Combination of Accumulated Motion and Color Segmentation for Human Activity Analysis

The automated analysis of activity in digital multimedia, and especially video, is gaining more and more importance due to the evolution of higher-level video processing systems and the development of relevant applications such as surveillance and sports. This paper presents a novel algorithm for the recognition and classification of human activities, which employs motion and color characteristics in a complementary manner, so as to extract the most information from both sources, and overcome their individual limitations. The proposed method accumulates the flow estimates in a video, and extracts “regions of activity†by processing their higher-order statistics. The shape of these activity areas can be used for the classification of the human activities and events taking place in a video and the subsequent extraction of higher-level semantics. Color segmentation of the active and static areas of each video frame is performed to complement this information. The color layers in the activity and background areas are compared using the earth mover's distance, in order to achieve accurate object segmentation. Thus, unlike much existing work on human activity analysis, the proposed approach is based on general color and motion processing methods, and not on specific models of the human body and its kinematics. The combined use of color and motion information increases the method robustness to illumination variations and measurement noise. Consequently, the proposed approach can lead to higher-level information about human activities, but its applicability is not limited to specific human actions. We present experiments with various real video sequences, from sports and surveillance domains, to demonstrate the effectiveness of our approach

Fast watermarking of MPEG-1/2 streams using compressed-domain perceptual embedding and a generalized correlator detector

A novel technique is proposed for watermarking of MPEG-1 and MPEG-2 compressed video streams. The proposed scheme is applied directly in the domain of MPEG-1 system streams and MPEG-2 program streams (multiplexed streams). Perceptual models are used during the embedding process in order to avoid degradation of the video quality. The watermark is detected without the use of the original video sequence. A modified correlation-based detector is introduced that applies nonlinear preprocessing before correlation. Experimental evaluation demonstrates that the proposed scheme is able to withstand several common attacks. The resulting watermarking system is very fast and therefore suitable for copyright protection of compressed video

Deep learning-based super-resolution and de-noising for XMM-newton images

The field of artificial intelligence based image enhancement has been rapidly evolving over the last few years and is able to produce impressive results on non-astronomical images. In this work, we present the first application of Machine Learning based super-resolution (SR) and de-noising (DN) to enhance X-ray images from the European Space Agency's XMM-Newton telescope. Using XMM-Newton images in band [0.5, 2] keV from the European Photon Imaging Camera pn detector (EPIC-pn), we develop XMM-SuperRes and XMM-DeNoise - deep learning-based models that can generate enhanced SR and DN images from real observations. The models are trained on realistic XMM-Newton simulations such that XMM-SuperRes will output images with two times smaller point-spread function and with improved noise characteristics. The XMM-DeNoise model is trained to produce images with 2.5× the input exposure time from 20 to 50 ks. When tested on real images, DN improves the image quality by 8.2 per cent, as quantified by the global peak-signal-to-noise ratio. These enhanced images allow identification of features that are otherwise hard or impossible to perceive in the original or in filtered/smoothed images with traditional methods. We demonstrate the feasibility of using our deep learning models to enhance XMM-Newton X-ray images to increase their scientific value in a way that could benefit the legacy of the XMM-Newton archive

MindSpaces:Art-driven Adaptive Outdoors and Indoors Design

MindSpaces provides solutions for creating functionally and emotionally appealing architectural designs in urban spaces. Social media services, physiological sensing devices and video cameras provide data from sensing environments. State-of-the-Art technology including VR, 3D design tools, emotion extraction, visual behaviour analysis, and textual analysis will be incorporated in MindSpaces platform for analysing data and adapting the design of spaces.</p

Fusion of Frequency and Spatial Domain Information for Motion Analysis

172 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2005.This thesis investigates new approaches for the analysis of multiple motions in video, which integrates frequency and spatial-domain information. The tasks of interest are finding the number of moving objects, velocity estimation, object tracking, and motion segmentation. The proposed hybrid approach performs the motion estimation based on frequency-domain information, but also uses spatial information for precise object localization. Unlike existing frequency-domain methods, the use of this hybrid approach is not limited to constant translational motions, but can also address the problem of roto-translational and nonconstant motions. Frequency information is also used to detect and characterize multiple periodic motions in a video sequence. For this purpose, two methods using time-frequency distributions are presented. The first method is based on the time-frequency analysis of spatial projections of the video sequence, which is computationally efficient and leads to reliable results. The second method overcomes errors introduced by the projection method, by performing the analysis of the sequence in two dimensions. The resulting period estimates are then used to extract the periodically moving objects. The validity, effectiveness, and potential of all proposed approaches is verified through experiments with both synthetic and real video sequences.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Fusion of Frequency and Spatial Domain Information for Motion Analysis

172 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2005.This thesis investigates new approaches for the analysis of multiple motions in video, which integrates frequency and spatial-domain information. The tasks of interest are finding the number of moving objects, velocity estimation, object tracking, and motion segmentation. The proposed hybrid approach performs the motion estimation based on frequency-domain information, but also uses spatial information for precise object localization. Unlike existing frequency-domain methods, the use of this hybrid approach is not limited to constant translational motions, but can also address the problem of roto-translational and nonconstant motions. Frequency information is also used to detect and characterize multiple periodic motions in a video sequence. For this purpose, two methods using time-frequency distributions are presented. The first method is based on the time-frequency analysis of spatial projections of the video sequence, which is computationally efficient and leads to reliable results. The second method overcomes errors introduced by the projection method, by performing the analysis of the sequence in two dimensions. The resulting period estimates are then used to extract the periodically moving objects. The validity, effectiveness, and potential of all proposed approaches is verified through experiments with both synthetic and real video sequences.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD