Jibitesh Mishra

Analysing Fractal Dimension of Color Images

Fractal Dimension (FD) is an important feature of fractal geometry that finds the significant application in different fields including texture segmentation, shape classification and image analysis. Various methods were proposed to estimate the fractal dimension of gray scale images. In this paper we found out a uniform fractal dimension for both Color and Grey Scale images using differential box counting (DBC) method.

An improved algorithm to estimate the fractal dimension of gray scale images

Fractal dimension is a very useful technique to estimate surface roughness of digital images; for this estimation many approaches have been implemented. Among them differential box counting mechanism are most popular and commonly used technique for computation of FD of gray scale images. This article presents an improved version of differential box counting method for improvement of accuracy in terms of less fit error and simultaneously provides wider range of FD in terms of least regression line as well as FD at each corresponding box size. Improvement version can achieve by adopting proper box height selection and exact box number calculation. The experimental work done through two sets of images and shows that our proposed methods able to capture accurate roughness and consistently give more satisfactory results as compared to other traditional method like DBC, RDBC and IDBC.

Ground truth study on fractal dimension of color images of similar texture

Abstract Fractal dimension (FD) estimation has become most popular in the field of image analysis and applications; especially estimating the roughness and smoothness of complex objects. In this present investigation, we are considering three most popular methods in the controlled images acquisition experimental setup for eight colored fabrics of the same texture, material property, and illumination. The concept of previous work and the results obtained by this ground truth experiment were discussed to indicate whether FD might be a worth useful metric for the study of color images with self-similarity, such as textures! Interestingly, we found ambiguous FD results here and tried to explain such phenomenon in relation to the development of CIE-based human perception model as texture perception are well associated with the viewing parameters and observer that perceives the image in terms of spatial distribution of color intensity.

Classification of Linear Fractals through L-System

Linear fractals are often referred as self-similar or self-affine sets. L-systems can be used to generate fractals by applying a turtle interpretation to the symbols of the grammar. Various authors with an initiator and generator concept generated a series of geometric fractals earlier. The purpose of this paper is to generate linear fractals using L-systems from the axioms and rules of famous classical fractals and making a classification of those based on their fractal nature.

A New Extended Differential Box-Counting Method by Adopting Unequal Partitioning of Grid for Estimation of Fractal Dimension of Grayscale Images

Fractal dimension (FD) is most useful research topic in the field of fractal geometry to evaluate surface roughness of digital images by using the concept of self-similarity, and the FD value should lie between 2 and 3 for surfaces of digital images. In this regard, many researchers have contributed their efforts to estimate FD in the digital domain as reported in many kinds of the literature. The differential box-counting (DBC) method is a well-recognized and commonly used technique in this domain. However, based on the DBC approach, several modified versions of DBC have been presented like relative DBC (RDBC), improved box counting (IBC), improved DBC (IDBC). However, the accuracy of an algorithm for FD estimation is still a great challenge. This article presents an improved version of DBC algorithm by partitioning the box of grid into two asymmetric patterns for more precision box count and provides accurate estimation of FD with less fit error as well as less computational time as compared to existing method like DBC, relative DBC (RDBC), improved box counting (IBC), and improved DBC (IDBC).

Fractal Dimension of GrayScale Images

Fractal dimension (FD) is a necessary aspect for characterizing the surface roughness and self-similarity of complex objects. However, fractal dimension gradually established its importance in the area of image processing. A number of algorithms for estimating fractal dimension of digital images have been reported in many literatures. However, different techniques lead to different results. Among them, the differential box-counting (DBC) was most popular and well-liked technique in digital domain. In this paper, we have presented an efficient differential box-counting mechanism for accurate estimation of FD with less fitting error as compared to existing methods like original DBC, relative DBC (RDBC), and improved box-counting (IBC) and improved DBC (IDBC). The experimental work is carried out by one set of fourteen Brodatz images. From this experimental result, we found that the proposed method performs best among the existing methods in terms of less fitting error.

A Novel Approach of Study of Universal E-Contract Framework for Ecommerce

Thisarticledescribeshowalongwiththegrowthofinternettechnologies,e-commerceactivitiesare growingexponentially.Electronicscontractingisamajorpartofthee-commerceactivity.Contracts betweentheexchangingsideshavebeenadoptedasasolutionthatguaranteestherightofparticipants andincreasesmutualtrust.Electroniccontracts(e-contract)orTradingPartnerAgreements(TPA) canbebetweenthestakeholdersinvolved,includingthepaymentauthority,buyerandseller.Since 1998,OpenBuyingontheInternet(OBI)protocolsareusedforbusinesstobusiness(B2B)internet commerce.XMLbasedframeworkssuchasBizTalk,commerceXML,eXML,eCoframeworksare increasinglybeingusedtobridgethegapbetweenthebuyerandseller.Multiplee-commercestandards needtobeintegratedinordertohaveastandardframework.Inthisarticle,theauthorshavestudied differentframeworksandproposeauniversalgeneralizedframeworkfore–contractfore-commerce likeB2B,B2C&C2Cintoconsideration. KEywoRdS B2B, B2C, C2C, Conceptual Design, E-Commerce, E-Commerce Framework, E-Contract, Universal E-Contract

Contextual Requirements for Mobile Native Applications

Mobile apps have found wide acceptance in today’s world which heavily depend on smart technology to access data over wide location. The apps are mostly of native type which can be used for accessing data even without the internet availability. In this paper the development of mobile native applications requires the assimilation of various analytical contexts depending on the requirement of users. We have done an empirical study of various papers based on ubiquitous systems and mobile apps for finding out the contexts in building mobile native apps and the mobile contexts are such as device context, user context, mobility context and social context. We have found that the overall weight of each mobile context is an empirical study. We have taken various activities which are performed among a user and mobile native apps and formed them into questionnaires which are sent to different mobile native app developers of different software industries. The mapping is done among these activities with the attributes and their associated mobile contexts. We have identified and obtained four contexts as main requirements for developing mobile native apps under any domain. The analysis of requirements is done modeling the contexts and their attributes through OWLDL language. We have determined from the empirical study that the overall weight of device context is more than the other contexts. Hence it is clear that the device context with its numerous features have a great impact on developing mobile native apps under any domain.

A Novel Approach for Designing Mobile Native Apps

A R T I C L E I N F O A B S T R A C T Article history: Received: 07 December, 2016 Accepted: 14 January, 2017 Online: 28 January, 2017 Mobile devices are differed from desktop based systems in terms of particular execution environment, constrained resources, and high mobility requirement. To overcome these shortcomings, various agile based methodologies are developed for native mobile applications such as Mobile-D, Scrum etc. These agile techniques are based on various phases and these phases begin from exploring, initializing and implementing the mobile apps. But these techniques are not focusing on elaborating design model for mobile native apps. The aim of the paper is to provide a layered approach or layered model for design of mobile native apps which can be used as a framework for developing mobile native apps. Any mobile native app developer can use this sequential approach or design model for design and development of mobile native apps. This design model gives a standard or framework, based on which generic native mobile apps can be designed and developed. This paper also shows an empirical analysis among the web app design models with the proposed design model for mobile native app development.

Modelling Social Aspects of E-Agriculture in India for Semantic Web

There exist various web-based agriculture information systems. These systems provide the required information to farmers about different crops, soil, different farming techniques, etc. These web-based agriculture information systems deal with numerous kinds of data but they do not maintain consistency and the semantics of the data. Hence an OWL (Web Ontology Language) is used for designing required information in the web which provides meaningful annotations and vocabulary of the terms about a certain domain area to achieve the semantics for the web-based systems. Here in this paper we are building ontology of an agriculture system which is modeled in web ontology language (OWL) in protege 5.0 framework for semantic web apps. In this paper, the usages of the famer’s or user’s aspect of various components of the e-agriculture systems are analyzed, with respect to the social web components for easy access through the semantic web.