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Mid-Level Vision Systems for Low Vision

针对低视力的中级视觉系统

基本信息

批准号：
8142000
负责人：
NORBERTO M GRZYWACZ
金额：
$ 114.11万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-09-30 至 2014-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8142000
关键词：
Age related macular degeneration Aging Algorithms Biological Biomedical Engineering Blindness California Cataract Cognitive Color Visions Complex Computer Vision Systems Consultations Contrast Sensitivity Crowding Development Devices Diffuse Disease Effectiveness Elderly Engineering Esthetics Evaluation Face Faculty Goals Human Image Image Analysis Inferior Injury Lead Learning Machine Learning Masks Measurement Medical Minor Modeling Motion Perception Nature Noise Operative Surgical Procedures Ophthalmology Optometry Patients Perceptual learning Peripheral Population Principal Investigator Property Psychologist Psychology Psychophysiology Reading Retina Schools Scientist Shapes Surface Surface Properties System Techniques Technology Testing Texture Universities Vision Visual Visual Acuity Visual Aid Visual Fields Visual impairment Visual system structure Visually Impaired Persons Work computational neuroscience computer science efficacy testing fovea centralis human subject improved lens medical schools meetings member object recognition symposium tool vision science visual performance

项目摘要

DESCRIPTION (provided by applicant): Low vision is a significant reduction of visual function that cannot be fully corrected by ordinary lenses, medical treatment, or surgery. Aging, injuries, and diseases can cause low vision. Its leading causes and those of blindness, include cataracts and age-related macular degeneration (AMD), both of which are more prevalent in the elderly population. Our overarching goal is to develop devices to aid people with low vision. We propose to use techniques of computer vision and computational neuroscience to build systems that enhance natural images. We plan test these systems on normal people and visually impaired older adults, with and without AMD. We have three milestones to reach: 1) Our first milestone will be to develop a system for low-noise image-contrast enhancement, which should help with AMD, because it causes lower contrast sensitivity. 2) Our second milestone will be a system that extracts the main contours of images in a cortical-like manner. Superimposing these contours on images should help with contrast-sensitivity problems at occlusion boundaries. Diffusing regions inside contours should help with crowding problems prominent in AMD. 3) Our third milestone is to probe whether these systems can help people with low vision people. For this purpose, we plan to use a battery of search and recognition psychophysical tests tailor-made for AMD. We put together an interdisciplinary team. The Principal Investigator is Dr. Norberto Grzywacz from Biomedical Engineering at USC. Drs. Gerard Medioni from Computer Science and Bartlett Mel from Biomedical Engineering at USC will lead the efforts in Specific Aims 1 and 2 respectively. Drs. Bosco Tjan from USC Psychology, Susana Chung from the University of Houston, and Eli Peli from Harvard Medical School will lead Specific Aim 3. Other scientists are from USC. They include Dr. Irving Biederman from Psychology, an object-recognition expert and Dr. Mark Humayun, from Ophthalmology, an AMD expert. They also include Dr. lone Fine, from Ophthalmology, an expert in people who recover vision after a prolonged period without it and Dr. Zhong-Lin Lu, an expert on motion perception and perceptual learning.

描述（由申请人提供）：低视力是视觉功能的显著降低，无法通过普通镜片、药物治疗或手术完全矫正。衰老、受伤和疾病会导致视力低下。其主要原因和失明的原因包括白内障和年龄相关性黄斑变性（AMD），这两种疾病在老年人中更为普遍。我们的首要目标是开发设备来帮助视力低下的人。我们建议使用计算机视觉和计算神经科学技术来构建增强自然图像的系统。我们计划在正常人和视力受损的老年人身上测试这些系统，有和没有AMD。我们有三个里程碑要达到：1）我们的第一个里程碑将是开发一个低噪声图像对比度增强系统，这将有助于AMD，因为它会导致较低的对比敏感度。2)我们的第二个里程碑将是一个以类似皮质的方式提取图像主要轮廓的系统。将这些轮廓叠加在图像上有助于解决遮挡边界处的对比度敏感性问题。轮廓内的扩散区域应该有助于AMD中突出的拥挤问题。3)我们的第三个里程碑是探索这些系统是否可以帮助视力低下的人。为此，我们计划使用一系列为AMD量身定制的搜索和识别心理物理测试。我们组建了一个跨学科的团队。主要研究者是南加州大学生物医学工程部的Norberto Grzywacz博士。来自南加州大学计算机科学的Gerard Medioni博士和来自生物医学工程的Bartlett Mel博士将分别领导具体目标1和2的工作。来自南加州大学心理学的Bosco Tjan博士，来自休斯顿大学的Susana Chung和来自哈佛医学院的Eli Peli将领导具体目标3。其他科学家来自南加州大学。他们包括来自心理学的物体识别专家欧文·比德曼博士和来自眼科的AMD专家马克·胡马云博士。他们还包括来自眼科的隆·法恩博士，他是一位长期失明后恢复视力的专家，以及运动感知和感知学习专家卢忠林博士。