Matching Items (6)
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- All Subjects: Visual perception
- Creators: Homa, Donald
- Creators: Karam, Lina J
Description
When people look for things in their environment they use a target template - a mental representation of the object they are attempting to locate - to guide their attention around a scene and to assess incoming visual input to determine if they have found that for which they are searching. However, unlike laboratory experiments, searchers in the real-world rarely have perfect knowledge regarding the appearance of their target. In five experiments (with nearly 1,000 participants), we examined how the precision of the observer's template affects their ability to conduct visual search. Specifically, we simulated template imprecision in two ways: First, by contaminating our searchers' templates with inaccurate features, and second, by introducing extraneous features to the template that were unhelpful. In those experiments we recorded the eye movements of our searchers in order to make inferences regarding the extent to which attentional guidance and decision-making are hindered by template imprecision. We also examined a third way in which templates may become imprecise; namely, that they may deteriorate over time. Overall, our findings support a dual-function theory of the target template, and highlight the importance of examining template precision in future research.
ContributorsHout, Michael C (Author) / Goldinger, Stephen D (Thesis advisor) / Azuma, Tamiko (Committee member) / Homa, Donald (Committee member) / Reichle, Erik (Committee member) / Arizona State University (Publisher)
Created2013
Description
Recognition memory was investigated for naturalistic dynamic scenes. Although visual recognition for static objects and scenes has been investigated previously and found to be extremely robust in terms of fidelity and retention, visual recognition for dynamic scenes has received much less attention. In four experiments, participants view a number of clips from novel films and are then tasked to complete a recognition test containing frames from the previously viewed films and difficult foil frames. Recognition performance is good when foils are taken from other parts of the same film (Experiment 1), but degrades greatly when foils are taken from unseen gaps from within the viewed footage (Experiments 3 and 4). Removing all non-target frames had a serious effect on recognition performance (Experiment 2). Across all experiments, presenting the films as a random series of clips seemed to have no effect on recognition performance. Patterns of accuracy and response latency in Experiments 3 and 4 appear to be a result of a serial-search process. It is concluded that visual representations of dynamic scenes may be stored as units of events, and participant's old
ew judgments of individual frames were better characterized by a cued-recall paradigm than traditional recognition judgments.
ew judgments of individual frames were better characterized by a cued-recall paradigm than traditional recognition judgments.
ContributorsFerguson, Ryan (Author) / Homa, Donald (Thesis advisor) / Goldinger, Stephen (Committee member) / Glenberg, Arthur (Committee member) / Brewer, Gene (Committee member) / Arizona State University (Publisher)
Created2014
Description
Previous research has shown that people can implicitly learn repeated visual contexts and use this information when locating relevant items. For example, when people are presented with repeated spatial configurations of distractor items or distractor identities in visual search, they become faster to find target stimuli in these repeated contexts over time (Chun and Jiang, 1998; 1999). Given that people learn these repeated distractor configurations and identities, might they also implicitly encode semantic information about distractors, if this information is predictive of the target location? We investigated this question with a series of visual search experiments using real-world stimuli within a contextual cueing paradigm (Chun and Jiang, 1998). Specifically, we tested whether participants could learn, through experience, that the target images they are searching for are always located near specific categories of distractors, such as food items or animals. We also varied the spatial consistency of target locations, in order to rule out implicit learning of repeated target locations. Results suggest that participants implicitly learned the target-predictive categories of distractors and used this information during search, although these results failed to reach significance. This lack of significance may have been due the relative simplicity of the search task, however, and several new experiments are proposed to further investigate whether repeated category information can benefit search.
ContributorsWalenchok, Stephen C (Author) / Goldinger, Stephen D (Thesis advisor) / Azuma, Tamiko (Committee member) / Homa, Donald (Committee member) / Hout, Michael C (Committee member) / Arizona State University (Publisher)
Created2014
Description
Super-Resolution (SR) techniques are widely developed to increase image resolution by fusing several Low-Resolution (LR) images of the same scene to overcome sensor hardware limitations and reduce media impairments in a cost-effective manner. When choosing a solution for the SR problem, there is always a trade-off between computational efficiency and High-Resolution (HR) image quality. Existing SR approaches suffer from extremely high computational requirements due to the high number of unknowns to be estimated in the solution of the SR inverse problem. This thesis proposes efficient iterative SR techniques based on Visual Attention (VA) and perceptual modeling of the human visual system. In the first part of this thesis, an efficient ATtentive-SELective Perceptual-based (AT-SELP) SR framework is presented, where only a subset of perceptually significant active pixels is selected for processing by the SR algorithm based on a local contrast sensitivity threshold model and a proposed low complexity saliency detector. The proposed saliency detector utilizes a probability of detection rule inspired by concepts of luminance masking and visual attention. The second part of this thesis further enhances on the efficiency of selective SR approaches by presenting an ATtentive (AT) SR framework that is completely driven by VA region detectors. Additionally, different VA techniques that combine several low-level features, such as center-surround differences in intensity and orientation, patch luminance and contrast, bandpass outputs of patch luminance and contrast, and difference of Gaussians of luminance intensity are integrated and analyzed to illustrate the effectiveness of the proposed selective SR frameworks. The proposed AT-SELP SR and AT-SR frameworks proved to be flexible by integrating a Maximum A Posteriori (MAP)-based SR algorithm as well as a fast two-stage Fusion-Restoration (FR) SR estimator. By adopting the proposed selective SR frameworks, simulation results show significant reduction on average in computational complexity with comparable visual quality in terms of quantitative metrics such as PSNR, SNR or MAE gains, and subjective assessment. The third part of this thesis proposes a Perceptually Weighted (WP) SR technique that incorporates unequal weighting parameters in the cost function of iterative SR problems. The proposed approach is inspired by the unequal processing of the Human Visual System (HVS) to different local image features in an image. Simulation results show an enhanced reconstruction quality and faster convergence rates when applied to the MAP-based and FR-based SR schemes.
ContributorsSadaka, Nabil (Author) / Karam, Lina J (Thesis advisor) / Spanias, Andreas S (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Abousleman, Glen P (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2011
Description
The present study explores the role of motion in the perception of form from dynamic occlusion, employing color to help isolate the contributions of both visual pathways. Although the cells that respond to color cues in the environment usually feed into the ventral stream, humans can perceive motion based on chromatic cues. The current study was designed to use grey, green, and red stimuli to successively limit the amount of information available to the dorsal stream pathway, while providing roughly equal information to the ventral system. Twenty-one participants identified shapes that were presented in grey, green, and red and were defined by dynamic occlusion. The shapes were then presented again in a static condition where the maximum occlusions were presented as before, but without motion. Results showed an interaction between the motion and static conditions in that when the speed of presentation increased, performance in the motion conditions became significantly less accurate than in the static conditions. The grey and green motion conditions crossed static performance at the same point, whereas the red motion condition crossed at a much slower speed. These data are consistent with a model of neural processing in which the main visual systems share information. Moreover, they support the notion that presenting stimuli in specific colors may help isolate perceptual pathways for scientific investigation. Given the potential for chromatic cues to target specific visual systems in the performance of dynamic object recognition, exploring these perceptual parameters may help our understanding of human visual processing.
ContributorsHolloway, Steven R. (Author) / McBeath, Michael K. (Thesis advisor) / Homa, Donald (Committee member) / Macknik, Stephen L. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Visual attention (VA) is the study of mechanisms that allow the human visual system (HVS) to selectively process relevant visual information. This work focuses on the subjective and objective evaluation of computational VA models for the distortion-free case as well as in the presence of image distortions.
Existing VA models are traditionally evaluated by using VA metrics that quantify the match between predicted saliency and fixation data obtained from eye-tracking experiments on human observers. Though there is a considerable number of objective VA metrics, there exists no study that validates that these metrics are adequate for the evaluation of VA models. This work constructs a VA Quality (VAQ) Database by subjectively assessing the prediction performance of VA models on distortion-free images. Additionally, shortcomings in existing metrics are discussed through illustrative examples and a new metric that uses local weights based on fixation density and that overcomes these flaws, is proposed. The proposed VA metric outperforms all other popular existing metrics in terms of the correlation with subjective ratings.
In practice, the image quality is affected by a host of factors at several stages of the image processing pipeline such as acquisition, compression, and transmission. However, none of the existing studies have discussed the subjective and objective evaluation of visual saliency models in the presence of distortion. In this work, a Distortion-based Visual Attention Quality (DVAQ) subjective database is constructed to evaluate the quality of VA maps for images in the presence of distortions. For creating this database, saliency maps obtained from images subjected to various types of distortions, including blur, noise and compression, and varying levels of distortion severity are rated by human observers in terms of their visual resemblance to corresponding ground-truth fixation density maps. The performance of traditionally used as well as recently proposed VA metrics are evaluated by correlating their scores with the human subjective ratings. In addition, an objective evaluation of 20 state-of-the-art VA models is performed using the top-performing VA metrics together with a study of how the VA models’ prediction performance changes with different types and levels of distortions.
Existing VA models are traditionally evaluated by using VA metrics that quantify the match between predicted saliency and fixation data obtained from eye-tracking experiments on human observers. Though there is a considerable number of objective VA metrics, there exists no study that validates that these metrics are adequate for the evaluation of VA models. This work constructs a VA Quality (VAQ) Database by subjectively assessing the prediction performance of VA models on distortion-free images. Additionally, shortcomings in existing metrics are discussed through illustrative examples and a new metric that uses local weights based on fixation density and that overcomes these flaws, is proposed. The proposed VA metric outperforms all other popular existing metrics in terms of the correlation with subjective ratings.
In practice, the image quality is affected by a host of factors at several stages of the image processing pipeline such as acquisition, compression, and transmission. However, none of the existing studies have discussed the subjective and objective evaluation of visual saliency models in the presence of distortion. In this work, a Distortion-based Visual Attention Quality (DVAQ) subjective database is constructed to evaluate the quality of VA maps for images in the presence of distortions. For creating this database, saliency maps obtained from images subjected to various types of distortions, including blur, noise and compression, and varying levels of distortion severity are rated by human observers in terms of their visual resemblance to corresponding ground-truth fixation density maps. The performance of traditionally used as well as recently proposed VA metrics are evaluated by correlating their scores with the human subjective ratings. In addition, an objective evaluation of 20 state-of-the-art VA models is performed using the top-performing VA metrics together with a study of how the VA models’ prediction performance changes with different types and levels of distortions.
ContributorsGide, Milind Subhash (Author) / Karam, Lina J (Thesis advisor) / Abousleman, Glen (Committee member) / Li, Baoxin (Committee member) / Reisslein, Martin (Committee member) / Arizona State University (Publisher)
Created2016