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High quality region-of-interest coding for video conferencing based remote general practitioner training

The impact of global and local features on multiple sequence alignment clustering-based near-duplicate video retrieval

  • Authors: Wang, Yandan , Lu, Guojun , Belkhatir, Mohammed , Messom, Christopher
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: 14th Pacific-Rim Conference on Multimedia p. 669-677
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  • Description: Traditionally, the performance of Near-Duplicate Video Retrieval (NDVR) is enhanced through different video features, matching scheme and indexing methods. The video features have been intensively investigated and it has been shown that local features outperform global features in terms of accuracy. However, local features have the expensive computational problem. Therefore, indexing structure is introduced to assist in scaling up, whilst the accuracy will drop slightly or dramatically in most time by using indexing approaches. Recent progress shows that NDVR based on clustering could reduce searching space while maintains equivalent retrieval accuracy compared to that of non-clustering based. In this paper, we will continue to evaluate clustering based NDVR, but using popular global and local features. Before conducting NDVR, dataset will be pre-processed offline into groups by using clustering algorithm that near-duplicate videos (NDVs) are assembled in the same cluster. Each cluster will be represented by member video or the centroid. The query video will then be compared to the representative videos instead of all videos in database (non-clustering based). Our experiment shows that clustering-based NDVR using global and local features outperforms than that of non-clustering based in terms of both retrieval accuracy and speed.

An improved curvature scale-space corner detector and a robust corner matching approach for transformed image identification

  • Authors: Awrangjeb, Mohammad , Lu, Guojun
  • Date: 2008
  • Type: Text , Journal article
  • Relation: Image Processing, IEEE Transactions Vol. 17, no. 12 (2008), p. 2425-2441
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  • Description: There are many applications, such as image copyright protection, where transformed images of a given test image need to be identified. The solution to this identification problem consists of two main stages. In stage one, certain representative features, such as corners, are detected in all images. In stage two, the representative features of the test image and the stored images are compared to identify the transformed images for the test image. Curvature scale-space (CSS) corner detectors look for curvature maxima or inflection points on planar curves. However, the arc-length used to parameterize the planar curves by the existing CSS detectors is not invariant to geometric transformations such as scaling. As a solution to stage one, this paper presents an improved CSS corner detector using the affine-length parameterization which is relatively invariant to affine transformations. We then present an improved corner matching technique as a solution to the stage two. Finally, we apply the proposed corner detection and matching techniques to identify the transformed images for a given image and report the promising results.

On feature combination for music classification

Integration of LIDAR data and orthoimage for automatic 3D building roof plane extraction

  • Authors: Awrangjeb, Mohammad , Fraser, Clive , Lu, Guojun
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: 2013 IEEE International Conference on Multimedia and Expo (ICME)
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  • Description: Automatic 3D extraction of building roofs from remotely sensed data is important for many applications including city modeling. This paper proposes a new method for automatic 3D roof extraction through an effective integration of LIDAR (Light Detection And Ranging) data and multispectral orthoimagery. Using the ground height from a DEM (Digital Elevation Model), the raw LIDAR points are separated into two groups. The first group contains the ground points that are exploited to constitute a `ground mask'. The second group contains the non-ground points which are segmented using an innovative image line guided segmentation technique to extract the roof planes. The image lines extracted from the grey-scale version of the orthoimage are classified into several classes such as `ground', `tree', `roof edge' and `roof ridge' using the ground mask and colour and texture information from the orthoimagery. During roof plane extraction the lines from the later two classes are used to fit roof planes to the neighbouring non-ground LIDAR points. Finally, a new rule-based procedure is applied to remove planes constructed on trees. Experimental results show that the proposed method successfully removes vegetation and offers high extraction rates.

Novel spectral descriptor for object shape

  • Authors: Sajjanhar, Atul , Lu, Guojun , Zhang, Dengsheng
  • Date: 2010
  • Type: Text , Book chapter
  • Relation: Proceedings of the 11th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing p. 58-67
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  • Description: In this paper, we propose a novel descriptor for shapes. The proposed descriptor is obtained from 3D spherical harmonics. The inadequacy of 2D spherical harmonics is addressed and the method to obtain 3D spherical harmonics is described. 3D spherical harmonics requires construction of a 3D model which implicitly represents rich features of objects. Spherical harmonics are used to obtain descriptors from the 3D models. The performance of the proposed method is compared against the CSS approach which is the MPEG-7 descriptor for shape contour. MPEG-7 dataset of shape contours, namely, CE-1 is used to perform the experiments. It is shown that the proposed method is effective

Multimodal image registration technique based on improved local feature descriptors

  • Authors: Teng, Shyh , Hossain, Tanvir , Lu, Guojun
  • Date: 2015
  • Type: Text , Journal article
  • Relation: Journal of Electronic Imaging Vol. 24, no. 1 (2015), p.
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  • Description: Multimodal image registration has received significant research attention over the past decade, and the majority of the techniques are global in nature. Although local techniques are widely used for general image registration, there are only limited studies on them for multimodal image registration. Scale invariant feature transform (SIFT) is a well-known general image registration technique. However, SIFT descriptors are not invariant to multimodality. We propose a SIFT-based technique that is modality invariant and still retains the strengths of local techniques. Moreover, our proposed histogram weighting strategies also improve the accuracy of descriptor matching, which is an important image registration step. As a result, our proposed strategies can not only improve the multimodal registration accuracy but also have the potential to improve the performance of all SIFT-based applications, e.g., general image registration and object recognition.

Automatic Extraction of Buildings in an Urban Region

  • Authors: Siddiqui, Fasahat , Teng, Shyh , Lu, Guojun , Awrangjeb, Mohammad
  • Date: 2014
  • Type: Text , Conference proceedings
  • Relation: 29th International Conference on Image and Vision Computing New Zealand, IVCNZ 2014; Hamilton; New Zealand; 19th-21st November 2014; published in ACM International Conference Proceeding Series p. 178-183
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  • Description: There are currently several automatic building extraction methods introduced in the literature, but none of them are capable to completely extract portions of a building that are below a pre-defined building minimum height threshold. This paper proposes a systematic method which analyzes the height differences between the extracted adjacent planes above and below the height threshold as well as the planes' connectivity, thereby, extracting all portions belonging to buildings more completely. In general, the height difference between the edges of the adjacent planes above and below the height threshold that belong to the same building is more uniform. In addition, the extracted planes below the height threshold that belong to a building and their adjacent ground planes also have a clear height difference. The proposed method incorporates such information to achieve better performance in building extraction. We have compared our proposed method to a current state-of-the-art building extraction method qualitatively and quantitatively. Our experimental results show that our proposed method successfully recovers portions of a building below the height threshold, thereby achieving relatively higher average completeness (an improvement of 1.14%) and quality (an improvement of 0.93%).

Automatic building extraction from LIDAR data covering complex urban scenes

  • Authors: Awrangjeb, Mohammad , Lu, Guojun , Fraser, Clive
  • Date: 2014
  • Type: Text , Conference proceedings
  • Relation: ISPRS Technical Commission III Symposium; Zurich, Switzerland; 5th-7th September 2014; published in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Vol. XL-3, p. 25-32
  • Relation: http://purl.org/au-research/grants/arc/DE120101778
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  • Description: This paper presents a new method for segmentation of LIDAR point cloud data for automatic building extraction. Using the ground height from a DEM (Digital Elevation Model), the non-ground points (mainly buildings and trees) are separated from the ground points. Points on walls are removed from the set of non-ground points by applying the following two approaches: If a plane fitted at a point and its neighbourhood is perpendicular to a fictitious horizontal plane, then this point is designated as a wall point. When LIDAR points are projected on a dense grid, points within a narrow area close to an imaginary vertical line on the wall should fall into the same grid cell. If three or more points fall into the same cell, then the intermediate points are removed as wall points. The remaining non-ground points are then divided into clusters based on height and local neighbourhood. One or more clusters are initialised based on the maximum height of the points and then each cluster is extended by applying height and neighbourhood constraints. Planar roof segments are extracted from each cluster of points following a region-growing technique. Planes are initialised using coplanar points as seed points and then grown using plane compatibility tests. If the estimated height of a point is similar to its LIDAR generated height, or if its normal distance to a plane is within a predefined limit, then the point is added to the plane. Once all the planar segments are extracted, the common points between the neghbouring planes are assigned to the appropriate planes based on the plane intersection line, locality and the angle between the normal at a common point and the corresponding plane. A rule-based procedure is applied to remove tree planes which are small in size and randomly oriented. The neighbouring planes are then merged to obtain individual building boundaries, which are regularised based on long line segments. Experimental results on ISPRS benchmark data sets show that the proposed method offers higher building detection and roof plane extraction rates than many existing methods, especially in complex urban scenes.

Optimizing cepstral features for audio classification

  • Authors: Fu, Zhouyu , Lu, Guojun , Ting, Kaiming , Zhang, Dengsheng
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: International Joint Conference on Artificial Intelligence p. 1330-1336
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  • Description: Cepstral features have been widely used in audio applications. Domain knowledge has played an important role in designing different types of cepstral features proposed in the literature. In this paper, we present a novel approach for learning optimized cepstral features directly from audio data to better discriminate between different categories of signals in classification tasks. We employ multi-layer feedforward neural networks to model the cepstral feature extraction process. The network weights are initialized to replicate a reference cepstral feature like the mel frequency cepstral coefficient. We then propose a embedded approach that integrates feature learning with the training of a support vector machine (SVM) classifier. A single optimization problem is formulated where the feature and classifier variables are optimized simultaneously so as to refine the initial features and minimize the classification risk. Experimental results have demonstrated the effectiveness of the proposed feature learning approach, outperforming competing methods by a large margin on benchmark data.

Building roof plane extraction from LIDAR data

  • Authors: Awrangjeb, Mohammad , Lu, Guojun
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
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  • Description: This paper presents a new segmentation technique to use LIDAR point cloud data for automatic extraction of building roof planes. The raw LIDAR points are first classified into two major groups: ground and non-ground points. The ground points are used to generate a 'building mask' in which the black areas represent the ground where there are no laser returns below a certain height. The non-ground points are segmented to extract the planar roof segments. First, the building mask is divided into small grid cells. The cells containing the black pixels are clustered such that each cluster represents an individual building or tree. Second, the non-ground points within a cluster are segmented based on their coplanarity and neighbourhood relations. Third, the planar segments are refined using a rule-based procedure that assigns the common points among the planar segments to the appropriate segments. Finally, another rule-based procedure is applied to remove tree planes which are generally small in size and randomly oriented. Experimental results on three Australian sites have shown that the proposed method offers high building detection and roof plane extraction rates.

A comparative study on contour-based corner detectors

  • Authors: Awrangjeb, Mohammad , Lu, Guojun , Fraser, Clive
  • Date: 2010
  • Type: Text , Conference paper
  • Relation: Digital Image Computing: Techniques and Applications (DICTA), 2010 International Conference
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  • Description: Contour-based corner detectors directly or indirectly estimate a significance measure (e.g. curvature) on the points of a planar curve and select the curvature extrema points as corners. While an extensive number of contour-based corner detectors have been proposed over the last four decades, there is no comparative study of recently proposed promising detectors. This paper is an attempt to fill this gap. We present the general frame-work of the contour-based corner detection technique and discuss two major issues - curve smoothing and curvature estimation, which have major impacts on the corner detection performance. A number of promising detectors are compared using an automatic evaluation system on a common large dataset. It is observed that while the detectors using indirect curvature estimation techniques are more robust, the detectors using direct curvature estimation techniques are faster.

Effective and efficient contour-based corner detectors

  • Authors: Teng, Shyh , Najmus Sadat, Rafi , Lu, Guojun
  • Date: 2015
  • Type: Text , Journal article
  • Relation: Pattern Recognition Vol. 48, no. 7 (2015), p. 2185-2197
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  • Description: Corner detection is an essential operation in many computer vision applications. Among the contour-based corner detectors in the literature, the Chord-to-Point Distance Accumulation (CPDA) detector is reported to have one of the highest repeatability in detecting robust corners and the lowest localization error. However, based on our analysis, we found that the CPDA detector often fails to accurately detect the true corners when a curve has multiple corners but the sharpness of one or a few of them is much more prominent than the rest. This detector also might not perform well when the corners are closely located. Furthermore, the CPDA detector is also computationally very expensive. To overcome these weaknesses, we propose two effective and efficient corner detectors using simple triangular theory and distance calculation. Our experimental results show that our proposed detectors outperform CPDA and nine other existing corner detectors in terms of repeatability. Our proposed detectors also have a relatively low or comparable localization error and are computationally more efficient. © 2015 Elsevier Ltd.

A novel multi-modal image registration method based on corners

  • Authors: Lv, Guohua , Teng, Shyh , Lu, Guojun
  • Date: 2015
  • Type: Text , Conference proceedings
  • Relation: 2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014, Wollongong, New South Wales, 25th-27th November 2014
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  • Description: This paper presents a novel method for registering multi-modal images, based on corners. The proposed method is motivated by the fact that large content differences are likely to occur in multi-modal images. Unlike traditional multi-modal image registration methods that utilize intensities or gradients for feature representation, we propose to use curvatures of corners. Moreover, a novel local descriptor called Distribution of Edge Pixels Along Contour (DEPAC) is proposed to represent the neighborhood of corners. Curvature and DEPAC similarities are combined in our method to improve the registration accuracy. Using a set of benchmark multi-modal images and multi-modal microscopic images, we demonstrate that our proposed method outperforms an existing state-of-the-art image registration method.

Improved spatial pyramid matching for image classification

  • Authors: Shahiduzzaman, Mohammad , Zhang, Dengsheng , Lu, Guojun
  • Date: 2010
  • Type: Text , Conference paper
  • Relation: 10th Asian Conference on Computer Vision p. 449-459
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  • Description: Spatial analysis of salient feature points has been shown to be promising in image analysis and classification. In the past, spatial pyramid matching makes use of both of salient feature points and spatial multiresolution blocks to match between images. However, it is shown that different images or blocks can still have similar features using spatial pyramid matching. The analysis and matching will be more accurate in scale space. In this paper, we propose to do spatial pyramid matching in scale space. Specifically, pyramid match histograms are computed in multiple scales to refine the kernel for support vector machine classification. We show that the combination of salient point features, scale space and spatial pyramid matching improves the original spatial pyramid matching significantly.

Fusion of LiDAR data and multispectral imagery for effective building detection based on graph and connected component analysis

  • Authors: Gilani, Alinaqi , Awrangjeb, Mohammad , Lu, Guojun
  • Date: 2015
  • Type: Text , Conference proceedings
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  • Description: Building detection in complex scenes is a non-trivial exercise due to building shape variability, irregular terrain, shadows, and occlusion by highly dense vegetation. In this research, we present a graph based algorithm, which combines multispectral imagery and airborne LiDAR information to completely delineate the building boundaries in urban and densely vegetated area. In the first phase, LiDAR data is divided into two groups: ground and non-ground data, using ground height from a bare-earth DEM. A mask, known as the primary building mask, is generated from the non-ground LiDAR points where the black region represents the elevated area (buildings and trees), while the white region describes the ground (earth). The second phase begins with the process of Connected Component Analysis (CCA) where the number of objects present in the test scene are identified followed by initial boundary detection and labelling. Additionally, a graph from the connected components is generated, where each black pixel corresponds to a node. An edge of a unit distance is defined between a black pixel and a neighbouring black pixel, if any. An edge does not exist from a black pixel to a neighbouring white pixel, if any. This phenomenon produces a disconnected components graph, where each component represents a prospective building or a dense vegetation (a contiguous block of black pixels from the primary mask). In the third phase, a clustering process clusters the segmented lines, extracted from multispectral imagery, around the graph components, if possible. In the fourth step, NDVI, image entropy, and LiDAR data are utilised to discriminate between vegetation, buildings, and isolated building's occluded parts. Finally, the initially extracted building boundary is extended pixel-wise using NDVI, entropy, and LiDAR data to completely delineate the building and to maximise the boundary reach towards building edges. The proposed technique is evaluated using two Australian data sets: Aitkenvale and Hervey Bay, for object-based and pixel-based completeness, correctness, and quality. The proposed technique detects buildings larger than 50 m2 and 10 m2 in the Aitkenvale site with 100% and 91% accuracy, respectively, while in the Hervey Bay site it performs better with 100% accuracy for buildings larger than 10 m2 in area.

An automatic building extraction and regularisation technique using LiDAR point cloud data and orthoimage

  • Authors: Gilani, Sayed Ali Naqi , Awrangjeb, Mohammad , Lu, Guojun
  • Date: 2016
  • Type: Text , Journal article
  • Relation: Remote Sensing Vol. 8, no. 3 (2016), p. 1-27
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  • Description: The development of robust and accurate methods for automatic building detection and regularisation using multisource data continues to be a challenge due to point cloud sparsity, high spectral variability, urban objects differences, surrounding complexity, and data misalignment. To address these challenges, constraints on object's size, height, area, and orientation are generally benefited which adversely affect the detection performance. Often the buildings either small in size, under shadows or partly occluded are ousted during elimination of superfluous objects. To overcome the limitations, a methodology is developed to extract and regularise the buildings using features from point cloud and orthoimagery. The building delineation process is carried out by identifying the candidate building regions and segmenting them into grids. Vegetation elimination, building detection and extraction of their partially occluded parts are achieved by synthesising the point cloud and image data. Finally, the detected buildings are regularised by exploiting the image lines in the building regularisation process. Detection and regularisation processes have been evaluated using the ISPRS benchmark and four Australian data sets which differ in point density (1 to 29 points/m2), building sizes, shadows, terrain, and vegetation. Results indicate that there is 83% to 93% per-area completeness with the correctness of above 95%, demonstrating the robustness of the approach. The absence of over- and many-to-many segmentation errors in the ISPRS data set indicate that the technique has higher per-object accuracy. While compared with six existing similar methods, the proposed detection and regularisation approach performs significantly better on more complex data sets (Australian) in contrast to the ISPRS benchmark, where it does better or equal to the counterparts. © 2016 by the authors.

Enhancing SIFT-based image registration performance by building and selecting highly discriminating descriptors

  • Authors: Lv, Guohua , Teng, Shyh , Lu, Guojun
  • Date: 2016
  • Type: Text , Journal article
  • Relation: Pattern Recognition Letters Vol. 84, no. (2016), p. 156-162
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  • Description: In this paper we will investigate the gradient utilization in building SIFT (Scale Invariant Feature Transform)-like descriptors for image registration. There are generally two types of gradient information, i.e. gradient magnitude and gradient occurrence, which can be used for building SIFT-like descriptors. We will provide a theoretical analysis on the effectiveness of each of the two types of gradient information when used individually. Based on our analysis, we will propose a novel technique which systematically uses both types of gradient information together for image registration. Moreover, we will propose a strategy to select keypoint matches with a higher discrimination. The proposed technique can be used for both mono-modal and multi-modal image registration. Our experimental results show that the proposed technique improves registration accuracy over existing SIFT-like descriptors. © 2016 Elsevier B.V.

Learning sparse kernel classifiers in the primal

  • Authors: Fu, Zhouyu , Lu, Guojun , Ting, Kaiming , Zhang, Dengsheng
  • Date: 2012
  • Type: Text , Conference paper
  • Relation: Joint IAPR International Workshop, SSPR&SPR 2012; Hiroshima, Japan; 7th-9th November 2012; published in Structural, Syntactic, and Statistical Pattern Recognition (part of the Lecture Notes in Computer Science) Vol. 7626, p. 60-69
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  • Description: The increasing number of classification applications in large data sets demands that efficient classifiers be designed not only in training but also for prediction. In this paper, we address the problem of learning kernel classifiers with reduced complexity and improved efficiency for prediction in comparison to those trained by standard methods. A single optimisation problem is formulated for classifier learning which optimises both classifier weights and eXpansion Vectors (XVs) that define the classification function in a joint fashion. Unlike the existing approach of Wu et al, which performs optimisation in the dual formulation, our approach solves the primal problem directly. The primal problem is much more efficient to solve, as it can be converted to the training of a linear classifier in each iteration, which scales linearly to the size of the data set and the number of expansions. This makes our primal approach highly desirable for large-scale applications, where the dual approach is inadequate and prohibitively slow due to the solution of cubic-time kernel SVM involved in each iteration. Experimental results have demonstrated the efficiency and effectiveness of the proposed primal approach for learning sparse kernel classifiers that clearly outperform the alternatives.

Improving SIFT's performance by incorporating appropriate gradient information

  • Authors: Lv, Guohua , Hossain, Md. Tanvir , Teng, Shyh , Lu, Guojun , Lackmann, Martin
  • Date: 2011
  • Type: Text , Conference paper
  • Relation: 26th Image and Vision Computing New Zealand Conference (IVCNZ 2011) p. 381 - 386
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  • Description: Scale Invariant Feature Transform (SIFT) has been applied in numerous applications especially in the domain of computer vision. In these applications, image information used for building the SIFT descriptor can have a significant impact on its performance. When building orientation histograms for descriptors, a critical step is how to increment the values in the orientation bins. The original scheme for this step in SIFT was improved in [6]. Two different types of gradient information are used for building orientation histograms. The limitations of the two schemes are identified in this paper and we then propose three new schemes which use both types of gradient information in the feature description and matching stages. Our experimental results show that the proposed schemes can achieve better registration performances than the schemes proposed in SIFT and [6].