显微立体视觉系统中立体匹配的研究
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摘要
随着科学技术的发展，许多领域越来越迫切地需要微型系统或微动系统，如生物细胞、聚合物的各种操作、微外科手术、扫描探针显微镜SPM、光纤对接和微细加工等；而且随着微技术的不断发展，以形状尺寸微小、操作尺度极小为特征的微机械已成为人们从微观角度认识和改造客观世界的一种高新技术；微机械技术还有望成为研究纳米技术的重要手段，因此在微观领域迫切要求显微立体视觉技术的发展。
通常显微立体视觉系统以体视显微镜为基础，通过CCD传感器采集图像，再经图像采集卡将图像数字化成为PC机能够读取的格式。本文着重研究了显微立体视觉中的立体匹配技术，具体为图像预处理、特征提取、立体匹配、深度恢复等。
针对显微图像的特点，对图像进行了高斯平滑和图像增强处理，有效的抑制了随机噪声对图像处理的影响，提高了物体与背景的对比度，有效的实现了物体与背景的分离，为边缘检测特征提取，立体匹配提供良好的图像信息。本文在使用Roberts，Sobel，Prewitt，Krisch，LoG和Canny边缘检测算子后，通过比较，选用最适合的Canny边缘检测算子作为最终选择。
立体匹配是本文研究的重点。应用Canny边缘检测后得到的图像边缘点作为匹配基元，在另一幅边缘图像中搜索对应点。由于边缘点的稀疏，匹配时选用的区域可以大些，可以得到准确的匹配点。同时检测出图像中的最大视差。边缘特征点的稀疏造成物体深度信息的严重缺乏，为了弥补信息缺乏的不足，本文提出了在边缘点特征匹配的基础上应用census提取像素邻域特征进行立体匹配。应用census是在边缘特征点匹配后所得最大视差的基础上进行的，那么此时的搜索范围可以控制在最大视差内，从而大大提高了运算速度。
With the development of science and technology, the need of micro system is more and more urgent in many technical fields, such as various operation of cell and polymerized substances, micro surgery, scanning probe microscope (SPM), butting optical fiber, fine manufacturing etc. With the development of micro-technology, micro mechanism, which has the character of micro size or micro motion is new high technology from microcosmic point of view understanding and reconstructing the world, micro mechanism technology is important means for researching nanotechnology, so the micro stereo vision(MSV)techniques are demanded urgently in microcosmic domain.
Stereo microscope and CCD sensors make the main components of a MSV system. The image information collected by CCD sensors is transformed as digital image information by image capture card. The digital information is easy to process by PC. This paper introduces the stereo matching techniques in MSV system, including image pre-processing, feature abstraction, stereo matching and depth information acquiring.
In order to obtain images easy to detect objects' edges, images have to processed by the techniques such as Guass smoothing and image enhancement. And then most of the noises in images are eliminated successfully. Several edge detection operators such as Roberts, Sobel, Prewitt, Krisch, LoG and Canny act on same images. Comparing the output images, I select Canny operator as optimizing edge detection operator.
This paper emphasizes on stereo matching. Firstly, selecting objects' edge pixels as matching elements. For the sake of sparseness of edge pixels, the matching area may big enough to find an accurate corresponding edge pixel. After this processing, we could obtain most edge pixels' corresponding pixels and maximum of disparity. In order to make up for the sparseness of edge pixels, a new matching algorithm named Census is introduced, which based on neighbor pixels' correlativity. Census's searching area is limited in a square whose side length is two times maximum of disparity. As the searching area's lessening, the operating speed increase greatly.
通常显微立体视觉系统以体视显微镜为基础，通过CCD传感器采集图像，再经图像采集卡将图像数字化成为PC机能够读取的格式。本文着重研究了显微立体视觉中的立体匹配技术，具体为图像预处理、特征提取、立体匹配、深度恢复等。
针对显微图像的特点，对图像进行了高斯平滑和图像增强处理，有效的抑制了随机噪声对图像处理的影响，提高了物体与背景的对比度，有效的实现了物体与背景的分离，为边缘检测特征提取，立体匹配提供良好的图像信息。本文在使用Roberts，Sobel，Prewitt，Krisch，LoG和Canny边缘检测算子后，通过比较，选用最适合的Canny边缘检测算子作为最终选择。
立体匹配是本文研究的重点。应用Canny边缘检测后得到的图像边缘点作为匹配基元，在另一幅边缘图像中搜索对应点。由于边缘点的稀疏，匹配时选用的区域可以大些，可以得到准确的匹配点。同时检测出图像中的最大视差。边缘特征点的稀疏造成物体深度信息的严重缺乏，为了弥补信息缺乏的不足，本文提出了在边缘点特征匹配的基础上应用census提取像素邻域特征进行立体匹配。应用census是在边缘特征点匹配后所得最大视差的基础上进行的，那么此时的搜索范围可以控制在最大视差内，从而大大提高了运算速度。
With the development of science and technology, the need of micro system is more and more urgent in many technical fields, such as various operation of cell and polymerized substances, micro surgery, scanning probe microscope (SPM), butting optical fiber, fine manufacturing etc. With the development of micro-technology, micro mechanism, which has the character of micro size or micro motion is new high technology from microcosmic point of view understanding and reconstructing the world, micro mechanism technology is important means for researching nanotechnology, so the micro stereo vision(MSV)techniques are demanded urgently in microcosmic domain.
Stereo microscope and CCD sensors make the main components of a MSV system. The image information collected by CCD sensors is transformed as digital image information by image capture card. The digital information is easy to process by PC. This paper introduces the stereo matching techniques in MSV system, including image pre-processing, feature abstraction, stereo matching and depth information acquiring.
In order to obtain images easy to detect objects' edges, images have to processed by the techniques such as Guass smoothing and image enhancement. And then most of the noises in images are eliminated successfully. Several edge detection operators such as Roberts, Sobel, Prewitt, Krisch, LoG and Canny act on same images. Comparing the output images, I select Canny operator as optimizing edge detection operator.
This paper emphasizes on stereo matching. Firstly, selecting objects' edge pixels as matching elements. For the sake of sparseness of edge pixels, the matching area may big enough to find an accurate corresponding edge pixel. After this processing, we could obtain most edge pixels' corresponding pixels and maximum of disparity. In order to make up for the sparseness of edge pixels, a new matching algorithm named Census is introduced, which based on neighbor pixels' correlativity. Census's searching area is limited in a square whose side length is two times maximum of disparity. As the searching area's lessening, the operating speed increase greatly.
引文
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