Analysis of textures requires the identification of proper attributes or features that differentiate the textures in the image for segmentation, classification, and recognition. The features are assumed to be uniform within the regions containing the same textures. From the cooccurrence matrices (C) obtained, significant features such as contrast, cluster shade, and cluster prominence are computed using the formulae given in (1) to (3). The architecture involves features value determination is given in the Fig.3. These feature values are subjected to either linear or logarithmic normalization, depending on their dynamic ranges. The contrast features have moderate values and hence they are subjected to linear normalization, while cluster shade and cluster prominence are subjected to logarithmic normalization, since they have very large dynamic range of values.Selection of seed block often can be based on the nature of the problem. When a priori information is not available, the procedure is to compute at every pixel or subregion. The same set of properties that ultimately will be used for the selection of seed and also for the growing process. The implementation of the sub-image block of size 16 x 16, with the maximum of combined normalized feature values of contrast, cluster shade, and cluster prominence (Shigh) is identified as seed block or seed window. The concept of cooccurrence features show that the feature values are high for a window that is surely a part of the target. Region growing is a region-based segmentation process in which subregions are grown into larger regions based on predefined criteria such as threshold and adjacency. In the current work, the region growing algorithm is based on mean distance method. In th...
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... 128 x 128 or 256 x 256 with 16 and 32 gray levels. These images are chosen with clear background. The target detection results obtained for the first four man-made and non-man made single-target images of size 128x 128 and 256 x 256 with 16 gray levels are shown in Fig 5 and Fig 6 respectively, where column (a) shows original images, while columns (b), (c), (d) and (e) show images with quantization, seed window, images after region growing process, and target detected images, respectively. From the figures, it is observed that for all the four images, the proposed algorithm results in a better detection process. The target detection results obtained for the two numbers of non man-made single-target images of size 128 x 128 and 256 x 256 with 32 gray levels are shown in Fig 7 and Fig 8 respectively. The results of the proposed algorithm are found to be satisfactory
...means and become familiar with K-means clustering and its usage. Then, we finish this part by different method of clustering. The K-nearest- neighbors is also discussed in this chapter. The KNN is simple for implication, programming, and one of the oldest techniques of data clustering as well. There are many applications existing for KNN and it is still growing. The PCA also discussed in this chapter as a method for dimension reduction, and then discrete wavelet transform is discussed. For the next chapter the combination of PCA and DWT, which can be useful in de-noising, come about. In this study, we have examined the neural network structure and modeling that is most of usage these days. The backpropagation is one of the common methods of training neural networks and for the last model, we discussed autoregressive model and the strategies to choose a model order.
The histogram of an input image is computed for selection of threshold value of a converted gray image. MATLABs ‘imhist(…)’ is the function that is used generate histogram. The appropriate threshold value has been selected, which is, then, applied to an image to threshold itself. Fig 8 and Fig 9 show an example of such images.
...a, M. Pietikainen and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns”, IEEE Trans. PAMI, vol.24, no.7, pp.971-987, Jul 2002.
A common characteristic of most of the images is that the neighboring pixels are highly correlated and therefore contain superfluous information. I...
The proposed multimodel segmentation was tested with almost all combination of mass shapes and margins in CC and MLO views and the segmented abnormal region was verified with ground truth table images in which abnormality marked by radiologist in the DDSM database. Further feature extraction methods and classifier has to be developed for fully automated diagnosis CAD system. Further study has to be carried out to test the algorithm for the segmentation of micro calcifications.
After the initial pre processing steps of smoothening and removal of noise, the edge strength is calculated by taking the gradient of the image. For the purpose of edge detection in an image, the Sobel operator first performs a 2-D spatial gradient measurement with the help of convolution masks. The convolution masks used is of the size 3X3, where one is used to calculate the horizontal gradient(Gx) while the other is used to calculate the vertical gradient(Gy). Then, the approximate absolute edge strength can be calculated at each point. The masks used for the convolution process is as shown
...ing Gradient and Curvature of Gray Scale Image', Patter Recognition vol. 35, no. 10, pp. 2051-9.
The assumption behind method is that smaller image regions are more likely to have approximately uniform illumination, thus being more suitable for thresholding. Firstly, we develop a method based on the local row average to binarise the current line using that threshold. We then extend this technique to a moving window of different sizes.
Image retrieval is the process of handling large volume of image database in order to achieve the efficiency in identifying similar images over the retrieved results. In Image retrieval, a choice of various techniques is used to represent images for searching, indexing and retrieval with either supervised or unsupervised learning models. The color feature extraction process consists of two parts: grid based representative of color selection [B.S.Manjunath, 2001] and discrete cosine transform with quantization. Color feature extraction is a very compact and resolution invariant representation of high speed image retrieval systems and it has been designed to efficiently represent the
Color moments are the measures that describe the color distribution of an image and the central moments are used to describe a probability distribution. Main purpose of color moment is to form the color indexing for image retrieval applications. The color moments are invariant for scaling and rotation. Here, the first three order moments are used as features for image retrieval. Three color moments are calculated for each channel. For example, 9 color moments are calculated for RGB and 12 color moments for CMYK. The color moments encode both shape and color information. It can be computed for each channel in any color model. Color model is a mathematical representation of color. Here, the color is represented
Image segmentation plays a vital role in Image Analysis and computer vision which is considered as the obstruction in the development of image processing technology, Image segmentation has been the subject of intensive research and a wide variety of segmentation techniques has been reported in the last two decades. Image segmentation is a classical and fundamental problem in computer vision. It refers to partitioning an image into several disjoint subsets such that each subset corresponds to a meaningful part of the image. As an integral step of many computer vision problems, the quality of segmentation output largely influences the performance of the whole vision system. In general terms, image segmentation divides an image into related sections or regions, consisting of image pixels having related data feature values. It is an essential issue since it is the first step for image understanding and any other, such as feature extraction and recognition, heavily depends on its results. Segmentation algorithms are based on two significant criteria: the homogeneity of a region and the discontinuity
Digital image processing is many area of research in the fields of computer aided manufacturing (CAM), biomedical instrumentation, electronics, robotics, consumer and entertainment electronics, control and instrumentation and communication engineering etc. important processing such as image restoration and image enhancement[2].
This paper describes the basic technological blee of Digital Image Processing . Image processing is basically classified in to three categories: The Rectification and Restoration, Enhancement and Information Extraction. The Rectification deals with incipient processing of raw image data to correct for geometric distortion, to calibrate the data radiometrically and to eliminate noise present in the data. The enhancement procedures are applied to image data in order to display the data for subsequent visual interpretation effectively. It involves various techniques for increasing the visual distinction between features in a scene. The objective of the information extraction operations is to replace visual analysis
By searching correct feature point and setting bidirectional threshold value,the matching process can be quickly and precisely implemented with optimistic result. The resemblance of two images is defined as the overall similarity between two families of image features[1]. Same proportion image matching algorithm using bi-directional threshold image matching technique is used. Small window of pixels in a reference image (template) is compared with equally sized windows of pixels in other (target) images. In FBM, instead of matching all pixels in an image, only selected points with certain features are to be matched. Area based matching provide low speed. feature based matching algorithm is faster in comparison to the area based matching technique. feature based matching time complexity depend on number of feature to be selected as well as right or wrong threshold. If the number of feature are high then sometimes it takes more computational time in comparison to area based feature. The number of features extracted from an image depends largely on the contents of an image. If there are high variations then features computed are high. This reduces time efficiency to
Hlavac and J. Fojt?k [11], in 1998, proposed a new method for lossless image compression of grey-level images. The image is treated as a set of stacked bit planes. The compressed version of the image is represented by residuals of a non-linear local predictor spanning the current bit plane as well as a few neighbouring ones. Predictor configurations are grouped in pairs differing in one bit of the representative point only. The frequency of predictor configurations is obtained from the input image. The predictor, as adapts automatically to the image and is able to estimate the influence of neighbouring cells, copes even with complicated structure or fine texture. The residuals between the original and the predicted image are those that correspond to the less frequent predictor configurations. Efficiently coded residuals constitute the output image. Good results were obtained for binary images, grey-level cartoons and man-made