Active Segmentation.
Active Segmentation.
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DOI：
10.1142/s0219843609001784
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Aloimonos Y
中科院分区：
文献类型：
--
作者：
Mishra A;Aloimonos Y
人类视觉系统通过进行一系列注视来观察和理解场景/图像。每个固定点位于一个特定的区域内的任意形状和大小的场景中，可以是一个对象或只是它的一部分。我们定义为一个基本的分割问题的任务，分割该区域包含的固定点。分割包含注视点的区域相当于找到围绕注视点的封闭轮廓-场景边缘图中的一组连接的边界边缘片段。这个封闭的轮廓应该是一个深度边界。我们在这里提出了一种新的算法，找到这个边界轮廓，并实现一个对象的分割，给定的固定。建议的分割框架结合了单眼线索（颜色/强度/纹理）与立体和/或运动，在一个线索独立的方式。在不久的将来，语义机器人将能够使用这种算法在任何环境中自动找到对象。自动分割视野中的物体的能力可以将视觉处理带到一个新的水平。我们的方法不同于目前的方法。虽然现有的工作试图将整个场景一次分割成许多区域，但我们只分割一个图像区域，特别是包含注视点的区域。我们的主动机器人和从已知的数据库收集的真实的图像的实验证明了这种方法的承诺。
The human visual system observes and understands a scene/image by making a series of fixations. Every fixation point lies inside a particular region of arbitrary shape and size in the scene which can either be an object or just a part of it. We define as a basic segmentation problem the task of segmenting that region containing the fixation point. Segmenting the region containing the fixation is equivalent to finding the enclosing contour- a connected set of boundary edge fragments in the edge map of the scene - around the fixation. This enclosing contour should be a depth boundary. We present here a novel algorithm that finds this bounding contour and achieves the segmentation of one object, given the fixation. The proposed segmentation framework combines monocular cues (color/intensity/texture) with stereo and/or motion, in a cue independent manner. The semantic robots of the immediate future will be able to use this algorithm to automatically find objects in any environment. The capability of automatically segmenting objects in their visual field can bring the visual processing to the next level. Our approach is different from current approaches. While existing work attempts to segment the whole scene at once into many areas, we segment only one image region, specifically the one containing the fixation point. Experiments with real imagery collected by our active robot and from the known databases demonstrate the promise of the approach.