Abstract 3D Face Reconstruction is a complex problem in Computer Vision. Until recently the existing methods were based on multiple image captures and solving complex dense correspondences between different face poses. Recent methods are based on volumetric CNNs and try to reconstruct the 3D face model from a single image. Accurate 3D face reconstructions…
Abstract In this paper we present a 3D facial reconstruction algorithm for facial shape classification. We propose a reconstruction method that preserves facial ratios independent on the captured poses allowing thus a precise facial shape classification/determination. We use an Active Appearance Model on a set of facial image captures with different poses on which…
Abstract The main incentive of this paper is to approach the sensitive subject of classical music synthesis in the form of musical scores by providing an analysis of different Recurrent Neural Network architectures. We will be discussing in a side-by-side comparison two of the most common neural network layers, namely Long-Short Term Memory and…
Abstract Breast cancer is one of the most common types of cancer amongst women, but it is also one of the most frequently cured cancers. Because of this, early detection is crucial, and this can be done through mammography screening. With the increasing need of an automated interpretation system, a lot of methods have…
Abstract Most users often find themselves in a situation when they need to answer the same question: which product, movie, vacation offer, restaurant or book is the best to choose? In order to answer this question, Recommendation Systems have been developed to generate the best suggestions according to the user’s interest. Recommendation Systems play…
Abstract The most common cancer type amongst women is the breast cancer with a large number of cases reported each year, many of them diagnosed in an advanced phase. In this paper our scope is to create a base for a support system that helps detecting breast cancer in an early stage. After defining…
Abstract We aimed to assess the reliability of an automatic solution for whole-heart segmentation of MRI images of patients with atrial fibrillation (AF). We propose a semi-interactive image segmentation algorithm based on region growing, GrowCut1, using novel neighborhood structures based on Cellular Automata. We complemented the algorithm with a global view of the signal…
Abstract We aimed to assess the reliability of teaching Artificial Intelligencefor Software Engineering master students. We propose a semi-interactive course where the students have to develop applications for solving real world problems by using various intelligent tools. We try to integrate these two disciplines, since both deal with modeling of the real case studies,…
Abstract Cellular automata have been widely used for solving the edge detection problem. This paper proposes an algorithm which optimizes cellular automata rules using Particle Swarm Optimization based on an existing method in the literature. Moreover, the method is extended from grayscale to RGB images by performing the optimization on each colour channel individually.…
Abstract In this paper, we introduce Competitive Unsupervised GrowCut, a cellular automata-based, unsupervised and autonomous algorithm that combines the label merging component of Unsupervised GrowCut with the soft label propagation mechanism of GrowCut. We evaluated our algorithm on two benchmark image segmentation datasets, along with two related methods proposed in the literature. We also…