Real-time Descriptor Research Articles

Time Evolution of Plasmonic Features in Pentagonal Ag Clusters.

In the present work, we apply recently developed real-time descriptors to study the time evolution of plasmonic features of pentagonal Ag clusters. The method is based on the propagation of the time-dependent Schrödinger equation within a singly excited TDDFT ansatz. We use transition contribution maps (TCMs) and induced density to characterize the optical longitudinal and transverse response of such clusters, when interacting with pulses resonant with the low-energy (around 2-3 eV, A1) size-dependent or the high-energy (around 4 eV, E1) size-independent peak. TCMs plots on the analyzed clusters, Ag25+ and Ag43+ show off-diagonal peaks consistent with a plasmonic response when a longitudinal pulse resonant at A1 frequency is applied, and dominant diagonal spots, typical of a molecular transition, when a transverse E1 pulse is employed. Induced densities confirm this behavior, with a dipole-like charge distribution in the first case. The optical features show a time delay with respect to the evolution of the external pulse, consistent with those found in the literature for real-time TDDFT calculations on metal clusters.

Evaluation of Indirect Methods for Motion Compensation in 2-D Focal Liver Lesion Contrast-Enhanced Ultrasound (CEUS) Imaging

This study investigates the application and evaluation of existing indirect methods, namely point-based registration techniques, for the estimation and compensation of observed motion included in the 2-D image plane of contrast-enhanced ultrasound (CEUS) cine-loops recorded for the characterization and diagnosis of focal liver lesions (FLLs). The value of applying motion compensation in the challenging modality of CEUS is to assist in the quantification of the perfusion dynamics of an FLL in relation to its parenchyma, allowing for a potentially accurate diagnostic suggestion. Towards this end, this study also proposes a novel quantitative multi-level framework for evaluating the quantification of FLLs, which to the best of our knowledge remains undefined, notwithstanding many relevant studies. Following quantitative evaluation of 19 indirect algorithms and configurations, while also considering the requirement for computational efficiency, our results suggest that the “compact and real-time descriptor” (CARD) is the optimal indirect motion compensation method in CEUS.

Design and Evaluation of Compact Real-time Descriptor for Camera Motion Estimation

This paper proposes the image processing algorithm of camera motion estimation that can be used for autonomous mobile robots. Most autonomous mobile robots need to quickly recognize the surroundings so as to move to the space. Moreover, the image processing algorithm requires low computation complexity because the mobile systems need to fulfill the immediate reaction for the spatial movement. Therefore, Compact and Real-time Descriptors (CARD) was proposed in order to extract local image features for corresponding to keypoints between two images. Based on CARD, the proposed image feature descriptor has the ability of scale-invariance and rotation-invariance for extracting image features and is represented by binary description so as to use small memory capacity. Our method uses a multi-scale image pyramid to extract coordinates for keypoints and a log-polar binning pattern for a patch around the keypoints. The results of correspondence for keypoints between two images were good enough to estimate the camera motion, where comparisons to other descriptors such as SIFT, SURF, and FREAK were evaluated.

Detecting violent and abnormal crowd activity using temporal analysis of grey level co-occurrence matrix (GLCM)-based texture measures

The severity of sustained injury resulting from assault-related violence can be minimised by reducing detection time. However, it has been shown that human operators perform poorly at detecting events found in video footage when presented with simultaneous feeds. We utilise computer vision techniques to develop an automated method of abnormal crowd detection that can aid a human operator in the detection of violent behaviour. We observed that behaviour in city centre environments often occurs in crowded areas, resulting in individual actions being occluded by other crowd members. We propose a real-time descriptor that models crowd dynamics by encoding changes in crowd texture using temporal summaries of grey level co-occurrence matrix features. We introduce a measure of inter-frame uniformity and demonstrate that the appearance of violent behaviour changes in a less uniform manner when compared to other types of crowd behaviour. Our proposed method is computationally cheap and offers real-time description. Evaluating our method using a privately held CCTV dataset and the publicly available Violent Flows, UCF Web Abnormality and UMN Abnormal Crowd datasets, we report a receiver operating characteristic score of 0.9782, 0.9403, 0.8218 and 0.9956, respectively.