Visual Histories of Sex

Increased access to visual archives and the proliferation of digitized images related to sexuality have led a growing number of scholars in recent years to place images and visual practices at the center of critical historical inquiries of sexual desire, subjectivity, and embodiment. At the same time, new critical histories of sexual science serve both to expand the temporal and geographical frames for investigating the historical relationships of sex and visual production, and to generate new lines of inquiry and reshape visual studies more broadly. The contributors to this issue invite us to ask: What new questions and challenges for the study of sex and sexual science are posed by critical studies of the visual? How are new visual methodologies that focus on archives changing the contours of historical knowledge about sex and sexuality? What—and where—are new methodologies still needed? “Visual Archives of Sex” aims to illuminate current research that centers visual media in the history of sexuality and that interrogates contemporary historiographies.

FON DUSHANBE CATALOGUE. RESULTS OF PROCESSING IN THE TYCHO-2 SYSTEM

In the Tycho-2 catalogue system the processing of 1529 photographic plates of the FON Dushanbe project from the collection of the Institute of Astrophysics of the National Academy of Sciences of Tajikistan was completed. The photographic plates with the size of 8º×8º (30x30 cm) were exposed in the zones from -8º to + 84º in the period of 1985-1992 years. In years 2017 – 2020 the plates were digitized using a Microtek ScanMaker 1000XL Plus scanner with the resolution of 1200 dpi, so the size of the digitized images is near 13000x13000 px. Based on the results of the processing of digitized images a catalogue of equatorial coordinates α, δ and B-magnitudes of stars for the northern hemisphere of the sky was created. The catalog contains about 30 million stars and galaxies for the epoch 1988.74. The average internal accuracy of the catalogue for all objects is σαδ = ±0.32" and σB = ± 0.11 m (for stars in the range of B = 8 m -14 m the errors are σαδ = ±0.19" and σB =±0.07 m ) for equatorial coordinates and B-magnitudes respectively. The convergence between calculated and reference positions from the Tycho-2 catalogue is σαδ = ±0.07" and the convergence with photoelectric B-magnitudes is σB = ±0.16 m . Five astronomical institutions took part in the processing of the photographic plates and in the creating of the FON-Dushanbe catalogue: Institute of Astrophysics of NAS of Tajikistan; Walter Hohmann Observatory, Essen, Germany; Ulugh Beg Astronomical Institute UAS, Uzbekistan; Research Institute “Mykolaiv Astronomical Observatory”, Ukraine and Main Astronomical Observatory NASU, Ukraine.

Mobile microscopy and telemedicine platform assisted by deep learning for quantification of Trichuris trichiura infection

Soil-transmitted helminths (STH) are the most prevalent pathogens among the group of neglected tropical diseases (NTDs). Kato-Katz technique is the diagnosis method recommended by WHO and although is generally more sensitive than other microscopic methods in high transmission settings, it often presents a decreased sensitivity in low transmission settings and it is labour intensive. Digitizing the samples could provide a solution which allows to store the samples in a digital database and perform remote analysis. Artificial intelligence methods based on digitized samples can support diagnostics efforts by support diagnostics efforts by performing an automatic and objective quantification of disease infection.In this work, we propose an end-to-end pipeline for microscopy image digitization and automatic analysis of digitized images of soil-transmitted helminths. Our solution includes (1) a digitalization system based on a mobile app that digitizes the microscope samples using a low-cost 3D-printed microscope adapter, (2) a telemedicine platform for remote analysis and labelling and (3) novel deep learning algorithms for automatic assessment and quantification of parasitological infection of STH.This work has been evaluated by comparing the STH quantification using both a manual remote analysis based on the digitized images and the AI-assisted quantification against the reference method based on conventional microscopy. The deep learning algorithm has been trained and tested on 41 slides of stool samples containing 949 eggs from 6 different subjects using a cross-validation strategy obtaining a mean precision of 98,44% and mean recall of 80,94%. The results also proved the potential of generalization capability of the method at identifying different types of helminth eggs.In conclusion, this work has presented a comprehensive pipeline using smartphone-based microscopy integrated with a telemedicine platform for automatic image analysis and quantification of STH infection using artificial intelligence models.

Development of a lung segmentation algorithm for analog imaged chest X-Ray: preliminary results

Analog X-Ray radiography is still used in many underdeveloped regions around the world. To allow these populations to benefit from advances in automatic computer-aided detection (CAD) systems, X-Ray films must be digitized. Unfortunately, this procedure may introduce artefacts which may severely impair the performance of such systems. This work investigates the impact digitized images may cause to deep neural networks trained for lung (semantic) segmentation on digital x-ray samples. While three public datasets for lung segmentation evaluation exist for digital samples, none are available for digitized data. To this end, a U-Net architecture was trained on publicly available data, and used to predict lung segmentation on a newly annotated set of digitized images. Our results show that the model is capable to effectively identify lung segmentation at digital X-Rays with a high intra-dataset (PR AUC: 0.99) and cross-dataset (PR AUC: 0.99) performances on unseen test data. When challenged against analog imaged films, the performance is substantially degraded (PR AUC: 0.90). Our analysis further suggests that the use of maximum F1 and precision-recall AUC (PR AUC) measures are not informative to identify segmentation problems in images.

Intellectual processing of spatial data in the GIS of landscape-ecological monitoring

An approach to the creation of modular subsystems for intelligent processing and compression of spatial data as a part of GIS landscape-ecological monitoring is proposed. The functions and methods of implementing the tasks of these subsystems are determined. The main modules include: a module for preliminary processing of spatial data with the formation of digitized images; module of image segmentation and edge highlighting; module for categorical classification of images of landscape objects; image compression module using a fractal model and a genetic algorithm; a module for compressing and restoring noisy digitized images using a noise-canceling autoencoder.

Imagens digitalizadas na avaliação do teste de tetrazólio em sementes de trigo

The tetrazolium test can be an alternative to obtain fast results of the physiological potential of wheat seeds. In this context, the objective of the present study was to evaluate the efficiency of the tetrazolium test through the evaluation by analysis of digitized images in the determination of the physiological quality of wheat seeds.The experiment was carried out with 22 lots of wheat seeds, submitted to the determination of the physiological potential, including the tetrazolium test evaluated by means of digitized image analysis.For digitization, after longitudinal bisection, and staining in 0.075% tetrazolium solution, the seeds were grouped on tabletop scanner glass, stored and classified into four vigor classes. Data were submitted to analysis of variance, with means grouped by the Scott-Knott test, at 5% of significance; we also performed an analysis of the simple correlation coefficients between tetrazolium test results and other tests.The evaluation of the tetrazolium test through the analysis of digitized images grouped the lots in three levels of vigor.The method is effective in determining the vigor and viability of wheat seeds, because the possibility of expanding the images that allows precise analysis of the embryonic axis structures.

STRUCTURE FROM MOTION PROCESSING OF ANALOGUE IMAGES CAPTURED BY ROLLEI METRIC CAMERA, DIGITIZED WITH VARIOUS SCANNING RESOLUTION

SfM processing of archived analogue images gives an opportunity to efficiently create new and valuable 2D and 3D results. The SfM processing of digitized analogue images brings some challenges. How to digitize negatives of photogrammetric images? What scanning resolution is the most beneficial for processing? How to preprocess the digitized images to be able to process them using the SfM method? What accuracy of results is possible to expect? This paper tries to deal with all these questions. For this paper, 7 negatives of former photogrammetric documentation of a vault were chosen. The negatives were captured by Rollei 3003 metric camera in 1999. Two pieces of software were chosen for the SfM processing. A commercial alternative Agisoft PhotoScan and free open-source alternative MicMac. The results of the SfM processing were compared to the results of an original photogrammetric method, which was used for former processing in 1999.

Yoruba Handwritten Character Recognition using Freeman Chain Code and K-Nearest Neighbor Classifier

This work presents a recognition system for Offline Yoruba characters recognition using Freeman chain code and K-Nearest Neighbor (KNN). Most of the Latin word recognition and character recognition have used k-nearest neighbor classifier and other classification algorithms. Research tends to explore the same recognition capability on Yoruba characters recognition. Data were collected from adult indigenous writers and the scanned images were subjected to some level of preprocessing to enhance the quality of the digitized images. Freeman chain code was used to extract the features of THE digitized images and KNN was used to classify the characters based on feature space. The performance of the KNN was compared with other classification algorithms that used Support Vector Machine (SVM) and Bayes classifier for recognition of Yoruba characters. It was observed that the recognition accuracy of the KNN classification algorithm and the Freeman chain code is 87.7%, which outperformed other classifiers used on Yoruba characters.