The mid-level vision toolbox for computing structural properties of real-world images

IF 2.4 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Dirk B. Walther, Delaram Farzanfar, Seohee Han, Morteza Rezanejad
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引用次数: 3

Abstract

Mid-level vision is the intermediate visual processing stage for generating representations of shapes and partial geometries of objects. Our mechanistic understanding of these operations is limited, in part, by a lack of computational tools for analyzing image properties at these levels of representation. We introduce the Mid-Level Vision (MLV) Toolbox, an open-source software that automatically processes low- and mid-level contour features and perceptual grouping cues from real-world images. The MLV toolbox takes vectorized line drawings of scenes as input and extracts structural contour properties. We also include tools for contour detection and tracing for the automatic generation of vectorized line drawings from photographs. Various statistical properties of the contours are computed: the distributions of orientations, contour curvature, and contour lengths, as well as counts and types of contour junctions. The toolbox includes an efficient algorithm for computing the medial axis transform of contour drawings and photographs. Based on the medial axis transform, we compute several scores for local mirror symmetry, local parallelism, and local contour separation. All properties are summarized in histograms that can serve as input into statistical models to relate image properties to human behavioral measures, such as esthetic pleasure, memorability, affective processing, and scene categorization. In addition to measuring contour properties, we include functions for manipulating drawings by separating contours according to their statistical properties, randomly shifting contours, or rotating drawings behind a circular aperture. Finally, the MLV Toolbox offers visualization functions for contour orientations, lengths, curvature, junctions, and medial axis properties on computer-generated and artist-generated line drawings. We include artist-generated vectorized drawings of the Toronto Scenes image set, the International Affective Picture System, and the Snodgrass and Vanderwart object images, as well as automatically traced vectorized drawings of set architectural scenes and the Open Affective Standardized Image Set (OASIS).
用于计算真实世界图像结构属性的中级视觉工具箱
中级视觉是生成物体形状和部分几何形状表示的中间视觉处理阶段。我们对这些操作的机械理解是有限的,部分原因是缺乏在这些表示级别上分析图像属性的计算工具。我们介绍了中级视觉(MLV)工具箱,这是一个开源软件,可以自动处理来自现实世界图像的低级和中级轮廓特征和感知分组线索。MLV工具箱以矢量化的场景线条图为输入,提取结构轮廓属性。我们还包括用于轮廓检测和跟踪的工具,用于从照片中自动生成矢量化线条图。计算了等高线的各种统计性质:方向分布、等高线曲率和等高线长度,以及等高线结点的数量和类型。该工具箱包括一个有效的算法,用于计算等高线图纸和照片的中间轴变换。基于中轴线变换,我们计算了局部镜像对称、局部平行度和局部轮廓分离的分数。所有属性都总结在直方图中,可以作为统计模型的输入,将图像属性与人类行为测量(如审美愉悦、记忆、情感处理和场景分类)联系起来。除了测量轮廓属性外,我们还包括通过根据统计属性分离轮廓、随机移动轮廓或在圆形孔径后面旋转绘图来操纵绘图的功能。最后,MLV工具箱为计算机生成和艺术家生成的线条图提供了轮廓方向、长度、曲率、连接点和中间轴属性的可视化功能。我们包括艺术家生成的多伦多场景图像集、国际情感图像系统、Snodgrass和Vanderwart对象图像的矢量化绘图,以及建筑场景集和开放情感标准化图像集(OASIS)的自动跟踪矢量化绘图。
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来源期刊
Frontiers in Computer Science
Frontiers in Computer Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.30
自引率
0.00%
发文量
152
审稿时长
13 weeks
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