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Trichromatic color vision is a fundamental aspect of the visual system shared by humans and non-human primates. In human observers, color has been shown to facilitate object identification. However, little is known about the role that color plays in higher level vision of non-human primates. Here, we addressed this question and studied the interaction between luminance- and color-based structural information for the recognition of natural scenes. We present psychophysical data showing that both monkey and human observers equally profited from color when recognizing natural scenes, and they were equally impaired when scenes were manipulated using colored noise. This effect was most prominent for degraded image conditions. By using a specific procedure for stimulus degradation, we found that the improvement as well as the impairment in visual memory performance is due to contribution of image color independent of luminance-based object information. Our results demonstrate that humans as well as non-human primates exploit their sensory ability of color vision to achieve higher performance in visual recognition tasks especially when shape features are degraded.
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http://dx.doi.org/10.1167/9.5.14 | DOI Listing |
IEEE Trans Neural Netw Learn Syst
September 2025
Considering how to make the model accurately understand and follow natural language instructions and perform actions consistent with world knowledge is a key challenge in robot manipulation. This mainly includes human fuzzy instruction reasoning and the following of physical knowledge. Therefore, the embodied intelligence agent must have the ability to model world knowledge from training data.
View Article and Find Full Text PDFSci Rep
August 2025
School of Electronic Information, Shanghai DianJi University, Shanghai, 201306, China.
Traditional visual SLAM systems are predominantly designed for static environments, where they encounter challenges in dynamic scenes, leading to increased system errors and redundancy. This paper introduces a dynamic feature detection and filtering algorithm. Through a feature point selection and optimization strategy within quadtree nodes, high-response feature points are prioritized.
View Article and Find Full Text PDFForensic Sci Int Genet
August 2025
Department of Forensic Medicine, School of Basic Medical Sciences, Central South University, No 172. Tongzipo Road, Changsha, Hunan 410013, PR China; Hebei Key Laboratory of Forensic Medicine, School of Forensic Medicine, Hebei Medical University, Shijiazhuang, PR China. Electronic address: 40409716
Blood is a critical and frequently encountered type of evidence at forensic crime scenes. Its detection can provide valuable insights into the nature of a case and help refine investigative focus. The blood test strip is the most commonly used method for blood detection.
View Article and Find Full Text PDFSci Rep
August 2025
College of Information Science and Technology, HaiNan Normal University, Haikou, 571158, China.
The speech recognition task of the HaiNan dialect faces significant differences in phonology, intonation, and grammatical structure among dialects, which in turn show significant regionalization characteristics, which makes the task of dialect-to-Mandarin conversion more complex. Currently, the research on the HaiNan dialect speech recognition is still in its early stages and lacks sufficient corpus resources, especially in the task of multi-dialect recognition. Traditional models are difficult to solve with the problem of data scarcity and diverse dialect characteristics effectively.
View Article and Find Full Text PDFSensors (Basel)
August 2025
School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
In recent years, indoor user identification via Wi-Fi signals has emerged as a vibrant research area in smart homes and the Internet of Things, thanks to its privacy preservation, immunity to lighting conditions, and ease of large-scale deployment. Conventional deep-learning classifiers, however, suffer from poor generalization and demand extensive pre-collected data for every new scenario. To overcome these limitations, we introduce SimID, a few-shot Wi-Fi user recognition framework based on identity-similarity learning rather than conventional classification.
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