of vision based gesture recognition is to design a system, which can understand the human actions and convey the acquired information with the help of captured images. An image restoration approach is extremely required whenever image gets blur during acquisition process since blurred images can severely degrade the performance of such systems. Image restoration recovers a true image from a degraded version. It is referred as blind restoration if blur information is unidentified. Blur identification is essential before application of any blind restoration algorithm. This paper presents a blur identification approach which categories a hand gesture image into one of the sharp, motion, defocus and combined blurred categories. Segmentation based fractal texture analysis extraction algorithm is utilized for featuring the neural network based classification system. The simulation results demonstrate the preciseness of proposed method

A bag of words description scheme for image quality assessment

Every day millions of images are obtained, processed, compressed, saved, transmitted and reproduced. All these operations can cause distortions that affect their quality. The quality of these images should be measured subjectively. However, that brings the disadvantage of achieving a considerable number of tests with individuals requested to provide a statistical analysis of an image’s perceptual quality. Several objective metrics have been developed, that try to model the human perception of quality. However, in most applications the representation of human quality perception given by these metrics is far from the desired representation. Therefore, this work proposes the usage of machine learning models that allow for a better approximation. In this work, definitions for image and quality are given and some of the difficulties of the study of image quality are mentioned. Moreover, three metrics are initially explained. One uses the image’s original quality has a reference (SSIM) while the other two are no reference (BRISQUE and QAC). A comparison is made, showing a large discrepancy of values between the two kinds of metrics. The database that is used for the tests is TID2013. This database was chosen due to its dimension and by the fact of considering a large number of distortions. A study of each type of distortion in this database is made. Furthermore, some concepts of machine learning are introduced along with algorithms relevant in the context of this dissertation, notably, K-means, KNN and SVM. Description aggregator algorithms like “bag of words” and “fisher-vectors” are also mentioned. This dissertation studies a new model that combines machine learning and a quality metric for quality estimation. This model is based on the division of images in cells, where a specific metric is computed. With this division, it is possible to obtain local quality descriptors that will be aggregated using “bag of words”. A SVM with an RBF kernel is trained and tested on the same database and the results of the model are evaluated using cross-validation. The results are analysed using Pearson, Spearman and Kendall correlations and the RMSE to evaluate the representation of the model when compared with the subjective results. The model improves the results of the metric that was used and shows a new path to apply machine learning for quality evaluation.No nosso dia-a-dia as imagens são obtidas, processadas, comprimidas, guardadas, transmitidas e reproduzidas. Em qualquer destas operações podem ocorrer distorções que prejudicam a sua qualidade. A qualidade destas imagens pode ser medida de forma subjectiva, o que tem a desvantagem de serem necessários vários testes, a um número considerável de indivíduos para ser feita uma análise estatística da qualidade perceptual de uma imagem. Foram desenvolvidas várias métricas objectivas, que de alguma forma tentam modelar a percepção humana de qualidade. Todavia, em muitas aplicações a representação de percepção de qualidade humana dada por estas métricas fica aquém do desejável, razão porque se propõe neste trabalho usar modelos de reconhecimento de padrões que permitam uma maior aproximação. Neste trabalho, são dadas definições para imagem e qualidade e algumas das dificuldades do estudo da qualidade de imagem são referidas. É referida a importância da qualidade de imagem como ramo de estudo, e são estudadas diversas métricas de qualidade. São explicadas três métricas, uma delas que usa a qualidade original como referência (SSIM) e duas métricas sem referência (BRISQUE e QAC). Uma comparação é feita entre elas, mostrando- – se uma grande discrepância de valores entre os dois tipos de métricas. Para os testes feitos é usada a base de dados TID2013, que é muitas vezes considerada para estudos de qualidade de métricas devido à sua dimensão e ao facto de considerar um grande número de distorções. Neste trabalho também se fez um estudo dos tipos de distorção incluidos nesta base de dados e como é que eles são simulados. São introduzidos também alguns conceitos teóricos de reconhecimento de padrões e alguns algoritmos relevantes no contexto da dissertação, são descritos como o K-means, KNN e as SVMs. Algoritmos de agregação de descritores como o “bag of words” e o “fisher-vectors” também são referidos. Esta dissertação adiciona métodos de reconhecimento de padrões a métricas objectivas de qua– lidade de imagem. Uma nova técnica é proposta, baseada na divisão de imagens em células, nas quais uma métrica será calculada. Esta divisão permite obter descritores locais de qualidade que serão agregados usando “bag of words”. Uma SVM com kernel RBF é treinada e testada na mesma base de dados e os resultados do modelo são mostrados usando cross-validation. Os resultados são analisados usando as correlações de Pearson, Spearman e Kendall e o RMSE que permitem avaliar a proximidade entre a métrica desenvolvida e os resultados subjectivos. Este modelo melhora os resultados obtidos com a métrica usada e demonstra uma nova forma de aplicar modelos de reconhecimento de padrões ao estudo de avaliação de qualidade

NASA JSC neural network survey results

A survey of Artificial Neural Systems in support of NASA's (Johnson Space Center) Automatic Perception for Mission Planning and Flight Control Research Program was conducted. Several of the world's leading researchers contributed papers containing their most recent results on artificial neural systems. These papers were broken into categories and descriptive accounts of the results make up a large part of this report. Also included is material on sources of information on artificial neural systems such as books, technical reports, software tools, etc

Blind Restoration of Motion Blurred Barcode Images using Ridgelet Transform and Radial Basis Function Neural Network

The aim of any image restoration techniques is recovering the original image from a degraded observation. One of the most common degradation phenomena in images is motion blur. In case of blind image restoration accurate estimation of motion blur parameters is required for deblurring of such images. This paper proposed a novel technique for estimating the parameters of motion blur using ridgelet transform. Initially, the energy of ridgelet coefficients is used to estimate the blur angle and then blur length is estimated using a radial biases function neural network. This work is tested on different barcode images with varying parameters of blur. The simulation results show that the proposed method improves the restoration performance