Robust Specularity Removal from Hand-held Videos

Specular reflection exists when one tries to record a photo or video through a transparent glass medium or opaque surfaces such as plastics, ceramics, polyester and human skin, which can be well described as the superposition of a transmitted layer and a reflection layer. These specular reflections often confound the algorithms developed for image analysis, computer vision and pattern recognition. To obtain a pure diffuse reflection component, specularity (highlights) needs to be removed. To handle this problem, a novel and robust algorithm is formulated. The contributions of this work are three-fold.;First, the smoothness of the video along with the temporal coherence and illumination changes are preserved by reducing the flickering and jagged edges caused by hand-held video acquisition and homography transformation respectively.;Second, this algorithm is designed to improve upon the state-of-art algorithms by automatically selecting the region of interest (ROI) for all the frames, reducing the computational time and complexity by utilizing the luminance (Y) channel and exploiting the Augmented Lagrange Multiplier (ALM) with Alternating Direction Minimizing (ADM) to facilitate the derivation of solution algorithms.;Third, a quantity metrics is devised, which objectively quantifies the amount of specularity in each frame of a hand-held video. The proposed specularity removal algorithm is compared against existing state-of-art algorithms using the newly-developed quantity metrics. Experimental results validate that the developed algorithm has superior performance in terms of computation time, quality and accuracy

Effectiveness of specularity removal from hyperspectral images on the quality of spectral signatures of food products

Specularity or highlight problem exists widely in hyperspectral images, provokes reflectance deviation from its true value, and can hide major defects in food objects or detecting spurious false defects causing failure of inspection and detection processes. In this study, a new non-iterative method based on the dichromatic reflection model and principle component analysis (PCA) was proposed to detect and remove specular highlight components from hyperspectral images acquired by various imaging modes and under different configurations for numerous agro-food products. To demonstrate the effectiveness of this approach, the details of the proposed method were described and the experimental results on various spectral images were presented. The results revealed that the method worked well on all hyperspectral and multispectral images examined in this study, effectively reduced the specularity and significantly improves the quality of the extracted spectral data. Besides the spectral images from available databases, the robustness of this approach was further validated with real captured hyperspectral images of different food materials. By using qualitative and quantitative evaluation based on running time and peak signal to noise ratio (PSNR), the experimental results showed that the proposed method outperforms other specularity removal methods over the datasets of hyperspectral and multispectral images.info:eu-repo/semantics/acceptedVersio

An investigation into the use of physical modelling for the prediction of various feature types visible from different view points

This paper describes a general purpose flexible technique which uses physical modelling techniques for determining the features of a 3D object that are visible from any predefined view. Physical modelling techniques are used to determine which of many different types of features are visible from a complete set of viewpoints. The power of this technique lies in its ability to detect and parameterise object features, regardless of object complexity. Raytracing is used to simulate the physical process by which object features are visible so that surface properties (eg specularity, transparency) as well as object boundaries can be used in the recognition process. Using this technique occluding and non-occluding edge based features are extracted using image processing techniques and then parameterised. Features caused by specularity are also extracted and qualitative descriptions for these are defined

Reflection Decomposition In Single Images Using An Optimum Thresholding-based Method

Traditional methods of separating reflection components have been developed based on multiple images. There are only few methods which are able to use a single image. However, their applicability is limited due to offline setting of its arbitrary parameter. In this study, we propose an effective method to separate specular components using a single image which based on an optimum thresholding-based technique. This method employs modified specular-free image and selects an optimum value for the offset parameter. In contrast to prior method, the proposed method processes all the steps automatically and produces better performance. Experimental results for inhomogeneous objects demonstrate the promising applicability for real-time implementation. However, this method is unsuitable for objects with strong specular reflection. An extension is suggested to include the specular lobe reflectance into Shafer dichromatic model

Computational and Physical Modelling of the Flow and Sediment Transport in a New Vortex-type Stormwater Retention Pond

Given the current water quality requirements for a stormwater retention pond, the civil and environmental engineering community requires accurate and efficient methods to explore the sediment removal of retention ponds. This research studied the use of Computational Fluid Dynamics (CFD) for modeling sediment retention ponds with comparison of the fluid flow results to in-house experimental data. This study provided insight on the pond design using single- and two-phase modeling approaches. This research highlighted the potential of using an Eulerian-Eulerian two-fluid model (TFM) approach, without the empirical ad hoc relations often used to determine the sediment concentration profile, for modeling flow and sediment in a new vortex-type pond design. This manuscript-based thesis documented four different studies. The first study summarized the fundamental concepts involved in the overall design of stormwater retention ponds. A comprehensive and in-depth description of different computational methods used in the literature for modeling stormwater retention ponds was given. Previous applications of CFD to modeling stormwater retention ponds was critically reviewed. The present position of multiphase modeling in the simulation of storage ponds was addressed, and possible directions for future development were outlined. The second study explored the potential of single-phase CFD modeling in a new vortex-type stormwater retention pond. The flow pattern in a 1:13.3 scale model of the vortex-type retention pond was characterized and some problematic recirculation zones were identified. The mean and fluctuating velocity fields in the pond were explored using computational and experimental methods. For the CFD modeling, the 3D Reynolds averaged Navier-Stokes (RANS) equations together with a k-ε turbulence model were solved using ANSYS Fluent 19.2. In general, the predictions and measurements were in good agreement. In the third study, an Eulerian-Eulerian TFM using constitutive equations based on granular kinetic theory, coupled with a low-Reynolds-number turbulence model, was used to predict the liquid and sediment transport in an equilibrium channel for fully-developed, steady, dilute flow. The particle-wall boundary condition was also investigated. The model predictions of the liquid and sediment velocity profiles, sediment concentration, turbulence statistics and fluctuating particle velocity field were documented against experimental data from the literature. In the last study, the TFM was implemented to assess pond performance and to provide insight on the sediment transport in the vortex-type stormwater retention pond for the case of steady, dilute flow with no sediment deposition. The model predictions of the liquid and sediment velocity profiles, and sediment concentration were documented. The study demonstrated the spatial distribution of sediment in the pond: the recirculation zones documented in the single-phase CFD study were characterized by relatively high concentrations of sediment. Overall, the current study demonstrated the application of single-phase CFD in detecting problematic regions such as low velocity zones and stagnation regions in a new pond design by providing a map of the flow patterns. This study also showed the application of two-phase CFD in the simulation of fluid and dilute sediment transport in the same pond as a step towards more comprehensive simulations, which in turn supports the goal of achieving higher water quality. No sediment deposition was included, which is the next step in applying the TFM formulation to retention pond studies

Embedded polarizing filters to separate diffuse and specular reflection

Polarizing filters provide a powerful way to separate diffuse and specular reflection; however, traditional methods rely on several captures and require proper alignment of the filters. Recently, camera manufacturers have proposed to embed polarizing micro-filters in front of the sensor, creating a mosaic of pixels with different polarizations. In this paper, we investigate the advantages of such camera designs. In particular, we consider different design patterns for the filter arrays and propose an algorithm to demosaic an image generated by such cameras. This essentially allows us to separate the diffuse and specular components using a single image. The performance of our algorithm is compared with a color-based method using synthetic and real data. Finally, we demonstrate how we can recover the normals of a scene using the diffuse images estimated by our method.Comment: ACCV 201