What is Texture Analysis in Image Processing
Image Processing , Technology & Science / February 18, 2018

In many machine vision and image processing algorithms, simplifying assumptions are made about the uniformity of intensities in local image regions. However, images of real objects often do not exhibit regions of uniform intensities. Extraction of effective features of objects is an important area of research in the intelligent processing of image data. Texture analysis is one of the fundamental aspects of human vision by which we discriminate between surfaces and objects. In a similar manner, computer vision can take advantage of the cues provided by surface texture to distinguish and recognize objects. In computer vision, texture analysis may be used alone or in combination with other sensed features (e.g. color, shape, or motion) to perform the task of recognition. Overview of Texture Analysis Texture analysis refers to the characterization of regions in an image by their texture content. Texture analysis attempts to quantify intuitive qualities described by terms such as rough, smooth, silky, or bumpy as a function of the spatial variation in pixel intensities. In this sense, the roughness or bumpiness refers to variations in the intensity values, or gray levels. Texture analysis is used in various applications, including remote sensing, automated inspection, and medical image processing. Texture analysis…

What is Bit Plane Slicing in Image processing
Image Processing / February 17, 2018

Bit plane slicing is well known technique used in Image processing. In image compression Bit plane slicing is used. Bit plane slicing is the conversion of image into multilevel binary image. These binary images are then compressed using different algorithm. With this technique, the valid bits from gray scale images can be separated, and it will be useful for processing these data in very less time complexity. Digitally, an image is represented in terms of pixels. These pixels can be expressed further in terms of bits. Separating a digital image into its bit-planes is useful for analyzing the relative importance played by each bit of image, a process aids in determining the adequacy of the no. of bits used to quantize each pixel. This type of decomposition is useful for image compression. This term of bit-plane extraction for an 8 bit image, it is not difficult to show that the (binary) image for bit-plane 7 can be obtained by processing input image with a thresholding gray-level transformation function. Overview of Bit Plane Slicing Instead of highlighting gray level images, highlighting the contribution made to total image appearance by specific bits might be desired. Suppose that each pixel in an image is…

What is Image Compression in Image Processing
Image Processing , Technology & Science / February 10, 2018

A marked progress has been made in the field of image compression and its application in various branches of engineering. Image compression is associated with removing redundant information of image data. It is a solution which associated with storage and data transmission problem of huge amounts of data for digital image. Compressing an image is significantly different than compressing raw binary data. Of course, general purpose compression programs can be used to compress images, but the result is less than optimal. This is because images have certain statistical properties which can be exploited by encoders specifically designed for them. Also, some of the finer details in the image can be sacrificed for the sake of saving a little more bandwidth or storage space. Overview of Image Compression Image compression is an application of data compression that encodes the original image with few bits. The objective of image compression is to reduce the redundancy of the image and to store or transmit data in an efficient form. Image compression is a type of an application for data/image compression in which the basic image gets encoded with the limited bits. To lower the irrelevance and the redundancy of image data is the…

Introduction of Template Matching in Image Processing
Image Processing , Technology & Science / January 31, 2018

Template matching is one of the areas of profound interests in recent times. It has turned out to be a revolution in the field of computer vision. Template Matching is a high-level machine vision technique that identifies the parts on an image that match a predefined template. Advanced template matching algorithms allow to find occurrences of the template regardless of their orientation and local brightness. Template Matching techniques are flexible and relatively straightforward to use, which makes them one of the most popular methods of object localization. Their applicability is limited mostly by the available computational power, as identification of big and complex templates can be time-consuming. What is Template Matching? Template matching is a technique used in classifying an object by comparing portions of images with another image. One of the important techniques in Digital image processing is template matching. Templates are usually employed to print characters, identify numbers, and other little, simple objects. It can be used for detection of edges in figures, in manufacturing as a part of quality control and a means to navigate a mobile robot. Figure 1: Example of Template Matching Figure 1 depict the example of template matching. Template Matching is a strategy for…

Grid Color Movement In Image Analysis
Image Processing , Technology & Science / January 29, 2018

Content-based image Retrieval techniques can be divided into two main domains: pixel and compressed domain techniques. In the pixel domain, the values of individual pixels in the image matrix are used directly for making visual indexes. In the compressed domain, transformed data, which is the result of mapping the original image matrix into another domain, is employed for feature extraction and retrieval. One of the main tasks for Content-based image Retrieval (CBIR) systems is similarity comparison, extracting feature of every image based on its pixel values and defining rules for comparing images. These features become the image representation for measuring similarity with other images in the database. Images are compared by calculating the difference of its feature components to other image descriptors i.e namely colour, texture and shape features. In this article we are going to discuss the color descriptors. Grid Color Movement Colour feature is one of the most widely used features in low level feature. Associated with shape feature, Colour feature and texture feature shows better stability and is more insensitive to the rotation and zoom of the image. Colour not only adds beauty to objects but also more information that is used as a powerful tool in content-based image retrieval….

How to Use OpenCV with Java under NetBeans IDE
Image Processing , Java , Technology & Science / January 25, 2018

This post will assist to developers who want to work  with Java and OpenCV environment. Here, we demonstrate how to use OpenCV with netbeans IDE.  In this post, we explain how to configure the netbeans IDE to facilitate execution of OpenCV applications. OpenCV 2.4.8 used for Java application, OpenCV supports Java from OpenCV 2.4.4 version. OpenCV OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code. Currently OpenCV supports a wide variety of programming languages like C++, Python, Java etc and is available on different platforms including Windows, Linux, OS X, Android, iOS etc. Also, interfaces based on CUDA and OpenCL are also under active development for high-speed GPU operations. OpenCV-Python is the Python API of OpenCV. It combines the best qualities of OpenCV C++ API and Python language. OpenCV Basics: How to Start Work? Start with Netbeans IDE: Configuration Process The subsequent steps show how to configure the netbeans 6.9.1 description with…

Introduction of Reinforcement Learning

The idea that we learn by interacting with our environment is probably the first to occur to us when we think about the nature of learning. When an infant plays, waves its arms, or looks about, it has no explicit teacher, but it does have a direct sensorimotor connection to its environment. Reinforcement learning is a computational approach to understanding and automating goal-directed learning and decision-making. It is distinguished from other computational approaches by its emphasis on learning. it is done by an agent from direct interaction with its environment. without relying on exemplary supervision or complete models of the environment. Overview of Reinforcement Learning Reinforcement learning is learning what to do–how to map situations to actions–so as to maximize a numerical reward signal. The learner is not told which actions to take, as in most forms of machine learning, but instead must discover which actions yield the most reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward but also the next situation and, through that, all subsequent rewards. These two characteristics–trial-and-error search and delayed reward–are the two most important distinguishing features of reinforcement learning.  Reinforcement Learning is a type…

Iris Recognition System

The pressures on today’s system administrators to have secure systems are ever increasing. One area where security can be improved is in authentication. Iris recognition, a biometric, provides one of the most secure methods of authentication and identification thanks to the unique characteristics of the iris. The iris recognition is now becoming a common authentication method in handheld consumer electronics devices, such as cellphones and tablets. The iris being a biometric parameter is a way better than password protection because of its uniqueness for each individual. General Overview of Iris Recognition System In today’s information technology world, security for systems is becoming more and more important. The number of systems that have been compromised is ever increasing and authentication plays a major role as a first line of defence against intruders. The three main types of authentication are something you know (such as a password), something you have (such as a card or token), and something you are (biometric). Passwords are notorious for being weak and easily crackable due to human nature and our tendency to make passwords easy to remember or writing them down somewhere easily accessible. Cards and tokens can be presented by anyone and although the token…

Signature Recognition and Applications

Signature is a special case of handwriting which includes special characters and flourishes. Many signatures can be unreadable. They are a kind of artistic handwriting objects. However, a signature can be handled as an image, and hence, it can be recognized using computer vision and artificial neural network techniques. Handwritten signatures are widely utilized as a form of personal recognition. However, they have the unfortunate shortcoming of being easily abused by those who would fake the identification or intent of an individual which might be very harmful. Therefore, the need for an automatic signature recognition system is crucial. Signature Recognition Overview The basic goal of the handwritten signatures is to provide an accurate method in order to verify a person’s identity based on the way in which he/she signs his/her name. Hence for this reason, the handwritten signatures are widely accepted, socially and legally throughout the world. There are basically two types of systems – online and offline. The hand-written signature verification uses the features conveyed by every signatory such that the features considered have a unique understanding and the way of signing presents the behavioral biostatistics. Some researchers considered common issues with the extraction of identification data from different…

Importance of Dimensionality Reduction in Data mining

The recent explosion of data set size, in number of records as well as of attributes, has triggered the development of a number of big data platforms as well as parallel data analytics algorithms. At the same time though, it has pushed for the usage of data dimensionality reduction procedures. Dealing with a lot of dimensions can be painful for machine learning algorithms. High dimensionality will increase the computational complexity, increase the risk of over fitting (as your algorithm has more degrees of freedom) and the sparsity of the data will grow. Hence, dimensionality reduction will project the data in a space with fewer dimensions to limit these phenomena. What is Dimensionality Reduction? The problem of unwanted increase in dimension is closely related to fixation of measuring / recording data at a far granular level then it was done in past. This is no way suggesting that this is a recent problem. It has started gaining more importance lately due to surge in data. In machine learning classification problems, there are often too many factors on the basis of which the final classification is done. These factors are basically variables called features. The higher the number of features, the harder…

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