When viewing reverspective stimuli, data-driven signals such as disparity, motion parallax, etc, help to recover veridical three-dimensional (3-D) shape. They compete against schema-driven influences such as experience with perspective, foreshortening, and other pictorial cues that favor the perception of an illusory depth inversion. We used three scaled-size versions of a reverspective to study the roles of retinal size, binocular disparity, and viewing distance--that influences both vergence and accommodation--in recovering the true 3-D shape. Experiment 1 used three conditions, in each of which a parameter was kept fixed across the three stimulus sizes: (a) fixed retinal size, (b) fixed viewing distance, (c) fixed disparity. The predominance of the veridical percept was recorded. Generally, the illusion strength was the same when the viewing distance was fixed, despite significantly different disparities and retinal sizes; conversely, illusion strength changed significantly in fixed-disparity and fixed-retinal-size conditions. Experiment 2 confirmed the results of experiment 1b (roughly equal performances for fixed viewing distance, independent of size) for two additional distances. Viewing distance and "scaled disparity" (disparity divided by retinal size) are good predictors of the data trends. We propose that disparity scaling is supported by both mathematical and 3-D shape considerations.
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Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden; Department of Oral and Maxillofacial Surgery, Skåne University Hospital, Malmö, Sweden; Department of Orthodontics, Malmö University, Malmö, Sweden.
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Research School of Biology, The Australian National University, 46 Sullivans Creek Road, Canberra ACT2601, Australia.
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College of Life Science and Technology, Xinjiang University, Urumqi 830017, China.
Beet crops are highly vulnerable to pest infestations throughout their growth cycle, which significantly affects crop development and yield. Timely and accurate pest identification is crucial for implementing effective control measures. Current pest detection tasks face two primary challenges: first, pests frequently blend into their environment due to similar colors, making it difficult to capture distinguishing features in the field; second, pest images exhibit scale variations under different viewing angles, lighting conditions, and distances, which complicates the detection process.
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