The Place of Hinduism Faith : Omkareshwar
/ November 30, 2017

Sometimes, beliefs in our land are based on a seamless meld of epics, legends and reality. The tales that wreath Omkareshwar in haloes of worship have their origins in such lore. Omkareshwar Jyotirlinga a shiv temple on Omkar Mountain an island in mid Narmada, revered Hindu temple is the center of extreme faith. There are 12 jyotirlinga temples in world and Omkareshwar is one of them. Omkareshwar : Inroduction Omkareshwar is considered to be one of the holiest Hindu sites in the nation. This is due to the presence of the Jyotirlingam, one of the twelve in India. Lingam is the symbol of Lord Shiva but the Jyotirlingam is special. Jyotirlingam is called the lingam of light. It is said to derive currents of power from within itself while, an ordinary lingam is ritually invested with mantra shakti (power invested by chants) by the priests. Figure 1: Omkareshwar Jyotirlinga View The Omkareshwar temple is one of the 12 revered Jyotirlinga shrines of Shiva. It is located on a huge island called Mandhata or Shivapuri along the banks of the Narmada river; the river Narmada, is here called “Rewa”. The shape of the island is said to be like the Hindu ?…

What is ACO (Ant Colony Optimization) Algorithm
/ November 29, 2017

There are even increasing efforts in searching and developing algorithms that can find solutions to combinatorial optimization problems. In this way, the Ant Colony Optimization Meta-heuristic takes inspiration from biology and proposes different versions of still more efficient algorithms. Ant Colony Optimization (ACO): Overview Ant Colony Optimization (ACO) is a paradigm for designing metaheuristic algorithms for combinatorial optimization problems. The essential trait of ACO algorithms is the combination of a priori information about the structure of a promising solution with a posteriori information about the structure of previously obtained good solutions. ACO is a class of algorithms, whose first member, called Ant System, was initially proposed by Colorni, Dorigo and Maniezzo The main underlying idea, loosely inspired by the behavior of real ants, is that of a parallel search over several constructive computational threads based on local problem data and on a dynamic memory structure containing information on the quality of previously obtained result. The collective behavior emerging from the interaction of the different search threads has proved effective in solving combinatorial optimization (CO) problems. More specifically, we can say that “Ant Colony Optimization (ACO) is a population-based, general search technique for the solution of difficult combinatorial problems which is…

Random Forests in Data Mining
/ November 28, 2017

Data analysis and machine learning have become an integrative part of the modern scientific methodology, offering automated procedures for the prediction of a phenomenon based on past observations, unraveling underlying patterns in data and providing insights about the problem. Since always, artificial intelligence has been driven by the ambition to understand and uncover complex relations in data. That is, to find models that can not only produce accurate predictions, but also be used to extract knowledge in an intelligible way. This section introduces random forest description in details. Random Forest Definition A Random Forest consists of a collection or ensemble of simple tree predictors, each capable of producing a response when presented with a set of predictor values. For classification problems, this response takes the form of a class membership, which associates, or classifies, a set of independent predictor values with one of the categories present in the dependent variable. Alternatively, for regression problems, the tree response is an estimate of the dependent variable given the predictors. A Random Forest consists of an arbitrary number of simple trees, which are used to determine the final outcome.  For classification problems, the ensemble of simple trees vote for the most popular class. In…

Evaluation of Image Edges using Gabor Filter
/ November 27, 2017

In the field of image processing, filters play an extremely important role. All image processing operations can be viewed as applying a series of filters to an image and transforming it in some way. Gabor filter is a particular type of filter, and it happens to be an important one. Gabor filter responses are widely and successfully used as general purpose features in many computer vision tasks, such as in texture segmentation, face detection and recognition, and iris recognition. In a typical feature construction the Gabor filters are utilized via multi-resolution structure, consisting of filters tuned to several different frequencies and orientations. The multi-resolution structure relates the Gabor features to wavelets, but the main difference, non-orthogonality, also is connected to the main weakness of the Gabor features: computational heaviness. The computational complexity prevents their use in many real-time or near real-time tasks, such as in object tracking. Gabor Filter Significance Gabor filters are orientation-sensitive filters, used for edge and texture analysis. It is named after Dennis Gabor. Certain specific bands of frequency components can be extracted by adjusting the orientation and center frequencies of the Gabor filter. They have enjoyed much attention in the field of 2D face recognition and…

Introduction of Canny Edge Detection
/ November 26, 2017

The early stages of vision processing identify features in images that are relevant to estimating the structure and properties of objects in a scene. Edges are one such feature. Edges are significant local changes in the image and are important features for analyzing images. Edges typically occur on the boundary between two different regions in an image. Edge detection is frequently the first step in recovering information from images. Due to its importance, edge detection continues to be an active research area. Here we discussed about canny edge detection statement: Canny Edge Detection: Overview Edge detection refers to the process of identifying and locating sharp discontinuities in an image. The discontinuities are abrupt changes in pixel intensity which characterize boundaries of objects in a scene. Classical methods of edge detection involve convolving the image with an operator (a 2-D filter), which is constructed to be sensitive to large gradients in the image while returning values of zero in uniform regions. There are an extremely large number of edge detection operators available, each designed to be sensitive to certain types of edges. Variables involved in the selection of an edge detection operator include: Edge Orientation: The geometry of the operator determines a…

Local Binary Pattern For Image Texture Analysis
/ November 25, 2017

Nowadays, applications in the field of surveillance, banking and multimedia equipment are becoming more important, but since each application related to face analysis demands different requirements on the analysis process, almost all algorithms and approaches for face analysis are application dependent and a standardization or generalization is quite difficult. Local Binary Patterns (LBP) is a non-parametric descriptor whose aim is to efficiently summarize the local structures of images. Local Binary Patterns (LBP): General Overview Local Binary Patterns were first used in order to describe ordinary textures where the spatial relation was not as significant as it is for face images. A face can be seen as a composition of micro textures depending on the local situation. The LBP is basically divided into two different descriptors: a global and a local. The global is used for discriminating the most non-face objects (blocks), whereas the second provides specific and detailed face information which can be used not only to select faces, but also to provide face information for recognition. So the whole descriptor consists of a global texture and a local texture representation calculated by dividing the image into blocks and computing the texture histogram for each one. The results will be…

introduction of Regression Analysis
/ November 24, 2017

Extracting patterns and models of interest from large databases is attracting much attention in a variety of disciplines. Knowledge discovery in databases (KDD) and data mining are areas of common interest to researchers in machine learning, pattern recognition, statistics, artificial intelligence, and high performance computing. Regression General Overview Regression is a data mining function that predicts a number. Profit, sales, mortgage rates, house values, square footage, temperature, or distance could all be predicted using regression techniques. For example, a regression model could be used to predict the value of a data warehouse based on web-marketing, number of data entries, size, and other factors. A regression task begins with a data set in which the target values are known. For example, a regression model that predicts data warehouse values could be developed based on observed data for many data warehouses over a period of time. In addition to the value, the data might track the age of the data warehouse, size and number of clusters and so on. Data warehouse value would be the target, the other attributes would be the predictors, and the data for each data warehouse would constitute a case. In the Regression is a data mining function…

Introduction of Motion Analysis
/ November 23, 2017

The ultimate goal of computer vision is to understand the scene correctly through various steps of acquiring, processing, analyzing and understanding different kinds of information obtained by different kinds of sensors. Human motion analysis, recognition, and understanding are one of the very hottest topics within computer vision. Aspect of Motion Analysis As one of the most active research areas in computer vision, visual analysis of human motion attempts to detect, track and identify people, and more generally, to interpret human behaviors, from image sequences involving humans. Human motion analysis has attracted great interests from computer vision researchers due to its promising applications in many areas such as visual surveillance, perceptual user interface, content-based image storage and retrieval, video conferencing, athletic performance analysis, virtual reality, etc. Vision based human motion recognition is a systematic approach to understand and analyze the movement of people in camera captured content. It comprises of fields such as Biomechanics, Machine Vision, Image Processing, Artificial Intelligence and Pattern Recognition. It is an interdisciplinary challenging field having grand applications with social, commercial, and educational benefits. A wide spectrum of applications demands human motion recognition. Human motion analysis is a broad concept. Figure 1: Directions of human motion analysis…

Social Network Analysis
/ November 22, 2017

Network analysis is still a growing field with a great deal of opportunity for new and transformative contributions. The term social network refers to the articulation of a social relationship, official or achieved, among individuals, families, households, villages, communities, regions, and so on. Each of them can play dual roles, acting both as a unit or node of a social network as well as a social actor Social Network Analysis : Definition Social network theory views a network as a group of actors who are connected by a set of relationships. Social networks develop when actors meet and form some kind of relation between each other. These can be of an informal as well as of a formal nature. Hereby actors are often people, but can also be nations, organizations, objects etc. Social Network Analysis (SNA) focuses on patterns of relations between these actors. It seeks to describe networks of relations as fully as possible. This includes teasing out the prominent patterns in such networks, tracing the flow of information through them, and discovering what effects these relations and networks have on people and organizations. It can therefore be used to study network patterns of organizations, ideas, and people that connected…

Forensics and Computer Vision: Blood Spatter Analysis
/ November 21, 2017

Often found at the scenes of violent crimes, the analysis of bloodstains can provide vital clues as to the occurrence of events Bloodstain Pattern Analysis is a forensic discipline in which, among others, the position of victims can be determined at crime scenes on which blood has been shed. Therefore, we studied in details about blood spatter analysis and their pattern analysis in image processing system. Blood Spatter Analysis : Significance Some scenes of violent crime contain blood stains. Blood spatter stains occur when blood falls passively due to force being applied to a body. There is a well established though extremely tedious technique by which a specially trained forensic technician can analyze the individual blood spots. Blood spatter analysis is performed by forensics experts at crime scenes where impact on a body has caused blood to fly off and land on surrounding surfaces. The resulting stains are affected by many physical variables, such as speed, liquid density, and the material properties of the surface. However, the shape of the stains, in this case the spatter pattern, does reveal information that can be useful to investigators. Subsequent developments have led to the emergence of Blood Spatter Analysis as a forensic…

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