Low-cost Automatic Inpainting for Artifact Suppression in Facial Images

Facial images are often used in applications that need to recognize or identify persons. Many existing facial recognition tools have limitations with respect to facial image quality attributes such as resolution, face position, and artifacts present in the image. In this paper we describe a new low-cost framework for preprocessing low-quality facial images in order to render them suitable for automatic recognition. For this, we first detect artifacts based on the statistical difference between the target image and a set of pre-processed images in the database. Next, we eliminate artifacts by an inpainting method which combines information from the target image and similar images in our database. Our method has low computational cost and is simple to implement, which makes it attractive for usage in low-budget environments. We illustrate our method on several images taken from public surveillance databases, and compare our results with existing inpainting techniques

A Framework for Interactive Visualization of Component-Based Software

In this paper, we advocate the use of visual tooling for the development and maintenance of component-based software systems. Our contribution is twofold. First, we demonstrate how an interactive visualization tool effectively supports understanding large component based software. Secondly, we show how to design such a tool in order to make it applicable for a wide range of component systems and investigation goals. We demonstrate our approach by several visualization scenarios for real-world systems. 1

A robust level-set algorithm for centerline extraction

We present a robust method for extracting 3D centerlines from volumetric datasets. We start from a 2D skeletonization method to locate voxels centered with respect to three orthogonal slicing directions. Next, we introduce a new detection criterion to extract the centerline voxels from the above skeletons, followed by a thinning, reconnection, and a ranking step. Overall, the proposed method produces centerlines that are object-centered, connected, one voxel thick, robust with respect to object noisiness, handles arbitrary object topologies, comes with a simple pruning threshold, and is fast to compute. We compare our results with two other methods on a variety of real-world datasets. 1

Empirical Software Engineering manuscript No. (will be inserted by the editor) Visual Querying and Analysis of Large Software Repositories

Abstract We present a software framework for visual mining of software repositories. Our framework addresses three aspects: data extraction from repositories, data analysis, and interactive visualization and exploration. Along these, it provides an open structure, extensible with new functionality. We first discuss the challenges of data extraction and storage, and propose a flexible way to deal with implementation inconsistencies in software repositories. We next present a new technique to enrich the raw data with information about artifacts showing similar evolution. Finally, we propose a visualization back-end that supports visual navigation and query of the extracted data at several levels of detail. We demonstrate the applicability of our framework by presenting several case studies performed on industry-size software repositories.