Segmentation of left and right ventricles plays a crucial role in quantitatively analyzing the global and regional information in the cardiac magnetic resonance imaging (MRI). In MRI, the intensity inhomogeneity and weak or blurred object boundaries are the problems, which makes it difficult for the intensity-based segmentation methods to properly delineate the regions of interests (ROI). In this paper, a hybrid signed pressure force function (SPF) is proposed, which yields both local and global image fitted differences in an additive fashion. A characteristic term is also introduced in the SPF function to restrict the contour within the ROI. The overlapping dice index and Hausdorff-Distance metrics have been used over cardiac datasets for quantitative validation. Using 2009 LV MICCAI validation dataset, the proposed method yields DSC values of 0.95 and 0.97 for endocardial and epicardial contours, respectively. Using 2012 RV MICCAI dataset, for the endocardial region, the proposed method yields DSC values of 0.97 and 0.90 and HD values of 8.51 and 7.67 for ED and ES, respectively. For the epicardial region, it yields DSC values of 0.92 and 0.91 and HD values of 6.47 and 9.34 for ED and ES, respectively. Results show its robustness in the segmentation application of the cardiac MRI.

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We present a method that estimates the physically accurate reflectance of materials from a single image and reproduces real world materials which can be used in well-known graphics engines and tools. Recovering the BRDF (bidirectional reflectance distribution function) from a single image is an ill-posed problem due to the insufficient irradiance and geometry information as well as the insufficient samples on the BRDF parameters. The problem could be alleviated with a simplified representation of the surface reflectance such as Phong reflection model. Recent works have appealed that convolutional neural network successfully predicts parameters of empirical BRDF models for non-Lambertian surfaces. However, parameters of the physically-based model confront the problem of having non-orthogonal space, making it difficult to estimate physically meaningful results. In this paper, we propose a method to estimate parameters of a physically-based BRDF model from a single image. We focus on the metallic property of the physically-based model to enhance the estimation accuracy. Since metals and nonmetals have very different characteristics, our method processes them separately. Our method also generates auxiliary maps using a cGAN (conditional generative adversarial network) architecture to help in estimating more accurate BRDF parameters. Based on the experimental results, the auxiliary map is selected as an irradiance environment map for the metallic and a specular map for the nonmetallic. These auxiliary maps help to clarify the contributions of different actors, including light color, material color, specular component, and diffuse component, to the surface color. Our method first estimates whether the material on the input image is metallic or nonmetallic. Then, it estimates BRDF parameters using CNN (convolutional neural networks) architecture guided by generated auxiliary maps. Our results show that our method is effective to estimate BRDF parameters both on synthesized as well as real images.

Recently, the size of models for real-time rendering has been significantly increasing for realism, and many graphics applications are being developed in mobile devices with relatively insufficient hardware power. Therefore, improving rendering speed is still important in graphics. Back-face culling is one of the core speed-up techniques to remove the back-facing polygons that are not drawn in the result image. In this paper, we present a mesh clustering and reordering method based on normal coherence for efficient back-face culling at an earlier stage than the current method, which removes back faces after the vertex shader on the GPU. In the pre-computation, our method first vertically clusters the mesh into multiple stripes based on the latitude of the face normal vector and sorts each stripe in ascending order of longitude. At runtime, our method computes a potentially visible set of faces at the current camera view by excluding back faces from the clustered and reordered faces list, and draws only the potentially visible set. Experiments have shown that the rendering using our method is more efficient than traditional methods, especially for large and static models.