266 research outputs found
Autofocus for digital Fresnel holograms by use of a Fresnelet-sparsity criterion
We propose a robust autofocus method for reconstructing digital Fresnel holograms. The numerical reconstruction
involves simulating the propagation of a complex wave front to the appropriate distance. Since the latter value is difficult to determine manually, it is desirable to rely on an automatic procedure for finding the optimal distance to achieve high-quality reconstructions. Our algorithm maximizes a sharpness metric related to the sparsity of the signal’s expansion in distance-dependent waveletlike Fresnelet bases. We show results from simulations and experimental situations that confirm its applicability
Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration
The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This article presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area
A novel face recognition method with feature combination
A novel combined personalized feature Framework is proposed for face recognition (FR). In the framework, the proposed linear discriminant analysis (LDA) makes use of the null space of the within-class scatter matrix effectively, and Global feature vectors (PCA-transformed) and local feature vectors (Gabor wavelet-transformed) are integrated by complex vectors as input feature of improved LDA. The proposed method is compared to other commonly used FR methods on two face databases (ORL and UMIST). Results demonstrated that the performance of the proposed method is superior to that of traditional FR approachesthe National Natural ScienceFoundation of China (No. 60275023
Mitigating the effect of covariates in face recognition
Current face recognition systems capture faces of cooperative individuals in controlled environment as part of the face recognition process. It is therefore possible to control lighting, pose, background, and quality of images. However, in a real world application, we have to deal with both ideal and imperfect data. Performance of current face recognition systems is affected for such non-ideal and challenging cases. This research focuses on designing algorithms to mitigate the effect of covariates in face recognition.;To address the challenge of facial aging, an age transformation algorithm is proposed that registers two face images and minimizes the aging variations. Unlike the conventional method, the gallery face image is transformed with respect to the probe face image and facial features are extracted from the registered gallery and probe face images. The variations due to disguises cause change in visual perception, alter actual data, make pertinent facial information disappear, mask features to varying degrees, or introduce extraneous artifacts in the face image. To recognize face images with variations due to age progression and disguises, a granular face verification approach is designed which uses dynamic feed-forward neural architecture to extract 2D log polar Gabor phase features at different granularity levels. The granular levels provide non-disjoint spatial information which is combined using the proposed likelihood ratio based Support Vector Machine match score fusion algorithm. The face verification algorithm is validated using five face databases including the Notre Dame face database, FG-Net face database and three disguise face databases.;The information in visible spectrum images is compromised due to improper illumination whereas infrared images provide invariance to illumination and expression. A multispectral face image fusion algorithm is proposed to address the variations in illumination. The Support Vector Machine based image fusion algorithm learns the properties of the multispectral face images at different resolution and granularity levels to determine optimal information and combines them to generate a fused image. Experiments on the Equinox and Notre Dame multispectral face databases show that the proposed algorithm outperforms existing algorithms. We next propose a face mosaicing algorithm to address the challenge due to pose variations. The mosaicing algorithm generates a composite face image during enrollment using the evidence provided by frontal and semiprofile face images of an individual. Face mosaicing obviates the need to store multiple face templates representing multiple poses of a users face image. Experiments conducted on three different databases indicate that face mosaicing offers significant benefits by accounting for the pose variations that are commonly observed in face images.;Finally, the concept of online learning is introduced to address the problem of classifier re-training and update. A learning scheme for Support Vector Machine is designed to train the classifier in online mode. This enables the classifier to update the decision hyperplane in order to account for the newly enrolled subjects. On a heterogeneous near infrared face database, the case study using Principal Component Analysis and C2 feature algorithms shows that the proposed online classifier significantly improves the verification performance both in terms of accuracy and computational time
Food Recognition and Volume Estimation in a Dietary Assessment System
Recently obesity has become an epidemic and one of the most serious worldwide public
health concerns of the 21st century. Obesity diminishes the average life expectancy and
there is now convincing evidence that poor diet, in combination with physical inactivity
are key determinants of an individual s risk of developing chronic diseases such as
cancer, cardiovascular disease or diabetes. Assessing what people eat is fundamental
to establishing the link between diet and disease. Food records are considered the best
approach for assessing energy intake. However, this method requires literate and highly
motivated subjects. This is a particular problem for adolescents and young adults who
are the least likely to undertake food records. The ready access of the majority of the
population to mobile phones (with integrated camera, improved memory capacity, network
connectivity and faster processing capability) has opened up new opportunities for
dietary assessment. The dietary information extracted from dietary assessment provide
valuable insights into the cause of diseases that greatly helps practicing dietitians and
researchers to develop subsequent approaches for mounting intervention programs for
prevention. In such systems, the camera in the mobile phone is used for capturing images
of food consumed and these images are then processed to automatically estimate the nutritional content of the food. However, food objects are deformable objects that
exhibit variations in appearance, shape, texture and color so the food classification and
volume estimation in these systems suffer from lower accuracy. The improvement of
the food recognition accuracy and volume estimation accuracy are challenging tasks.
This thesis presents new techniques for food classification and food volume estimation.
For food recognition, emphasis was given to texture features. The existing food
recognition techniques assume that the food images will be viewed at similar scales and
from the same viewpoints. However, this assumption fails in practical applications, because
it is difficult to ensure that a user in a dietary assessment system will put his/her
camera at the same scale and orientation to capture food images as that of the target food
images in the database. A new scale and rotation invariant feature generation approach
that applies Gabor filter banks is proposed. To obtain scale and rotation invariance,
the proposed approach identifies the dominant orientation of the filtered coefficient and
applies a circular shifting operation to place this value at the first scale of dominant
direction. The advantages of this technique are it does not require the scale factor to
be known in advance and it is scale/and rotation invariant separately and concurrently.
This approach is modified to achieve improved accuracy by applying a Gaussian window
along the scale dimension which reduces the impact of high and low frequencies of
the filter outputs enabling better matching between the same classes. Besides automatic
classification, semi automatic classification and group classification are also considered
to have an idea about the improvement. To estimate the volume of a food item, a stereo pair is used to recover the structure as a 3D point cloud. A slice based volume estimation
approach is proposed that converts the 3D point cloud to a series of 2D slices.
The proposed approach eliminates the problem of knowing the distance between two
cameras with the help of disparities and depth information from a fiducial marker. The
experimental results show that the proposed approach can provide an accurate estimate
of food volume
Real-time people tracking in a camera network
Visual tracking is a fundamental key to the recognition and analysis of human behaviour.
In this thesis we present an approach to track several subjects using multiple
cameras in real time. The tracking framework employs a numerical Bayesian estimator,
also known as a particle lter, which has been developed for parallel implementation on
a Graphics Processing Unit (GPU). In order to integrate multiple cameras into a single
tracking unit we represent the human body by a parametric ellipsoid in a 3D world.
The elliptical boundary can be projected rapidly, several hundred times per subject per
frame, onto any image for comparison with the image data within a likelihood model.
Adding variables to encode visibility and persistence into the state vector, we tackle the
problems of distraction and short-period occlusion. However, subjects may also disappear
for longer periods due to blind spots between cameras elds of view. To recognise
a desired subject after such a long-period, we add coloured texture to the ellipsoid surface,
which is learnt and retained during the tracking process. This texture signature
improves the recall rate from 60% to 70-80% when compared to state only data association.
Compared to a standard Central Processing Unit (CPU) implementation, there
is a signi cant speed-up ratio
Deep learning in food category recognition
Integrating artificial intelligence with food category recognition has been a field of interest for research for the
past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The
modern advent of big data and the development of data-oriented fields like deep learning have provided advancements
in food category recognition. With increasing computational power and ever-larger food datasets,
the approach’s potential has yet to be realized. This survey provides an overview of methods that can be applied
to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We
survey the core components for constructing a machine learning system for food category recognition, including
datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a
particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer
learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments
in food category recognition for research and industrial applicationsMRC (MC_PC_17171)Royal Society (RP202G0230)BHF (AA/18/3/34220)Hope Foundation for Cancer Research (RM60G0680)GCRF (P202PF11)Sino-UK Industrial
Fund (RP202G0289)LIAS (P202ED10Data Science
Enhancement Fund (P202RE237)Fight for Sight (24NN201);Sino-UK
Education Fund (OP202006)BBSRC (RM32G0178B8
Quantitative spatiotemporal mapping of thermal runaway propagation rates in lithium-ion cells using cross-correlated Gabor filtering
Abuse testing of lithium-ion batteries is widely performed in order to develop new safety standards and strategies. However, testing methodologies are not standardised across the research community, especially with failure mechanisms being inherently difficult to reproduce. High-speed X-ray radiography is proven to be a valuable tool to capture events occurring during cell failure, but the observations made remain largely qualitative. We have therefore developed a robust image processing toolbox that can quantify, for the first time, the rate of propagation of battery failure mechanisms revealed by high-speed X-ray radiography. Using Gabor filter, the toolbox selectively tracks the electrode structure at the onset of failure. This facilitated the estimation of the displacement of electrodes undergoing abuse via nail penetration, and also the tracking of objects, such as the nail, as it propagates through a cell. Further, by cross-correlating the Gabor signals, we have produced practical, illustrative spatiotemporal maps of the failure events. From these, we can quantify the propagation rates of electrode displacement prior to the onset of thermal runaway. The highest recorded acceleration (≈514 mm s−2) was when a nail penetrated a cell radially (perpendicular to the electrodes) as opposed to axially (parallel to the electrodes). The initiation of thermal runaway was also resolved in combination with electrode displacement, which occurred at a lower acceleration (≈108 mm s−2). Our assistive toolbox can also be used to study other types of failure mechanisms, extracting otherwise unattainable kinetic data. Ultimately, this tool can be used to not only validate existing theoretical mechanical models, but also standardise battery failure testing procedures
- …