CIF: Small: RUI: Fixation-Driven Contour Integration of Natural Images for Early Visual Processing

CIF：小：RUI：用于早期视觉处理的自然图像的注视驱动轮廓集成

• 批准号: 1117439
• 负责人: Toshiro Kubota
• 金额: $ 17.06万
• 依托单位: Susquehanna University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117439&HistoricalAwards=false
• 关键词: CIF; Small; RUI; Fixation; Driven

A reliable system for automated image understanding can have immense impacts on many applications including image search, video surveillance, autonomous vehicles, and robotics, to name a few. However, the progress of the technology has been slow compared to text understanding and speech recognition. The problem can be attributed to lacking ways of breaking an image into a set of meaningful components analogous to words in text and speech processing. This research will develop an algorithm to partition a digital image into such meaningful components efficiently and effectively. To reach the goal, the investigators focus on discontinuities in color and brightness often called "edges" and study algorithmic ways to group them and delineate objects found in the image. Undergraduate students will actively participate in interdisciplinary research involving computer science, mathematics, statistics, psychology and biology. The PI will also develop an interdisciplinary course that integrates cognitive, neuro, and computer sciences. The resulting source code, software tools, data, and visual materials will be made publicly available to promote STEM education.More specifically, the study centers on two recent innovations developed by the investigators: semi-group smoothing with a matrix of linear filters, and successive partitioning of a graph with increasing complexity. The former is an affine commutative linear operator that is shown to be effective in extracting high-curvature points while robust against aliasing. It will be used to smoothen, partition, and characterize contour fragments. The latter generates a set of closed contours surrounding a focal point successively from a simple shape to more complex ones. Furthermore, the study investigates new context sensitive perceptual saliency metrics that quantify the importance of edges based on their surroundings.

可靠的自动图像理解系统可以对许多应用产生巨大影响，包括图像搜索、视频监控、自动驾驶车辆和机器人等。然而，与文本理解和语音识别相比，该技术的进展缓慢。该问题可归因于缺乏将图像分解为一组有意义的组件（类似于文本和语音处理中的单词）的方法。这项研究将开发一种算法，将数字图像有效地分割成这些有意义的组成部分。为了实现这一目标，研究人员重点关注颜色和亮度的不连续性（通常称为“边缘”），并研究对它们进行分组并描绘图像中发现的对象的算法方法。本科生将积极参与涉及计算机科学、数学、统计学、心理学和生物学的跨学科研究。 PI 还将开发一门整合认知、神经和计算机科学的跨学科课程。由此产生的源代码、软件工具、数据和视觉材料将公开，以促进 STEM 教育。更具体地说，该研究集中在研究人员最近开发的两项创新上：使用线性滤波器矩阵进行半群平滑，以及对复杂性不断增加的图进行连续划分。前者是一种仿射交换线性算子，被证明可以有效提取高曲率点，同时对混叠具有鲁棒性。它将用于平滑、分割和表征轮廓片段。后者依次生成一组围绕焦点的闭合轮廓，从简单的形状到更复杂的形状。此外，该研究还研究了新的上下文敏感感知显着性指标，该指标根据周围环境量化边缘的重要性。