A Fast Dense Feature-Matching Model for Cross-Track Pushbroom Satellite Imagery.

Feature-based matching can provide high robust correspondences and it is usually invariant to image scale and rotation. Nevertheless, in remote sensing, the robust feature-matching algorithms often require costly computations for matching dense features extracted from high-resolution satellite images due to that the computational complexity of conventional feature-matching model is O ( N 2 ) . For replacing the conventional feature-matching model, a fast dense (FD) feature-matching model is proposed in this paper. The FD model reduces the computational complexity to linear by splitting the global one-to-one matching into a set of local matchings based on a classic frame-based rectification method. To investigate the possibility of applying the classic frame-based method on cross-track pushbroom images, a feasibility study is given by testing the frame-based method on 2.1 million independent experiments provided by a pushbroom based feature-correspondences simulation platform. Moreover, to improve the stability of the frame-based method, a correspondence-direction-constraint algorithm is proposed for providing the most favourable seed-matches/control-points. The performances of the FD and the conventional models are evaluated on both an automatic feature-matching evaluation platform and real satellite images. The evaluation results show that, for the feature-matching algorithms which have high computational complexity, their running time for matching dense features reduces from hours level to minutes level when they are operated on the FD model. Meanwhile, based the FD method, feature-matching algorithms can achieve comparable matching results as they achieved based on the conventional model.

Effect of silencing the T­Box transcription factor TBX2 in prostate cancer PC3 and LNCaP cells.

T­Box (TBX)­2 is a member of the T­box gene family, which is aberrantly expressed in numerous types of malignant tumors, and has previously been demonstrated to be conducive to tumor progression by acting as a transcription factor. However, specific information regarding the expression and function of TBX2 in prostate cancer cells remains to be elucidated. The present study demonstrated that silencing of TBX2 by TBX2 small interfering RNA inhibited cell proliferation and promoted cell senescence. It was demonstrated that knockdown of TBX2 inhibited cell metastatic abilities by upregulating E­cadherin and downregulating N­cadherin, Vimentin and fibronectin. In addition, the expression of TBX2 in prostate cancer tissues and tumor adjacent tissues was detected by immunohistochemistry. The results indicated that the expression rates of TBX2 were significantly increased in the cancerous tissues, compared with the healthy tumor adjacent tissue, and TBX2 increased staining was associated with the clinical stage and pathological grade. The findings of the present study therefore suggest that TBX2 expression is markedly increased in prostate cancer and TBX2 may act as a potential beneficial therapeutic target for the future treatment of prostate cancer.