Bit-Planes: Dense Subpixel Alignment of Binary Descriptors
Bit-Planes: Dense Subpixel Alignment of Binary Descriptors
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位平面：二进制描述符的密集子像素对齐
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
S. Lucey
中科院分区：
文献类型：
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作者：
Hatem Alismail;Brett Browning;S. Lucey
二进制描述符已经在最近的计算效率的稀疏图像对齐算法的演变。然而，视觉界越来越对密集图像对齐方法感兴趣，这些方法更适合于估计来自高帧速率相机的对应性，因为它们不依赖于穷举搜索。然而，经典的密集对齐方法对照明变化敏感。在本文中，我们提出了一个易于实现和低复杂度的密集二进制描述符，我们称之为位平面，可以无缝集成在一个多通道卢卡斯和Kanade框架。这种新方法结合了二进制描述符的鲁棒性和密集对齐方法的速度和准确性。该方法在模板跟踪问题上进行了演示，在消费类笔记本电脑（单核Intel i7上为400+ fps）和手持移动的设备（iPad Air 2上为100+ fps）上实现了最先进的鲁棒性和比实时性能更快的速度。
Binary descriptors have been instrumental in the recent evolution of computationally efficient sparse image alignment algorithms. Increasingly, however, the vision community is interested in dense image alignment methods, which are more suitable for estimating correspondences from high frame rate cameras as they do not rely on exhaustive search. However, classic dense alignment approaches are sensitive to illumination change. In this paper, we propose an easy to implement and low complexity dense binary descriptor, which we refer to as bit-planes, that can be seamlessly integrated within a multi-channel Lucas & Kanade framework. This novel approach combines the robustness of binary descriptors with the speed and accuracy of dense alignment methods. The approach is demonstrated on a template tracking problem achieving state-of-the-art robustness and faster than real-time performance on consumer laptops (400+ fps on a single core Intel i7) and hand-held mobile devices (100+ fps on an iPad Air 2).
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DOI：
10.1007/978-1-4939-7647-8_1
发表时间：
2018
期刊：
Neuromethods
影响因子：
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作者：
Joshi,AnandA
通讯作者：
Joshi,AnandA