Self-adjusted active contours using multi-directional texture cues

Parameterization is an open issue in active contour research, associated with the cumbersome and time-consuming process of empirical adjustment. This work introduces a novel framework for self-adjustment of region-based active contours, based on multi-directional texture cues. The latter are mined by applying filtering transforms characterized by multi-resolution, anisotropy, localization and directionality. This process yields to entropy-based image “heatmaps”, used to weight the regularization and data fidelity terms, which guide contour evolution. Experimental evaluation is performed on a large benchmark dataset as well as on textured images. Τhe segmentation results demonstrate that the proposed framework is capable of accelerating contour convergence, maintaining a segmentation quality which is comparable to the one obtained by empirically adjusted active contours

PU learning-based recognition of structural elements in architectural floor plans

This work introduces a computational method for the recognition of structural elements in architectural floor plans. The proposed method requires minimal user interaction and is capable of effectively analysing floor plans in order to identify different types of structural elements in various notation styles. It employs feature extraction based on Haar kernels and PU learning, in order to retrieve image regions, which are similar to a user-defined query. Most importantly, apart from this user-defined query, the proposed method is not dependent on learning from labelled samples. Therefore, there is no need for laborious annotations to form large datasets in various notation styles. The experimental evaluation has been performed on a publicly available and diverse dataset of floor plans. The results show that the proposed method outperforms a state-of-the-art method, with respect to retrieval accuracy. Further experiments on additional floor plans of various notation styles, demonstrate its general applicability. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature

A computer-aided system for malignancy risk assessment of nodules in thyroid US images based on boundary features

In this paper, a novel computer-based approach is proposed for malignancy risk assessment of thyroid nodules in ultrasound images. The proposed approach is based on boundary features and is motivated by the correlation which has been addressed in medical literature between nodule boundary irregularity and malignancy risk. In addition, local echogenicity variance is utilized so as to incorporate information associated with local echogenicity distribution within nodule boundary neighborhood. Such information is valuable for the discrimination of high-risk nodules with blurred boundaries from medium-risk nodules with regular boundaries. Analysis of variance is performed, indicating that each boundary feature under study provides statistically significant information for the discrimination of thyroid nodules in ultrasound images, in terms of malignancy risk. k-nearest neighbor and support vector machine classifiers are employed for the classification tasks, utilizing feature vectors derived from all combinations of features under study. The classification results are evaluated with the use of the receiver operating characteristic. It is derived that the proposed approach is capable of discriminating between medium-risk and high-risk nodules, obtaining an area under curve, which reaches 0.95. © 2009 Elsevier Ireland Ltd. All rights reserved

Classification of Driving Behaviour using Short-term and Long-term Summaries of Sensor Data

The classification of driving behaviour is important for monitoring driving risk and fuel efficiency, as well as for adaptive driving assistance and car insurance industry. Starting from raw measurements of acceleration and speed, as provided by a telematics device placed on each vehicle, we define features summarizing instantaneous, short-term and long-term driving behaviour. We use these features along with conventional classification approaches, such as k-NN, SVM and decision trees, to distinguish between different types of driving behaviour. Experiments are performed on a dataset comprising time series of measurements. The results lead to the conclusion that the proposed features, along with decision trees, achieve the highest classification accuracy, whereas they outperform RNN-based approaches. © 2020 IEEE

H-V shadow detection based on electromagnetism-like optimization

Shadow detection is useful in a variety of image analysis applications, as it can improve scene understanding. Most of the recent shadow detection approaches use near-infrared (NIR) cameras and deep learning to provide enhanced segmentation of the shadow areas in images. In this paper a novel shadow detection method is proposed, exploiting the perceptual color representation of the HSV color space and a physics-inspired optimization algorithm for image segmentation. The comparative advantage of this method over the state-of-the-art ones is that its performance is comparable without requiring any special equipment, such as NIR cameras, while it is simpler. Quantitative and qualitative experiments on publicly available datasets in comparison with three state-of-the-art methods, validate its effectiveness. © 2021 European Signal Processing Conference, EUSIPCO. All rights reserved

A Variable Background Active Contour Model for Automatic Detection of Thyroid Nodules in Ultrasound Images

Abstract — A novel active contour model named Variable Background Active Contour model is proposed and applied for the detection of thyroid nodules in ultrasound images. The new model offers edge independency, no need for smoothing, ability for topological changes and it is more accurate when compared to the Active Contour Without Edges model. Improved accuracy is achieved by introducing as background a limited image subset which appropriately changes shape to reduce the effects of background inhomogeneity. We validated the proposed model on ultrasound images acquired from 24 patients and the results demonstrate an improvement in accuracy when compared to the Active Contour Without Edges model. I