Learning-based hierarchical graph for unsupervised matting and foreground estimation.

Automatically extracting foreground objects from a natural image remains a challenging task. This paper presents a learning-based hierarchical graph for unsupervised matting. The proposed hierarchical framework progressively condenses image data from pixels into cells, from cells into components, and finally from components into matting layers. First, in the proposed framework, a graph-based contraction process is proposed to condense image pixels into cells in order to reduce the computational loads in the subsequent processes. Cells are further mapped into matting components using spectral clustering over a learning based graph. The graph affinity is efficiently learnt from image patches of different resolutions and the inclusion of multiscale information can effectively improve the performance of spectral clustering. In the final stage of the hierarchical scheme, we propose a multilayer foreground estimation process to assemble matting components into a set of matting layers. Unlike conventional approaches, which typically address binary foreground/background partitioning, the proposed method provides a set of multilayer interpretations for unsupervised matting. Experimental results show that the proposed approach can generate more consistent and accurate results as compared with state-of-the-art techniques.

Famprofazone use can be misinterpreted as methamphetamine abuse.

Famprofazone, a major ingredient of Gewolen, is an analgesic that has been demonstrated to be metabolized to methamphetamine (MA) and amphetamine (AM) following administration. Therefore, a famprofazone user may be interpreted as an illicit MA abuser in Taiwan because the user's urine tested positive for MA. The purpose of this study was to investigate whether the concentration of MA metabolized from a single dose of Gewolen users would offend the official controlled substance regulation and be identified as MA-positive. Subjects (n = 6) received 25 mg of famprofazone and collected all urine specimens at certain timed intervals for 48 h after drug administration. The urine specimens were screened by immunoassay and then confirmed by gas chromatography-mass spectrometry (GC-MS). The highest concentration of amphetamines by immunoassay was 1954 ng/mL, and 18.8% of the urine specimens' amphetamines concentrations exceeded 500 ng/mL. The MA and AM concentrations by GC-MS analysis of these urine specimens ranged from 901 to 2670 ng/mL and 208 to 711 ng/mL, respectively. These urine specimens were interpreted as MA-positive (>or= 500 ng/mL MA and >or= 100 ng/mL AM), according to the official test methods of Taiwan. The MA positive results appeared within 2-34 h. It is therefore clearly possible to misinterpret the legitimate famprofazone user as an MA abuser in Taiwan.