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Colletotrichum species are well-known plant pathogens, saprobes, endophytes, human pathogens and entomopathogens. However, little is known about Colletotrichum as endophytes of plants and cultivars including Citrus grandis cv. “Tomentosa”. In the present study, 12 endophytic Colletotrichum isolates were obtained from this host in Huazhou, Guangdong Province (China) in 2019. Based on morphology and combined multigene phylogeny [nuclear ribosomal internal transcribed spacer (ITS), glyceraldehyde-3-phosphate dehydrogenase (gapdh), chitin synthase 1 (chs-1), histone H3 (his3) actin (act), beta-tubulin (β-tubulin) and glutamine synthetase (gs)], six Colletotrichum species were identified, including two new species, namely Colletotrichum guangdongense and C. tomentosae. Colletotrichum asianum, C. plurivorum, C. siamense and C. tainanense are identified as being the first reports on C. grandis cv. “Tomentosa” worldwide. This study is the first comprehensive study on endophytic Colletotrichum species on C. grandis cv. “Tomentosa” in China.

Pogostemon cablin is one of the well-known Southern Chinese medicinal plants with detoxification, anti-bacterial, anti-inflammatory, and other pharmacological functions. Identification and characterization of phytopathogens on P. cablin are of great significance for the prevention and control of diseases. From spring to summer of 2019 and 2020, a leaf spot disease on Pogostemon cablin was observed in Guangdong Province, South China. The pathogen was isolated and identified based on both morphological and DNA molecular approaches. The molecular identification was conducted using multi-gene sequence analysis of large subunit (LSU), the nuclear ribosomal internal transcribed spacer (ITS), beta-tubulin (β-tubulin), and RNA polymerase II (rpb2) genes. The causal organism was identified as Stagonosporopsis pogostemonis, a novel fungal species. Pathogenicity of Stagonosporopsis pogostemonis on P. cablin was fulfilled via confining the Koch’s postulates, causing leaf spots and stem blight disease. This is the first report of leaf spot diseases on P. cablin caused by Stagonosporopsis species worldwide.

An improved detection algorithm was presented.Firstly,an improved skin color model,which adopted many preprocessing including optical offset,auto white balance,and auto gamma correction,was built to detect candidate skin pixels.Secondly,the image was filtered with erosion and dilation to exclude unconnected candidate pixels.Lastly,gray co-matrix was used to exclude those candidate human skin regions with complex texture.Experimental results show that this algorithm improves the skin region detection precision.

To reduce the detection time efficiency under large scale data environment, a rapid algorithm was proposed on compressed domain using a two-level hierarchical detection scheme.The ordinal measures of DCT coefficients were adopted as visual features for similarity-matching in order to reduce the computational load in video decoding.Inverted indexing structure was used to accelerate the first level detection process.The experiment results show, compared with the previous algorithm, the algorithm can improve the detection speed obviously with the similar detection precision.

A new method for video text extraction in order to overcome the weakness of existing methods was proposed.First,multi-scale text location and text region refinement in text location step were adopted.Then an improved K-means clustering method was used to segment text regions to obtain a binarized image in text segmentation step.Finally,OCR software was used to recognize text.The results of experiment show that the method can effctively used in sensitive words detection in videos from the Internet.

Coded aperture imaging systems have recently shown great success in recovering scene depth and extending the depth-of-field. Tremendous efforts have been focused on designing good coded patterns. The ideal pattern, however, would have to serve two conflicting purposes: 1) it should be broadband to ensure robust deconvolution and 2) it should have sufficient zero-crossings for a high depth discrepancy. This paper presents a simple but effective scene-adaptive coded aperture solution to bridge this gap. We observe that natural scenes often exhibit a dominant edge direction that can be caused by structure, texture, shading, etc. We therefore apply depth-discriminative codes along the dominant direction and broadband codes along its orthogonal direction. To physically implement this scheme, we adopt a two-shot scheme. Firstly, we capture a scene image with a pinhole aperture and analyze image content to determine the dominant direction. Secondly, we apply the proposed coding scheme using the programmable Liquid Crystal on Silicon (LCoS). Experiments on a broad range of synthetic and real scenes show that our technique is able to combine advantages of the state-of-the-art patterns for recovering better quality depth map and all-focus images. [ABSTRACT FROM AUTHOR]