81 research outputs found
TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition
This work tackles the face recognition task on images captured using thermal
camera sensors which can operate in the non-light environment. 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). This is
partly due to the much smaller amount number of thermal imagery data collected
compared to the VLD data. Unfortunately, direct application of the existing
very strong face recognition models trained using VLD data into the thermal
imagery data will not produce a satisfactory performance. This is due to the
existence of the domain gap between the thermal and VLD images. To this end, we
propose a Thermal-to-Visible Generative Adversarial Network (TV-GAN) that is
able to transform thermal face images into their corresponding VLD images
whilst maintaining identity information which is sufficient enough for the
existing VLD face recognition models to perform recognition. Some examples are
presented in Figure 1. Unlike the previous methods, our proposed TV-GAN uses an
explicit closed-set face recognition loss to regularize the discriminator
network training. This information will then be conveyed into the generator
network in the forms of gradient loss. In the experiment, we show that by using
this additional explicit regularization for the discriminator network, the
TV-GAN is able to preserve more identity information when translating a thermal
image of a person which is not seen before by the TV-GAN
Efficient Clustering on Riemannian Manifolds: A Kernelised Random Projection Approach
Reformulating computer vision problems over Riemannian manifolds has
demonstrated superior performance in various computer vision applications. This
is because visual data often forms a special structure lying on a lower
dimensional space embedded in a higher dimensional space. However, since these
manifolds belong to non-Euclidean topological spaces, exploiting their
structures is computationally expensive, especially when one considers the
clustering analysis of massive amounts of data. To this end, we propose an
efficient framework to address the clustering problem on Riemannian manifolds.
This framework implements random projections for manifold points via kernel
space, which can preserve the geometric structure of the original space, but is
computationally efficient. Here, we introduce three methods that follow our
framework. We then validate our framework on several computer vision
applications by comparing against popular clustering methods on Riemannian
manifolds. Experimental results demonstrate that our framework maintains the
performance of the clustering whilst massively reducing computational
complexity by over two orders of magnitude in some cases
Matching Image Sets via Adaptive Multi Convex Hull
Traditional nearest points methods use all the samples in an image set to
construct a single convex or affine hull model for classification. However,
strong artificial features and noisy data may be generated from combinations of
training samples when significant intra-class variations and/or noise occur in
the image set. Existing multi-model approaches extract local models by
clustering each image set individually only once, with fixed clusters used for
matching with various image sets. This may not be optimal for discrimination,
as undesirable environmental conditions (eg. illumination and pose variations)
may result in the two closest clusters representing different characteristics
of an object (eg. frontal face being compared to non-frontal face). To address
the above problem, we propose a novel approach to enhance nearest points based
methods by integrating affine/convex hull classification with an adapted
multi-model approach. We first extract multiple local convex hulls from a query
image set via maximum margin clustering to diminish the artificial variations
and constrain the noise in local convex hulls. 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. By applying ARC, noisy clusters in the query set can be discarded.
Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method
outperforms single model approaches and other recent techniques, such as Sparse
Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant
Analysis.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV),
201
Does Interference Exist When Training a Once-For-All Network?
The Once-For-All (OFA) method offers an excellent pathway to deploy a trained
neural network model into multiple target platforms by utilising the
supernet-subnet architecture. Once trained, a subnet can be derived from the
supernet (both architecture and trained weights) and deployed directly to the
target platform with little to no retraining or fine-tuning. To train the
subnet population, OFA uses a novel training method called Progressive
Shrinking (PS) which is designed to limit the negative impact of interference
during training. It is believed that higher interference during training
results in lower subnet population accuracies. In this work we take a second
look at this interference effect. Surprisingly, we find that interference
mitigation strategies do not have a large impact on the overall subnet
population performance. Instead, we find the subnet architecture selection bias
during training to be a more important aspect. To show this, we propose a
simple-yet-effective method called Random Subnet Sampling (RSS), which does not
have mitigation on the interference effect. Despite no mitigation, RSS is able
to produce a better performing subnet population than PS in four
small-to-medium-sized datasets; suggesting that the interference effect does
not play a pivotal role in these datasets. Due to its simplicity, RSS provides
a reduction in training times compared to PS. A
reduction can also be achieved with a reasonable drop in performance when the
number of RSS training epochs are reduced. Code available at
https://github.com/Jordan-HS/RSS-Interference-CVPRW2022.Comment: Accepted to CVPR Embedded Vision Workshop 202
Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching
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. The
IIF protocol on HEp-2 cells has been the hallmark method to identify the
presence of ANAs, due to its high sensitivity and the large range of antigens
that can be detected. However, it suffers from numerous shortcomings, such as
being subjective as well as time and labour intensive. Computer Aided
Diagnostic (CAD) systems have been developed to address these problems, which
automatically classify a HEp-2 cell image into one of its known patterns (eg.
speckled, homogeneous). Most of the existing CAD systems use handpicked
features to represent a HEp-2 cell image, which may only work in limited
scenarios. We propose a novel automatic cell image classification method termed
Cell Pyramid Matching (CPM), which is comprised of regional histograms of
visual words coupled with the Multiple Kernel Learning framework. We present a
study of several variations of generating histograms and show the efficacy of
the system on two publicly available datasets: the ICPR HEp-2 cell
classification contest dataset and the SNPHEp-2 dataset.Comment: arXiv admin note: substantial text overlap with arXiv:1304.126
SafeSea: Synthetic Data Generation for Adverse & Low Probability Maritime Conditions
High-quality training data is essential for enhancing the robustness of
object detection models. Within the maritime domain, obtaining a diverse real
image dataset is particularly challenging due to the difficulty of capturing
sea images with the presence of maritime objects , especially in stormy
conditions. These challenges arise due to resource limitations, in addition to
the unpredictable appearance of maritime objects. Nevertheless, acquiring data
from stormy conditions is essential for training effective maritime detection
models, particularly for search and rescue, where real-world conditions can be
unpredictable. In this work, we introduce SafeSea, which is a stepping stone
towards transforming actual sea images with various Sea State backgrounds while
retaining maritime objects. Compared to existing generative methods such as
Stable Diffusion Inpainting~\cite{stableDiffusion}, this approach reduces the
time and effort required to create synthetic datasets for training maritime
object detection models. The proposed method uses two automated filters to only
pass generated images that meet the criteria. In particular, these filters will
first classify the sea condition according to its Sea State level and then it
will check whether the objects from the input image are still preserved. This
method enabled the creation of the SafeSea dataset, offering diverse weather
condition backgrounds to supplement the training of maritime models. Lastly, we
observed that a maritime object detection model faced challenges in detecting
objects in stormy sea backgrounds, emphasizing the impact of weather conditions
on detection accuracy. The code, and dataset are available at
https://github.com/martin-3240/SafeSea.Comment: Accepted to WACV 2024 workshop on Maritime Computer Visio
It takes two to tango: cascading off-the-shelf face detectors
Recent face detection methods have achieved high detection rates in unconstrained environments. However, as they still generate excessive false positives, any method for reducing false positives is highly desirable. This work aims to massively reduce false positives of existing face detection methods whilst maintaining the true detection rate. In addition, the proposed method also aims to sidestep the detector retraining task which generally requires enormous effort. To this end, we propose a two-stage framework which cascades two off-the-shelf face detectors. Not all face detectors can be cascaded and achieve good performance. Thus, we study three properties that allow us to determine the best pair of detectors. These three properties are: (1) correlation of true positives; (2) diversity of false positives and (3) detector runtime. Experimental results on recent large benchmark datasets such as FDDB and WIDER FACE support our findings that the false positives of a face detector could be potentially reduced by 90% whilst still maintaining high true positive detection rate. In addition, with a slight decrease in true positives, we found a pair of face detector that achieves significantly lower false positives, while being five times faster than the current state-of-the-art detector
Classification of Human Epithelial Type 2 Cell Indirect Immunofluoresence Images via Codebook Based Descriptors
The Anti-Nuclear Antibody (ANA) clinical pathology test is commonly used to
identify the existence of various diseases. A hallmark method for identifying
the presence of ANAs is the Indirect Immunofluorescence method on Human
Epithelial (HEp-2) cells, due to its high sensitivity and the large range of
antigens that can be detected. However, the method suffers from numerous
shortcomings, such as being subjective as well as time and labour intensive.
Computer Aided Diagnostic (CAD) systems have been developed to address these
problems, which automatically classify a HEp-2 cell image into one of its known
patterns (eg., speckled, homogeneous). Most of the existing CAD systems use
handpicked features to represent a HEp-2 cell image, which may only work in
limited scenarios. In this paper, we propose a cell classification system
comprised of a dual-region codebook-based descriptor, combined with the Nearest
Convex Hull Classifier. We evaluate the performance of several variants of the
descriptor on two publicly available datasets: ICPR HEp-2 cell classification
contest dataset and the new SNPHEp-2 dataset. To our knowledge, this is the
first time codebook-based descriptors are applied and studied in this domain.
Experiments show that the proposed system has consistent high performance and
is more robust than two recent CAD systems
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