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透明目标识别的加性潜在特征模型
An Additive Latent Feature Model for Transparent Object Recognition |
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| 课程网址: |
http://videolectures.net/nips09_fritz_alfm/
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| 主讲教师: |
Mario Fritz
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| 开课单位: |
加州大学伯克利分校
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| 开课时间: |
2010-01-19
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| 课程语种: |
英语
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| 中文简介: |
当在透明表面（例如玻璃或塑料物体）上存在局部特征时，用于基于量化的局部特征识别对象实例和类别的现有方法可能表现不佳。对于透明对象的局部外观存在特征模式，但是它们可能不能通过到各个示例的距离或通过矢量量化获得的局部模式码本来很好地捕获。透明贴片的外观部分地由背景图案通过透明介质的折射确定：来自背景的能量通常主导贴片外观。我们使用潜在因子的加性模型来模拟透明局部斑块外观：由于场景内容导致的背景因素，以及捕获折射的局部边缘能量分布特征的因素。我们使用一种新的LDA SIFT公式实现我们的方法，该公式在任何矢量量化步骤之前执行LDA;我们发现潜在的主题，这些主题是特定透明贴片的特征，并根据潜在主题维度将SIFT空间量化为“透明视觉词”。在测试时不需要了解背景场景;我们展示了在家庭环境中识别透明眼镜的例子。
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| 课程简介: |
Existing methods for recognition of object instances and categories based on quantized local features can perform poorly when local features exist on transparent surfaces, such as glass or plastic objects. There are characteristic patterns to the local appearance of transparent objects, but they may not be well captured by distances to individual examples or by a local pattern codebook obtained by vector quantization. The appearance of a transparent patch is determined in part by the refraction of a background pattern through a transparent medium: the energy from the background usually dominates the patch appearance. We model transparent local patch appearance using an additive model of latent factors: background factors due to scene content, and factors which capture a local edge energy distribution characteristic of the refraction. We implement our method using a novel LDA-SIFT formulation which performs LDA prior to any vector quantization step; we discover latent topics which are characteristic of particular transparent patches and quantize the SIFT space into 'transparent visual words' according to the latent topic dimensions. No knowledge of the background scene is required at test time; we show examples recognizing transparent glasses in a domestic environment.
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| 关 键 词: |
塑料物体; 透明对象; 矢量量化
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-07-24：cwx
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| 阅读次数: |
45
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