Yankelovich Albert, Spitzer Hedva
Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel.
Faculty of Engineering, School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel.
Front Comput Neurosci. 2019 Jan 18;12:106. doi: 10.3389/fncom.2018.00106. eCollection 2018.
Boundary completion is one of the desired properties of a robust object boundary detection model, since in real-word images the object boundaries are commonly not fully and clearly seen. An extreme example of boundary completion occurs in images with illusory contours, where the visual system completes boundaries in locations without intensity gradient. Most illusory contour models extract special image features, such as L and T junctions, while the task is known to be a difficult issue in real-world images. The proposed model uses a functional optimization approach, in which a cost value is assigned to any boundary arrangement to find the arrangement with minimal cost. The functional accounts for basic object properties, such as alignment with the image, object boundary continuity, and boundary simplicity. The encoding of these properties in the functional does not require special features extraction, since the alignment with the image only requires extraction of the image edges. The boundary arrangement is represented by a border ownership map, holding object boundary segments in discrete locations and directions. The model finds multiple possible image interpretations, which are ranked according to the probability that they are supposed to be perceived. This is achieved by using a novel approach to represent the different image interpretations by multiple functional local minima. The model is successfully applied to objects with real and illusory contours. In the case of Kanizsa illusion the model predicts both illusory and real (pacman) image interpretations. The model is a proof of concept and is currently restricted to synthetic gray-scale images with solid regions.
边界补全是强大的目标边界检测模型所期望具备的特性之一，因为在真实世界的图像中，目标边界通常无法被完整且清晰地看到。边界补全的一个极端例子出现在具有虚幻轮廓的图像中，在这种情况下，视觉系统会在没有强度梯度的位置补全边界。大多数虚幻轮廓模型会提取特殊的图像特征，比如L形和T形交叉点，然而在真实世界的图像中，这项任务是一个难题。所提出的模型使用一种功能优化方法，在该方法中，会为任何边界排列分配一个代价值，以找到代价最小的排列。该功能考虑了基本的目标属性，例如与图像的对齐、目标边界的连续性以及边界的简洁性。这些属性在功能中的编码不需要特殊的特征提取，因为与图像的对齐仅需要提取图像边缘。边界排列由一个边界所有权图表示，该图在离散的位置和方向上保存目标边界段。该模型会找到多种可能的图像解释，并根据它们被感知的概率进行排序。这是通过一种新颖的方法实现的，即通过多个功能局部最小值来表示不同的图像解释。该模型已成功应用于具有真实和虚幻轮廓的目标。在卡尼兹错觉的情况下，该模型预测了虚幻和真实（吃豆人）两种图像解释。该模型是一个概念验证，目前仅限于具有实心区域的合成灰度图像。
