Reflections in a “Black Mirror”

The focus of this work is to analyse two episodes of the lucky TV series Black Mirror in an attempt to examine the descriptive trajectory of an increasingly technology-driven society. It has to be noted that the two chosen episodes describe a society seemingly working as a technological grammar which breaks down and rebuilds ubiquitous experiences and life stories more and more beyond the limit. This is not a criminalization of technology but, rather, a condemnation of lifestyles which lose their identity and become aspatial. Thus, conserving memories of the past or creating reputation become hybridized and twisted behavioural realities, which concur to structure a strongly ‘oligotrophic' nature: that of the post-human versions, that of technological mediations and organic dominions which meet the inorganic and meld with it. The authors analyse these aspects through a diachronic perspective that minimizes dialectic polarizations in order to examine the exegeses of the post-human concept within a medial representation that intensifies the discriminating and causative factors.

Medial Representation Based Font Recognition Method

In this article, a method of font recognition based on the medial representation, integrated into the font recognition system based on a digital image of text is described. This system searches for similar fonts, ordered by similarity, to the font shown in the user-entered text image. The system is based on solving two machine learning problems: text recognition on an image and font recognition on a text image. To solve the first problem, we use the concept of a mathematical model of a grapheme based on a continuous medial representation of a symbol. The solution to the font recognition problem is based on the concept of the morphological width of the figure, which is also closely related to the medial representation. We propose a method for using the morphological width function to find the most similar fonts from a known database. The experiments show high accuracy of searching for the most similar fonts. For a database consisting of 2543 fonts, the accuracy is 0.991 according to the metric top@5 for correctly recognized text in the font size of 100 pixels in the image.

Concerning intent, interpretation, memory and ambiguity in the work of an informal collective working on the Western Sahara conflict

In October 2016 I made my first visit to the refugee camps of Western Sahara’s Saharawi people near the Algerian town of Tindouf. This was an opportunity to advance my research on the work of an “informal collective” who work with a collection of photographs belonging to Moroccan soldiers, seized by SPLA (Saharawi People’s Liberation Army) over the course of 15 years spent fighting Moroccan forces. In this essay, I conceptualize the relationship between two disparate practices centering around photography—that of the Saharawi’s political organization, the Front Polisario, and the work undertaken by this informal collective. The latter’s work involves exploring the ontological coordinates of these photographs in a dialogical setting. Besides probing the many resonances between the group’s work and the Polisario’s treatment of the photographs of Moroccans in their possession, this essay is also concerned with the relationship between the conflict and its medial representation.

NEURAL NETWORKS FOR SHAPE RECOGNITION BY MEDIAL REPRESENTATION

<p><strong>Abstract.</strong> The article is dedicated to the development of neural networks that process data of a special kind – a medial representation of the shape, which is considered as a special case of an undirected graph. Methods for solving problems that complicate the processing of data of this type by traditional neural networks – different length of input data, heterogeneity of its structure, unordered constituent elements – are proposed. Skeletal counterparts of standard operations used in convolutional neural networks are formulated. Experiments on character recognition for various fonts, on classification of handwritten digits and data compression using the autoencoder-style architecture are carried out.</p>

FOREGROUND DETECTION ON DEPTH MAPS USING SKELETAL REPRESENTATION OF OBJECT SILHOUETTES

This article considers the problem of foreground detection on depth maps. The problem of finding objects of interest on images appears in many object detection, recognition and tracking applications as one of the first steps. However, this problem becomes too complicated for RGB images with multicolored or constantly changing background and in presence of occlusions. Depth maps provide valuable information about distance to the camera for each point of the scene, making it possible to explore object detection methods, based on depth features. We define foreground as a set of objects silhouettes, nearest to the camera relative to the local background. We propose a method of foreground detection on depth maps based on medial representation of objects silhouettes which does not require any machine learning procedures and is able to detect foreground in near real-time in complex scenes with occlusions, using a single depth map. Proposed method is implemented to depth maps, obtained from Kinect sensor.