Morphogenesis Analysis for Digital Image Production with L-System

The process of forming an image requires a correct color composition, location and distance between the lines to produce a good image. Human abilities in both creativity and high imagination are very limited, especially in forming new images by utilizing existing image patterns or images that resemble old images. Here we showed the implementation of L-System to generate new image generations with additional flame as a fire effect/glow on images for image transformation. This research used the L-System algorithm, Iterated Function System, and Voronoi Diagram to improve the result of image transformation. The results of this study indicated that mathematical calculations can be applied in the formation of images and the resulting images can be abstract and symmetrical. The next generation of images produced in this research can be in unlimited numbers as the generation of morphogenesis processes. The process of generating images is carried out randomly by merging the two existing images with morphogenesis analogy. The resulting images can be exported into jpg, png, and svg formats. Furthermore, this research showed that the implementation of the calculation for the variation reach the value of 99.48% while the image variation composition has a value of 99.29%.

Evidence for, and challenges to, sensory recruitment models of visual working memory

Visual working memory refers to the ability to temporarily hold information in mind in the service of behavior. Often, it is not sufficient to hold an abstract idea in mind to achieve our goals. Rather, we must maintain vivid sensory details. For example, when buying a spool of thread to repair a much-loved shirt, holding an abstract category in mind is not sufficient to buy the correct color (e.g. ‘blue’)—instead, you need a precise visual memory of the color (e.g., a particular gray-ish shade of blue). One proposal for how we maintain vivid, detailed information in mind is the sensory recruitment hypothesis. Sensory recruitment proposes that neural circuits already specialized for encoding sensory details during perception are likewise recruited to help maintain this information in working memory. In this review, we recount evidence that is consistent with a key role for early visual cortex in supporting visual working memory, we discuss key debates about the role of early sensory activity in supporting memory maintenance, and we outline a framework in which sensory codes are one part of a flexible, multi-level working memory representation.

Method of numerical estimating aerial images indicators quality for mapping purposes

The task of assessing the quality of aerial imagery, obtained for mapping, in terms of vision properties, is very ambiguous due to the lack of objective criteria and evaluation methods. A system of indicators for aerial images quality and methods of their numerical assessment is presented. The fine aerial image’s quality is characterized by a set of its structural and gradation properties. The structural properties of the image are determined by the actual spatial resolution and photographic sharpness. Gradation properties of an image are characterized by the correct color rendering, the level of random noise and information completeness indicators – haze, radiometric resolution and the percentage of information loss in illumination and shadows.Methods of evaluating these indicators are formulated, and their recommended and acceptable numerical values are determined analytically. To clarify and correct the obtained analytical recommended and acceptable numerical values of the image quality indicators of their practical application possibility and further experimental studies are necessary with materials, obtained through various airborne imaging sensors for mapping.

Knowledge and practices of health workers on safe disposal of sharp medical wastes in selected hospitals

Sharp medical waste is infectious and hazardous because of their high potential diseases transmission and injury. It poses serious threats to environmental health. A cross sectional study was done to assess the level of knowledge and practices of health workers on safe disposal of sharp medical wastes. This study was done in Pirojpur Sadar Hospital, Nazirpur Upazilla Health Complex and Zia Nagar Upazilla Health Complex. Sample size was 200 Health Workers. A Semi-structured interviewer administered questionnaire was used for data collection. The collected data were processed and analyzed with the help of SPSS (Version 20) software on the basis of different variables. The period of study was from January to December 2016. The study result shows that among 200 respondent mean age was 44.05 (±7.01) years. The designation varieties were nurses (70.0%), doctor (10.0%), Aya/ward boy (15.0%). This study found that, majority of the respondents had knowledge about the concept of medical waste and sharp medical waste. Among the respondents (90.0%) respondents knew the correct color bin to segregate sharp medical waste. Multiple responses of the respondents (80%) mentioned that they got information from Course curriculum. Knowledge of the respondents on sharp medical waste disposal (64.5%) had Good knowledge, (23%) had Fair Knowledge and (12.5%) had Poor Knowledge. Most of the respondents (47.5%) had Good practices, (33%) had Fair and (20%) had Poor practices regarding safe disposal of sharp medical waste disposal. Adequate supply of equipment’s and strict monitoring system should be established to improvement the practice of the health care providers regarding safe disposal of sharp medical waste. Training on sharp medical waste disposal will help the participants to improve their knowledge and practices. Asian J. Med. Biol. Res. December 2020, 6(4): 802-808

Optimization of automotive color filter arrays for traffic light color separation

Traffic light (TL) classification is an important feature for automated driving, and it requires correct color separation of the TL signals captured using cameras. A key camera component for the color separation performance is the color filter array (CFA). For common automotive-specific CFAs, we have observed unsatisfactory performance for TL color separation, which indicates the need for an optimization. Based on typical scenarios for TL classification and a set of recorded TL signals, we evaluate the performance of common automotive CFAs. For a quantitative evaluation, we propose a suitable color distance metric. We also propose a method for optimization of the CFA and show that using this method, reference color separation performance can be achieved, trading in only a small amount of sensitivity.

A Simple, Reliable, and Inexpensive Solution for Contact Color Measurement in Small Plant Samples

Correct color measurement by contact-type color measuring devices requires that the sample surface fully covers the head of the device, so their use on small samples remains a challenge. Here, we propose to use cardboard adaptors on the two aperture masks (3 and 8 mm diameter measuring area) of a broadly used portable spectrophotometer. Adaptors in black and white to reduce the measuring area by 50% and 70% were applied in this study. Representatives of the family Campanulaceae have been used to test the methodology, given the occurrence of small leaves. Our results show that, following colorimetric criteria, the only setting providing indistinguishable colors according to the perception of the human eye is the use of a 50%-reducing adaptor on the 3-mm aperture. In addition, statistical analysis suggests the use of the white adaptor. Our contribution offers a sound measurement technique to gather ecological information from the color of leaves, petals, and other small samples.

EEMEFN: Low-Light Image Enhancement via Edge-Enhanced Multi-Exposure Fusion Network

This work focuses on the extremely low-light image enhancement, which aims to improve image brightness and reveal hidden information in darken areas. Recently, image enhancement approaches have yielded impressive progress. However, existing methods still suffer from three main problems: (1) low-light images usually are high-contrast. Existing methods may fail to recover images details in extremely dark or bright areas; (2) current methods cannot precisely correct the color of low-light images; (3) when the object edges are unclear, the pixel-wise loss may treat pixels of different objects equally and produce blurry images. In this paper, we propose a two-stage method called Edge-Enhanced Multi-Exposure Fusion Network (EEMEFN) to enhance extremely low-light images. In the first stage, we employ a multi-exposure fusion module to address the high contrast and color bias issues. We synthesize a set of images with different exposure time from a single image and construct an accurate normal-light image by combining well-exposed areas under different illumination conditions. Thus, it can produce realistic initial images with correct color from extremely noisy and low-light images. Secondly, we introduce an edge enhancement module to refine the initial images with the help of the edge information. Therefore, our method can reconstruct high-quality images with sharp edges when minimizing the pixel-wise loss. Experiments on the See-in-the-Dark dataset indicate that our EEMEFN approach achieves state-of-the-art performance.

Dominant Color Extraction with K-Means for Camera Characterization in Cultural Heritage Documentation

The camera characterization procedure has been recognized as a convenient methodology to correct color recordings in cultural heritage documentation and preservation tasks. Instead of using a whole color checker as a training sample set, in this paper, we introduce a novel framework named the Patch Adaptive Selection with K-Means (P-ASK) to extract a subset of dominant colors from a digital image and automatically identify their corresponding chips in the color chart used as characterizing colorimetric reference. We tested the methodology on a set of rock art painting images captured with a number of digital cameras. The characterization approach based on the P-ASK framework allows the reduction of the training sample size and a better color adjustment to the chromatic range of the input scene. In addition, the computing time required for model training is less than in the regular approach with all color chips, and obtained average color differences Δ E a b * lower than two CIELAB units. Furthermore, the graphic and numeric results obtained for the characterized images are encouraging and confirms that the P-ASK framework based on the K-means algorithm is suitable for automatic patch selection for camera characterization purposes.

Correction to: Genetic and clinical characterization of BRCA-associated hereditary breast and ovarian cancer in Navarra (Spain)

Following publication of the original article [1], the authors reported an error in Figure 2, where the color code of the text boxes is reversed. Figure 2-amended shows the correct color association between the text boxes and the different areas in the map: Navarra, neighbouring communities and other Spanish communities.

Current knowledge, attitude and practices of dental residents towards biomedical waste management: A cross sectional study

Aim: To assess the knowledge, attitude and practices towards biomedical waste (BMW) management among residents of dental colleges of Davangere city. Methodology: A cross- sectional questionnaire based study was carried out among 150 residents from dental colleges (85 from College of Dental Sciences and 65 from Bapuji Dental College and Hospital) in Davangere Karnataka. The responses were analyzed using Frequency distribution and Pearson Chi Square test using SPSS (Statistical package for social sciences, software Version 22.0) Results: 94% residents said that they were aware about BMW management out of which 90% could correctly describe the BM waste. Out of 150, 71% residents were aware about the storage duration. Only 41.3% residents knew the correct symbol for biohazard. When asked about color codes for disposal of waste only 50.7% residents knew the color code for the disposal of human anatomical waste and 58.7% residents knew the correct color code for the disposal of used gloves and masks. Conclusion: Though majority of the residents had fair knowledge of BMW management, the need for training regarding their practice of BMW disposal should be the focus.