The Mechanism of Orientation Detection Based on Artificial Visual System

As an important part of the nervous system, the human visual system can provide visual perception for humans. The research on it is of great significance to improve our understanding of biological vision and the human brain. Orientation detection, in which visual cortex neurons respond only to linear stimuli in specific orientations, is an important driving force in computer vision and biological vision. However, the principle of orientation detection is still unknown. This paper proposes an orientation detection mechanism based on dendrite calculation of local orientation detection neurons. We hypothesized the existence of orientation detection neurons that only respond to specific orientations and designed eight neurons that can detect local orientation information. These neurons interact with each other based on the nonlinearity of dendrite generation. Then, local orientation detection neurons are used to extract local orientation information, and global orientation information is deduced from local orientation information. The effectiveness of the mechanism is verified by computer simulation, which shows that the machine can perform orientation detection well in all experiments, regardless of the size, shape, and position of objects. This is consistent with most known physiological experiments.

The Vision of a Deductive Meta-Biology: From Goodwin to Spinoza

This paper attempts to delineate a 20th century movement towards the formation of a truly 'deductive' metabiology. The aim is to reify and crystallize the historical reality of this new meta-biological ‘movement’ by suggesting that what essentially united it was an ontological or epistemically heuristic commitment to a biological vision whose grammar and syntax were provided by utilising non-classical forms of qualitative rather than quantitative mathematical modelling and expression. The paper primarily focusses on the anti-Darwinian metabiology of the Canadian born, yet UK domiciled, biologist Brian Goodwin, as well as comparing and contrasting his ideas withose those of other metabiological such as Per Alberch, Hal Waddington, Rene Thom and Vladimir Vernadsky. It is the contention of his paper that, historically, the purest expression and the philosophical consummation of many of the meta-biological ideas propounded by Goodwin and others discussed in this paper can actually be found in the extraordinary metaphysical vision of Benedict de Spinoza (1632-1677) particularly as expressed in his posthumously published masterpiece the Ethics (1677), and so this paper incorporates an account of the meta-biological relevance of Spinoza’s thinking in relation to these more modern metabiological thinkers, not in order to indicate direct influence, but as an attempt to reveal the potential metabiological inspiration of such an metaphysical hermeneutic for those concerned to increasingly understand the phenomenon of 'life' in deductive terms.

Image Augmentation based on GAN deep learning approach with Textual Content Descriptors

Computer vision, also known as computational visual perception, is a branch of artificial intelligence that allows computers to interpret digital pictures and videos in a manner comparable to biological vision. It entails the development of techniques for simulating biological vision. The aim of computer vision is to extract more meaningful information from visual input than that of a biological vision. Computer vision is exploding due to the avalanche of data being produced today. Powerful generative models, such as Generative Adversarial Networks (GANs), are responsible for significant advances in the field of picture creation. The focus of this research is to concentrate on textual content descriptors in the images used by GANs to generate synthetic data from the MNIST dataset to either supplement or replace the original data while training classifiers. This can provide better performance than other traditional image enlarging procedures due to the good handling of synthetic data. It shows that training classifiers on synthetic data are as effective as training them on pure data alone, and it also reveals that, for small training data sets, supplementing the dataset by first training GANs on the data may lead to a significant increase in classifier performance.

RECORDING OF RESEARCH RESULTS IN THE COURSE OF FORENSIC HISTOLOGICAL EXAMINATION IN RUSSIA

The objective of the study was to identify the features of recording microscopy results in case of forensic histological examination in Russia at present and the prospects for its development in the future. Research material included conclusions of an expert (specialist) of state forensic medical institutions of Russia. The methods used to study the material were as follows: comparative-historical analysis, microscopic, thanatogenetic analysis, microphotography and description of histological sections. The authors analyzed in details two methods of recording the results obtained by microscopy, which are mostly often used while performing forensic histological examinations at present in Russia. The article reflects the historical, legal and forensic aspects, as well as some unresolved problems of recording information obtained during forensic histological research. The authors also express their opinion about the use of computer vision in the microscopy of histological sections in the near future, as a supplement but not the opposite of biological vision.

An Automatic 3D Point Cloud Registration Method Based on Biological Vision

When measuring surface deformation, because the overlap of point clouds before and after deformation is small and the accuracy of the initial value of point cloud registration cannot be guaranteed, traditional point cloud registration methods cannot be applied. In order to solve this problem, a complete solution is proposed, first, by fixing at least three cones to the target. Then, through cone vertices, initial values of the transformation matrix can be calculated. On the basis of this, the point cloud registration can be performed accurately through the iterative closest point (ICP) algorithm using the neighboring point clouds of cone vertices. To improve the automation of this solution, an accurate and automatic point cloud registration method based on biological vision is proposed. First, the three-dimensional (3D) coordinates of cone vertices are obtained through multi-view observation, feature detection, data fusion, and shape fitting. In shape fitting, a closed-form solution of cone vertices is derived on the basis of the quadratic form. Second, a random strategy is designed to calculate the initial values of the transformation matrix between two point clouds. Then, combined with ICP, point cloud registration is realized automatically and precisely. The simulation results showed that, when the intensity of Gaussian noise ranged from 0 to 1 mr (where mr denotes the average mesh resolution of the models), the rotation and translation errors of point cloud registration were less than 0.1° and 1 mr, respectively. Lastly, a camera-projector system to dynamically measure the surface deformation during ablation tests in an arc-heated wind tunnel was developed, and the experimental results showed that the measuring precision for surface deformation exceeded 0.05 mm when surface deformation was smaller than 4 mm.

Deep CNN and Deep GAN in Computational Visual Perception-Driven Image Analysis

Computational visual perception, also known as computer vision, is a field of artificial intelligence that enables computers to process digital images and videos in a similar way as biological vision does. It involves methods to be developed to replicate the capabilities of biological vision. The computer vision’s goal is to surpass the capabilities of biological vision in extracting useful information from visual data. The massive data generated today is one of the driving factors for the tremendous growth of computer vision. This survey incorporates an overview of existing applications of deep learning in computational visual perception. The survey explores various deep learning techniques adapted to solve computer vision problems using deep convolutional neural networks and deep generative adversarial networks. The pitfalls of deep learning and their solutions are briefly discussed. The solutions discussed were dropout and augmentation. The results show that there is a significant improvement in the accuracy using dropout and data augmentation. Deep convolutional neural networks’ applications, namely, image classification, localization and detection, document analysis, and speech recognition, are discussed in detail. In-depth analysis of deep generative adversarial network applications, namely, image-to-image translation, image denoising, face aging, and facial attribute editing, is done. The deep generative adversarial network is unsupervised learning, but adding a certain number of labels in practical applications can improve its generating ability. However, it is challenging to acquire many data labels, but a small number of data labels can be acquired. Therefore, combining semisupervised learning and generative adversarial networks is one of the future directions. This article surveys the recent developments in this direction and provides a critical review of the related significant aspects, investigates the current opportunities and future challenges in all the emerging domains, and discusses the current opportunities in many emerging fields such as handwriting recognition, semantic mapping, webcam-based eye trackers, lumen center detection, query-by-string word, intermittently closed and open lakes and lagoons, and landslides.

El Mago (de) Matthew Barney: Influencias Renacentistas en el Ciclo Cremaster

In this article we will try to offer a new interpretation of the video art work of the artist Matthew Barney (California, 1967) the Cremaster Cycle, approaching a more humanistic and less biological vision, the last one being the principal study aspect for the experts on Matthew Barney. We will focus on the character of the Magician, a key figure in the Cremaster Cycle. The Magician lighting the pass to a possible interpretation of the Cycle in Renaissance key, the magician character while being a personification of aesthetic- artistic interests of Matthew Barney is also an approximation to the ideal of the Renaissance man: philosopher, magician, healer. Alter-ego of Matthew Barney, the escape artist Harry Houdini represents (and through him all other magical branches) a possible link with the powerful magician Renaissance described by the philosopher Giordano Bruno (1548-1600), able to metamorphose, influence in the sensitivities of his objects, through self-knowledge and knowledge of the Divine.