Two experiments evaluated the importance of temporal integration for the perception and discrimination of solid object shape. In Experiment 1, observers anorthoscopically viewed moving or stationary cast shadows of naturally shaped solid objects (bell peppers, Capsicum annuum) through narrow (4-mm wide) slits. At any given moment, observers could only see a very small portion of the overall object shape (generally less than 10%). The results showed that the observers' discrimination performance for the moving cast shadows was much higher than that obtained for the stationary shadows, demonstrating the ability to temporally integrate the piecemeal momentary information about shape that was available through the narrow apertures. In a second experiment, estimates of the strength of the observers' impressions of solid shapes rotating in depth were obtained as well as discrimination accuracies; perceptions of the original moving condition were compared with a new condition where the frames of the apparent motion sequences depicting solid objects in continuous motion (behind the slits) were randomly scrambled. The observers perceived the anorthoscopic displays as depicting solid objects rotating in depth, but only in the continuous motion condition. Interestingly, the discrimination performance in the scrambled condition remained relatively high-observers were still able to integrate information across the multiple scrambled frames in order to produce discrimination performance that was significantly higher than that obtained in the stationary shadow condition. This study was the first to thoroughly evaluate whether and to what extent human observers can effectively discriminate and perceive solid object shape anorthoscopically.
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http://dx.doi.org/10.3758/s13414-020-02031-0 | DOI Listing |
Polymers (Basel)
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Department of Chemistry and Pharmacy, Interdisciplinary Center for Molecular Materials, Friedrich-Alexander Universität Erlangen-Nürnberg, Egerlandstr. 3, 91058 Erlangen, Germany.
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January 2025
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
With the rapid development of AI algorithms and computational power, object recognition based on deep learning frameworks has become a major research direction in computer vision. UAVs equipped with object detection systems are increasingly used in fields like smart transportation, disaster warning, and emergency rescue. However, due to factors such as the environment, lighting, altitude, and angle, UAV images face challenges like small object sizes, high object density, and significant background interference, making object detection tasks difficult.
View Article and Find Full Text PDFSensors (Basel)
January 2025
College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China.
This paper aims to address the challenge of precise robotic grasping of molecular sieve drying bags during automated packaging by proposing a six-dimensional (6D) pose estimation method based on an red green blue-depth (RGB-D) camera. The method consists of three components: point cloud pre-segmentation, target extraction, and pose estimation. A minimum bounding box-based pre-segmentation method was designed to minimize the impact of packaging wrinkles and skirt curling.
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Zhejiang Sunny Optical Company, Yuyao 315400, China.
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View Article and Find Full Text PDFHealthcare (Basel)
January 2025
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Penetrating orbit injury is a rare but complex and life-threatening occurrence that may easily be overlooked. Management in the emergency department requires an early multidisciplinary approach but still lacks standard guidelines. This narrative review aims to provide a systematic approach to the management of penetrating orbital injuries for emergency clinicians.
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