Resilient Image Feature Description through Evolution

Content-Based Image Feature Description and Retrieving

Image alignment via kernelized feature learning

Machine learning is an application of artificial intelligence that is able to automatically learn and improve from experience without being explicitly programmed. The primary assumption for most of the machine learning algorithms is that the training set (source domain) and the test set (target domain) follow from the same probability distribution. However, in most of the real-world application...

Analyzing software evolution through feature views

Features encapsulate the domain knowledge of a software system and thus are valuable sources of information for a reverse engineer. When analyzing the evolution of a system, we need to know how and which features were modified to recover both the change intention and extent, namely which source artifacts are affected. Typically, the implementation of a feature crosscuts a number of source artif...

Image Segmentation through Fuzzy Feature Space Analysis

We propose an image segmentation algorithm based on local measures and fuzzy feature space analysis. We address the problem of assigning individual pixels to multiple classes based on computed pixel properties. This new segmentation algorithm extends an object/background segmentation approach introduced in 1992. The idea of analyzing trajectories in fuzzy feature space leads to the partitioning...

Resilient Image Fusion

The paper describes a distributed spectral-screening PCT algorithm for fusing hyper-spectral images in remote sensing applications. The algorithm provides intrusion tolerance from information warfare attacks using the notion of computational resiliency. This concept uses replication to achieve fault tolerance, but goes further to dynamically regenerate replication in response to an attack or fa...