Medical image enhancement

Each image acquired from a medical imaging system is often part of a two-dimensional (2-D) image set whose total presents a three-dimensional (3-D) object for diagnosis. Unfortunately, sometimes these images are of poor quality. These distortions cause an inadequate object-of-interest presentation, which can result in inaccurate image analysis. Blurring is considered a serious problem. Therefore, “deblurring” an image to obtain better quality is an important issue in medical image processing. In our research, the image is initially decomposed. Contrast improvement is achieved by modifying the coefficients obtained from the decomposed image. Small coefficient values represent subtle details and are amplified to improve the visibility of the corresponding details. The stronger image density variations make a major contribution to the overall dynamic range, and have large coefficient values. These values can be reduced without much information loss

From receptive profiles to a metric model of V1

In this work we show how to construct connectivity kernels induced by the receptive profiles of simple cells of the primary visual cortex (V1). These kernels are directly defined by the shape of such profiles: this provides a metric model for the functional architecture of V1, whose global geometry is determined by the reciprocal interactions between local elements. Our construction adapts to any bank of filters chosen to represent a set of receptive profiles, since it does not require any structure on the parameterization of the family. The connectivity kernel that we define carries a geometrical structure consistent with the well-known properties of long-range horizontal connections in V1, and it is compatible with the perceptual rules synthesized by the concept of association field. These characteristics are still present when the kernel is constructed from a bank of filters arising from an unsupervised learning algorithm

Reconhecimento biométrico da íris na região de comprimentos de onda do infravermelho próximo e do visível

Neste trabalho é proposta e concretizada a criação de uma base de dados com imagens da íris adquiridas e registadas de forma cooperativa em condições controladas de iluminação. A base de dados criada integra imagens da íris adquiridas simultaneamente nas regiões do visível (RGB) e infravermelho próximo (NIR) do espectro electromagnético. São ainda propostos e testados dois métodos de reconhecimento da íris com base na informação contida nos quatro canais RGB e NIR um dos métodos consiste num modelo linear cujos coeficientes foram calculados em função da base de dados criada enquanto que o outro consiste num modelo neuronal treinado com dados da mesma base de dados. Após a segmentação e normalização das imagens das íris procede-se à optimização dos parâmetros de filtros de Gabor a aplicar às regiões visível e infravermelho próximo do espectro, obtendo a informação relativa aos quatro canais: vermelho, verde, azul (Red, Green e Blue - RGB) e ao canal do infravermelho próximo (Near Infrared - NIR). Estes filtros optimizados são usados para a codificação final das imagens segmentadas das íris a cada um dos quatro canais anteriormente referidos, o desempenho da codificação das íris é analisado em cada um destes canais em separado. Por fim, foram criados dois modelos de reconhecimento da íris, um linear e outro utilizando redes neuronais que fundem a informação dos canais do visível RGB, ou em alternativa usam a informação associada aos quatro canais RGB e NIR. Os resultados mostraram que o modelo que tem o melhor desempenho no reconhecimento da íris é o modelo linear em que a informação dos quatro canais RGB e NIR é usada.In this paper is proposed and implemented the creation of a database for iris images acquired and registered in a cooperative way under controlled conditions of illumination. The created database includes iris images acquired simultaneously in the regions of the visible (RGB) and near infrared (NIR) of the electromagnetic spectrum. It’s also proposed and tested two methods for iris recognition with the information contained in the four acquired channels RGB and NIR. The first method is a linear model whose coefficients were calculated according to the created database while the other method consists of a neuronal model trained with the data from the same database. After iris images segmentation and normalization the next step was the optimization of the Gabor filters parameters to be applied to corresponding segmented iris images in the visible and near infrared spectrum, obtaining the information in the four channels: red, green, blue (Red, Green and Blue - RGB) and near-infrared channel (Near infrared - NIR). These optimal filters were then used to encode the segmented images of the iris on each of the four channels mentioned above. The performance of the encoding was also analyzed in each channel separately. Finally, two iris recognition models were built, the first was a linear model and the second uses a neural network that merge information from the visible RGB channels, or alternatively uses the information association of all of the four channels RGB and NIR. The results show that the model that has the better performance in iris recognition is the linear model which holds the information of the four channels RGB and NIR.In this paper is proposed and implemented the creation of a database for iris images acquired and registered in a cooperative way under controlled conditions of illumination. The created database includes iris images acquired simultaneously in the regions of the visible (RGB) and near infrared (NIR) of the electromagnetic spectrum. It’s also proposed and tested two methods for iris recognition with the information contained in the four acquired channels RGB and NIR. The first method is a linear model whose coefficients were calculated according to the created database while the other method consists of a neuronal model trained with the data from the same database. After iris images segmentation and normalization the next step was the optimization of the Gabor filters parameters to be applied to corresponding segmented iris images in the visible and near infrared spectrum, obtaining the information in the four channels: red, green, blue (Red, Green and Blue - RGB) and near-infrared channel (Near infrared - NIR). These optimal filters were then used to encode the segmented images of the iris on each of the four channels mentioned above. The performance of the encoding was also analyzed in each channel separately. Finally, two iris recognition models were built, the first was a linear model and the second uses a neural network that merge information from the visible RGB channels, or alternatively uses the information association of all of the four channels RGB and NIR. The results show that the model that has the better performance in iris recognition is the linear model which holds the information of the four channels RGB and NIR

A Computational Neuroscience Approach to Higher-Order Texture Perception

Natural images contain large amounts of structural information characterised by higher-order spatial correlations. Neurons have limited capacities, so the visual system must filter out non-salient information, but retain that which is behaviourally relevant. Previous research has concentrated on two-point correlations; there has been less research into higher-order correlations, although the visual system is sensitive to them. Isotrigon textures can be used for this purpose. Their salient structure is exclusively due to fourth- and higher-order spatial correlations and they have the same structural features that create salience in natural images. In Chapter 2, we evaluated human texture discrimination using 10 novel isotrigon textures (VnL2) and 17 standard V3L2 isotrigon textures. Factor analysis revealed that as few as 3 mechanisms may govern the detection of fourth- and higher-order image structure. The Maddess group has previously published evidence that the number of independent mechanisms is less than 10 and perhaps as small as 3-4. The computation of higher-order correlations by the brain is neuro-physiologically plausible via nonlinear combinations of recursive and/or rectifying processes. In Chapter 3, we utilised the crowdsourcing platform “mTurk” to implement a large texture discrimination study. Under laboratory conditions, we showed that the testing modality was robust across a range of browsers, resolutions, contrasts and screen sizes. Texture discrimination data was gathered from 121 naïve subjects and compared to 2 independent laboratory data sets. Factor analysis indicated the presence of 3-4 factors, consistent with previous studies. Based on Pearson's correlation and coefficients of repeatability, mTurk is capable of producing data of comparable quality to laboratory studies. This is significant as mTurk has not previously been systematically evaluated for visual psychometric research. In Chapter 4, we employed a set of statistically controlled ternary textures. The textures were constrained (spatial correlations from 1st to 4th order) and their salience could be independently controlled by the addition of noise. To the ideal observer, all textures defined by a given amount of noise are equally detectable. However, humans are not ideal observers; their visual perceptual resources are restricted. Because of the number of textures available, we used mTurk to gather performance functions from 928 subjects for a subset of the texture space. Perceptual salience varied for each image statistic, with rank order: gamma > beta_hv > beta_diag > alpha > theta. This supports the order previously published for the related binary stochastic textures. The two least salient directions were consistently white:black and grey-bias (for gammas and betas), and black:grey and grey:white (for thetas and alphas). Such differences reflect the sensitivities and limitations of neural processing and are a manifestation of efficient coding. We hypothesised that the grey token conferred non-salience. Indeed, for gammas and betas, the grey-bias was consistently the second least salient. However, this did not hold for thetas or alphas. Counter-intuitively, the order of texture presentation did not significantly affect discrimination performance. An analysis of 31 repeat Workers found evidence of learning for beta textures, whereas performance for other textures was already maximal. This thesis concludes by considering future research

Unfamiliar facial identity registration and recognition performance enhancement

The work in this thesis aims at studying the problems related to the robustness of a face recognition system where specific attention is given to the issues of handling the image variation complexity and inherent limited Unique Characteristic Information (UCI) within the scope of unfamiliar identity recognition environment. These issues will be the main themes in developing a mutual understanding of extraction and classification tasking strategies and are carried out as a two interdependent but related blocks of research work. Naturally, the complexity of the image variation problem is built up from factors including the viewing geometry, illumination, occlusion and other kind of intrinsic and extrinsic image variation. Ideally, the recognition performance will be increased whenever the variation is reduced and/or the UCI is increased. However, the variation reduction on 2D facial images may result in loss of important clues or UCI data for a particular face alternatively increasing the UCI may also increase the image variation. To reduce the lost of information, while reducing or compensating the variation complexity, a hybrid technique is proposed in this thesis. The technique is derived from three conventional approaches for the variation compensation and feature extraction tasks. In this first research block, transformation, modelling and compensation approaches are combined to deal with the variation complexity. The ultimate aim of this combination is to represent (transformation) the UCI without losing the important features by modelling and discard (compensation) and reduce the level of the variation complexity of a given face image. Experimental results have shown that discarding a certain obvious variation will enhance the desired information rather than sceptical in losing the interested UCI. The modelling and compensation stages will benefit both variation reduction and UCI enhancement. Colour, gray level and edge image information are used to manipulate the UCI which involve the analysis on the skin colour, facial texture and features measurement respectively. The Derivative Linear Binary transformation (DLBT) technique is proposed for the features measurement consistency. Prior knowledge of input image with symmetrical properties, the informative region and consistency of some features will be fully utilized in preserving the UCI feature information. As a result, the similarity and dissimilarity representation for identity parameters or classes are obtained from the selected UCI representation which involves the derivative features size and distance measurement, facial texture and skin colour. These are mainly used to accommodate the strategy of unfamiliar identity classification in the second block of the research work. Since all faces share similar structure, classification technique should be able to increase the similarities within the class while increase the dissimilarity between the classes. Furthermore, a smaller class will result on less burden on the identification or recognition processes. The proposed method or collateral classification strategy of identity representation introduced in this thesis is by manipulating the availability of the collateral UCI for classifying the identity parameters of regional appearance, gender and age classes. In this regard, the registration of collateral UCI s have been made in such a way to collect more identity information. As a result, the performance of unfamiliar identity recognition positively is upgraded with respect to the special UCI for the class recognition and possibly with the small size of the class. The experiment was done using data from our developed database and open database comprising three different regional appearances, two different age groups and two different genders and is incorporated with pose and illumination image variations