Extensive research has been performed in image processing to find the best edge detector, from the gradient-based operators to evolved Cellular Automata (CA). Some of these detectors have weak points, such as disconnected edges, the incapacity of detecting the branching edges or the need of a ground truth that is not always available. To…
Abstract Cellular Automata (CA) are simple, easily parallelizable models that have been used extensively for various computational tasks. Such models are especially useful for image processing, as mapping automaton cells to image pixels is straightforward and intuitive. This paper proposes a novel optimization framework for CA rules based on evolutionary algorithms and used in…
One significant and complex image processing task that may be used as a step in various complex processes is the edge detection. The mechanism of detecting the edges is called edge detector. The scope of this paper is to propose an edge detector based on evolved Cellular Automata (CA), for binary images. It was…
One important image processing task is the edge detection in intensity images. Some approaches were proposed based on computing the gradient of the signal, but not many approaches were proposed based on Cellular Automata (CA). In this paper, an edge detection method based on CA evolved by the means of a Genetic Algorithm (GA)…
Cellular Automata (CA) can be successfully applied in various image processing tasks because they have a number of advantages over the traditional methods of computations: simplicity of implementation, the complexity of behaviour, parallelisation, extensibility, scalability, robustness. In this paper, an edge detection method for binary images, based on CA and Evolutionary Algorithms (EA) is…
Citare A. Marinescu, A. Dărăbant, T. Ileni, Optimal Stereo Camera Calibration via Genetic Algorithms, IJCAI 2021 AI4AD Workshop, Artificial Intelligence for Autonomous Driving
We have continued our work in the field of AI-driven music synthesis and have improved upon our previous 3-layer gated recurrent unit neural network, with results confirming higher accuracy and much smaller validation loss. In order to achieve this, we have designed a recurrent neural architecture that is more suited to learning the musical…
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…
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 Gated…