Improving Texture Categorization with Biologically Inspired Filtering

Within the domain of texture classification, a lot of effort has been spent on local descriptors, leading to many powerful algorithms. However, preprocessing techniques have received much less attention despite their important potential for improving the overall classification performance. We address this question by proposing a novel, simple, yet very powerful biologically-inspired filtering (BF) which simulates the performance of human retina. In the proposed approach, given a texture image, after applying a DoG filter to detect the "edges", we first split the filtered image into two "maps" alongside the sides of its edges. The feature extraction step is then carried out on the two "maps" instead of the input image. Our algorithm has several advantages such as simplicity, robustness to illumination and noise, and discriminative power. Experimental results on three large texture databases show that with an extremely low computational cost, the proposed method improves significantly the performance of many texture classification systems, notably in noisy environments. The source codes of the proposed algorithm can be downloaded from https://sites.google.com/site/nsonvu/code.Comment: 11 page

Adherence to highly active antiretroviral therapy among people living with HIV and associated high-risk behaviours and clinical characteristics: A cross-sectional survey in Vietnam

Although Vietnam has promoted the utilisation of highly active antiretroviral therapy (HAART) towards HIV elimination targets, adherence to treatment has remained under-investigated. We aimed to describe high-risk behaviours and clinical characteristics by adherence status and to identify the factors associated with non-adherence. We included 426 people living with HIV (PLWH) currently or previously involved in HAART. Most participants were men (75.4%), young (33.6 years), with low income and low education levels. Non-adherent PLWH (11.5%) were more likely to have a larger number of sex partners (p-value = 0.053), sex without condom use (p-value = 0.007) and not receive result at hospital or voluntary test centre (p-value = 0.001). Multiple logistic regression analysis showed that demographic (education levels), sexual risk behaviours (multiple sex partners and sex without using condom) and clinical characteristics (time and facility at first time received HIV-positive result) were associated with HAART non-adherence. There are differences in associated factors between women (education levels and place of HIV testing) and men (multiple sex partners). Gender-specific programs, changing risky behaviours and reducing harms among PLWH may benefit adherence. We highlight the need to improve the quantity and quality of HIV/AIDS services in Vietnam, especially in pre- and post-test counselling, to achieve better HAART adherence, working towards ending AIDS in 2030. © The Author(s) 2021. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Huy Nguyen” is provided in this record*

Statistical binary patterns for rotational invariant texture classification

International audienceA new texture representation framework called statistical binary patterns (SBP) is presented. It consists in applying rotation invariant local binary pattern operators (LBP riu2) to a series of moment images, defined by local statistics uniformly computed using a given spatial support. It can be seen as a generalisation of the commonly used complementation approach (CLBP), since it extends the local description not only to local contrast information, but to higher order local variations. In short, SBPs aim at expanding LBP self-similarity operator from the local gray level to the regional distribution level. Thanks to a richer local description, the SBPs have better discrimination power than other LBP variants. Furthermore, thanks to the regularisation effect of the statistical moments, the SBP descriptors show better noise robustness than classical CLBPs. The interest of the approach is validated through a large experimental study performed on five texture databases: KTH-TIPS, KTH-TIPS 2b, CUReT, UIUC and DTD. The results show that, for the four first datasets, the SBPs are comparable or outperform the recent state-of-the-art methods, even using small support for the LBP operator, and using limited size spatial support for the computation of the local statistics

Volumes of Blurred-Invariant Gaussians for Dynamic Texture Classification

International audienceAn effective model, which jointly captures shape and motion cues, for dynamic texture (DT) description is introduced by taking into account advantages of volumes of blurred-invariant features in three main following stages. First, a 3-dimensional Gaussian kernel is used to form smoothed sequences that allow to deal with well-known limitations of local encoding such as near uniform regions and sensitivity to noise. Second , a receptive volume of the Difference of Gaussians (DoG) is figured out to mitigate the negative impacts of environmental and illumination changes which are major challenges in DT understanding. Finally, a local encoding operator is addressed to construct a discriminative descriptor of enhancing patterns extracted from the filtered volumes. Evaluations on benchmark datasets (i.e., UCLA, DynTex, and DynTex++) for issue of DT classification have positively validated our crucial contributions

EXTRACTION AND PROPERTY STUDIES OF COENZYME Q10 FROM RECOMBINANT AGROBACTERIUM TUMEFACIENS

In this report, some results of extraction and characterization of CoQ10 from recombinant A. tumefaciens are presented. Four different cell breaking methods (sonication, acidic treatment, ethanol treatment, and enzymatic lysis) in combination with the extracting steps were carried out to extract CoQ10 and the results showed that ethanol treatment was the most efficient method. Appropriate conditions for CoQ10 extraction were 25 oC, 24 hours incubation and ethanol solvent/biomass ratio of 10:1 (ml/g). Characterization of extracted CoQ10 showed that CoQ10 was sensitive to light, but stable in the temperature ranges of 4 – 60 oC and the pH range of 6.0 – 9.0. Obtained results in present study should be applied in the large scale for CoQ10 extraction, providing the CoQ10 product for testing production of functional foods

Detecting Machine-Translated Text using Back Translation

Machine-translated text plays a crucial role in the communication of people using different languages. However, adversaries can use such text for malicious purposes such as plagiarism and fake review. The existing methods detected a machine-translated text only using the text's intrinsic content, but they are unsuitable for classifying the machine-translated and human-written texts with the same meanings. We have proposed a method to extract features used to distinguish machine/human text based on the similarity between the intrinsic text and its back-translation. The evaluation of detecting translated sentences with French shows that our method achieves 75.0% of both accuracy and F-score. It outperforms the existing methods whose the best accuracy is 62.8% and the F-score is 62.7%. The proposed method even detects more efficiently the back-translated text with 83.4% of accuracy, which is higher than 66.7% of the best previous accuracy. We also achieve similar results not only with F-score but also with similar experiments related to Japanese. Moreover, we prove that our detector can recognize both machine-translated and machine-back-translated texts without the language information which is used to generate these machine texts. It demonstrates the persistence of our method in various applications in both low- and rich-resource languages.Comment: INLG 2019, 9 page