Evidence from eye-tracking experiments has provided mixed support for saliency map models of inspection, with the task set for the viewer accounting for some of the discrepancies between predictions and observations. In the present experiment viewers inspected pictures of road scenes with the task being to decide whether or not they would enter a highway from a junction. Road safety observations have concluded that highly visible road users are less likely to be involved in crashes, suggesting that saliency is important in real-world tasks. The saliency of a critical vehicle was varied in the present task, as was the type of vehicle and the preferred vehicle of the viewer. Decisions were influenced by saliency, with more risky decisions when low saliency motorcycles were present. Given that the vehicles were invariably inspected, this may relate to the high incidence of "looked-but-failed-to-see" crashes involving motorcycles and to prevalence effects in visual search. Eye-tracking measures indicated effects of saliency on the fixation preceding inspection of the critical vehicle (as well as effects on inspection of the vehicle itself), suggesting that high saliency can attract an early fixation. These results have implications for recommendations about the conspicuity of vulnerable road users.
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http://dx.doi.org/10.1016/j.visres.2011.07.020 | DOI Listing |
Sci Rep
December 2024
Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, China.
The intelligent identification of wear particles in ferrography is a critical bottleneck that hampers the development and widespread adoption of ferrography technology. To address challenges such as false detection, missed detection of small wear particles, difficulty in distinguishing overlapping and similar abrasions, and handling complex image backgrounds, this paper proposes an algorithm called TCBGY-Net for detecting wear particles in ferrography images. The proposed TCBGY-Net uses YOLOv5s as the backbone network, which is enhanced with several advanced modules to improve detection performance.
View Article and Find Full Text PDFNat Commun
December 2024
Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
Mid-infrared photoacoustic microscopy can capture biochemical information without staining. However, the long mid-infrared optical wavelengths make the spatial resolution of photoacoustic microscopy significantly poorer than that of conventional confocal fluorescence microscopy. Here, we demonstrate an explainable deep learning-based unsupervised inter-domain transformation of low-resolution unlabeled mid-infrared photoacoustic microscopy images into confocal-like virtually fluorescence-stained high-resolution images.
View Article and Find Full Text PDFPsychoradiology
November 2024
Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.
Background: The lack of clearly defined neuromodulation targets has contributed to the inconsistent results of real-time fMRI-based neurofeedback (rt-fMRI-NF) for the treatment of chronic pain. Functional neurosurgery (funcSurg) approaches have shown more consistent effects in reducing pain in patients with severe chronic pain.
Objective: This study aims to redefine rt-fMRI-NF targets for chronic pain management informed by funcSurg studies.
Netw Neurosci
December 2024
McLean Imaging Center, McLean Hospital, Harvard Medical School, Belmont, MA, USA.
The atypical static brain functions related to the executive control network (ECN), default mode network (DMN), and salience network (SN) in people with autism spectrum disorder (ASD) has been widely reported. However, their transient functions in ASD are not clear. We aim to identify transient network states (TNSs) using coactivation pattern (CAP) analysis to characterize the age-related atypical transient functions in ASD.
View Article and Find Full Text PDFWorld J Surg
December 2024
Monash University Endocrine Surgery Unit, Department of General Surgery, Alfred Hospital, Melbourne, Victoria, Australia.
Background: Despite widespread use of standardized classification systems, risk stratification of thyroid nodules is nuanced and often requires diagnostic surgery. Genomic sequencing is available for this dilemma however, costs and access restricts global applicability. Artificial intelligence (AI) has the potential to overcome this issue nevertheless, the need for black-box interpretability is pertinent.
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