Advances in microsensors, microprocessors, and microdisplays are creating new opportunities for improving vision in degraded environments through the use of head-mounted displays. Initially, the cutting-edge technology used in these new displays will be expensive. Inevitably, the cost of providing the additional sensor and processing required to support binocularity brings the value of binocularity into question. Several assessments comparing binocular, binocular, and monocular head-mounted displays for aided vision have concluded that the additional performance, if any, provided by binocular head-mounted displays does not justify the cost. The selection of a biocular [corrected] display for use in the F-35 is a current example of this recurring decision process. It is possible that the human binocularity advantage does not carry over to the aided vision application, but more likely the experimental approaches used in the past have been too coarse to measure its subtle but important benefits. Evaluating the value of binocularity in aided vision applications requires an understanding of the characteristics of both human vision and head-mounted displays. With this understanding, the value of binocularity in aided vision can be estimated and experimental evidence can be collected to confirm or reject the presumed binocular advantage, enabling improved decisions in aided vision system design. This paper describes four computational approaches-geometry of stereopsis, modulation transfer function area for stereopsis, probability summation, and binocular summation-that may be useful in quantifying the advantage of binocularity in aided vision.
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http://dx.doi.org/10.3357/ASEM.3976.2014 | DOI Listing |
Sci Rep
January 2025
Center for Advanced Laser Technologies (CETAL), National Institute for Lasers, Plasma and Radiation Physics, Magurele-Ilfov, 077125, Romania.
Nature offers unique examples that help humans produce artificial systems which mimic specific functions of living organisms and provide solutions to complex technical problems of the modern world. For example, the development of 3D micro-nanostructures that mimic nocturnal insect eyes (optimized for night vision), emerges as promising technology for detection in IR spectral region. Here, we report a proof of principle concerning the design and laser 3D printing of all ultrastructural details of nocturnal moth Grapholita Funebrana eyes, for potential use as microlens arrays for IR detection systems.
View Article and Find Full Text PDFComput Biol Med
January 2025
Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124, Cagliari, Italy.
Background: Malaria is a critical and potentially fatal disease caused by the Plasmodium parasite and is responsible for more than 600,000 deaths globally. Early and accurate detection of malaria parasites is crucial for effective treatment, yet conventional microscopy faces limitations in variability and efficiency.
Methods: We propose a novel computer-aided detection framework based on deep learning and attention mechanisms, extending the YOLO-SPAM and YOLO-PAM models.
Comput Methods Programs Biomed
January 2025
Regional Institute of Ophthalmology, Indira Gandhi Institute of Medical Sciences, Patna, 800025, Bihar, India.
Background And Objectives: Hypertensive Retinopathy (HR) is a retinal manifestation resulting from persistently elevated blood pressure. Severity grading of HR is essential for patient risk stratification, effective management, progression monitoring, timely intervention, and minimizing the risk of vision impairment. Computer-aided diagnosis and artificial intelligence (AI) systems play vital roles in the diagnosis and grading of HR.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Earth, Environment and Geospatial Sciences, Saint Louis University, Saint Louis, MO 63108, USA.
Wheat is a globally cultivated cereal crop with substantial protein content present in its seeds. This research aimed to develop robust methods for predicting seed protein concentration in wheat seeds using bench-top hyperspectral imaging in the visible, near-infrared (VNIR), and shortwave infrared (SWIR) regions. To fully utilize the spectral and texture features of the full VNIR and SWIR spectral domains, a computer-vision-aided image co-registration methodology was implemented to seamlessly align the VNIR and SWIR bands.
View Article and Find Full Text PDFBMC Oral Health
January 2025
Center of Digital Dentistry, Peking University School and Hospital of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, No.22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, PR China.
Background: Establishing accurate, reliable, and convenient methods for enamel segmentation and analysis is crucial for effectively planning endodontic, orthodontic, and restorative treatments, as well as exploring the evolutionary patterns of mammals. However, no mature, non-destructive method currently exists in clinical dentistry to quickly, accurately, and comprehensively assess the integrity and thickness of enamel chair-side. This study aims to develop a deep learning work, 2.
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