Live Image Colour Segmentation Using Different Methods of ANN

Machine learning is a new dimension of science since last 2 decade which motivates algorithms that can learn from data by building a model, based on inputs and using that to make predications or decisions, rather than following only explicitly programmed instructions. Machine learning is sometimes conflated with data mining, which focuses more on exploratory data analysis. Data mining is the extraction of interesting (non-trivial, implicit, previously unknow and potential useful) patterns of knowledge from huge amount of data In computer vision image segmentation is the process of partitioning a digital image into multiple segments (set of pixels, also known as super-pixels). The goals of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics. After that by considering region co-ordinates it separates all color in different figure

Cognitive and Simulation Modeling of Regional Economic System Development

Sustainable development of regional economy is the declared as a strategic objective of the state. From these positions, studying of the regional socio-economic phenomena and processes, development of the corresponding research and managerial tools are actual tasks. Authors consider the regional economy as a complex hierarchical system. This requires identification of its state, structure, particularities in its development and governance. These factors are investigated using the means of simulation modeling. Authors suggest to use the developed cognitive and simulation modeling methodology, which is based on cognitive approach, the theory of hierarchical multilevel structures, fixed and fuzzy directed graphs. The study illustrates possibilities of cognitive-simulation modeling and foresight of the socio-economic system development at the regional level. Keywords: simulation modeling, regional development, sustainable development JEL Classifications: C63, R5

Tourism Industry Perspectives in the Context of the COVID-19 Pandemic Based on the Sustainable Development Concept

The relevance of this study is in the growing popularity of the concept of sustainable development in the tourism sector. The purpose of the article is to determine a systematic basis for the assessment of the possibility of sustainable tourism development at the regional level, as well as determining relevant vectors. In addition, the study considers a number of studies that allow for defining sustainable tourism, as well as determining groups of indicators affecting it. The leading method of studying tourism structure as part of a regional socio-economic system is topological analysis, which allows for identifying functionally significant combinations of factors. Incidence matrices of the structure of indicators with included weighting factors influencing the sustainable development of tourism, analysis of their q-connectivity, the results of the dimension of simplexes, the number of connected components and communication chains, the structural vectors of the complexes were determined and presented. The study proved the presence of simplexes in the complexes. The effects on simplexes can bring the desired result in the quickest and most efficient way. Since tourism is an integral part of environmental, social and economic sectors, and the sustainable development itself can be regarded as a unified system of interaction between them, it is possible to use the above factors in each of the sectors on a case-by-case basis in any territory or enterprise to conserve resources, eradicate poverty and ensure well-being. This research attempts to formalize the factors that determine the sustainable development of tourist destination that gives the full basis for a systematic study of the territory to assess the sustainability of tourism development. The topological analysis shows the mutual influence of simplicial complexes by means of a chain of connections leading to sustainable development

Wheat Ears Counting in Field Conditions Based on Multi-Feature Optimization and TWSVM

The number of wheat ears in the field is very important data for predicting crop growth and estimating crop yield and as such is receiving ever-increasing research attention. To obtain such data, we propose a novel algorithm that uses computer vision to accurately recognize wheat ears in a digital image. First, red-green-blue images acquired by a manned ground vehicle are selected based on light intensity to ensure that this method is robust with respect to light intensity. Next, the selected images are cut to ensure that the target can be identified in the remaining parts. The simple linear iterative clustering method, which is based on superpixel theory, is then used to generate a patch from the selected images. After manually labeling each patch, they are divided into two categories: wheat ears and background. The color feature “Color Coherence Vectors,” the texture feature “Gray Level Co-Occurrence Matrix,” and a special image feature “Edge Histogram Descriptor” are then exacted from these patches to generate a high-dimensional matrix called the “feature matrix.” Because each feature plays a different role in the classification process, a feature-weighting fusion based on kernel principal component analysis is used to redistribute the feature weights. Finally, a twin-support-vector-machine segmentation (TWSVM-Seg) model is trained to understand the differences between the two types of patches through the features, and the TWSVM-Seg model finally achieves the correct classification of each pixel from the testing sample and outputs the results in the form of binary image. This process thus segments the image. Next, we use a statistical function in Matlab to get the exact a precise number of ears. To verify these statistical numerical results, we compare them with field measurements of the wheat plots. The result of applying the proposed algorithm to ground-shooting image data sets correlates strongly (with a precision of 0.79–0.82) with the data obtained by manual counting. An average running time of 0.1 s is required to successfully extract the correct number of ears from the background, which shows that the proposed algorithm is computationally efficient. These results indicate that the proposed method provides accurate phenotypic data on wheat seedlings

Scalable image segmentation via decoupled sub-graph compression

The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.patcog.2017.11.028 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/Dealing with large images is an on-going challenge in image segmentation, where many of the current methods run into computational and/or memory complexity issues. This work presents a novel decoupled sub-graph compression (DSC) approach for efficient and scalable image segmentation. In DSC, the image is modeled as a region graph, which is then decoupled into small sub-graphs. The sub-graphs undergo a compression process, which simplifies the graph, reducing the number of vertices and edges, while keeping the overall graph structure. Finally, the compressed sub-graphs are re-coupled and re-compressed to form a final compressed graph representing the final image segmentation. Experimental results based on a dataset of high resolution images (1000 × 1500) show that the DSC method achieves better segmentation performance when compared to state-of-the-art segmentation methods (PRI=0.84 and F=0.61), while having significantly lower computational and memory complexity