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This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. First, a sub image containing WBC is separated from the original cell image. Then, scale-space filtering is used to extract nucleus region from sub image. Later, a watershed clustering in 3-D HSV histogram is processed to extract cytoplasm region. Finally, morphological operations are performed to obtain the entire connective WBC region. Through feature space clustering techniques, this scheme successfully avoids the variety and complexity in image space, and can effectively extract WBC regions from various cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs much better than former methods.

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A fast algorithm is pre.sented in Ihis paper to track a/Metes in diving videos based on pose learning. In lhis algorilhm, each dil'ing mOlion is considered as a pari iCII/ar sequence of poses and each pose is represented by a set of landmarks. Firstly, the transformations belween poses of slIccessive frames are learned FOIll sample I'ideos. Then in the video to be tracked, an initial pose is designated to the first Fame and fits itself 10 the body l'Ontolir by a cllrve-jitling algorithm. Later in evelY following frame, the curren! pose tramjorms according to Ihe parameters learned before, adjus!s itself Fom the temporal information andf i ts itself to the body contour. In this way ti,e athlete in a diving video is ejJixtively tracked Ollt. Ex p eriments are carried out and the tracking results show that the p ro p osed algorithm guarantee both ef ficiency and robllslness.