Eun-Jung Holden

A fast and fully automatic registration approach based on point features for multi-source remote-sensing images

Automatic registration of multi-source remote-sensing images is a difficult task as it must deal with the varying illuminations and resolutions of the images, different perspectives and the local deformations within the images. This paper proposes a fully automatic and fast non-rigid image registration technique that addresses those issues. The proposed technique performs a pre-registration process that coarsely aligns the input image to the reference image by automatically detecting their matching points by using the scale invariant feature transform (SIFT) method and an affine transformation model. Once the coarse registration is completed, it performs a fine-scale registration process based on a piecewise linear transformation technique using feature points that are detected by the Harris corner detector. The registration process firstly finds in succession, tie point pairs between the input and the reference image by detecting Harris corners and applying a cross-matching strategy based on a wavelet pyramid for a fast search speed. Tie point pairs with large errors are pruned by an error-checking step. The input image is then rectified by using triangulated irregular networks (TINs) to deal with irregular local deformations caused by the fluctuation of the terrain. For each triangular facet of the TIN, affine transformations are estimated and applied for rectification. Experiments with Quickbird, SPOT5, SPOT4, TM remote-sensing images of the Hangzhou area in China demonstrate the efficiency and the accuracy of the proposed technique for multi-source remote-sensing image registration.

Australian sign language recognition

This paper presents an automatic Australian sign language (Auslan) recognition system, which tracks multiple target objects (the face and hands) throughout an image sequence and extracts features for the recognition of sign phrases. Tracking is performed using correspondences of simple geometrical features between the target objects within the current and the previous frames. In signing, the face and a hand of a signer often overlap, thus the system needs to segment these for the purpose of feature extraction. Our system deals with the occlusion of the face and a hand by detecting the contour of the foreground moving object using a combination of motion cues and the snake algorithm. To represent signs, features that are invariant to scaling, 2D rotations and signing speed are used for recognition. The features represent the relative geometrical positioning and shapes of the target objects, as well as their directions of motion. These are used to recognise Auslan phrases using Hidden Markov Models. Experiments were conducted using 163 test sign phrases with varying grammatical formations. Using a known grammar, the system achieved over 97% recognition rate on a sentence level and 99% success rate at a word level.

Towards the automated analysis of regional aeromagnetic data to identify regions prospective for gold deposits

Quantitative analysis of geoscientific data to determine areas most likely to contain mineral deposits is becoming increasingly common in the mining industry. The approach is based on characterising areas known to contain deposits and seeking similar areas elsewhere. This paper presents an automatic image processing technique for the prospectivity analysis of Archaean lode-gold deposits, which differs from previous methods in that it is based solely on aeromagnetic data and does not require knowledge of the location of existing deposits. Instead, the aeromagnetic expressions of what are perceived to be geologically significant characteristics are sought within the aeromagnetic data. Gold mineralisation is known to occur near major crustal breaks manifesting as large-scale shear zones, which act as conduits for mineralising fluids. Mineralisation occurs in regions of structural complexity adjacent to the shear zones. Progressing towards the automatic detection of such regions, the proposed system finds firstly regions of magnetic discontinuity that correspond to both lithological boundaries and shear zones using a combination of texture analysis and symmetry feature detection techniques. Secondly, it examines the data using fractal analysis to find areas nearby with a complex magnetic expression (zones of structural complexity). The most prospective areas are those where inferred structural complexity occurs adjacent to the regions of magnetic discontinuity. A preliminary experiment was conducted using aeromagnetic data from the Yilgarn Craton in Western Australia and the regions selected by the proposed system contained over 76% of all known deposit locations and 82% of the greater than 1 tonne deposit locations.

Towards the automatic selection of optimal seam line locations when merging optical remote-sensing images

Image mosaicking is an important task in remote sensing due to the need for imagery with a greater spatial extent than provided by individual scenes. Merging of images requires the selection of a seam line within their area of overlap along which the scenes are merged. The seam line has a better chance of being invisible if it lies in regions where the images to be merged are very similar. The automatic detection of an optimal seam line is not a trivial task as it is difficult to find laterally continuous regions with high image similarity, and to identify image similarities when there are variations in the images, for example due to different illuminations or viewing directions, or shadow differences of tall structures. This article presents an automatic seam line location technique for remote-sensing images and achieves the following three objectives: to trace along the locations with minimal image difference so that the merged data set appears seamless; to avoid creating discontinuities within salient features within the images; and to ensure that the more accurate radiometric values that are associated with the least distance from the nadir point are better preserved in the mosaic image. Therefore, our method uses pixel-based image similarity measurement to choose the locations with high colour, edge and texture similarity; a region-based saliency map that is based on a human attention model to identify and avoid the areas with visibly dominant foreground objects; and location preference to encourage the seam line to lie as close as possible to an equal distance from the two nadir points of the images being merged. These measures are used as input to a cost function and the estimated costs are used to guide the tracing of the seam line in a dynamic programming algorithm. Our experiments demonstrate that the consideration of a combination of factors produces superior results to using just one or two of the variables as controls when merging high-resolution images containing complex structures.

Automatic Recognition of Colloquial Australian Sign Language

This paper presents an automatic Australian sign language (Auslan) recognition system, which tracks multiple target objects (the face and hands) throughout an image sequence and extracts features for the recognition of sign phrases. Tracking is performed using correspondences of simple geometrical features between the target objects within the current and the previous frames. In signing, the face and a hand of a signer often overlap, thus the system needs to segment these for the purpose of feature extraction. Our system deals with the occlusion of the face and a hand by detecting the contour of the foreground moving object using a combination of motion cues and the snake algorithm. To represent signs, features that are invariant to scaling, 2D rotations, and signing speed are used for recognition. The features represent the relative geometrical positioning and shapes of the target objects, as well as their directions of motion. These are used to recognise Auslan phrases using Hidden Markov Models. Experiments were conducted using 163 test sign phrases with varying grammatical formations. Using a known grammar, the system achieved over 97% recognition rate on a sentence level and 99% success rate at a word level.

Automatic detection of anisotropic features on rock surfaces

Surface roughness is an important rock property that is measured for structural geology and engineering purposes. We have developed an automatic technique to detect anisotropic features on rock faces based on fractal analysis. The analysis method has been applied to synthetic surfaces, and to digitally mapped point clouds of natural rock surfaces shaped by weathering, fault wear, and mining. We illustrate the technique using field examples from Permian sandstones containing brittle shear zones in northeast England, the surface of a neotectonic fault in Turkey, Proterozoic quartzite from central Australia, and Devonian Quartzite in an aggregate quarry in Germany. Roughness analysis of these natural examples suggests that a significant change of roughness value, anisotropy, and anisotropy direction can exist across scale. Our analysis method represents a step toward developing a toolkit to automatically detect and interpret surface characteristics from digitally acquired data sets. It has widespread potential for applications in rock engineering and the geosciences.

Dynamic Fingerspelling Recognition using Geometric and Motion Features

This paper presents the Australian sign language (Auslan) fingerspelling recognizer (APR): a system capable of recognizing signs consisting of Auslan manual alphabet letters from video sequences. The APR system uses a combination of geometric features and motion features based on optical flow which are extracted from video sequences. The sequence of features are then classified using hidden Markov models (HMMs). Tests using a vocabulary of twenty signed words showed the system could achieve 97% accuracy at the letter level and 88% at the word level by using a finite state grammar network and embedded training.

Interactive multi-image blending for visualization and interpretation

The need to integrate information from images of different modalities is an increasingly common problem for the geosciences. Interactive multi-image blending is presented as a tool for facilitating the interpretation of complex information from multiple data sources. Traditionally, image blending has only been considered for cross-dissolving effects between two images. The emphasis of this work is on image blending for the effective visualization of data, rather than for attractive visual effects. To achieve this we have developed blending techniques that allow for the simultaneous presentation of more than two images. We present a family of different image blending techniques that support the blending of multiple images under a range of different situations. For image blending to be a useful tool for data interpretation it is important that the input images remain distinct within the blend. We argue that interactivity of the blend is an important component for achieving this. Blending can also be usefully employed to interactively explore parameter variations for enhancement techniques. Often the best parameter values to use cannot be known beforehand, and it is common for different regions of an image to require different parameter values for best enhancement. HighlightsGeological interpretation requires integration of data from multiple sources.Interactive blending of two or more images facilitates interpretation.We present a series of image blending techniques designed for different applications.Interactivity is crucial for ensuring input images remain identifiable within a blend.Blending can be used to interactively explore parameter variations for enhancement.

Recognising moving hand shapes

The paper presents a new hand shape representation technique that characterises the finger-only topology of the hand, by adapting an existing technique from speech signal processing. From a moving hand sequence, the tracking algorithm determines the centre of the largest convex subset of the hand, using a combination of pattern matching and condensation algorithms. A hand shape feature represents the topological formation of the finger-only regions of the hand using a linear predictive coding parameter set called cepstral coefficients. Experimental results demonstrate the effectiveness of detecting the shape feature from motion sequences.

The Graphical Translation of English Text into Signed English in the Hand Sign Translator System

Signed English is a manual interpretation of English using fingerspelling and signs. A prototype of the Hand Sign Translator (HST) system was developed to graphically translate English into Signed English, using two‐handed animation. The HST consists of a practical interface that aims to help users learn Signed English, and the translation process where English text is transformed into a series of images that represent corresponding signs. This paper describes the translation process which involves two stages; the input environment and the animation process. The input environment consists of text analysis in order to extract corresponding kinematic data from the database, named English‐Sign Dictionary (ESD). The data is then used as an input to the animation process, Firstly, the skeleton models of keyframe images and their in‐between poses are calculated. Secondly, appropriate volume models are applied in order to surround the surface of skin. Then the shapes that are suitable for painting are generated, and finally images are drawn and rendered using a smooth animation technique.