Search Results for author: Brian C. Lovell

Found 38 papers, 5 papers with code

Few-Shot Class-Incremental Learning from an Open-Set Perspective

1 code implementation30 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.

Data Augmentation Face Recognition +2

DIODE: Dilatable Incremental Object Detection

no code implementations12 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).

Incremental Learning Object +2

Scalable Bayesian Deep Learning with Kernel Seed Networks

no code implementations19 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.

Faster ILOD: Incremental Learning for Object Detectors based on Faster RCNN

1 code implementation9 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.

Incremental Learning Knowledge Distillation +2

Unsupervised Domain Adaptive Object Detection using Forward-Backward Cyclic Adaptation

no code implementations3 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.

Image Classification object-detection +2

To What Extent Does Downsampling, Compression, and Data Scarcity Impact Renal Image Analysis?

no code implementations22 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.

Image Compression

CORAL8: Concurrent Object Regression for Area Localization in Medical Image Panels

no code implementations24 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.


Deep Instance-Level Hard Negative Mining Model for Histopathology Images

1 code implementation24 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.

General Classification Multiple Instance Learning

Convex Class Model on Symmetric Positive Definite Manifolds

no code implementations14 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.

Classification General Classification +4

SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep Neural Networks

no code implementations20 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.

Using LIP to Gloss Over Faces in Single-Stage Face Detection Networks

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.

Face Detection

TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition

2 code implementations7 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).

Face Recognition Generative Adversarial Network

What is the Best Way for Extracting Meaningful Attributes from Pictures?

no code implementations17 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.


Determining the best attributes for surveillance video keywords generation

no code implementations21 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.


Automatic and Quantitative evaluation of attribute discovery methods

no code implementations5 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.

Attribute Image Classification

Efficient Clustering on Riemannian Manifolds: A Kernelised Random Projection Approach

no code implementations18 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.


Multi-Action Recognition via Stochastic Modelling of Optical Flow and Gradients

no code implementations6 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.

Action Recognition Classification +4

MRF-based Background Initialisation for Improved Foreground Detection in Cluttered Surveillance Videos

no code implementations19 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.

Object Tracking Semantic Segmentation

Domain Adaptation on the Statistical Manifold

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.

Object Recognition Unsupervised Domain Adaptation

Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching

no code implementations15 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.

General Classification Image Classification

K-Tangent Spaces on Riemannian Manifolds for Improved Pedestrian Detection

no code implementations5 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.

Pedestrian Detection

Random Projections on Manifolds of Symmetric Positive Definite Matrices for Image Classification

no code implementations4 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.

Face Recognition General Classification +3

Object Tracking via Non-Euclidean Geometry: A Grassmann Approach

no code implementations3 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.

Object Object Tracking +1

Matching Image Sets via Adaptive Multi Convex Hull

no code implementations3 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.

Clustering General Classification

Dictionary Learning and Sparse Coding on Grassmann Manifolds: An Extrinsic Solution

no code implementations18 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.

Action Recognition Dictionary Learning +5

Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach

1 code implementation16 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.

Dictionary Learning Face Recognition +3

Dynamic Amelioration of Resolution Mismatches for Local Feature Based Identity Inference

no code implementations8 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.

Face Recognition

Classification of Human Epithelial Type 2 Cell Indirect Immunofluoresence Images via Codebook Based Descriptors

no code implementations4 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.

General Classification

Shadow Detection: A Survey and Comparative Evaluation of Recent Methods

no code implementations4 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.

object-detection Object Detection +2

Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognition

no code implementations3 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.

Face Image Quality Face Image Quality Assessment +3

Improved Anomaly Detection in Crowded Scenes via Cell-based Analysis of Foreground Speed, Size and Texture

no code implementations3 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.

Anomaly Detection Density Estimation +2

Video Face Matching using Subset Selection and Clustering of Probabilistic Multi-Region Histograms

no code implementations26 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.

Clustering Computational Efficiency +3

Spatio-Temporal Covariance Descriptors for Action and Gesture Recognition

no code implementations25 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.

General Classification Gesture Recognition +1

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