This paper presents a change detection system using Planet's mosaic dataset. This dataset has higher resolution but fewer bands than data captured from Landsat or Sentinel satellites. Here, an object-based random forest regressor is used to detect vegetation change. The mosaic was separated into individual images, which were then ranked in a list. Results indicate adequate performance with a tradeoff in precision and recall.

Engineers must have deep and accurate conceptual understanding of their field and Concept inventories (CIs) are one method of assessing conceptual understanding and providing formative feedback. Current CI tests use Multiple Choice Questions (MCQ) to identify misconceptions and have undergone reliability and validity testing to assess conceptual understanding. However, they do not readily provide the diagnostic information about students' reasoning and therefore do not effectively point to specific actions that can be taken to improve student learning. We piloted the textual component of our diagnostic CI on electrical engineering students using items from the signals and systems CI. We then analysed the textual responses using automated lexical analysis software to test the effectiveness of these types of software and interviewed the students regarding their experience using the textual component. Results from the automated text analysis revealed that students held both incorrect and correct ideas for certain conceptual areas and provided indications of student misconceptions. User feedback also revealed that the inclusion of the textual component is helpful to students in assessing and reflecting on their own understanding.

In this paper, we present three counterfeiting attacks on the block-wise dependent fragile watermarking schemes. We consider vulnerabilities such as the exploitation of a weak correlation among block-wise dependent watermarks to modify valid watermarked images, where they could still be verified as authentic, though they are actually not. Experimental results successfully demonstrate the practicability and consequences of the proposed attacks for some relevant schemes. The development of the proposed attack models can be used as a means to systematically examine the security levels of similar watermarking schemes.

Aerial photography obtained by unmanned aerial vehicles (UAVs) is a rising market for their civil application. Small UAVs are believed to close gaps in niche markets, such as acquiring airborne image data for remote sensing purposes. Small UAVs can fly at low altitudes, in dangerous environments, and over long periods of time. However, their small lightweight construction leads to new problems, such as higher agility and more susceptibility to turbulence, which has a big impact on the quality of the data and their suitability for aerial photography. This paper investigates the use of fish-eye lenses to overcome field-of-view (FOV) issues for highly agile UAV platforms susceptible to turbulence. The fish-eye lens has the benefit of a large observation area (large FOV) and does not add additional weight to the aircraft, such as traditional mechanical stabilizing systems. We present the implementation of a fish-eye lens for aerial photography and mapping purposes, with potential use in remote sensing applications. We describe a detailed investigation from the fish-eye lens distortion to the registering of the images. Results of the process are presented using low-quality sensors typically found on small UAVs. The system was flown on a midsize platform (a more stable Cessna aircraft) and also on ARCAA's small (<10 kg) UAV platform. The effectiveness of the approach is compared for the two sized platforms.

We introduce multiple-control fuzzy vaults allowing generalized threshold, compartmented and multilevel access structure. The presented schemes enable many useful applications employing multiple users and/or multiple locking sets. Introducing the original single control fuzzy vault of Juels and Sudan we identify several similarities and differences between their vault and secret sharing schemes which influence how best to obtain working generalizations. We design multiple-control fuzzy vaults suggesting applications using biometric credentials as locking and unlocking values. Furthermore we assess the security of our obtained generalizations for insider/ outsider attacks and examine the access-complexity for legitimate vault owners.

This paper considers an application of scale-invariant feature detection using scale-space analysis suitable for use with wide field of view cameras. Rather than obtain scale- space images via convolution with the Gaussian function on the image plane, we map the image to the sphere and obtain scale-space images as the solution to the heat (diffusion) equation on the sphere which is implemented in the frequency domain using spherical harmonics. The percentage correlation of scale-invariant features that may be matched between any two wide-angle images subject to change in camera pose is then compared using each of these methods. We also present a means by which the required sampling bandwidth may be determined and propose a suitable anti-aliasing filter which may be used when this bandwidth exceeds the maximum permissible due to computational requirements. The results show improved performance using scale-space images obtained as the solution of the diffusion equation on the sphere, with additional improvements observed using the anti-aliasing filter.

Numerous scale-invariant feature matching algorithms using scale-space analysis have been proposed for use with perspective cameras, where scale-space is defined as convolution with a Gaussian. The contribution of this work is a method suitable for use with wide angle cameras. Given an input image, we map it to the unit sphere and obtain scale-space images by convolution with the solution of the spherical diffusion equation on the sphere which we implement in the spherical Fourier domain. Using such an approach, the scale-space response of a point in space is independent of its position on the image plane for a camera subject to pure rotation. Scale-invariant features are then found as local extrema in scale-space. Given this set of scale-invariant features, we then generate feature descriptors by considering a circular support region defined on the sphere whose size is selected relative to the feature scale. We compare our method to a naive implementation of SIFT where the image is treated as perspective, where our results show an improvement in matching performance.

The problem of determining the script and language of a document image has a number of important applications in the field of document analysis, such as indexing and sorting of large collections of such images, or as a precursor to optical character recognition (OCR). In this paper, we investigate the use of texture as a tool for determining the script of a document image, based on the observation that text has a distinct visual texture. An experimental evaluation of a number of commonly used texture features is conducted on a newly created script database, providing a qualitative measure of which features are most appropriate for this task. Strategies for improving classification results in situations with limited training data and multiple font types are also proposed.

The coefficients of the wavelet transform have been widely used for texture analysis tasks, including segmentation, classification and synthesis. Second order statistics of such values have been shown to give excellent performance in these applications, and are typically calculated using co-occurrence matrices, which require quantisation of the coefficients. In this paper, we propose a non-linear quantisation function which is experimentally shown to better characterise textured images, and use this to formulate a new set of texture features, the wavelet log co-occurrence signatures.

It has been documented in the literature that texture can be well characterised by features obtained from its multi-scale representation. Typically, the textured image being analysed is decomposed into separate frequency and/or orientation bands, and features extracted separately from each such band. In this paper, we propose that features modelling the relationships between scale bands of such a representation provide a better characterisation of textured images than features extracted from individual bands alone. Using this conjecture, we develop a novel feature set for texture classification, and demonstrate its effectiveness using a set of images obtained from the Brodatz texture album.

A new theory that qualitatively and quantitatively describes shape is presented. Signals are treated as ordered sets and shape as a property of the order. Measures of shape are derived starting from simple two element sets. It is shown that the degree of asymmetry of relative contrast at various scales is a sufficient descriptor of shape. The shape of a signal is a composite of the shape of all possible ordered subsets. A distinction is drawn between statistical and shape measures within a unified theoretical framework which may make it possible compare diverse pattern recognition approaches in terms robustness and discrimination power.

A technique is developed to construct a representation of planar objects undergoing a general affine transformation. The representation can be used to describe planar or nearly planar objects in a three-dimensional space, observed by a camera under arbitrary orientations. The technique is based upon object contours, parameterized by an affine invariant parameter and the dyadic wavelet transform. The role of the wavelet transform is the extraction of multiresolution affine invariant features from the affine invariant contour representation. A dissimilarity function is also developed and used to distinguish among different object representations. This function makes use of the extrema on the representations, thus making its computation very efficient. A study of the effect of using different wavelet functions and their order or vanishing moments is also carried out. Experimental results show that the performance of the proposed representation is better than that of other existing methods, particularly when objects are heavily corrupted with noise.

A new algorithm to recognize a two-dimensional object of arbitrary shape is presented. The object boundary is first represented by a one-dimensional signal. This signal is then used to build the wavelet transform zero-crossing representation of the object. The algorithm is invariant to translation, rotation and scaling. Experimental results show that, compared with the use of Fourier descriptors, our algorithm gives more stable and accurate results.

A wavelet based representation of planar objects is introduced. Based on this representation, a matching algorithm using indexing techniques is also developed for identifying unknown objects. Rather than considering all points on the representation, only extrema are used in constructing the look-up table and for matching. Simulations demonstrate that the proposed algorithm is effective and accurate in classifying objects under similarity transformations and in a noisy environment.

A technique for representing and recognising 3-D or space curves is presented. In the proposed algorithm, the space curves are represented by a set of two zero-crossing representations which are constructed based on the dyadic wavelet transform. These representations are then described in the form of an ordered set of complex numbers which is referred to as the compact representation of the space curves. A string-matching technique is adapted for comparing two curves using their compact representations. Experimental results show that the proposed technique can be used for recognising space curves under similarity transformation with and without additive noise.

We present the theory of multiscale dilation-erosion scale-space and the process of feature extraction via morphological scale-space fingerprints. We then discuss the reduced form of the fingerprints and state the scale-space causality theorem. These fingerprints are then applied to the recognition of multiple objects from range data. The proposed recognition system is invariant to translation, rotation, scale, and partial occlusion. We demonstrate results for the recognition of human faces in a scene, and the recognition of mountain features in a digital elevation map.>

An algorithm for recognising space curves is introduced. The algorithm includes two stages : construction of space curve representation and a matching procedure. The representation is based on a dyadic wavelet transform. A dissimilarity function is defined and used in the matching procedure. Experimental results show that for 3D objects that can be represented by space curves, this algorithm is robust and efficient in extracting and matching object information.>

An algorithm based on the dyadic wavelet transform and complex Daubechies wavelets to recognise a planar object undergoing a general affine transformation is presented. The affine transformation most appropriately represents shape distortions arising from observing an object by a camera under arbitrary orientations. The proposed algorithm includes two steps: constructing the representation and the matching process. Experimental results show that the representation is robust and combined with the matching algorithm, it efficiently classifies unknown objects.>