No-reference Stereoscopic Image Quality Assessment Using Natural Scene Statistics

We present two contributions in this work: (i) a bivariate generalized Gaussian distribution (BGGD) model for the joint distribution of luminance and disparity subband coefficients of natural stereoscopic scenes and (ii) a no-reference (NR) stereo image quality assessment algorithm based on the BGGD model. We first empirically show that a BGGD accurately models the joint distribution of luminance and disparity subband coefficients. We then show that the model parameters form good discriminatory features for NR quality assessment. Additionally, we rely on the previously established result that luminance and disparity subband coefficients of natural stereo scenes are correlated, and show that correlation also forms a good feature for NR quality assessment. These features are computed for both the left and right luminance-disparity pairs in the stereo image and consolidated into one feature vector per stereo pair. This feature set and the stereo pair׳s difference mean opinion score (DMOS) (labels) are used for supervised learning with a support vector machine (SVM). Support vector regression is used to estimate the perceptual quality of a test stereo image pair. The performance of the algorithm is evaluated over popular databases and shown to be competitive with the state-of-the-art no-reference quality assessment algorithms. Further, the strength of the proposed algorithm is demonstrated by its consistently good performance over both symmetric and asymmetric distortion types. Our algorithm is called Stereo QUality Evaluator (StereoQUE)

Stereoscopic video quality assessment using binocular energy

Stereoscopic imaging is becoming increasingly popular. However, to ensure the best quality of experience, there is a need to develop more robust and accurate objective metrics for stereoscopic content quality assessment. Existing stereoscopic image and video metrics are either extensions of conventional 2D metrics (with added depth or disparity information) or are based on relatively simple perceptual models. Consequently, they tend to lack the accuracy and robustness required for stereoscopic content quality assessment. This paper introduces full-reference stereoscopic image and video quality metrics based on a Human Visual System (HVS) model incorporating important physiological findings on binocular vision. The proposed approach is based on the following three contributions. First, it introduces a novel HVS model extending previous models to include the phenomena of binocular suppression and recurrent excitation. Second, an image quality metric based on the novel HVS model is proposed. Finally, an optimised temporal pooling strategy is introduced to extend the metric to the video domain. Both image and video quality metrics are obtained via a training procedure to establish a relationship between subjective scores and objective measures of the HVS model. The metrics are evaluated using publicly available stereoscopic image/video databases as well as a new stereoscopic video database. An extensive experimental evaluation demonstrates the robustness of the proposed quality metrics. This indicates a considerable improvement with respect to the state-of-the-art with average correlations with subjective scores of 0.86 for the proposed stereoscopic image metric and 0.89 and 0.91 for the proposed stereoscopic video metrics

Automatic face recognition using stereo images

Face recognition is an important pattern recognition problem, in the study of both natural and artificial learning problems. Compaxed to other biometrics, it is non-intrusive, non- invasive and requires no paxticipation from the subjects. As a result, it has many applications varying from human-computer-interaction to access control and law-enforcement to crowd surveillance. In typical optical image based face recognition systems, the systematic vaxiability arising from representing the three-dimensional (3D) shape of a face by a two-dimensional (21)) illumination intensity matrix is treated as random vaxiability. Multiple examples of the face displaying vaxying pose and expressions axe captured in different imaging conditions. The imaging environment, pose and expressions are strictly controlled and the images undergo rigorous normalisation and pre-processing. This may be implemented in a paxtially or a fully automated system. Although these systems report high classification accuracies (>90%), they lack versatility and tend to fail when deployed outside laboratory conditions. Recently, more sophisticated 3D face recognition systems haxnessing the depth information have emerged. These systems usually employ specialist equipment such as laser scanners and structured light projectors. Although more accurate than 2D optical image based recognition, these systems are equally difficult to implement in a non-co-operative environment. Existing face recognition systems, both 2D and 3D, detract from the main advantages of face recognition and fail to fully exploit its non-intrusive capacity. This is either because they rely too much on subject co-operation, which is not always available, or because they cannot cope with noisy data. The main objective of this work was to investigate the role of depth information in face recognition in a noisy environment. A stereo-based system, inspired by the human binocular vision, was devised using a pair of manually calibrated digital off-the-shelf cameras in a stereo setup to compute depth information. Depth values extracted from 2D intensity images using stereoscopy are extremely noisy, and as a result this approach for face recognition is rare. This was cofirmed by the results of our experimental work. Noise in the set of correspondences, camera calibration and triangulation led to inaccurate depth reconstruction, which in turn led to poor classifier accuracy for both 3D surface matching and 211) 2 depth maps. Recognition experiments axe performed on the Sheffield Dataset, consisting 692 images of 22 individuals with varying pose, illumination and expressions

Food Recognition and Volume Estimation in a Dietary Assessment System

Recently obesity has become an epidemic and one of the most serious worldwide public health concerns of the 21st century. Obesity diminishes the average life expectancy and there is now convincing evidence that poor diet, in combination with physical inactivity are key determinants of an individual s risk of developing chronic diseases such as cancer, cardiovascular disease or diabetes. Assessing what people eat is fundamental to establishing the link between diet and disease. Food records are considered the best approach for assessing energy intake. However, this method requires literate and highly motivated subjects. This is a particular problem for adolescents and young adults who are the least likely to undertake food records. The ready access of the majority of the population to mobile phones (with integrated camera, improved memory capacity, network connectivity and faster processing capability) has opened up new opportunities for dietary assessment. The dietary information extracted from dietary assessment provide valuable insights into the cause of diseases that greatly helps practicing dietitians and researchers to develop subsequent approaches for mounting intervention programs for prevention. In such systems, the camera in the mobile phone is used for capturing images of food consumed and these images are then processed to automatically estimate the nutritional content of the food. However, food objects are deformable objects that exhibit variations in appearance, shape, texture and color so the food classification and volume estimation in these systems suffer from lower accuracy. The improvement of the food recognition accuracy and volume estimation accuracy are challenging tasks. This thesis presents new techniques for food classification and food volume estimation. For food recognition, emphasis was given to texture features. The existing food recognition techniques assume that the food images will be viewed at similar scales and from the same viewpoints. However, this assumption fails in practical applications, because it is difficult to ensure that a user in a dietary assessment system will put his/her camera at the same scale and orientation to capture food images as that of the target food images in the database. A new scale and rotation invariant feature generation approach that applies Gabor filter banks is proposed. To obtain scale and rotation invariance, the proposed approach identifies the dominant orientation of the filtered coefficient and applies a circular shifting operation to place this value at the first scale of dominant direction. The advantages of this technique are it does not require the scale factor to be known in advance and it is scale/and rotation invariant separately and concurrently. This approach is modified to achieve improved accuracy by applying a Gaussian window along the scale dimension which reduces the impact of high and low frequencies of the filter outputs enabling better matching between the same classes. Besides automatic classification, semi automatic classification and group classification are also considered to have an idea about the improvement. To estimate the volume of a food item, a stereo pair is used to recover the structure as a 3D point cloud. A slice based volume estimation approach is proposed that converts the 3D point cloud to a series of 2D slices. The proposed approach eliminates the problem of knowing the distance between two cameras with the help of disparities and depth information from a fiducial marker. The experimental results show that the proposed approach can provide an accurate estimate of food volume

Full-reference stereoscopic video quality assessment using a motion sensitive HVS model

Stereoscopic video quality assessment has become a major research topic in recent years. Existing stereoscopic video quality metrics are predominantly based on stereoscopic image quality metrics extended to the time domain via for example temporal pooling. These approaches do not explicitly consider the motion sensitivity of the Human Visual System (HVS). To address this limitation, this paper introduces a novel HVS model inspired by physiological findings characterising the motion sensitive response of complex cells in the primary visual cortex (V1 area). The proposed HVS model generalises previous HVS models, which characterised the behaviour of simple and complex cells but ignored motion sensitivity, by estimating optical flow to measure scene velocity at different scales and orientations. The local motion characteristics (direction and amplitude) are used to modulate the output of complex cells. The model is applied to develop a new type of full-reference stereoscopic video quality metrics which uniquely combine non-motion sensitive and motion sensitive energy terms to mimic the response of the HVS. A tailored two-stage multi-variate stepwise regression algorithm is introduced to determine the optimal contribution of each energy term. The two proposed stereoscopic video quality metrics are evaluated on three stereoscopic video datasets. Results indicate that they achieve average correlations with subjective scores of 0.9257 (PLCC), 0.9338 and 0.9120 (SRCC), 0.8622 and 0.8306 (KRCC), and outperform previous stereoscopic video quality metrics including other recent HVS-based metrics

Stereo Reconstruction using Induced Symmetry and 3D scene priors

Tese de doutoramento em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e Tecnologia da Universidade de CoimbraRecuperar a geometria 3D a partir de dois vistas, conhecida como reconstrução estéreo, é um dos tópicos mais antigos e mais investigado em visão por computador. A computação de modelos 3D do ambiente é útil para uma grande número de aplicações, desde a robótica‎, passando pela sua utilização do consumidor comum, até a procedimentos médicos. O princípio para recuperar a estrutura 3D cena é bastante simples, no entanto, existem algumas situações que complicam consideravelmente o processo de reconstrução. Objetos que contêm estruturas pouco texturadas ou repetitivas, e superfícies com bastante inclinação ainda colocam em dificuldade os algoritmos state-of-the-art. Esta tese de doutoramento aborda estas questões e apresenta um novo framework estéreo que é completamente diferente das abordagens convencionais. Propomos a utilização de simetria em vez de foto-similaridade para avaliar a verosimilhança de pontos em duas imagens distintas serem uma correspondência. O framework é chamado SymStereo, e baseia-se no efeito de espelhagem que surge sempre que uma imagem é mapeada para a outra câmera usando a homografia induzida por um plano de corte virtual que intersecta a baseline. Experiências em estéreo denso comprovam que as nossas funções de custo baseadas em simetria se comparam favoravelmente com os custos baseados em foto-consistência de melhor desempenho. Param além disso, investigamos a possibilidade de realizar Stereo-Rangefinding, que consiste em usar estéreo passivo para recuperar exclusivamente a profundidade ao longo de um plano de varrimento. Experiências abrangentes fornecem evidência de que estéreo baseada em simetria induzida é especialmente eficaz para esta finalidade. Como segunda linha de investigação, propomos superar os problemas descritos anteriormente usando informação a priori sobre o ambiente 3D, com o objectivo de aumentar a robustez do processo de reconstrução. Para tal, apresentamos uma nova abordagem global para detectar pontos de desvanecimento e grupos de direcções de desvanecimento mutuamente ortogonais em ambientes Manhattan. Experiências quer em imagens sintéticas quer em imagens reais demonstram que os nossos algoritmos superaram os métodos state-of-the-art, mantendo a computação aceitável. Além disso, mostramos pela primeira vez resultados na detecção simultânea de múltiplas configurações de Manhattan. Esta informação a priori sobre a estrutura da cena é depois usada numa pipeline de reconstrução que gera modelos piecewise planares de ambientes urbanos a partir de duas vistas calibradas. A nossa formulação combina SymStereo e o algoritmo de clustering PEARL [3], e alterna entre um passo de otimização discreto, que funde hipóteses de superfícies planares e descarta detecções com pouco suporte, e uma etapa de otimização contínua, que refina as poses dos planos. Experiências com pares estéreo de ambientes interiores e exteriores confirmam melhorias significativas sobre métodos state-of-the-art relativamente a precisão e robustez. Finalmente, e como terceira contribuição para melhorar a visão estéreo na presença de superfícies inclinadas, estendemos o recente framework de agregação estéreo baseada em histogramas [4]. O algoritmo original utiliza janelas de suporte fronto-paralelas para a agregação de custo, o que leva a resultados imprecisos na presença de superfícies com inclinação significativa. Nós abordamos o problema considerando hipóteses de orientação discretas. Os resultados experimentais obtidos comprovam a eficácia do método, permitindo melhorar a precisção de correspondência, preservando simultaneamente uma baixa complexidade computacional.Recovering the 3D geometry from two or more views, known as stereo reconstruction, is one of the earliest and most investigated topics in computer vision. The computation of 3D models of an environment is useful for a very large number of applications, ranging from robotics, consumer utilization to medical procedures. The principle to recover the 3D scene structure is quite simple, however, there are some issues that considerable complicate the reconstruction process. Objects containing complicated structures, including low and repetitive textures, and highly slanted surfaces still pose difficulties to state-of-the-art algorithms. This PhD thesis tackles this issues and introduces a new stereo framework that is completely different from conventional approaches. We propose to use symmetry instead of photo-similarity for assessing the likelihood of two image locations being a match. The framework is called SymStereo, and is based on the mirroring effect that arises whenever one view is mapped into the other using the homography induced by a virtual cut plane that intersects the baseline. Extensive experiments in dense stereo show that our symmetry-based cost functions compare favorably against the best performing photo-similarity matching costs. In addition, we investigate the possibility of accomplishing Stereo-Rangefinding that consists in using passive stereo to exclusively recover depth along a scan plane. Thorough experiments provide evidence that Stereo from Induced Symmetry is specially well suited for this purpose. As a second research line, we propose to overcome the previous issues using priors about the 3D scene for increasing the robustness of the reconstruction process. For this purpose, we present a new global approach for detecting vanishing points and groups of mutually orthogonal vanishing directions in man-made environments. Experiments in both synthetic and real images show that our algorithms outperform the state-of-the-art methods while keeping computation tractable. In addition, we show for the first time results in simultaneously detecting multiple Manhattan-world configurations. This prior information about the scene structure is then included in a reconstruction pipeline that generates piece-wise planar models of man-made environments from two calibrated views. Our formulation combines SymStereo and PEARL clustering [3], and alternates between a discrete optimization step, that merges planar surface hypotheses and discards detections with poor support, and a continuous optimization step, that refines the plane poses. Experiments with both indoor and outdoor stereo pairs show significant improvements over state-of-the-art methods with respect to accuracy and robustness. Finally, and as a third contribution to improve stereo matching in the presence of surface slant, we extend the recent framework of Histogram Aggregation [4]. The original algorithm uses a fronto-parallel support window for cost aggregation, leading to inaccurate results in the presence of significant surface slant. We address the problem by considering discrete orientation hypotheses. The experimental results prove the effectiveness of the approach, which enables to improve the matching accuracy while preserving a low computational complexity