no code implementations • 12 Mar 2013 • Conrad Sanderson, Mehrtash T. Harandi, Yongkang Wong, Brian C. Lovell
In contrast to comparing faces via single exemplars, matching sets of face images increases robustness and discrimination performance.
no code implementations • 25 Mar 2013 • Andres Sanin, Conrad Sanderson, Mehrtash T. Harandi, Brian C. Lovell
We propose a new action and gesture recognition method based on spatio-temporal covariance descriptors and a weighted Riemannian locality preserving projection approach that takes into account the curved space formed by the descriptors.
Ranked #2 on Hand Gesture Recognition on Cambridge
no code implementations • 26 Mar 2013 • Sandra Mau, Shaokang Chen, Conrad Sanderson, Brian C. Lovell
This paper presents a video face recognition system based on probabilistic Multi-Region Histograms to characterise performance trade-offs in: (i) selecting a subset of faces compared to using all faces, and (ii) combining information from all faces via clustering.
no code implementations • 3 Apr 2013 • Vikas Reddy, Conrad Sanderson, Brian C. Lovell
The motion and size features are modelled by an approximated version of kernel density estimation, which is computationally efficient even for large training datasets.
no code implementations • 3 Apr 2013 • Yongkang Wong, Shaokang Chen, Sandra Mau, Conrad Sanderson, Brian C. Lovell
In video based face recognition, face images are typically captured over multiple frames in uncontrolled conditions, where head pose, illumination, shadowing, motion blur and focus change over the sequence.
no code implementations • 4 Apr 2013 • Arnold Wiliem, Yongkang Wong, Conrad Sanderson, Peter Hobson, Shaokang Chen, Brian C. Lovell
In this paper, we propose a cell classification system comprised of a dual-region codebook-based descriptor, combined with the Nearest Convex Hull Classifier.
no code implementations • 4 Apr 2013 • Andres Sanin, Conrad Sanderson, Brian C. Lovell
Furthermore, we propose the use of tracking performance as an unbiased approach for determining the practical usefulness of shadow detection methods.
no code implementations • 8 Apr 2013 • Yongkang Wong, Conrad Sanderson, Sandra Mau, Brian C. Lovell
While existing face recognition systems based on local features are robust to issues such as misalignment, they can exhibit accuracy degradation when comparing images of differing resolutions.
1 code implementation • 16 Apr 2013 • Mehrtash T. Harandi, Conrad Sanderson, Richard Hartley, Brian C. Lovell
Recent advances suggest that a wide range of computer vision problems can be addressed more appropriately by considering non-Euclidean geometry.
no code implementations • CVPR 2013 • Shaokang Chen, Conrad Sanderson, Mehrtash T. Harandi, Brian C. Lovell
We show that by minimising the sparse representation reconstruction error, we approach the nearest point on a Grassmann manifold.
no code implementations • 18 Oct 2013 • Mehrtash Harandi, Conrad Sanderson, Chunhua Shen, Brian C. Lovell
Recent advances in computer vision and machine learning suggest that a wide range of problems can be addressed more appropriately by considering non-Euclidean geometry.
no code implementations • 3 Mar 2014 • Shaokang Chen, Arnold Wiliem, Conrad Sanderson, Brian C. Lovell
We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set.
no code implementations • 3 Mar 2014 • Sareh Shirazi, Mehrtash T. Harandi, Brian C. Lovell, Conrad Sanderson
A robust visual tracking system requires an object appearance model that is able to handle occlusion, pose, and illumination variations in the video stream.
no code implementations • 4 Mar 2014 • Azadeh Alavi, Arnold Wiliem, Kun Zhao, Brian C. Lovell, Conrad Sanderson
Recent advances suggest that encoding images through Symmetric Positive Definite (SPD) matrices and then interpreting such matrices as points on Riemannian manifolds can lead to increased classification performance.
no code implementations • 5 Mar 2014 • Andres Sanin, Conrad Sanderson, Mehrtash T. Harandi, Brian C. Lovell
For covariance-based image descriptors, taking into account the curvature of the corresponding feature space has been shown to improve discrimination performance.
no code implementations • 15 Mar 2014 • Arnold Wiliem, Conrad Sanderson, Yongkang Wong, Peter Hobson, Rodney F. Minchin, Brian C. Lovell
This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol.
no code implementations • CVPR 2014 • Mahsa Baktashmotlagh, Mehrtash T. Harandi, Brian C. Lovell, Mathieu Salzmann
Here, we propose to make better use of the structure of this manifold and rely on the distance on the manifold to compare the source and target distributions.
no code implementations • 19 Jun 2014 • Vikas Reddy, Conrad Sanderson, Andres Sanin, Brian C. Lovell
Robust foreground object segmentation via background modelling is a difficult problem in cluttered environments, where obtaining a clear view of the background to model is almost impossible.
no code implementations • 28 Jul 2014 • Arnold Wiliem, Peter Hobson, Brian C. Lovell
In our work, a specimen image descriptor is represented by its overall cell attribute descriptors.
no code implementations • 6 Feb 2015 • Johanna Carvajal, Conrad Sanderson, Chris McCool, Brian C. Lovell
In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification.
no code implementations • 18 Sep 2015 • Kun Zhao, Azadeh Alavi, Arnold Wiliem, Brian C. Lovell
We then validate our framework on several computer vision applications by comparing against popular clustering methods on Riemannian manifolds.
no code implementations • 5 Feb 2016 • Liangchen Liu, Arnold Wiliem, Shaokang Chen, Brian C. Lovell
In our evaluation, we gleaned some insights that could be beneficial in developing automatic attribute discovery methods to generate meaningful attributes.
no code implementations • 21 Feb 2016 • Liangchen Liu, Arnold Wiliem, Shaokang Chen, Kun Zhao, Brian C. Lovell
In this paper, we propose a novel approach, based on the shared structure exhibited amongst meaningful attributes, that enables us to compare between different automatic attribute discovery approaches. We then validate our approach by comparing various attribute discovery methods such as PiCoDeS on two attribute datasets.
no code implementations • 17 Oct 2016 • Liangchen Liu, Arnold Wiliem, Shaokang Chen, Brian C. Lovell
With this metric, automatic quantitative evaluation can be performed on the attribute sets; thus, reducing the enormous effort to perform manual evaluation.
2 code implementations • 7 Dec 2017 • Teng Zhang, Arnold Wiliem, Siqi Yang, Brian C. Lovell
While it can greatly increase the scope and benefits of the current security surveillance systems, performing such a task using thermal images is a challenging problem compared to face recognition task in the Visible Light Domain (VLD).
no code implementations • ECCV 2018 • Siqi Yang, Arnold Wiliem, Shaokang Chen, Brian C. Lovell
We show that existing adversarial perturbation methods are not effective to perform such an attack, especially when there are multiple faces in the input image.
no code implementations • 20 Mar 2018 • Teng Zhang, Johanna Carvajal, Daniel F. Smith, Kun Zhao, Arnold Wiliem, Peter Hobson, Anthony Jennings, Brian C. Lovell
In order to address the quality assessment problem, we propose a deep neural network based framework to automatically assess the slide quality in a semantic way.
no code implementations • 14 Jun 2018 • Kun Zhao, Arnold Wiliem, Shaokang Chen, Brian C. Lovell
Our proposed framework, named Manifold Convex Class Model, represents each class on SPD manifolds using a convex model, and classification can be performed by computing distances to the convex models.
no code implementations • 24 Jun 2019 • Sam Maksoud, Arnold Wiliem, Kun Zhao, Teng Zhang, Lin Wu, Brian C. Lovell
This is because the system can ignore the attention mechanism by assigning equal weights for all members.
1 code implementation • 24 Jun 2019 • Meng Li, Lin Wu, Arnold Wiliem, Kun Zhao, Teng Zhang, Brian C. Lovell
Histopathology image analysis can be considered as a Multiple instance learning (MIL) problem, where the whole slide histopathology image (WSI) is regarded as a bag of instances (i. e, patches) and the task is to predict a single class label to the WSI.
no code implementations • 22 Sep 2019 • Can Peng, Kun Zhao, Arnold Wiliem, Teng Zhang, Peter Hobson, Anthony Jennings, Brian C. Lovell
Critical findings are observed: (1) The best balance between detection accuracy, detection speed and file size is achieved at 8 times downsampling captured with a $40\times$ objective; (2) compression which reduces the file size dramatically, does not necessarily have an adverse effect on overall accuracy; (3) reducing the amount of training data to some extents causes a drop in precision but has a negligible impact on the recall; (4) in most cases, Faster R-CNN achieves the best accuracy in the glomerulus detection task.
no code implementations • 3 Feb 2020 • Siqi Yang, Lin Wu, Arnold Wiliem, Brian C. Lovell
To achieve gradient alignment, we propose Forward-Backward Cyclic Adaptation, which iteratively computes adaptation from source to target via backward hopping and from target to source via forward passing.
1 code implementation • 9 Mar 2020 • Can Peng, Kun Zhao, Brian C. Lovell
To address this problem, incremental learning methods have been explored which preserve the old knowledge of deep learning models.
no code implementations • 31 Dec 2020 • Can Peng, Kun Zhao, Sam Maksoud, Meng Li, Brian C. Lovell
Incremental learning requires a model to continually learn new tasks from streaming data.
no code implementations • 19 Apr 2021 • Sam Maksoud, Kun Zhao, Can Peng, Brian C. Lovell
To address this problem we present a method for performing BDL, namely Kernel Seed Networks (KSN), which does not require a 2-fold increase in the number of parameters.
no code implementations • 12 Aug 2021 • Can Peng, Kun Zhao, Sam Maksoud, Tianren Wang, Brian C. Lovell
In this paper, we aim to alleviate this performance decay on multi-step incremental detection tasks by proposing a dilatable incremental object detector (DIODE).
1 code implementation • 30 Jul 2022 • Can Peng, Kun Zhao, Tianren Wang, Meng Li, Brian C. Lovell
The continual appearance of new objects in the visual world poses considerable challenges for current deep learning methods in real-world deployments.
no code implementations • 24 Sep 2023 • Can Peng, Piotr Koniusz, Kaiyu Guo, Brian C. Lovell, Peyman Moghadam
Deep learning models suffer from catastrophic forgetting when being fine-tuned with samples of new classes.