Matching Items (4)

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The Role of Visual Attention In Auditory Localization

Description

Hearing and vision are two senses that most individuals use on a daily basis. The simultaneous presentation of competing visual and auditory stimuli often affects our sensory perception. It is

Hearing and vision are two senses that most individuals use on a daily basis. The simultaneous presentation of competing visual and auditory stimuli often affects our sensory perception. It is often believed that vision is the more dominant sense over audition in spatial localization tasks. Recent work suggests that visual information can influence auditory localization when the sound is emanating from a physical location or from a phantom location generated through stereophony (the so-called "summing localization"). The present study investigates the role of cross-modal fusion in an auditory localization task. The focuses of the experiments are two-fold: (1) reveal the extent of fusion between auditory and visual stimuli and (2) investigate how fusion is correlated with the amount of visual bias a subject experiences. We found that fusion often occurs when light flash and "summing localization" stimuli were presented from the same hemifield. However, little correlation was observed between the magnitude of visual bias and the extent of perceived fusion between light and sound stimuli. In some cases, subjects reported distinctive locations for light and sound and still experienced visual capture.

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Created

Date Created
  • 2016-05

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Efficient perceptual super-resolution

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

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.

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Agent

Created

Date Created
  • 2011

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Quiet Eye Training and the Focus of Visual Attention in Golf Putting

Description

Previous research has shown that training visual attention can improve golf putting performance. A technique called the Quiet Eye focuses on increasing a player’s length of fixation between the ball

Previous research has shown that training visual attention can improve golf putting performance. A technique called the Quiet Eye focuses on increasing a player’s length of fixation between the ball and the hole. When putting, the final fixation is made on the ball before executing the stroke leaving players to rely on their memory of the hole’s distance and location. The present study aimed to test the effectiveness of Quiet Eye training for final fixation on the hole. Twelve Arizona State University (ASU) students with minimal golf experience putted while wearing eye tracking glasses under the following conditions: from three feet with final fixation on the ball, from six feet with final fixation on the ball, from three feet with final fixation on the hole and from six feet with final fixation on the hole. Participant’s performance was measured before training, following quiet eye training, and under simulated pressure conditions. Putting performance was not significantly affected by final fixation for all conditions. The number of total putts made was significantly greater when putting from three feet for all conditions. Future research should test the effects of this training with expert golfers whose processes are more automatic compared to novices and can afford to look at the hole while putting.

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Created

Date Created
  • 2019

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Texture structure analysis

Description

Texture analysis plays an important role in applications like automated pattern inspection, image and video compression, content-based image retrieval, remote-sensing, medical imaging and document processing, to name a few. Texture

Texture analysis plays an important role in applications like automated pattern inspection, image and video compression, content-based image retrieval, remote-sensing, medical imaging and document processing, to name a few. Texture Structure Analysis is the process of studying the structure present in the textures. This structure can be expressed in terms of perceived regularity. Our human visual system (HVS) uses the perceived regularity as one of the important pre-attentive cues in low-level image understanding. Similar to the HVS, image processing and computer vision systems can make fast and efficient decisions if they can quantify this regularity automatically. In this work, the problem of quantifying the degree of perceived regularity when looking at an arbitrary texture is introduced and addressed. One key contribution of this work is in proposing an objective no-reference perceptual texture regularity metric based on visual saliency. Other key contributions include an adaptive texture synthesis method based on texture regularity, and a low-complexity reduced-reference visual quality metric for assessing the quality of synthesized textures. In order to use the best performing visual attention model on textures, the performance of the most popular visual attention models to predict the visual saliency on textures is evaluated. Since there is no publicly available database with ground-truth saliency maps on images with exclusive texture content, a new eye-tracking database is systematically built. Using the Visual Saliency Map (VSM) generated by the best visual attention model, the proposed texture regularity metric is computed. The proposed metric is based on the observation that VSM characteristics differ between textures of differing regularity. The proposed texture regularity metric is based on two texture regularity scores, namely a textural similarity score and a spatial distribution score. In order to evaluate the performance of the proposed regularity metric, a texture regularity database called RegTEX, is built as a part of this work. It is shown through subjective testing that the proposed metric has a strong correlation with the Mean Opinion Score (MOS) for the perceived regularity of textures. The proposed method is also shown to be robust to geometric and photometric transformations and outperforms some of the popular texture regularity metrics in predicting the perceived regularity. The impact of the proposed metric to improve the performance of many image-processing applications is also presented. The influence of the perceived texture regularity on the perceptual quality of synthesized textures is demonstrated through building a synthesized textures database named SynTEX. It is shown through subjective testing that textures with different degrees of perceived regularities exhibit different degrees of vulnerability to artifacts resulting from different texture synthesis approaches. This work also proposes an algorithm for adaptively selecting the appropriate texture synthesis method based on the perceived regularity of the original texture. A reduced-reference texture quality metric for texture synthesis is also proposed as part of this work. The metric is based on the change in perceived regularity and the change in perceived granularity between the original and the synthesized textures. The perceived granularity is quantified through a new granularity metric that is proposed in this work. It is shown through subjective testing that the proposed quality metric, using just 2 parameters, has a strong correlation with the MOS for the fidelity of synthesized textures and outperforms the state-of-the-art full-reference quality metrics on 3 different texture databases. Finally, the ability of the proposed regularity metric in predicting the perceived degradation of textures due to compression and blur artifacts is also established.

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Agent

Created

Date Created
  • 2014