Modeling Bottom-Up Visual Attention Using Dihedral Group D4

Published version. Source at http://dx.doi.org/10.3390/sym8080079 In this paper, first, we briefly describe the dihedral group D4 that serves as the basis for calculating saliency in our proposed model. Second, our saliency model makes two major changes in a latest state-of-the-art model known as group-based asymmetry. First, based on the properties of the dihedral group D4, we simplify the asymmetry calculations associated with the measurement of saliency. This results is an algorithm that reduces the number of calculations by at least half that makes it the fastest among the six best algorithms used in this research article. Second, in order to maximize the information across different chromatic and multi-resolution features, the color image space is de-correlated. We evaluate our algorithm against 10 state-of-the-art saliency models. Our results show that by using optimal parameters for a given dataset, our proposed model can outperform the best saliency algorithm in the literature. However, as the differences among the (few) best saliency models are small, we would like to suggest that our proposed model is among the best and the fastest among the best. Finally, as a part of future work, we suggest that our proposed approach on saliency can be extended to include three-dimensional image data

Unsupervised brain anomaly detection in MR images

Brain disorders are characterized by morphological deformations in shape and size of (sub)cortical structures in one or both hemispheres. These deformations cause deviations from the normal pattern of brain asymmetries, resulting in asymmetric lesions that directly affect the patient’s condition. Unsupervised methods aim to learn a model from unlabeled healthy images, so that an unseen image that breaks priors of this model, i.e., an outlier, is considered an anomaly. Consequently, they are generic in detecting any lesions, e.g., coming from multiple diseases, as long as these notably differ from healthy training images. This thesis addresses the development of solutions to leverage unsupervised machine learning for the detection/analysis of abnormal brain asymmetries related to anomalies in magnetic resonance (MR) images. First, we propose an automatic probabilistic-atlas-based approach for anomalous brain image segmentation. Second, we explore an automatic method for the detection of abnormal hippocampi from abnormal asymmetries based on deep generative networks and a one-class classifier. Third, we present a more generic framework to detect abnormal asymmetries in the entire brain hemispheres. Our approach extracts pairs of symmetric regions — called supervoxels — in both hemispheres of a test image under study. One-class classifiers then analyze the asymmetries present in each pair. Experimental results on 3D MR-T1 images from healthy subjects and patients with a variety of lesions show the effectiveness and robustness of the proposed unsupervised approaches for brain anomaly detection

Monitoring Processes in Visual Search Enhanced by Professional Experience: The Case of Orange Quality-Control Workers

Visual search tasks have often been used to investigate how cognitive processes change with expertise. Several studies have shown visual experts' advantages in detecting objects related to their expertise. Here, we tried to extend these findings by investigating whether professional search experience could boost top-down monitoring processes involved in visual search, independently of advantages specific to objects of expertise. To this aim, we recruited a group of quality-control workers employed in citrus farms. Given the specific features of this type of job, we expected that the extensive employment of monitoring mechanisms during orange selection could enhance these mechanisms even in search situations in which orange-related expertise is not suitable. To test this hypothesis, we compared performance of our experimental group and of a well-matched control group on a computerized visual search task. In one block the target was an orange (expertise target) while in the other block the target was a Smurfette doll (neutral target). The a priori hypothesis was to find an advantage for quality-controllers in those situations in which monitoring was especially involved, that is, when deciding the presence/absence of the target required a more extensive inspection of the search array. Results were consistent with our hypothesis. Quality-controllers were faster in those conditions that extensively required monitoring processes, specifically, the Smurfette-present and both target-absent conditions. No differences emerged in the orange-present condition, which resulted to mainly rely on bottom-up processes. These results suggest that top-down processes in visual search can be enhanced through immersive real-life experience beyond visual expertise advantages

Principles of energy optimization underlying human walking gait adaptations

Learning to move in novel situations is a complex process. We need to continually learn the changing situations and determine the best way to move. Optimization is a widely accepted framework for this process. However, little is known about algorithms used by the nervous system to perform this optimization. Our lab recently found evidence that people can continuously optimize energy during walking. My goal in this thesis is to identify principles of optimization, particularly energy optimization in walking, that govern our choice of movement in novel situations. I used two novel walking tasks for this purpose. For the first task, I designed, built, and tested a mechatronic system that can quickly, accurately, and precisely apply forces to a user’s torso. It changes the relationship between a walking gait and its associated energetic cost—cost landscape—to shift the energy optimal walking gait. Participants shift their gait towards the new optimum in these landscapes. In my second project, I aimed to understand how the nervous system identifies when to initiate optimization. I used my system to create cost landscapes of three different cost gradients. I found that experiencing a steeper cost gradient through natural variability is not sufficient to cue the nervous system to initiate optimization. For my third and fourth projects, I used the task of split-belt walking. I collaborated with another research group to analyse the mechanics and energetics of walking with different step lengths on a split-belt treadmill. I found that people can harness energy from a split-belt treadmill by placing their leading leg further forward on the fast belt, and that there may be an energy optimal gait. In my fourth project, I used computer modelling to identify that there may exist an energy optimal gait due to the trade-off between the cost of swinging the leg and the cost of redirecting the body center of mass when transitioning from step to step. Together, these projects develop a new system and a new approach to understand energy optimization in walking. They uncover principles governing the initiation of this process and our ability to benefit from it

Uncertainty, generalization, and neural representation of relevant variables for decision making

Dissertation presented to obtain the Ph.D degree in Biology, Computational Biology.Understanding decision making in various contexts is fundamental to understanding human behavior. This thesis presents several studies that examine decision making from many different points of view using a variety of research tools.(...

Understanding deep learning

Deep neural networks have reached impressive performance in many tasks in computer vision and its applications. However, research into understanding deep neural networks is challenging due to the evaluation. Since it is unknown which features deep neural networks use, it is hard to empirically evaluate whether a result for which feature is used by a deep neural network is correct. The state- of-the-art for understanding which features a deep neural network uses to reach its prediction is sailiency maps. However, all methods built on sailiency maps share shortcomings that open a gap between the current state-of-the-art and the requirements for understanding deep neural networks. This work describes a method that does not suffer from these shortcomings. To this end, we employ the framework of causal modeling to determine whether a feature is used by the neural network. We present theoretical evidence that our method is able to correctly identify if a feature is used. Furthermore, we demonstrate two studies as empirical evidence. First, we show that our method can further the understanding of automatic skin lesion classifiers. There, we find that some of the features in the ABCD rule are used by the classifiers to identify melanoma but not to identify seborrheic keratosis. In contrast, all classifiers highly rely on the bias variables, particularly the age of the patient and the existence of colorful patches in the input image. Second we apply our method to adversarial debiasing. In adversarial debiasing, we want to stop a neural network from using a known bias variable. We demonstrate in a toy example and an example on real- world images that our approach outperforms the state-of-the-art in adversarial debiasing

Cortical spatio-temporal dimensionality reduction for visual grouping

The visual systems of many mammals, including humans, is able to integrate the geometric information of visual stimuli and to perform cognitive tasks already at the first stages of the cortical processing. This is thought to be the result of a combination of mechanisms, which include feature extraction at single cell level and geometric processing by means of cells connectivity. We present a geometric model of such connectivities in the space of detected features associated to spatio-temporal visual stimuli, and show how they can be used to obtain low-level object segmentation. The main idea is that of defining a spectral clustering procedure with anisotropic affinities over datasets consisting of embeddings of the visual stimuli into higher dimensional spaces. Neural plausibility of the proposed arguments will be discussed