Spatiotemporal Similarity Research Articles

Direction of Auditory Pitch-Change Influences Visual Search for Slope From Graphs.

Linear trend (slope) is important information conveyed by graphs. We investigated how sounds influenced slope detection in a visual search paradigm. Four bar graphs or scatter plots were presented on each trial. Participants looked for a positive-slope or a negative-slope target (in blocked trials), and responded to targets in a go or no-go fashion. For example, in a positive-slope-target block, the target graph displayed a positive slope while other graphs displayed negative slopes (a go trial), or all graphs displayed negative slopes (a no-go trial). When an ascending or descending sound was presented concurrently, ascending sounds slowed detection of negative-slope targets whereas descending sounds slowed detection of positive-slope targets. The sounds had no effect when they immediately preceded the visual search displays, suggesting that the results were due to crossmodal interaction rather than priming. The sounds also had no effect when targets were words describing slopes, such as "positive," "negative," "increasing," or "decreasing," suggesting that the results were unlikely due to semantic-level interactions. Manipulations of spatiotemporal similarity between sounds and graphs had little effect. These results suggest that ascending and descending sounds influence visual search for slope based on a general association between the direction of auditory pitch-change and visual linear trend.

Diffusion and the Production of Eastern Europe

This article argues that Eastern Europe continues to be defined and redefined not just by the actual patterns of socio-economic and political reproduction of the distant and proximate regimes governing the region but also through the perceptions of such legacies as generating fundamental similarities. Such perceived similarities, whether or not closely mapped on the objective parallels among countries in Eastern Europe, facilitate intra-region diffusion that results in (further) spatio-temporal socio-economic and political similarities specific to the region. To illustrate this relationship between precommunist and communist legacies, intra-region diffusion, and the production of Eastern Europe, the article examines Slovakia’s diffusion entrepreneurship in the wave of electoral breakthroughs in Eastern Europe in the late 1990s and early 2000s. This article contributes to the literatures on Eastern Europe and comparative democratization in two main ways. First, it adds to the work on the impact of spatio-temporal dependence on transition outcomes, such as democratization, in Eastern Europe. Second, by doing so, the article also documents the impact of an understudied set of democracy promoters—the Eastern European countries.

4-D spatiotemporal analysis of ultrasound contrast agent dispersion for prostate cancer localization: a feasibility study.

Currently, nonradical treatment for prostate cancer is hampered by the lack of reliable diagnostics. Contrastultrasound dispersion imaging (CUDI) has recently shown great potential as a prostate cancer imaging technique. CUDI estimates the local dispersion of intravenously injected contrast agents, imaged by transrectal dynamic contrast-enhanced ultrasound (DCE-US), to detect angiogenic processes related to tumor growth. The best CUDI results have so far been obtained by similarity analysis of the contrast kinetics in neighboring pixels. To date, CUDI has been investigated in 2-D only. In this paper, an implementation of 3-D CUDI based on spatiotemporal similarity analysis of 4-D DCE-US is described. Different from 2-D methods, 3-D CUDI permits analysis of the entire prostate using a single injection of contrast agent. To perform 3-D CUDI, a new strategy was designed to estimate the similarity in the contrast kinetics at each voxel, and data processing steps were adjusted to the characteristics of 4-D DCE-US images. The technical feasibility of 4-D DCE-US in 3-D CUDI was assessed and confirmed. Additionally, in a preliminary validation in two patients, dispersion maps by 3-D CUDI were quantitatively compared with those by 2-D CUDI and with 12-core systematic biopsies with promising results.

Classifying dynamic textures via spatiotemporal fractal analysis

The large-scale images and videos are one kind of the main source of big data. Dynamic texture (DT) is essential for understanding the video sequences with spatio-temporal similarities. This paper presents a powerful tool called dynamic fractal analysis to DT description and classification, which integrates rich description of DT with strong robustness to environmental changes. The proposed dynamic fractal spectrum (DFS) for DT sequences is composed of two components. The first one is a volumetric dynamic fractal spectrum component (V-DFS) that captures the stochastic self-similarities of DT sequences by treating them as 3D volumes; the second one is a multi-slice dynamic fractal spectrum component (S-DFS) that encodes fractal structures of repetitive DT patterns on 2D slices along different views of the 3D volume. To fully exploit various types of dynamic patterns in DT, five measurements of DT pixels are collected for the analysis on DT sequences from different perspectives. We evaluated our method on four publicly available benchmark datasets. All the experimental results have demonstrated the excellent performance of our method in comparison with state-of-the-art approaches.

Technical Framework of Feature Extraction Based on Pixel-Level SAR Image Time Series

This study proposes a novel technical framework of feature extraction based on pixel-level synthetic aperture radar (SAR) image time series, to exploit the application potential of SAR image data with low and medium spatial resolution. This framework comprises three key parts: 1) construction of the pixel-level SAR image time series using a new matching technique based on progressive binary partition; 2) pixel-level similarity measurement via dynamic time warping (DTW); and 3) a new spatiotemporal similarity analysis method that improves feature extraction by considering both the similarity of a feature’s pixel-level time series and its spatial correlation. Two locations, covered by 31 low-resolution (150 m) and 26 medium-resolution (30 m) ENVISAT ASAR images, respectively, were selected as test cases to validate the proposed framework. Results show that the framework can identify features with a high level of accuracy, completeness, and correctness, outperforming methods using multitemporal images, as well as the time series-only (nonspatial) method, and other methods of spatiotemporal similarity analysis that use alternative similarity measures.

Spatio-Temporal Signatures of User-Centric Data: How Similar Are We?

Much work has been done on understanding and predicting human mobility in time. In this work, we are interested in obtaining a set of users who are spatio-temporally most similar to a query user. We propose an efficient way of user data representation called Spatio-Temporal Signatures to keep track of complete record of user movement. We define a measure called Spatio-Temporal similarity for comparing a given pair of users. Although computing exact pairwise Spatio-Temporal similarities between query user with all users is inefficient, we show that with our hybrid pruning scheme the most similar users can be obtained in logarithmic time with in a (1+\epsilon) factor approximation of the optimal. We are developing a framework to test our models against a real dataset of urban users.

Spatiotemporal characterization of water chemistry and pollution sources of the Umhlatuzana, Umbilo and Amanzimnyama River catchments of Durban, KwaZulu-Natal, South Africa

The physical, chemical and microbiological properties of three freshwater systems contributing inflows to the Bayhead Canal of the Durban Harbour: the Umhlatuzana and Umbilo Rivers, and the Amanzimnyama Canal of KwaZulu-Natal, South Africa are presented. Parameters targeted for analysis collectively included pH, total dissolved solids, dissolved oxygen, biological oxygen demand, chemical oxygen demand, conductivity, ammonium ions, phosphorous, sodium ions, sulphur, copper, calcium, magnesium, chromium, aluminium, nickel, lead, vanadium, mercury, potassium ions, Escherichiacoli and total coliforms. These parameters were analyzed seasonally during the wet and dry seasons in relation to land use change for spatial characterization. Comparisons with relevant South African Water Quality Guidelines for freshwater systems showed that pollution associated with catchment activities was the main factor governing water quality, with nutrient concentrations that frequently exceeded prescribed standards and often rendered the system hypertrophic. In addition, the sanitary state of the rivers across all land use types was shown to be contaminated and polluted. This study also attempted to determine spatiotemporal (dis)similarity in the water quality of sample sites through Principal Components Analysis (PCA) with varimax rotation. Results show that although these systems were separated on the basis of water quality (both spatially and temporally), there were generally no apparent trends in water quality based on specific land use patterns which linked sites across different catchments. In the wet season, a few exceptions were noted for residential sites across the Umbilo and Umhlatuzana catchments and industrial sites across the Amanzimnyama catchment which showed spatial similarity. Finally, the study examined the impacts of the three freshwater systems on the water quality of the Bayhead Canal of the Durban Harbour, and identified the Amanzimnyama Canal as the most influential on heavy metal and microbiological contamination near the confluence.

Learning-Based Video Superresolution Reconstruction Using Spatiotemporal Nonlocal Similarity

Aiming at improving the video visual resolution quality and details clarity, a novel learning-based video superresolution reconstruction algorithm using spatiotemporal nonlocal similarity is proposed in this paper. Objective high-resolution (HR) estimations of low-resolution (LR) video frames can be obtained by learning LR-HR correlation mapping and fusing spatiotemporal nonlocal similarities between video frames. With the objective of improving algorithm efficiency while guaranteeing superresolution quality, a novel visual saliency-based LR-HR correlation mapping strategy between LR and HR patches is proposed based on semicoupled dictionary learning. Moreover, aiming at improving performance and efficiency of spatiotemporal similarity matching and fusion, an improved spatiotemporal nonlocal fuzzy registration scheme is established using the similarity weighting strategy based on pseudo-Zernike moment feature similarity and structural similarity, and the self-adaptive regional correlation evaluation strategy. The proposed spatiotemporal fuzzy registration scheme does not rely on accurate estimation of subpixel motion, and therefore it can be adapted to complex motion patterns and is robust to noise and rotation. Experimental results demonstrate that the proposed algorithm achieves competitive superresolution quality compared to other state-of-the-art algorithms in terms of both subjective and objective evaluations.

Layered Jurassic intrusions in Antarctica

The paper addresses the current understanding of the inner structure of the layered intrusions at Dufek Massif in the Pensacola Mountains and the Utpostane and Muren intrusions in Queen Maud Land, Antarctica, which are still poorly known to Russian geologists. The magmatic events at approximately 180 Ma, including the emplacement of layered intrusions, are thought to had predated the breakup of the Gondwana supercontinent. The spatiotemporal similarities of the intrusions determine the importance of the problem of whether they were produced by a single or more than one parental magmas, which are thought to have been derived under the effect of a superplume.

Would the ‘real’ observed dataset stand up? A critical examination of eight observed gridded climate datasets for China

This research compared and evaluated the spatio-temporal similarities and differences of eight widely used gridded datasets. The datasets include daily precipitation over East Asia (EA), the Climate Research Unit (CRU) product, the Global Precipitation Climatology Centre (GPCC) product, the University of Delaware (UDEL) product, Precipitation Reconstruction over Land (PREC/L), the Asian Precipitation Highly Resolved Observational (APHRO) product, the Institute of Atmospheric Physics (IAP) dataset from the Chinese Academy of Sciences, and the National Meteorological Information Center dataset from the China Meteorological Administration (CN05). The meteorological variables focus on surface air temperature (SAT) or precipitation (PR) in China. All datasets presented general agreement on the whole spatio-temporal scale, but some differences appeared for specific periods and regions. On a temporal scale, EA shows the highest amount of PR, while APHRO shows the lowest. CRU and UDEL show higher SAT than IAP or CN05. On a spatial scale, the most significant differences occur in western China for PR and SAT. For PR, the difference between EA and CRU is the largest. When compared with CN05, CRU shows higher SAT in the central and southern Northwest river drainage basin, UDEL exhibits higher SAT over the Southwest river drainage system, and IAP has lower SAT in the Tibetan Plateau. The differences in annual mean PR and SAT primarily come from summer and winter, respectively. Finally, potential factors impacting agreement among gridded climate datasets are discussed, including raw data sources, quality control (QC) schemes, orographic correction, and interpolation techniques. The implications and challenges of these results for climate research are also briefly addressed.

Temporally Coherent Video Saliency Using Regional Dynamic Contrast

Saliency detection for images and videos has become increasingly popular due to its wide applicability. In this paper, we present a new method that takes advantage of region-based visual dynamic contrast to generate temporally coherent video saliency maps. The concept of visual dynamics is formulated to represent both visual and motional variabilities of video content. Moreover, the regions are regarded as primitives for saliency computation by using spatiotemporal appearance contrasts. Then, region matching is performed across successive video frames to form temporally coherent regions, which are computed on the basis of spatiotemporal similarity in the visual dynamics of the different regions along the optical flow in the video. The region matching can effectively eliminate saliency discontinuities, particularly in the areas of oversegmentation that are otherwise highly problematic. The proposed approach is tested on a challenging set of video sequences and is compared with contemporary methods to demonstrate its superior performance in terms of its computational efficiency and ability to detect salient video content.

Salient Motion Detection in Compressed Domain

We propose a novel motion saliency framework using DCT coefficients and motion vectors as features from MPEG-2 videos. The DCT coefficients of the luma and chroma components are used to calculate the spatial saliency of a frame while the motion vectors are utilized to refine it. Spatio-temporal similarity maps are calculated separately in order to cater to tracking shots. Results show that the proposed motion saliency framework is computationally nearly an 8-fold improvement over the best performing state-of-the-art method while nearly equalling its performance in identifying salient regions.

VideoGraph: a non-linear video representation for efficient exploration

In this paper we introduce VideoGraph, a novel non-linear representation for scene structure of a video. Unlike classical linear sequential organization, VideoGraph concentrates the video content across the time line by structuring scenes and materializes with two-dimensional graph, which enables non-linear exploration on the scenes and their transitions. To construct VideoGraph, we adopt a sub-shot induced method to evaluate the spatio-temporal similarity between shot segments of video. Then, scene structure is derived by grouping similar shots and identifying the valid transitions between scenes. The final stage is to represent the scene structure using a graph with respect to scene transition topology. Our VideoGraph can provide a condensed representation in the scene level and facilitate a non-linear manner to browse videos. Experimental results are presented to demonstrate the effectiveness and efficiency by using VideoGraph to explore and access the video content.

Contrast-Ultrasound Dispersion Imaging for Prostate Cancer Localization by Improved Spatiotemporal Similarity Analysis

Angiogenesis plays a major role in prostate cancer growth. Despite extensive research on blood perfusion imaging aimed at angiogenesis detection, the diagnosis of prostate cancer still requires systematic biopsies. This may be due to the complex relationship between angiogenesis and microvascular perfusion. Analysis of ultrasound-contrast-agent dispersion kinetics, determined by multipath trajectories in the microcirculation, may provide better characterization of the microvascular architecture. We propose the physical rationale for dispersion estimation by an existing spatiotemporal similarity analysis. After an intravenous ultrasound-contrast-agent bolus injection, dispersion is estimated by coherence analysis among time-intensity curves measured at neighbor pixels. The accuracy of the method is increased by time-domain windowing and anisotropic spatial filtering for speckle regularization. The results in 12 patient data sets indicated superior agreement with histology (receiver operating characteristic curve area = 0.88) compared with those obtained by reported perfusion and dispersion analyses, providing a valuable contribution to prostate cancer localization.

Spatio-temporal similarity of network-constrained moving object trajectories using sequence alignment of travel locations

Data analysis based on the similarity of vehicle trajectories in a vehicular network is emerging as a new exciting paradigm that is important for law enforcement applications (e.g., the analysis of criminal tracking, road traffic security and traffic scheduling). Spatio-temporal data analysis plays an important role in many applications, including transportation infrastructure, border security and inland security. To analyze the moving patterns of vehicles on a road network, a measure for determining the similarity of vehicle trajectories with respect to space and time has to be defined. Although previous research has addressed the trajectory similarity problem, most of the studies focus on Euclidian distance instead of network distance. This paper deals with the variations in applying a spatio-temporal similarity measure with given Points of Interest (POI) and Time of Interest (TOI), treating spatial similarity as a combination of structural and sequence similarities that is evaluated using the techniques of dynamic programming. The similarity set thus formed will be used by the remote database to broadcast trigger-based messages to participating vehicles in a neighborhood for future route- and information-sharing activities. The performance of the scheme is evaluated using experiments on standard real-life data.

Combined shape and feature-based video analysis and its application to non-rigid object tracking

Many video object tracking systems use block matching algorithm (BMA) because of its simple computational structure and robust performance. The BMA, however, exhibits fundamental limitations resulting from non-rigid shapes and similar patterns to the background. The authors propose a combined shape and feature-based non-rigid object tracking algorithm, which is tightly coupled with an adaptive background generation to overcome the limit of block matching. The proposed algorithm is robust to the object's sudden movement or the change of features. This becomes possible by tracking both feature points and their neighbouring regions. Combination of background and shape boundary information significantly improves the tracking performance because the target object and the corresponding feature points on the boundary can be easily found. The shape control points (SCPs) are regularly distributed on the contour of the object, and the authors compare and update the centroid during the tracking process, where straying SCPs are removed, and the tracking continues with only qualified SCPs. As a result, the proposed method becomes free from potential failing factors such as spatio-temporal similarity between object and background, object deformation and occlusion, to name a few. Experiments have been performed using several in-house video sequences including various objects such as a moving robot, swimming fish and walking people. In order to demonstrate the performance of the proposed tracking algorithm, a number of experiments have been performed under noisy and low-contrast environment. For more objective comparison, performance evaluation of tracking surveillance 2002 data sets were also used.

A New Measurement Method to Calculate Similarity of Moving Object Spatio-Temporal Trajectories by Compact Representation

A new measurement method is proposed to calculate spatio-temporal trajectory similarity, which can reflect the similar degree between two moving object spatio-temporal trajectories compressed by the Maximal Bounding Boxes (MBB). Firstly, the similarity between two trajectories is replaced by the similarity of MBB sequences in respective trajectories which can dramatically decrease the storage volume of the trajectory data. Secondly, some factors affected the similar degree of MBB sequences are analyzed systematically, such as the time duration of overlap between two MBBs in different trajectories, space distance and the density of data points inside the boxes. And then, a similarity measurement formula is proposed by integrating these factors. Experiments show that the proposed measurement formula can improve the value of clustering index Dunn.

Detecting Space-Time Alternating Biological Signals Close to the Bifurcation Point

Time-alternating biological signals, i.e., alternans, arise in variety of physiological states marked by dynamic instabilities, e.g., period doubling. Normally, a sequence of large-small-large transients, they can exhibit variable patterns over time and space, including spatial discordance. Capture of the early formation of such alternating regions is challenging because of the spatiotemporal similarities between noise and the small-amplitude alternating signals close to the bifurcation point. We present a new approach for automatic detection of alternating signals in large noisy spatiotemporal datasets by exploiting quantitative measures of alternans evolution, e.g., temporal persistence, and by preserving phase information. The technique specifically targets low amplitude, relatively short alternating sequences and is validated by combinatorics-derived probabilities and empirical datasets with white noise. Using high-resolution optical mapping in live cardiomyocyte networks, exhibiting calcium alternans, we reveal for the first time early fine-scale alternans, close to the noise level, which are linked to the later formation of larger regions and evolution of spatially discordant alternans. This robust method aims at quantification and better understanding of the onset of cardiac arrhythmias and can be applied to general analysis of space-time alternating signals, including the vicinity of the bifurcation point.

A New Similar Trajectory Search Algorithm Based on Spatio-Temporal Similarity Measure for Moving Objects in Road Networks

The deployment of historical trajectories of moving objects has greatly increased for various applications in road networks. For instance, similar patterns of moving-object trajectories are very useful for designing the transportation network of a new city. In this paper, we define a spatio-temporal similarity measure based on a road network distance, rather than a Euclidean distance. We also propose a new similar trajectory search algorithm based on the spatio-temporal measure by using an efficient pruning mechanism. Finally, we show the efficiency of our algorithm, both in terms of retrieval accuracy and retrieval efficiency.

An automatic identification and removal method for eye-blink artifacts in event-related magnetoencephalographic measurements

While measuring event-related magnetoencephalographic (MEG) signals using visual stimuli, eye blinks were inevitable and generated large magnetic artifacts. Since trials containing eye blinks were excluded from the analyses, the signal-to-noise ratio of the event-related signals was decreased. In this study, we propose a method to identify the eye blink magnetic artifacts and remove them automatically using independent component analysis preprocessed by principal component analysis. The method evaluates the spatiotemporal similarity between independent components and both MEG and electro-oculogram data based on a newly devised cost function. Testing of the method on event-related MEG signals measured by a 306-channel whole-head system in a visual perception task led to the successful identification and removal of eye-blink artifacts in all trials containing eye blinks from all the seven subjects.