Robust visual recognition of high-level form in human observers

***视觉识别很难。物体在各种光照、观看距离和方向不同的条件下被看到，这会给它们的图像带来巨大的变化。此外，观察者的因素（从眼睛和大脑引入的扭曲到观察者一生中不同的视觉体验）都会影响视觉识别。尽管取得了重大进展，但对这些因素在视觉识别基础计算中的影响的全面理解仍然难以实现。******我们最近的工作表明，视觉系统利用视觉输入中的持续规律性（例如大小或表情）来促进面部识别。在一项自然观察研究中，我们通过眼镜嵌入式摄像机获取了日常视觉体验的镜头。对这段视频的分析表明，大多数面部视图都是来自社交互动距离，即视觉上很大。事实上，我们发现社交互动中最常见的面部尺寸与更好的识别能力相关。我们对大小和模糊规律的研究还发现了与该提案的总体假设相一致的证据，即视觉系统根据环境中最常见的视觉输入类型采取策略来优化识别性能。******我研究的长期目标是：（1）描述人类观察者视觉输入的各种统计规律及其对识别的影响，包括物理规律，例如光学几何产生的规律，以及观察者与其环境交互方式的结果的规律， （2）揭示视觉系统利用这些规律来促进识别的策略，以及（3）开发针对人类视觉系统定制的新颖应用程序，例如计算机程序，以增强低能见度条件下的识别，例如颗粒图像。我的短期目标侧重于以下方面的特定统计规律：(1) 人脸观看距离（例如，视觉大小和模糊），(2) 社会环境中最常见的人脸种族，以及 (3) 总体人脸暴露持续时间，以实现上述长期目标。********在下一个 5 年周期中，我们将继续依靠我们在行为测试（例如视觉心理物理学）、计算模型（例如理想视觉）等方法论方面的专业知识。观察者），以及视觉图像统计的自然观察（例如，基于通过眼镜嵌入式摄像机获取的镜头）。各级高素质人才将获得科学和技术技能，为他们未来在学术界、工业界和医学界的职业生涯服务。这项工作将增进我们对大脑视觉识别基本原理的理解，并为开发工具提供信息，以提高从计算机应用到低能见度条件下的视觉辅助设备等各种环境中对高级形式的识别。

***Visual recognition is hard. Objects are seen in a variety of conditions differing in illumination, viewing distance, and orientation, which introduce drastic changes to their image. In addition, observer factorsfrom distortions introduced by the eye and the brain to varying visual experience over the observer's lifespaninfluence visual recognition. Despite significant advances, a comprehensive understanding of the impact of these factors in computations underlying visual recognition remains elusive.******Our recent work showed that the visual system takes advantage of sustained regularities, e.g., those in size or expression, in the visual input to facilitate recognition of faces. In a naturalistic observation study, we acquired footage of daily visual experiences via eyewear-embedded cameras. Analysis of this footage revealed that most views of faces are from social interaction distances, i.e., visually large. Indeed, we found that face sizes most common in social interactions are associated with better recognition. Our work on regularities in size and blur, also uncovered evidence consistent with the overarching hypothesis of this proposal that the visual system adopts strategies to optimize recognition performance based on the most frequent types of visual input in the environment.******Long-term objectives of my research are: (1) Describe various statistical regularities in human observers' visual input and their impact on recognition, including physical regularities, such as those that arise from the geometry of optics, as well as those that are consequences of the ways in which observers interact with their environment, (2) Uncover strategies of the visual system that utilize these regularities to facilitate recognition, and (3) Develop novel applications, such as computer programs, tailored to the human visual system to enhance recognition in low visibility conditions, e.g., of grainy images. My short-term objectives focus on specific statistical regularities that arise from (1) viewing distance to faces (e.g., visual size and blur), (2) ethnicities of faces most prevalent in the social environment, and (3) overall face exposure duration, in pursuit of the long-term objectives above.******In the next 5-year cycle, we will continue to rely on our expertise in methodologies such as behavioural testing (e.g., visual psychophysics), computational modelling (e.g., ideal observers), and naturalistic observation of visual image statistics, (e.g., based on footage acquired through eyewear-embedded cameras). Highly qualified personnel at all levels will gain scientific and technical skills that will serve them in future careers in academia, industry and medicine. This work will advance our understanding of the fundamental principles underlying visual recognition in the brain and inform the development of tools that improve recognition of high-level form in a variety of settings from computer applications to visual aids in low-visibility conditions.