Feature-based tracking of urethral motion in low-resolution trans-perineal ultrasound

This paper describes a novel algorithm for tracking the motion of the urethra from trans-perineal ultrasound. Our work is based on the structure-from-motion paradigm and therefore handles well structures with ill-defined and partially missing boundaries. The proposed approach is particularly well-suited for video sequences of low resolution and variable levels of blurriness introduced by anatomical motion of variable speed. Our tracking method identifies feature points on a frame by frame basis using the SURF detector/descriptor. Inter-frame correspondence is achieved using nearest-neighbor matching in the feature space. The motion is estimated using a non-linear bi-quadratic model, which adequately describes the deformable motion of the urethra. Experimental results are promising and show that our algorithm performs well when compared to manual tracking

Skeleton-based temporal segmentation of human activities from video sequences

This paper presents a new multi-step, skeleton-based approach for the temporal segmentation of human activities from video sequences. Several signals are first extracted from a skeleton sequence. These signals are then segmented individually to localize their cyclic segments. Finally, all individual segmentations are merged with respect to the global set of signals. Our approach requires no prior knowledge on human activities and can use any generic stick-model. Two different techniques for signal segmentation and for the fusion of the individual segmentations are proposed and tested on a database of fifteen video sequences of variable level of complexity

Computing viewnormalized body parts trajectories

This paper proposes an approach to compute viewnormalized body part trajectories of pedestrians from monocular video sequences. The proposed approach first extracts the 2D trajectories of both feet and of the head from tracked silhouettes. On that basis, it segments the walking trajectory into piecewise linear segments. Finally, a normalization process is applied to head and feet trajectories over each obtained straight walking segment. View normalization makes head and feet trajectories appear as if seen from a fronto-parallel viewpoint. The latter is assumed to be optimal for gait modeling and recognition purposes. The proposed approach is fully automatic as it requires neither manual initialization nor camera calibration. 1

Semantic segmentation of textured mosaics

Abstract This paper investigates deep learning (DL)-based semantic segmentation of textured mosaics. Existing popular datasets for mosaic texture segmentation, designed prior to the DL era, have several limitations: (1) training images are single-textured and thus differ from the multi-textured test images; (2) training and test textures are typically cut out from the same raw images, which may hinder model generalization; (3) each test image has its own limited set of training images, thus forcing an inefficient training of one model per test image from few data. We propose two texture segmentation datasets, based on the existing Outex and DTD datasets, that are suitable for training semantic segmentation networks and that address the above limitations: SemSegOutex focuses on materials acquired under controlled conditions, and SemSegDTD focuses on visual attributes of textures acquired in the wild. We also generate a synthetic version of SemSegOutex via texture synthesis that can be used in the same way as standard random data augmentation. Finally, we study the performance of the state-of-the-art DeepLabv3+ for textured mosaic segmentation, which is excellent for SemSegOutex and variable for SemSegDTD. Our datasets allow us to analyze results according to the type of material, visual attributes, various image acquisition artifacts, and natural versus synthetic aspects, yielding new insights into the possible usage of recent DL technologies for texture analysis