A new design tool for feature extraction in noisy images based on grayscale hit-or-miss transforms

The Hit-or-Miss transform (HMT) is a well known morphological transform capable of identifying features in digital images. When image features contain noise, texture or some other distortion, the HMT may fail. Various researchers have extended the HMT in different ways to make it more robust to noise. The most successful, and most recent extensions of the HMT for noise robustness, use rank order operators in place of standard morphological erosions and dilations. A major issue with the proposed methods is that no technique is provided for calculating the parameters that are introduced to generalize the HMT, and, in most cases, these parameters are determined empirically. We present here, a new conceptual interpretation of the HMT which uses a percentage occupancy (PO) function to implement the erosion and dilation operators in a single pass of the image. Further, we present a novel design tool, derived from this PO function that can be used to determine the only parameter for our routine and for other generalizations of the HMT proposed in the literature. We demonstrate the power of our technique using a set of very noisy images and draw a comparison between our method and the most recent extensions of the HMT

Outlier and target detection in aerial hyperspectral imagery : a comparison of traditional and percentage occupancy hit or miss transform techniques

The use of aerial hyperspectral imagery for the purpose of remote sensing is a rapidly growing research area. Currently, targets are generally detected by looking for distinct spectral features of the objects under surveillance. For example, a camouflaged vehicle, deliberately designed to blend into background trees and grass in the visible spectrum, can be revealed using spectral features in the near-infrared spectrum. This work aims to develop improved target detection methods, using a two-stage approach, firstly by development of a physics-based atmospheric correction algorithm to convert radiance into reflectance hyperspectral image data and secondly by use of improved outlier detection techniques. In this paper the use of the Percentage Occupancy Hit or Miss Transform is explored to provide an automated method for target detection in aerial hyperspectral imagery

A fast method for computing the output of rank order filters within arbitrarily shaped windows

Rank order filters are used in a multitude of image processing tasks. Their application can range from simple preprocessing tasks which aim to reduce/remove noise, to more complex problems where such filters can be used to detect and segment image features. There is, therefore, a need to develop fast algorithms to compute the output of this class of filter. A number of methods for efficiently computing the output of specific rank order filters have been proposed [1]. For example, numerous fast algorithms exist that can be used for calculating the output of the median filter. Fast algorithms for calculating morphological erosions and dilations - which are also a special case of the more general rank order filter - have also been proposed. In this paper we present an extension of a recently introduced method for computing fast morphological operators to the more general case of rank order filters. Using our method, we are able to efficiently compute any rank, using any arbitrarily shaped window, such that it is possible to quickly compute the output of any rank order filter. We demonstrate the usefulness and efficiency of our technique by implementing a fast method for computing a recent generalisation of the morphological Hit-or-Miss Transform which makes it more robust in the presence of noise. We also compare the speed and efficiency of this routine with similar techniques that have been proposed in the literature

Long-term experiments with an adaptive spherical view representation for navigation in changing environments

Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metric-topological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability

Object detection and classification in aerial hyperspectral imagery using a multivariate hit-or-miss transform

High resolution aerial and satellite borne hyperspectral imagery provides a wealth of information about an imaged scene allowing for many earth observation applications to be investigated. Such applications include geological exploration, soil characterisation, land usage, change monitoring as well as military applications such as anomaly and target detection. While this sheer volume of data provides an invaluable resource, with it comes the curse of dimensionality and the necessity for smart processing techniques as analysing this large quantity of data can be a lengthy and problematic task. In order to aid this analysis dimensionality reduction techniques can be employed to simplify the task by reducing the volume of data and describing it (or most of it) in an alternate way. This work aims to apply this notion of dimensionality reduction based hyperspectral analysis to target detection using a multivariate Percentage Occupancy Hit or Miss Transform that detects objects based on their size shape and spectral properties. We also investigate the effects of noise and distortion and how incorporating these factors in the design of necessary structuring elements allows for a more accurate representation of the desired targets and therefore a more accurate detection. We also compare our method with various other common Target Detection and Anomaly Detection techniques

Atmospheric correction and target detection in aerial hyperspectral imagery

The use of aerial hyperspectral imagery (HSI) in remote sensing is a rapidly growing research area. Currently, targets are generally detected by looking for distinct spectral features of objects under surveillance. For example, a camouflaged vehicle, designed to blend into background trees and grass in the visible spectrum, can be revealed using spectral features in the near-infrared region of the electromagnetic (EM) spectrum

A colour hit-or-miss transform based on a rank ordered distance measure

The Hit-or-Miss Transform (HMT) is a powerful morphological operation that can be utilised in many digital image analysis problems. Its original binary definition and its extension to grey-level images have seen it applied to various template matching and object detection tasks. However, further extending the transform to incorporate colour or multivariate images is problematic since there is no general or intuitive way of ordering data which allows the formal definition of morphological operations in the traditional manner. In this paper, instead of following the usual strategy for Mathematical Morphology, based on the definition of a total order in the colour space, we propose a transform that relies on a colour or multivariate distance measure. As with the traditional HMT operator, our proposed transform uses two structuring elements (SE) - one for the foreground and one for the background - and retains the idea that a good fitting is obtained when the foreground SE is a close match to the image and the background SE matches the image complement. This allows for both flat and non-flat structuring elements to be used in object detection. Furthermore, the use of ranking operations on the computed distances allows the operator to be robust to noise and partial occlusion of objects

Spartan Daily, September 21, 2005

Volume 125, Issue 15https://scholarworks.sjsu.edu/spartandaily/10158/thumbnail.jp