Drastic change of luminescent QY was found in a serial of guest-tuned lanthanide CPs. An octupolar-like structure lanthanide CP shows a high two-photon luminescence.

Superficial angiomyxoma (SAM) is an extremely rare soft tissue tumor. It is especially rare in the vulva, with only a few such cases reported in the medical literature. Here, we report a case of SAM of the vulva that was initially suspected to be a Bartholin gland cyst. The patient underwent local excision of the vulvar cyst under lumbar anesthesia. Clinical manifestations and B-scan ultrasonographic features are similar between SAM and cysts. Echoes in the mass are uneven and exhibit low echoes and punctate hyperechoic floating. Thus, increasing sonographers’ awareness of the high-frequency ultrasonography findings associated with this rare tumor could broaden their knowledge base.

This paper is focused on camera calibration, image matching, both of which are the key issues in three-dimensional (3D) reconstruction. In terms of camera calibration firstly, we adopt the method based on the method proposed by Zhengyou Zhang. In addition to this, it is selective for us to deal with tangential distortion. In respect of image matching, we use the SIFT algorithm, which is invariant to image translation, scaling, rotation, and partially invariant to illumination changes and to affine or 3D projections. It performs well in the follow-up matching the corresponding points. Lastly, we perform 3D reconstruction of the surface of the target object. A Graphical User Interface is designed to help us to realize the key function of binocular stereo vision, with better visualization. Apparently, the entire GUI brings convenience to the follow-up work.

Image segmentation with low computational burden has been highly regarded as important goal for researchers. One of the popular image segmentation methods is normalized cut algorithm. But it is unfavorable for high resolution image segmentation because the amount of segmentation computation is very huge [1]. To solve this problem, we propose a novel approach for high resolution image segmentation based on the Normalized Cuts. The proposed method preprocesses an image by using the normalized cut algorithm to form segmented regions, and then use k-Means clustering on the regions. The experimental results verify that the proposed algorithm behaves an improved performance comparing to the normalized cut algorithm.

Object extraction, which aims to accurately separate a foreground object from its background in still images, plays an important role in many computer vision applications. An interactive object extraction method based on MSRM (maximal similarity based region merging) is presented in this paper. We can manually mark the target and background only one time in any one image of the image sequence to obtain the object extraction result of the image sequence. Compared to currently used method based on graph cut algorithm that manually marks the target and background on all the images one by one to get the object extraction result, our method is more efficient and the result is as precious as with other methods.