Analysis : The Representation Of Haar-Like Features

For detecting various types of textures it uses local masks. To compute the energy of texture it uses convolution masks of 5×5 which is represented by a nine element vector for each pixel.

The Haar transform is based on the calculations of the averages (approximation co-efficient) and differences (detail co-efficient). Given two adjacent pixels a and b, the principle is to calculate the average and the difference . If a and b are similar, s will be similar to both and d will be small, i.e., require few bits to represent. This transform is reversible, since and and it can be written using matrix notation as

Feature selection (FS) methods have been used in the since 70s, using in the fields of statistics and pattern recognition. Pattern recognition system is one of the most important and indispensable tasks in overcome the curse of dimensionality problem, which forms a motivation for using a suitable feature selection method. According to their working principles, there are two types of methods are using in feature selection: methods which select the best subset of features that has a certain number of features And methods which select the best subset of features according to their own principles, independent of outside size measures [base].

This research also requires depth knowledge of image processing. Fetching out each position of the palm image through the reading it pixel by pixel. Some kinds of reading patterns of image should be crystal clear in the mind while working with the features of this topic.

Features are essential for any classification or analysis in image processing. There are many types of features which can be extracted from the images each gives its identical informations about the image. Here MA region supposed with properties such as shape, color and size which appears as a dark red colored circle shape. To identify MA and non- MA region feature vectors are formed for each candidate regions.

where B and C are normalizing factors to be determined. 2-D Gabor filters have rich applications in image processing, especially in feature extraction for texture analysis and segmentation.[8] f defines the frequency being looked for in the texture. By varying

The specific problem to be addressed is one modified segmentation method using MRF model, the neighborhood of pixels, this is a little improving in neighborhood model choosing which is really efficient. As there are

Various classification methods have been developed to extract information from imageries. The two main types are pixel and object-based methods. Pixel-based methods can be cluster based unsupervised or supervised classification whereas the later uses statistical (e.g., maximum likelihood algorism) and non-statistical algorithms (e.g., support vector machines) (Lu, Li, Kuang, & Moran, 2014). The object-based classification which overcomes some of the particular problems encountered with pixel-based classification (Blaschke, 2010) was used to analyze

Classification between the objects is easy task for humans but it has proved to be a complex problem for machines. The raise of high-capacity computers, the availability of high quality and low-priced video cameras, and the increasing need for automatic video analysis has generated an interest in object classification algorithms. A simple classification system consists of a camera fixed high above the interested zone, where images are captured and consequently processed. Classification includes image sensors, image preprocessing, object detection, object segmentation, feature extraction and object classification. Classification system consists of database that contains predefined patterns that compares with detected object to classify in to proper category. Image classification is an important and challenging task in various application domains, including biomedical imaging, biometry, video surveillance, vehicle navigation, industrial visual inspection, robot navigation, and remote sensing.

(x i)(y j) 3) Histogram Matching: As previously mentioned Image fusion is the approach of combining two or more images of same scene to obtain the more informative image. The histogram matching is important step in the preprocessing for the image fusion. The histogram of an image illustrates the frequency of

Abstract— This paper proposed two set of edge-texture features is such as Discriminative Robust Local Binary Pattern (DRLBP) and Discriminative Robust Local Ternary Pattern (DRLTP) for object recognition. The proposed DLBP and DRLTP are resultant from the disadvantage of the Local Binary Pattern (LBP), Local Ternary Pattern (LTP) and Robust LBP (RLBP).The LBP system and the RLBP system are mapped in the same block .The proposed feature indentifies the problem of discrimination between a bright object against dark background and vice-versa. The proposed feature also retains contrast information for object contours the LBP, LTP and RLBP discards. By the proposed features the objects is further analyzed for the accurate position of the object in the given image.

Feature plays a very important role in the area of image processing. Different feature extraction techniques are applied on different types of images to get features that will be useful in classifying and recognition of images. Features describes the important information of images that helps to classify images correctly and remarkably reduce the dimension of the images.

Pattern recognition is a technique to differentiate different pattern into classes through the help of supervised or unsupervised technique. We have developed highly sophisticated skills for sensing the environment and taking actions according to what we observe. This sensing and understanding is mostly dependent on ability to differentiate between patterns. The pattern recognition ability if with the help of machine learning can be applied in machine. The machine ability to make decision like human being will be enhanced. Many applications such as data mining, web searching, face recognition etc has already been in uses which are based on the pattern recognitions. The objective of this review paper is to summarize and compare some of the well-known methods and application used in pattern recognition system.

Assume that an image f(x,y) is sampled so that the resulting image has M rows and N columns so, the image size is M x N. The values of the coordinates are discrete quantities. The image origin is usually defined to be at (x, y) = (0, 0). The next coordinate values along the first row of the image are (x, y) = (0, 1). The notation (0, 1) is used to signify the second sample along the first row. It does not mean that these are the actual values of physical coordinates when the image was sampled. Fig. (5.3) shows this coordinate convention, where x ranges from 0 to (M-1) and y from 0 to (N-1) in integer increments [11].

Segmenting or dividing a digital image into region of interests or meaningful structures in general plays a momentous role in quite a few image processing tasks. Image analysis, image visualization, object representation are some of them. The prime objective of segmenting a digital image is to change its representation so that it looks more expressive for image analysis. During the course of action in image segmentation, each and every pixel of the image segmentation is assigned a label or value. The pixels that share the same value also share homogeneous traits. The examples can include color, texture, intensity or some other features. Image segmentation can be defined as the technique to divide the an image f (x, y) into a non empty subset f1, f2, ...., fn which is continuous and disconnected. This step contributes in feature extraction. There are quite a few applications where image segmentation plays a pivotal role. These applications vary from image filtering, face recognition, medical imaging