Video-frame-rate millimetre-wave imaging has recently been
demonstrated with a quality similar to that of a low-quality
uncooled thermal imager. In this paper we will discuss initial
investigations into the transfer of image processing algorithms from
more mature imaging modalities to millimetre-wave imagery.
The current aim is to develop body segmentation algorithms for use
in object detection and analysis. However, this requires a variety
of image processing algorithms from different domains, including
image de-noising, segmentation and motion tracking. This paper
focuses on results from the segmentation of a body from the
millimetre-wave images and a qualitative comparison of different
approaches is presented. Their performance is analysed and any
characteristics which enhance or limit their application are
discussed.
While it is possible to apply image processing algorithms developed
for the visible-band directly to millimetre-wave images, the physics
of the image formation process is very different. This paper
discusses the potential for exploiting an understanding of the
physics of image formation in the image segmentation process to
enhance classification of scene components and, thereby, improve
segmentation performance. This paper presents some results from a
millimetre-wave image formation simulator, including synthetic
images with multiple objects in the scene.
In speckled radar images, filtering must achieve a tradeoff between smoothing of homogeneous areas and edge and texture preservation. Multiscale analysis splits up the image information content, such as edges and texture, according to a scale factor by successive lowpass and highpass filterings followed by downsampling. The speckle noise is present on each downsampled image. Each image level is then filtered in order to reduce the speckle noise. High frequency images are processed by median filtering or spatial filtering, or by using a threshold. On low frequency images a distinction is made between homogeneous areas, textural areas and areas including edges according to the values of the variation coefficient. Each class is processed differently. A Wiener filter including a multiplicative noise hypothesis for the speckle is used for textured areas. For homogeneous areas the pixel value is simply replaced by the mean value. For areas containing edges, the pixel value is let unchanged. The filtered image is finally obtained y synthesis from these images. This algorithm has been applied to an ERS1 image.
Conference Committee Involvement (2)
Machine Vision Applications in Industrial Inspection XV
29 January 2007 | San Jose, CA, United States
Machine Vision Applications in Industrial Inspection XIV
16 January 2006 | San Jose, California, United States
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