Abstract The main incentive of this paper is to provide an enhanced approach for 2D medical image segmentation based on the Unsupervised Grow Cut algorithm, a method that requires no prior training. This paper assumes that the reader is, to some extent, familiar with cellular automata and their function as they make up the…
Abstract Regression testing is the testing activity performed after changes occurred on software. Its aim is to increase confidence that achieved software adjustments have no negative impact on the already functional parts of the software. Test case prioritization is one technique that could be applied in regression testing with the aim to find faults…
Abstract This paper presents the results obtained by investigating a new hybrid topology for the GrowCut algorithm. The main purpose is to study whether choosing neighbors situated at a certain distance from a pixel (in addition to the pixels from a classical neighbourhood) can influence the results of image segmentation. Experiments were performed on…
Abstract The majority of Cellular Automata (CA) described in the literature are binary or three-state. While several abstractions are possible to generalise to more than three states, only a negligible number of multi-state CA rules exist with concrete practical applications.This paper proposes a generic rule for multi-state CA. The rule allows for any number…
Abstract Cellular Automata have been considered for a series of applications among which several image processing tasks. The goal of this paper is to investigate such existing methods, supporting the broader goal of identifying Cellular Automata rules able to automatically segment images. With the same broader goal in mind as future work, a detailed…
Abstract This paper investigates a novel update rule for multi-state Cellular Automata (CA) in the context of greyscale image segmentation. The update rule is parameterized and takes into account the features of neighbouring cells compared to the features of the current cell. We use the resulting CA to segment several real-world images. During this…
Abstract Traffic scene object detection and recognition is extensively researched in the field of roadside assistance. Due to its importance, many methods have been proposed to solve the classification of objects in traffic and aim classification in different lighting conditions, scaling, orientation and shape of objects. Although most methods for classification are binary classification,…
Abstract This paper presents the first results obtained by exploring different neighborhoods in two-dimensional Cellular Automata applied for the difficult task of automatic image segmentation. Numerical experiments have been performed on several real-world and synthetic images for which the ground truth is known, being therefore able to compute the algorithm performance by comparing the…
Abstract Cellular Automata (CA) can be successfully applied to the task of image segmentation. The CA-based GrowCut algorithm is able to perform such a task and we aim to investigate the full emergence phenomenon that arises during the segmentation process. In fact, we want to investigate how the segmentation performance could depend on the…
Abstract Multiclass classification is an extensively researched topic due to its importance in making the binary classification problems a complex and well tuned system and minimising the running time for multiple classification problems. In the traffic scenes one can encounter several types of obstacles like cars, pedestrians, animals, low elevated objects, road signs that…