Unsupervised Segmentation of Action Segments in Egocentric Videos using Gaze

Unsupervised segmentation of action segments in egocentric videos is a desirable feature in tasks such as activity recognition and content-based video retrieval. Reducing the search space into a finite set of action segments facilitates a faster and less noisy matching. However, there exist a substantial gap in machine understanding of natural temporal cuts during a continuous human activity. This work reports on a novel gaze-based approach for segmenting action segments in videos captured using an egocentric camera. Gaze is used to locate the region-of-interest inside a frame. By tracking two simple motion-based parameters inside successive regions-of-interest, we discover a finite set of temporal cuts. We present several results using combinations (of the two parameters) on a dataset, i.e., BRISGAZE-ACTIONS. The dataset contains egocentric videos depicting several daily-living activities. The quality of the temporal cuts is further improved by implementing two entropy measures.Comment: To appear in 2017 IEEE International Conference On Signal and Image Processing Application

Automatic detection of malignant prostatic gland units in cross-sectional microscopic images

Prostate cancer is the second most frequent cause of cancer deaths among men in the US. In the most reliable screening method, histological images from a biopsy are examined under a microscope by pathologists. In an early stage of prostate cancer, only relatively few gland units in a large region become malignant. Discovering such sparse malignant gland units using a microscope is a labor-intensive and error-prone task for pathologists. In this paper, we develop effective image segmentation and classification methods for automatic detection of malignant gland units in microscopic images. Both segmentation and classification methods are based on carefully designed feature descriptors, including color histograms and texton co-occurrence tables. © 2010 IEEE.published_or_final_versionThe 17th IEEE International Conference on Image Processing (ICIP 2010), Hong Kong, China, 26-29 September 2010. In Proceedings of the 17th ICIP, 2010, p. 1057-106

Automated Extraction of Biomarkers for Alzheimer's Disease from Brain Magnetic Resonance Images

In this work, different techniques for the automated extraction of biomarkers for Alzheimer's disease (AD) from brain magnetic resonance imaging (MRI) are proposed. The described work forms part of PredictAD (www.predictad.eu), a joined European research project aiming at the identification of a unified biomarker for AD combining different clinical and imaging measurements. Two different approaches are followed in this thesis towards the extraction of MRI-based biomarkers: (I) the extraction of traditional morphological biomarkers based on neuronatomical structures and (II) the extraction of data-driven biomarkers applying machine-learning techniques. A novel method for a unified and automated estimation of structural volumes and volume changes is proposed. Furthermore, a new technique that allows the low-dimensional representation of a high-dimensional image population for data analysis and visualization is described. All presented methods are evaluated on images from the Alzheimer's Disease Neuroimaging Initiative (ADNI), providing a large and diverse clinical database. A rigorous evaluation of the power of all identified biomarkers to discriminate between clinical subject groups is presented. In addition, the agreement of automatically derived volumes with reference labels as well as the power of the proposed method to measure changes in a subject's atrophy rate are assessed. The proposed methods compare favorably to state-of-the art techniques in neuroimaging in terms of accuracy, robustness and run-time

Efficient Scene Text Localization and Recognition with Local Character Refinement

An unconstrained end-to-end text localization and recognition method is presented. The method detects initial text hypothesis in a single pass by an efficient region-based method and subsequently refines the text hypothesis using a more robust local text model, which deviates from the common assumption of region-based methods that all characters are detected as connected components. Additionally, a novel feature based on character stroke area estimation is introduced. The feature is efficiently computed from a region distance map, it is invariant to scaling and rotations and allows to efficiently detect text regions regardless of what portion of text they capture. The method runs in real time and achieves state-of-the-art text localization and recognition results on the ICDAR 2013 Robust Reading dataset

Automatic segmentation of object region using Graph Cuts based on saliency maps and AdaBoost

Abstract—In conventional methods for region segmentation of objects, the best segmentation results have been obtained by semi-automatic or interactive methods that require a small amount of user input. In this study, we propose a new technique for automatically obtaining segmentation of a flower region by using visual attention (saliency maps) as the prior probability in Graph Cuts. First, AdaBoost determines an approximate flower location using a rectangular window in order to learn the object and background color information using two Gaussian mixture models. We then extract visual attention using saliency maps of the image, and used them as a prior probability of the object model (spatial information). Bayes ’ theorem gives a posterior probability using the prior probability and the likelihood from GMMs, and the posterior probability is used as t-link cost in Graph Cuts, where no manual labeling of image regions is required. The effectiveness of our approach is confirmed by experiments of region segmentation on flower images. I