8 research outputs found
Unsupervised Image Segmentation using the Deffuant-Weisbuch Model from Social Dynamics
Unsupervised image segmentation algorithms aim at identifying disjoint
homogeneous regions in an image, and have been subject to considerable
attention in the machine vision community. In this paper, a popular theoretical
model with it's origins in statistical physics and social dynamics, known as
the Deffuant-Weisbuch model, is applied to the image segmentation problem. The
Deffuant-Weisbuch model has been found to be useful in modelling the evolution
of a closed system of interacting agents characterised by their opinions or
beliefs, leading to the formation of clusters of agents who share a similar
opinion or belief at steady state. In the context of image segmentation, this
paper considers a pixel as an agent and it's colour property as it's opinion,
with opinion updates as per the Deffuant-Weisbuch model. Apart from applying
the basic model to image segmentation, this paper incorporates adjacency and
neighbourhood information in the model, which factors in the local similarity
and smoothness properties of images. Convergence is reached when the number of
unique pixel opinions, i.e., the number of colour centres, matches the
pre-specified number of clusters. Experiments are performed on a set of images
from the Berkeley Image Segmentation Dataset and the results are analysed both
qualitatively and quantitatively, which indicate that this simple and intuitive
method is promising for image segmentation. To the best of the knowledge of the
author, this is the first work where a theoretical model from statistical
physics and social dynamics has been successfully applied to image processing.Comment: This paper is under consideration at Signal Image and Video
Processing journa
Face Detection and Recognition using Skin Segmentation and Elastic Bunch Graph Matching
Recently, face detection and recognition is attracting a lot of interest in areas such as network security, content indexing and retrieval, and video compression, because ‘people’ are the object of attention in a lot of video or images. To perform such real-time detection and recognition, novel algorithms are needed, which better current efficiencies and speeds. This project is aimed at developing an efficient algorithm for face detection and recognition.
This project is divided into two parts, the detection of a face from a complex environment and the subsequent recognition by comparison. For the detection portion, we present an algorithm based on skin segmentation, morphological operators and template matching. The skin segmentation isolates the face-like regions in a complex image and the following operations of morphology and template matching help reject false matches and extract faces from regions containing multiple faces.
For the recognition of the face, we have chosen to use the ‘EGBM’ (Elastic Bunch Graph Matching) algorithm. For identifying faces, this system uses single images out of a database having one image per person. The task is complex because of variation in terms of position, size, expression, and pose. The system decreases this variance by extracting face descriptions in the form of image graphs. In this, the node points (chosen as eyes, nose, lips and chin) are described by sets of wavelet components (called ‘jets’). Image graph extraction is based on an approach called the ‘bunch graph’, which is constructed from a set of sample image graphs. Recognition is based on a directly comparing these graphs. The advantage of this method is in its tolerance to lighting conditions and requirement of less number of images per person in the database for comparison
A Framework for Auditing Multilevel Models using Explainability Methods
Applications of multilevel models usually result in binary classification
within groups or hierarchies based on a set of input features. For transparent
and ethical applications of such models, sound audit frameworks need to be
developed. In this paper, an audit framework for technical assessment of
regression MLMs is proposed. The focus is on three aspects, model,
discrimination, and transparency and explainability. These aspects are
subsequently divided into sub aspects. Contributors, such as inter MLM group
fairness, feature contribution order, and aggregated feature contribution, are
identified for each of these sub aspects. To measure the performance of the
contributors, the framework proposes a shortlist of KPIs. A traffic light risk
assessment method is furthermore coupled to these KPIs. For assessing
transparency and explainability, different explainability methods (SHAP and
LIME) are used, which are compared with a model intrinsic method using
quantitative methods and machine learning modelling. Using an open source
dataset, a model is trained and tested and the KPIs are computed. It is
demonstrated that popular explainability methods, such as SHAP and LIME,
underperform in accuracy when interpreting these models. They fail to predict
the order of feature importance, the magnitudes, and occasionally even the
nature of the feature contribution. For other contributors, such as group
fairness and their associated KPIs, similar analysis and calculations have been
performed with the aim of adding profundity to the proposed audit framework.
The framework is expected to assist regulatory bodies in performing conformity
assessments of AI systems using multilevel binomial classification models at
businesses. It will also benefit businesses deploying MLMs to be future proof
and aligned with the European Commission proposed Regulation on Artificial
Intelligence.Comment: Submitted at ECIAIR 202
Spectral Data Augmentation Techniques to quantify Lung Pathology from CT-images
Data augmentation is of paramount importance in biomedical image processing
tasks, characterized by inadequate amounts of labelled data, to best use all of
the data that is present. In-use techniques range from intensity
transformations and elastic deformations, to linearly combining existing data
points to make new ones. In this work, we propose the use of spectral
techniques for data augmentation, using the discrete cosine and wavelet
transforms. We empirically evaluate our approaches on a CT texture analysis
task to detect abnormal lung-tissue in patients with cystic fibrosis. Empirical
experiments show that the proposed spectral methods perform favourably as
compared to the existing methods. When used in combination with existing
methods, our proposed approach can increase the relative minor class
segmentation performance by 44.1% over a simple replication baseline.Comment: 5 pages including references, accepted as Oral presentation at IEEE
ISBI 202
Spectral Data Augmentation Techniques to Quantify Lung Pathology from CT-Images
Data augmentation is of paramount importance in biomedical image processing tasks, characterized by inadequate amounts of labelled data, to best use all of the data that is present. In-use techniques range from intensity transformations and elastic deformations, to linearly combining existing data points to make new ones. In this work, we propose the use of spectral techniques for data augmentation, using the discrete cosine and wavelet transforms. We empirically evaluate our approaches on a CT texture analysis task to detect abnormal lung-tissue in patients with cystic fibrosis. Empirical experiments show that the proposed spectral methods perform favourably as compared to the existing methods. When used in combination with existing methods, our proposed approach can increase the relative minor class segmentation performance by 44.1% over a simple replication baseline
Region-of-interest guided supervoxel inpainting for self-supervision
Self-supervised learning has proven to be invaluable in making best use of all of the available data in biomedical image segmentation. One particularly simple and effective mechanism to achieve self-supervision is inpainting, the task of predicting arbitrary missing areas based on the rest of an image. In this work, we focus on image inpainting as the self-supervised proxy task, and propose two novel structural changes to further enhance the performance. Our method can be regarded as an efficient addition to self-supervision, where we guide the process of generating images to inpaint by using supervoxel-based masking instead of random masking, and also by focusing on the area to be segmented in the primary task, which we term as the region-of-interest. We postulate that these additions force the network to learn semantics that are more attuned to the primary task, and test our hypotheses on two applications: brain tumour and white matter hyperintensities segmentation. We empirically show that our proposed approach consistently outperforms both supervised CNNs, without any self-supervision, and conventional inpainting-based self-supervision methods on both large and small training set sizes