Extraction and Removal of Layers from Map Imagery Data

1994

Abstract In this paper, we derive the optimal structuring elements of morphological filters in image restoration. The expected pattern transformation of random sets is presented. An estimation theory framework for random sets is subsequently proposed. This framework is based on the least mean difference (LMD) estimator. The LMD estimator is defined to minimize the cardinality of the expected pattern transformation of the set-difference of the parameter and the estimate.

2)2: 685-698] © 2017 IJSRSET | Volume 3 | Issue 2 | Print ABSTRACT Today, Image Processing has become one of the popular and essential research domains in the field of computer science and information technology. Important tasks of image processing are image enhancement, image compression and information extraction. Image enhancement techniques are used to improve the quality of image where as information extraction techniques extract the statistical information about portion of an image or any particular image. Image compression is reducing the size of the digital image and allows more images to be stored in a computer disk. In this digital era, people can handle large amount of digital documents and with this documents we need to perform several tasks. Digital images may contain both machine printed documents and handwritten documents. Machine printed document images might have been produced using various technologies with good quality. A handwritten digital document has poor quality like blurred images, old handwritten manuscripts and handwritten variations, hence difficult to identify the texts. Hence the main objective of this paper is to enhance the handwritten image quality using various image enhancement techniques like Noise Removal, Contrast Stretching, Morphological Operations and Interpolations.

Morphological operations are simple to use and works on the basis of set theory. The objective of using morphological operations is to remove the imperfections in the structure of image. Most of the operations used here are combination of two processes, dilation and erosion. The operation uses a small matrix structure called as structuring element. The shape and size of the structuring element has significant impact on the final result. In this paper an attempt is made to understand the basic morphological operations by using them on some standard images. Matlab software is used as the tool for experimenting the morphological operations.

This paper presents a comprehensive approach to effectively extract cartographic urban data from high resolution satellite imagery. It consists of a sequence of image processing techniques, for image segmentation, based on RGB band separation, analysis and preprocessing, followed by a morphological-based approach for data segmentation. The chosen image objects for this study are roof-tile buildings and road network. The first step of this approach consists of a spectral response evaluation for roof-tile building objects in a dense urban environment, being those enhanced through proper sequence of standard arithmetic operators, applied to RGB bands, segmented and generalized. The second step aims at urban road network segmentation for cartographic representation purposes, by combining watershed and dual reconstruction morphological transformations, which characterize the hierarchical waterfall concept. For the latter concept, a new approach is developed in order to improve hierarchical segmentation procedure, to better induce object discrimination. Each one of the referred objects will be segmented in separate. The road network segmentation will have in consideration the previous result of roof-tile buildings extraction. Finally, segmented objects will be compared with other extraction results in order to do proper validation. The method is applied over high-resolution ortho-rectified images, taken from satellite, of the city of Lisbon. Results will show the practicality of the method for purposes of cartographic data structures acquisition, from highresolution satellite imagery, aiming urban management and GIS applications.

… Technology, 2008. ITSim …, 2008

Mathematical morphological operations are commonly used as a tool in image processing for extracting image components that are useful in the representation and description of region shape. In this paper, six basic morphological operations are investigated to remove noise and enhance the appearance of binary images. Dilation, erosion, opening, closing, fill and majority operations are tested on twenty-five images and subjectively evaluated based on perceived quality of the enhanced images. Results of the experiments showed that noise can be effectively removed from binary images using combinations of erode-dilate operations. Also, the binary images are significantly enhanced using combinations of majority-close operations.

Morphological filters erosion and dilation plays an important role for highlighting the buildings and roads areas in aerial images. These filters give better results for classification of particular building and road areas in Images which are our region of interest. Image processing is a dynamic field and in recent years it is applied in many areas like medical sciences, remote sensing, military defense system, physical sciences and aerospace for useful purposes. This research paper focuses the study of highly resoluted aerial image of populated area. By applying different image processing techniques on aerial images like red band extraction, edge detection, enhanced gray level, texture features, red band replacement, binary threshold and finding the objects and classes of the image. By considering a particular range of buildings and roads objects then reconstruct the subjected images. At the end these buildings and road areas are highlighted by applying morphological filter erosion and dilation. We have taken the aerial image of Lahore Canal bank Road by Google earth and implemented all the above discussed techniques on it. The implemented techniques highlighted the building and road areas with red color.

2002

In an experimental study on colour and grey scale images coveting a range of corruption from 50 to 95%, good restoration was achieved using a new morphological method. The nearest good neighbour (NGN) morphological filter copies grey scale values from the 'good' pixels in a regular manner so that these pixels become seeds for the restoration during successive iterations. A complementary propagation method is also described. In this context, sparse data refers to an image in which a large fraction of the data has been replaced by impulsive noise. The impulse noise may have a high value or range or a zero or n u l l v a l u e . R a n d o m l o s s o f a n i m a g e communication channel will result in a sparse image which may be restored by these methods. Range images or optic flow data may be processed by these methods also. On a face image which had lost 95% of its data, the restored image had a signal to noise ratio of 16dB and all features were clearly discernable. The filter had an 8 pixel neighbourhood and took about 0.5 second per iteration to filter the image on a 386 standard PC.

Document Recognition III, 1996

This paper discusses a method for binary morphological lter design to restore document images degraded by subtractive or additive noise, given a constraint on the size of lters. With a lter size restriction (for example 3 3), each pixel in output image depends only on its ( 3 3 ) neighborhood of input image. Therefore, we can construct a look-up table between input and output. Each output image pixel is determined by this table. So the lter design becomes the search for the optimal look-up table. By considering the degradation condition of the input image, we provide a methodology for knowledge based look-up table design, to achieve computational tractability. The methodology can be applied iteratively so that the nal output image is the input image after being transformed through successive 3 3 operations.

Landscape Ecology, 2007

We use morphological image processing for classifying spatial patterns at the pixel level on binary land-cover maps. Land-cover pattern is classified as 'perforated,' 'edge,' 'patch,' and 'core' with higher spatial precision and thematic accuracy compared to a previous approach based on image convolution, while retaining the capability to label these features at the pixel level for any scale of observation. The implementation of morphological image processing is explained and then demonstrated, with comparisons to results from image convolution, for a forest map of the Val Grande National Park in North Italy.

Journal of Analysis and Computation (JAC, 2018

morphology in image processing is a tool forextracting image components that are useful in the representation and description of region shape, such as boundaries and skeletons. The field of mathematical morphology contributes a wide range of operators to image processing, all based around a few simple mathematical concepts from set theory. The operators are particularly useful for the analysis of binary images and common usages include edge detection, noise removal, image enhancement and image segmentation.