scholarly journals Overview of the machine vision methods at agrorobots

2021 ◽  
Vol 13 (30) ◽  
pp. 13-19
Author(s):  
Vera Stefanova ◽  
◽  
Georgi Komitov ◽  
Keyword(s):  
Machine Vision ◽  
Computer Technology ◽  
Focal Length ◽  
Climatic Conditions ◽  
Vision Systems ◽  
Laser Technology ◽  
Visual Identification ◽  
Different Types ◽  
Wide Assortment ◽  
Modern Machine

The idea of "seeing" machines developed violently after the entry of computer technology and industry 4.0 into mass production. The first task of the machine vision is the processing of images and their adjacent equipment, and its systems then include devices with digital inputs/outputs. These inputs/outputs are designed to control some kind of movement or operation of equipment, such as manipulators or automatic lines. The modern machine vision systems are a modern direction of technology that contains elements of computer technology, optics, mechanical systems and industrial automation. With their help, modern agrorobots perform orderly tasks such as visual identification, visual control, obstacle and position determination. This article presents an overview of certain applications of different machine vision systems suitable for use in agrorobots. It is known that they are autonomously powered and work in poor climatic conditions. In other cases, the machine vision in agrorobots must necessarily work in random oscillations and variable focal length, as well as with a wide assortment of objects for research. The article discusses the capabilities of different types of cameras and related software, the application of QR and laser technology for agrorobots.

Deep Learning: The Good, the Bad, and the Ugly

2019 ◽  
Vol 5 (1) ◽  
pp. 399-426 ◽  
Author(s):  
Thomas Serre
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Machine Vision ◽  
Artificial Vision ◽  
Vision Systems ◽  
Comprehensive Overview ◽  
Biological Vision ◽  
Recent Developments ◽  
Visual Intelligence ◽  
Modern Machine

Artificial vision has often been described as one of the key remaining challenges to be solved before machines can act intelligently. Recent developments in a branch of machine learning known as deep learning have catalyzed impressive gains in machine vision—giving a sense that the problem of vision is getting closer to being solved. The goal of this review is to provide a comprehensive overview of recent deep learning developments and to critically assess actual progress toward achieving human-level visual intelligence. I discuss the implications of the successes and limitations of modern machine vision algorithms for biological vision and the prospect for neuroscience to inform the design of future artificial vision systems.

scholarly journals Aeromycology: studies of fungi in aeroplankton

2014 ◽  
Vol 10 ◽  
pp. 18-26 ◽  
Author(s):  
Małgorzata Jędryczka
Keyword(s):  
Fungal Spores ◽  
Climatic Conditions ◽  
Species Level ◽  
Cultivated Plants ◽  
Wind Force ◽  
Rna Amplification ◽  
Arable Crops ◽  
Visual Identification ◽  
Different Types ◽  
Alternaria Species

Air is a natural environment for spores of many genera and species of fungi. Despite its small size and a significant dispersion they have a great impact on human health and different areas of our activities, such as agricultural production. The study on spores of fungi that belong to aeroplankton or bioaerosole is called aeromycology. The most frequent fungi present in the air are Cladosporium and Alternaria species. Their numbers are abundant regardless of latitude and height above the sea level and above the ground. They mostly originate from agricultural environment. Other frequently listed species of fungi, whose spores are present in the air include of Aspergillus, Penicillium, Fusarium, Sclerotinia and Ganoderma. The concentration of spores in the air strongly depends on the abundance of their formation during the studied period. This in turn relates to geobotanical region, vegetation, degree of urbanization, climatic conditions, season, current weather, wind force and direction, local microclimate, and many other factors. Changes in humidity affect the concentration of different types of fungal spores. In general they are divided to ‘dry’ (Alternaria, Cladosporium, Puccinia, Ustilago, Melampsora, Epicoccum, Drechslera) and ‘wet’ (Didymella, Fusarium, Ganoderma, Gliocladium, Leptosphaeria, Verticillium). Study of the composition of species and genera are being done using different types of spore samplers, mostly volumetric instruments. Visual identification is based on colony morphology of the fungus and the shape and size of spores. The identification at the species level is possible with molecular tools. Methods based on DNA/RNA amplification are very sensitive and accurate. They allow the identification below the species level, e.g. chemotypes, mating types or isolates with genes or alleles of interest. Aerobiological monitoring is widely used in the epidemiology of human diseases (inhalant allergies) and infections of arable crops (decision support systems for the protection of cultivated plants). Aeromycology is interconnected with such diverse areas as industrial aerobiology, bioterrorism, ecology, climatology or even speleology and cultural heritage.

Effect of Field Setup on Accuracy of Traffic Data Collection Using Machine Vision Technology

2002 ◽  
Vol 1804 (1) ◽  
pp. 62-66 ◽  
Author(s):  
Sunita Nadella ◽  
Lloyd A. Herman
Keyword(s):  
Machine Vision ◽  
Focal Length ◽  
Reference Points ◽  
Specific Reference ◽  
Traffic Data ◽  
Video Traffic ◽  
Vertical Angle ◽  
Traffic Data Collection ◽  
Camera Position ◽  
Parameter Ranges

Video traffic data were collected in 24 combinations of four different camera position parameters. A machine vision processor was used to detect vehicle speeds and volumes from the videotapes. The machine vision results were then compared with the actual vehicle volumes and speeds to give the percentage errors in each case. The results of the study provide a procedure with which to establish camera position parameters with specific reference points to help machine vision users select suitable camera positions and develop appropriate measurement error expectations. The camera position parameters that were most likely to produce the least overall volume and speed errors, for the specific site and field setup with the parameter ranges used in this study, were the low height of approximately 7.6 m (25 ft), with an upstream orientation (traffic moving toward the camera), a 50-mm (midangle) focal length, and a 15° vertical angle.

The method of repairing OHE damaged ancient painted murals based on machine vision

2021 ◽  
pp. 1-15
Author(s):  
Hao Li
Keyword(s):  
Machine Vision ◽  
Two Dimensional ◽  
Traditional Methods ◽  
Dimensional Image ◽  
Two Dimensional Image ◽  
Different Types ◽  
Damage Data ◽  
Incomplete Damage

Traditional mural repair methods only observe the texture of murals when segmenting the repair area, but ignore the extraction of a mural damage data, resulting in incomplete damage crack information. For this reason, the method of repairing the damaged murals based on machine vision is studied. Using machine vision, it can get two-dimensional image of a mural, preprocess the image, extract the damaged data of a mural, and then divide the repair area and repair degree index. According to different types of damage, it can choose the corresponding repair methods to achieve the repair of damaged mural. The results show: Compared with the reference [1] method and reference [2] method, the number of repair points and repair cracks extracted by the proposed method is more than that of the two traditional methods, which can more accurately and comprehensively extract the repair information of murals.

scholarly journals Machine Vision Systems for Plant Growth Measurement and Modeling

2006 ◽  
Vol 44 (3) ◽  
pp. 181-187 ◽  
Author(s):  
Ta-Te LIN ◽  
Chung-Fang CHIEN ◽  
Wen-Chi LIAO ◽  
Kuo-Chi CHUNG ◽  
Jen-Min CHANG
Keyword(s):  
Plant Growth ◽  
Machine Vision ◽  
Vision Systems ◽  
Growth Measurement ◽  
Plant Growth Measurement

scholarly journals Epidermis structure in Mesembryanthemaceae

Bothalia ◽  
1983 ◽  
Vol 14 (3/4) ◽  
pp. 937-937
Author(s):  
H. -D. Ihlenfeldt
Keyword(s):  
Cell Walls ◽  
Climatic Conditions ◽  
Radiation Balance ◽  
Outer Cell ◽  
Extreme Conditions ◽  
Prominent Feature ◽  
Oxalate Crystals ◽  
Different Types ◽  
Bladder Cells ◽  
Epidermis Structure

The Mesembryanthemaceae, which inhabit the deserts and semi-deserts of Southern Africa, exhibit two very different types of epidermis. The first is characterized by thick outer cell walls encrusted by oxalate crystals, a thick cuticle and thick wax layers. In the second type, the epidermal cells exhibit only very thin outer cell walls, not encrusted by oxalate crystals, a very thin cuticle, and wax layers are poorly developed; the most prominent feature of this type is the occurrence of huge idioblasts often protruding as ‘bladder cells’. With regard to the climatic conditions in the native habitats this type of epidermis must be called ‘abnormal’. Both types of epidermis may form sculptures of three distinct size classes, which are not necessarily homologous. The function of these sculptures is still poorly understood. There is evidence that the sculptures influence the optical properties of the surfaces and thereby the radiation balance (and heat stress) of the leaves.From recent research, it has become evident that the two different types of epidermis are the anatomical expression of two different strategies for survival under extreme conditions. Species with the idioblast type of epidermis exhibit a strategy that might be called ‘opportunistic’. These species have developed a mechanism of gaining water from the atmosphere during the night under favourable conditions. There are indications that the idioblasts are involved in this mechanism.

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