113 research outputs found
A Universal Latent Fingerprint Enhancer Using Transformers
Forensic science heavily relies on analyzing latent fingerprints, which are
crucial for criminal investigations. However, various challenges, such as
background noise, overlapping prints, and contamination, make the
identification process difficult. Moreover, limited access to real crime scene
and laboratory-generated databases hinders the development of efficient
recognition algorithms. This study aims to develop a fast method, which we call
ULPrint, to enhance various latent fingerprint types, including those obtained
from real crime scenes and laboratory-created samples, to boost fingerprint
recognition system performance. In closed-set identification accuracy
experiments, the enhanced image was able to improve the performance of the
MSU-AFIS from 61.56\% to 75.19\% in the NIST SD27 database, from 67.63\% to
77.02\% in the MSP Latent database, and from 46.90\% to 52.12\% in the NIST
SD302 database. Our contributions include (1) the development of a two-step
latent fingerprint enhancement method that combines Ridge Segmentation with
UNet and Mix Visual Transformer (MiT) SegFormer-B5 encoder architecture, (2)
the implementation of multiple dilated convolutions in the UNet architecture to
capture intricate, non-local patterns better and enhance ridge segmentation,
and (3) the guided blending of the predicted ridge mask with the latent
fingerprint. This novel approach, ULPrint, streamlines the enhancement process,
addressing challenges across diverse latent fingerprint types to improve
forensic investigations and criminal justice outcomes
Towards Better Image Embeddings Using Neural Networks
The primary focus of this dissertation is to study image embeddings extracted by neural networks. Deep Learning (DL) is preferred over traditional Machine Learning (ML) for the reason that feature representations can be automatically constructed from data without human involvement. On account of the effectiveness of deep features, the last decade has witnessed unprecedented advances in Computer Vision (CV), and more real-world applications are expected to be introduced in the coming years.
A diverse collection of studies has been included, covering areas such as person re-identification, vehicle attribute recognition, neural image compression, clustering and unsupervised anomaly detection. More specifically, three aspects of feature representations have been thoroughly analyzed. Firstly, features should be distinctive, i.e., features of samples from distinct categories ought to differ significantly. Extracting distinctive features is essential for image retrieval systems, in which an algorithm finds the gallery sample that is closest to a query sample. Secondly, features should be privacy-preserving, i.e., inferring sensitive information from features must be infeasible. With the widespread adoption of Machine Learning as a Service (MLaaS), utilizing privacy-preserving features prevents privacy violations even if the server has been compromised. Thirdly, features should be compressible, i.e., compact features are preferable as they require less storage space. Obtaining compressible features plays a vital role in data compression.
Towards the goal of deriving distinctive, privacy-preserving and compressible feature representations, research articles included in this dissertation reveal different approaches to improving image embeddings learned by neural networks. This topic remains a fundamental challenge in Machine Learning, and further research is needed to gain a deeper understanding
Learning from small and imbalanced dataset of images using generative adversarial neural networks.
The performance of deep learning models is unmatched by any other approach in supervised computer vision tasks such as image classification. However, training these models requires a lot of labeled data, which are not always available. Labelling a massive dataset is largely a manual and very demanding process. Thus, this problem has led to the development of techniques that bypass the need for labelling at scale. Despite this, existing techniques such as transfer learning, data augmentation and semi-supervised learning have not lived up to expectations. Some of these techniques do not account for other classification challenges, such as a class-imbalance problem. Thus, these techniques mostly underperform when compared with fully supervised approaches. In this thesis, we propose new methods to train a deep model on image classification with a limited number of labeled examples. This was achieved by extending state-of-the-art generative adversarial networks with multiple fake classes and network switchers. These new features enabled us to train a classifier using large unlabeled data, while generating class specific samples. The proposed model is label agnostic and is suitable for different classification scenarios, ranging from weakly supervised to fully supervised settings. This was used to address classification challenges with limited labeled data and a class-imbalance problem. Extensive experiments were carried out on different benchmark datasets. Firstly, the proposed approach was used to train a classification model and our findings indicated that the proposed approach achieved better classification accuracies, especially when the number of labeled samples is small. Secondly, the proposed approach was able to generate high-quality samples from class-imbalance datasets. The samples' quality is evident in improved classification performances when generated samples were used in neutralising class-imbalance. The results are thoroughly analyzed and, overall, our method showed superior performances over popular resampling technique and the AC-GAN model. Finally, we successfully applied the proposed approach as a new augmentation technique to two challenging real-world problems: face with attributes and legacy engineering drawings. The results obtained demonstrate that the proposed approach is effective even in extreme cases
A Comprehensive Overview of Computational Nuclei Segmentation Methods in Digital Pathology
In the cancer diagnosis pipeline, digital pathology plays an instrumental
role in the identification, staging, and grading of malignant areas on biopsy
tissue specimens. High resolution histology images are subject to high variance
in appearance, sourcing either from the acquisition devices or the H\&E
staining process. Nuclei segmentation is an important task, as it detects the
nuclei cells over background tissue and gives rise to the topology, size, and
count of nuclei which are determinant factors for cancer detection. Yet, it is
a fairly time consuming task for pathologists, with reportedly high
subjectivity. Computer Aided Diagnosis (CAD) tools empowered by modern
Artificial Intelligence (AI) models enable the automation of nuclei
segmentation. This can reduce the subjectivity in analysis and reading time.
This paper provides an extensive review, beginning from earlier works use
traditional image processing techniques and reaching up to modern approaches
following the Deep Learning (DL) paradigm. Our review also focuses on the weak
supervision aspect of the problem, motivated by the fact that annotated data is
scarce. At the end, the advantages of different models and types of supervision
are thoroughly discussed. Furthermore, we try to extrapolate and envision how
future research lines will potentially be, so as to minimize the need for
labeled data while maintaining high performance. Future methods should
emphasize efficient and explainable models with a transparent underlying
process so that physicians can trust their output.Comment: 47 pages, 27 figures, 9 table
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