The left intraparietal sulcus modulates the selection of low salient stimuli

Neuropsychological and functional imaging studies have suggested a general right hemisphere advantage for processing global visual information and a left hemisphere advantage for processing local information. In contrast, a recent transcranial magnetic stimulation study [Mevorach, C., Humphreys, G. W., & Shalev, L. Opposite biases in salience-based selection for the left and right posterior parietal cortex. Nature Neuroscience, 9, 740-742, 2006b] demonstrated that functional lateralization of selection in the parietal cortices on the basis of the relative salience of stimuli might provide an alternative explanation for previous results. In the present study, we applied a whole-brain analysis of the functional magnetic resonance signal when participants responded to either the local or the global levels of hierarchical figures. The task (respond to local or global) was crossed with the saliency of the target level (local salient, global salient) to provide, for the first time, a direct contrast between brain activation related to the stimulus level and that related to relative saliency. We found evidence for lateralization of salience-based selection but not for selection based on the level of processing. Activation along the left intraparietal sulcus (IPS) was found when a low saliency stimulus had to be selected irrespective of its level. A control task showed that this was not simply an effect of task difficulty. The data suggest a specific role for regions along the left IPS in salience-based selection, supporting the argument that previous reports of lateralized responses to local and global stimuli were contaminated by effects of saliency

Semantic guidance of eye movements in real-world scenes

AbstractThe perception of objects in our visual world is influenced by not only their low-level visual features such as shape and color, but also their high-level features such as meaning and semantic relations among them. While it has been shown that low-level features in real-world scenes guide eye movements during scene inspection and search, the influence of semantic similarity among scene objects on eye movements in such situations has not been investigated. Here we study guidance of eye movements by semantic similarity among objects during real-world scene inspection and search. By selecting scenes from the LabelMe object-annotated image database and applying latent semantic analysis (LSA) to the object labels, we generated semantic saliency maps of real-world scenes based on the semantic similarity of scene objects to the currently fixated object or the search target. An ROC analysis of these maps as predictors of subjects’ gaze transitions between objects during scene inspection revealed a preference for transitions to objects that were semantically similar to the currently inspected one. Furthermore, during the course of a scene search, subjects’ eye movements were progressively guided toward objects that were semantically similar to the search target. These findings demonstrate substantial semantic guidance of eye movements in real-world scenes and show its importance for understanding real-world attentional control

Doctor of Philosophy

dissertationDigital image processing has wide ranging applications in combustion research. The analysis of digital images is used in practically every scale of studying combustion phenomena from the scale of individual atoms to diagnosing and controlling large-scale combustors. Digital image processing is one of the fastest-growing scientific areas in the world today. From being able to reconstruct low-resolution grayscale images from transmitted signals, the capabilities have grown to enabling machines carrying out tasks that would normally require human vision, perception, and reasoning. Certain applications in combustion science benefit greatly from recent advances in image processing. Unfortunately, since the two fields - combustion and image processing research - stand relatively far from each other, the most recent results are often not known well enough in the areas where they may be applied with great benefits. This work aims to improve the accuracy and reliability of certain measurements in combustion science by selecting, adapting, and implementing the appropriate techniques originally developed in the image processing area. A number of specific applications were chosen that cover a wide range of physical scales of combustion phenomena, and specific image processing methodologies were proposed to improve or enable measurements in studying such phenomena. The selected applications include the description and quantification of combustion-derived carbon nanostructure, the three-dimensional optical diagnostics of combusting pulverized-coal particles and the optical flow velocimetry and quantitative radiation imaging of a pilot-scale oxy-coal flame. In the field of the structural analysis of soot, new structural parameters were derived and the extraction and fidelity of existing ones were improved. In the field of pulverized-coal combustion, the developed methodologies allow for studying the detailed mechanisms of particle combustion in three dimensions. At larger scales, the simultaneous measurement of flame velocity, spectral radiation, and pyrometric properties were realized

Unmanned Aerial Systems for Wildland and Forest Fires

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at: https://doi.org/10.3390/drones501001

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas