Robust visual recognition of high-level form in human observers

*视觉识别很困难。在各种不同的条件下可以看到物体，这些条件在照度、观察距离和方向上都不同，这给它们的图像带来了巨大的变化。此外，观察者的因素，从眼睛和大脑引起的扭曲，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，但对这些因素在计算中对视觉识别的影响的全面理解仍然难以捉摸。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，来促进人脸识别。在一项自然主义的观察研究中，我们通过佩戴眼镜的相机获取了日常视觉体验的镜头。对这段视频的分析表明，大多数人看到的人脸都来自社交距离，即视觉上很大的距离。事实上，我们发现，社交互动中最常见的脸型尺寸与更好的识别能力有关。我们对大小和模糊的规律性的研究也发现了与这一建议的总体假设一致的证据，即视觉系统采用基于环境中最频繁的视觉输入类型来优化识别性能的策略。*我的研究的长期目标是：(1)描述人类视觉输入中的各种统计规律性及其对识别的影响，包括物理规律性，例如那些由光学几何产生的规律性，以及那些观察者与其环境交互的方式的结果，(2)揭示利用这些规律性来促进识别的视觉系统的策略，以及(3)开发适合人类视觉系统的新型应用，例如计算机程序，以增强在低能见度条件下(例如，颗粒状图像)的识别。我的短期目标侧重于特定的统计规律，这些规律源于(1)面部的观察距离(例如，视觉大小和模糊)，(2)在社会环境中最普遍的面部的种族，以及(3)总体面部暴露时间，以追求上述长期目标。*在下一个5年周期中，我们将继续依靠我们在方法学方面的专业知识，例如行为测试(例如，视觉心理物理学)、计算建模(例如，理想的观察者)和视觉图像统计的自然观察，(例如，基于通过佩戴眼镜的摄像头获取的镜头)。高素质的各级人员将获得科学和技术技能，这些技能将为他们未来在学术界、工业界和医学界的职业生涯服务。这项工作将促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，这些工具可以在从计算机应用到低能见度条件下的视觉辅助的各种环境中改善对高级形式的识别。

***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.