A multi-scale approach for estimating gradient from volumetric data.

Gradient is mostly used to approximate a quantity called normal vector, which is widely used in techniques such as raycasting or shading to calculate the shade of a pixel. Central Different Equation (CDE), Forward Difference Equation (FDE) and Backward Difference Equation (BDE) [1] are examples of common finite-difference gradient filters to estimate gradient from discrete data

Reconstruction of 3D faces by shape estimation and texture interpolation

This paper aims to address the ill-posed problem of reconstructing 3D faces from single 2D face images. An extended Tikhonov regularization method is connected with the standard 3D morphable model in order to reconstruct the 3D face shapes from a small set of 2D facial points. Further, by interpolating the input 2D texture with the model texture and warping the interpolated texture to the reconstructed face shapes, 3D face reconstruction is achieved. For the texture warping, the 2D face deformation has been learned from the model texture using a set of facial landmarks. Our experimental results justify the robustness of the proposed approach with respect to the reconstruction of realistic 3D face shapes

Optimized Segmentation of Cellular Tomography through Organelles' Morphology and Image Features

Computational tracing of cellular images generally requires painstaking job in optimizing parameter(s). By incorporating prior knowledge about the organelle’s morphology and image features, the required number of parameter tweaking can be reduced substantially. In practical applications, however, the general organelles’ features are often known in advance, yet the actual organelles’ morphology is not elaborated. Two primary contributions of this paper are firstly the classification of insulin granules based on its image features and morphology for accurate segmentation – mainly focused at pre-processing image segmentation and secondly the new hybrid meshing quantification is presented. The method proposed in this study is validated on a set of manually defined ground truths. The study of insulin granules in particular; the location, and its image features has also opened up other options for future studies

Reconstructing 3D face shapes from single 2D images using an adaptive deformation model

The Representational Power (RP) of an example-based model is its capability to depict a new 3D face for a given 2D face image. In this contribution, a novel approach is proposed to increase the RP of the 3D reconstruction PCA-based model by deforming a set of examples in the training dataset. By adding these deformed samples together with the original training samples we gain more RP. A 3D PCA-based model is adapted for each new input face image by deforming 3D faces in the training data set. This adapted model is used to reconstruct the 3D face shape for the given input 2D near frontal face image. Our experimental results justify that the proposed adaptive model considerably improves the RP of the conventional PCA-based model

Adaptive face modelling for reconstructing 3D face shapes from single 2D images

Example-based statistical face models using principle component analysis (PCA) have been widely deployed for three-dimensional (3D) face reconstruction and face recognition. The two common factors that are generally concerned with such models are the size of the training dataset and the selection of different examples in the training set. The representational power (RP) of an example-based model is its capability to depict a new 3D face for a given 2D face image. The RP of the model can be increased by correspondingly increasing the number of training samples. In this contribution, a novel approach is proposed to increase the RP of the 3D face reconstruction model by deforming a set of examples in the training dataset. A PCA-based 3D face model is adapted for each new near frontal input face image to reconstruct the 3D face shape. Further an extended Tikhonov regularisation method has been

Model Centred Approach to Scientific Visualization

One of the crucial requirements for a Scientific Visualization system is to produce reliable and accurate results. There are many possible sources of errors that could jeopardise these efforts, such as measurement or simulation errors in the pre-analysis stage, or errors generated in the visualization process itself. Our focus here is to control errors introduced during the visualization processes. To this end we propose a conceptual model for visualization known as the Model Centred Approach (MCA). This new paradigm separates the modelling and viewing processes in visualization, and this provides the opportunity to consistently utilise a single modelling function throughout the visualization process. Results show that consistent visualizations are produced by our approach when compared to conventional methods

Model Centred Approach to Scientific Visualization

One of the crucial requirements for a Scientific Visualization system is to produce reliable and accurate results. There are many possible sources of errors that could jeopardise these efforts, such as measurement or simulation errors in the pre-analysis stage, or errors generated in the visualization process itself. Our focus here is to control errors introduced during the visualization processes. To this end we propose a conceptual model for visualization known as the Model Centred Approach (MCA). This new paradigm separates the modelling and viewing processes in visualization, and this provides the opportunity to consistently utilise a single modelling function throughout the visualization process