Sliding Window Framework Research Articles

Identification of Road Black Spots Based on the Sliding Window Optimization and Safety Performance Function Development

The sliding window method is a road network screening approach commonly used for identifying black spots. Previous studies have indicated that the selection of window length significantly impacts the black spot identification process. This research proposes a new method that optimizes the sliding window framework by examining its characteristics. The optimization methodology employed in this study is as follows: Firstly, the road is segmented, and for each segment, different scenarios of window lengths are chosen using the Density-Based Spatial Clustering of Applications with Noise algorithm. Next, a Safety Performance Function is developed to calculate the predicted and expected number of crashes, as well as the Potential Safety Improvement, for each window movement across all selected scenarios within the segment. Subsequently, the average differences are calculated using the analysis of variance, and the window length with the lowest dispersion of difference values from the mean is identified as the optimal length for each segment. The case study yielded noteworthy results, indicating that the utilization of the sliding window with optimal lengths led to the identification of 122 high-risk black spot-candidates. These points exhibit a higher crash density, effective length, and greater value in quantitative evaluation tests compared to the results obtained using windows with common fixed lengths.

Robust Depth-Aided RGBD-Inertial Odometry for Indoor Localization

RGB-D cameras such as RealSense and Structure Sensors have been widely used in most robotics systems. This paper presents a system for estimating the trajectory of an RGB-D camera and IMU in indoor environments. The system uses a novel relative pose estimation method that utilizes depth measurements and epipolar constraints for initialization. An adaptive depth estimation method is also proposed, which fuses a depth uncertainty model and multi-view triangulation. In the backend, a sliding window framework is used to optimize the system state by minimizing the residuals of pre-integrated IMU, 3D features re-projection, and 2D features epipolar constraint. The effectiveness of the system is evaluated using publicly available datasets with ground truth trajectories.

Robust RGB-D SLAM Using Point and Line Features for Low Textured Scene.

Three-dimensional (3D) reconstruction using RGB-D camera with simultaneous color image and depth information is attractive as it can significantly reduce the cost of equipment and time for data collection. Point feature is commonly used for aligning two RGB-D frames. Due to lacking reliable point features, RGB-D simultaneous localization and mapping (SLAM) is easy to fail in low textured scenes. To overcome the problem, this paper proposes a robust RGB-D SLAM system fusing both points and lines, because lines can provide robust geometry constraints when points are insufficient. To comprehensively fuse line constraints, we combine 2D and 3D line reprojection error with point reprojection error in a novel cost function. To solve the cost function and filter out wrong feature matches, we build a robust pose solver using the Gauss–Newton method and Chi-Square test. To correct the drift of camera poses, we maintain a sliding-window framework to update the keyframe poses and related features. We evaluate the proposed system on both public datasets and real-world experiments. It is demonstrated that it is comparable to or better than state-of-the-art methods in consideration with both accuracy and robustness.

Acute ischaemic stroke alters the brain's preference for distinct dynamic connectivity states.

Acute ischaemic stroke disturbs healthy brain organization, prompting subsequent plasticity and reorganization to compensate for the loss of specialized neural tissue and function. Static resting state functional MRI studies have already furthered our understanding of cerebral reorganization by estimating stroke-induced changes in network connectivity aggregated over the duration of several minutes. In this study, we used dynamic resting state functional MRI analyses to increase temporal resolution to seconds and explore transient configurations of motor network connectivity in acute stroke. To this end, we collected resting state functional MRI data of 31 patients with acute ischaemic stroke and 17 age-matched healthy control subjects. Stroke patients presented with moderate to severe hand motor deficits. By estimating dynamic functional connectivity within a sliding window framework, we identified three distinct connectivity configurations of motor-related networks. Motor networks were organized into three regional domains, i.e. a cortical, subcortical and cerebellar domain. The dynamic connectivity patterns of stroke patients diverged from those of healthy controls depending on the severity of the initial motor impairment. Moderately affected patients (n = 18) spent significantly more time in a weakly connected configuration that was characterized by low levels of connectivity, both locally as well as between distant regions. In contrast, severely affected patients (n = 13) showed a significant preference for transitions into a spatially segregated connectivity configuration. This configuration featured particularly high levels of local connectivity within the three regional domains as well as anti-correlated connectivity between distant networks across domains. A third connectivity configuration represented an intermediate connectivity pattern compared to the preceding two, and predominantly encompassed decreased interhemispheric connectivity between cortical motor networks independent of individual deficit severity. Alterations within this third configuration thus closely resembled previously reported ones originating from static resting state functional MRI studies post-stroke. In summary, acute ischaemic stroke not only prompted changes in connectivity between distinct networks, but it also caused characteristic changes in temporal properties of large-scale network interactions depending on the severity of the individual deficit. These findings offer new vistas on the dynamic neural mechanisms underlying acute neurological symptoms, cortical reorganization and treatment effects in stroke patients.

Constant-Time Sliding Window Framework with Reduced Memory Footprint and Efficient Bulk Evictions

The fast evolution of data analytics platforms has resulted in an increasing demand for real-time data stream processing. From Internet of Things applications to the monitoring of telemetry generated in large data centers, a common demand for currently emerging scenarios is the need to process vast amounts of data with low latencies, generally performing the analysis process as close to the data source as possible. Stream processing platforms are required to be malleable and absorb spikes generated by fluctuations of data generation rates. Data is usually produced as time series that have to be aggregated using multiple operators, being sliding windows one of the most common abstractions used to process data in real-time. To satisfy the above-mentioned demands, efficient stream processing techniques that aggregate data with minimal computational cost need to be developed. In this paper we present the Monoid Tree Aggregator general sliding window aggregation framework, which seamlessly combines the following features: amortized $O(1)$ time complexity and a worst-case of $O(\log {n})$ between insertions; it provides both a window aggregation mechanism and a window slide policy that are user programmable; the enforcement of the window sliding policy exhibits amortized $O(1)$ computational cost for single evictions and supports bulk evictions with cost $O(\log {n})$ ; and it requires a local memory space of $O(\log {n})$ . The framework can compute aggregations over multiple data dimensions, and has been designed to support decoupling computation and data storage through the use of distributed Key-Value Stores to keep window elements and partial aggregations.

Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging.

Brain functional imaging data, especially functional magnetic resonance imaging (fMRI) data, have been employed to reflect functional integration of the brain. Alteration in brain functional connectivity (FC) is expected to provide potential biomarkers for classifying or predicting brain disorders. In this paper, we present a comprehensive review in order to provide guidance about the available brain FC measures and typical classification strategies. We survey the state-of-the-art FC analysis methods including widely used static functional connectivity (SFC) and more recently proposed dynamic functional connectivity (DFC). Temporal correlations among regions of interest (ROIs), data-driven spatial network and functional network connectivity (FNC) are often computed to reflect SFC from different angles. SFC can be extended to DFC using a sliding-window framework, and intrinsic connectivity states along the time-varying connectivity patterns are typically extracted using clustering or decomposition approaches. We also briefly summarize window-less DFC approaches. Subsequently, we highlight various strategies for feature selection including the filter, wrapper and embedded methods. In terms of model building, we include traditional classifiers as well as more recently applied deep learning methods. Moreover, we review representative applications with remarkable classification accuracy for psychosis and mood disorders, neurodevelopmental disorder, and neurological disorders using fMRI data. Schizophrenia, bipolar disorder, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease and mild cognitive impairment (MCI) are discussed. Finally, challenges in the field are pointed out with respect to the inaccurate diagnosis labeling, the abundant number of possible features and the difficulty in validation. Some suggestions for future work are also provided.

An Effective Method Based on ACF for Aircraft Detection in Remote Sensing Images

Detecting artificial targets, such as aircraft, in satellite images is significant in military and civil applications. Although the performance has improved with the use of more complicated features and better learning methods, effectively handling aircraft with variations of type, pose, and size is still very challenging. To solve this problem, we propose a multiscale sliding-window framework based on aggregate channel features, well-designed features that contain rich information. We also employ a fast feature pyramids algorithm to accelerate multiscale aircraft detection. In this framework, features are trained by Cascade AdaBoost including multiple rounds of bootstrapping that leads to improved overall accuracy. A two-step nonmaximum suppression algorithm is carefully designed based on a given set of detections. Our method shows a competitive performance on the QuickBird images of 0.6 m resolution.

Visual Object Detection Using Cascades of Binary and One-Class Classifiers

We describe an efficient approach to visual object detection that uses short cascades of asymmetric `one class' classifiers to quickly reject negatives (windows not centered on an object of the desired class) within a sliding window framework. Current detectors typically use binary discriminants such as Support Vector Machines or Boosting to implement each stage of the cascade. These treat the positive and negative classes symmetrically. We argue that this is suboptimal because object detectors typically see a great many negative windows with extremely diverse contents and only a few positive ones with comparatively coherent contents. We show that asymmetric representations that focus on tightly modeling the extent of the rare, coherent positive class can lead to simpler classifiers and faster rejection. Our cascades use asymmetric classifiers based on simple convex models to progressively tighten the bound on the positive class. They typically start with a conventional linear SVM for initial pruning, followed by a cascade of linear distance-to-hyperplane and interior-of-hypersphere classifiers and finishing with a kernelized hypersphere classifier. We show that the resulting detectors have competitive performance on the Labeled Faces in the Wild dataset and state-of-the-art performance on the FDDB face detection, ESOGU face detection and INRIA Person datasets. The results on the PASCAL VOC 2007 dataset are also respectable given that they use neither object parts nor context. The one-class formulations provide significant reductions in classifier complexity relative to the corresponding two-class ones, making them suitable for real-world applications.

Representation of binary feature pooling for detection of insulator strings in infrared images

Insulators are the most common equipment in power systems, and the failure of insulators would be the direct threat to the stability and safety of the system. With the advantages of being non-contact and non-destructive, infrared imaging technology is efficient for monitoring and evaluating the thermal condition of insulators. Detecting the position of the insulator string in an infrared image is a crucial step for automatic diagnosis, thus an efficient and robust representation of insulator string is necessary. This paper proposes a novel method for insulator strings detection, and presents an appearance representation for insulator strings in infrared image based on Binary Robust Invariant Scalable Keypoints (BRISK) and Vector of Locally Aggregated Descriptors (VLAD). We name this feature generation method Binary Feature Pooling. A classification model based on Support Vector Machine (SVM) is integrated into multi-scale sliding window framework for locating insulator string in infrared image. Then redundant regions are merged by non-maximum suppression and shape prior knowledge constraints. The classification accuracy of the proposed method is 89.1026% on our standard infrared insulator dataset, and insulator strings under various conditions can be successfully detected. The results show that this method can detect multiple insulator strings in infrared images with low resolution and complex background.

Patch-wise skin segmentation of human body parts via deep neural networks

In human-centric technologies, skin segmentation of body parts is a prerequisite for high-level processing. The traditional method of skin detection is pixel-wise detection coupled with morphological operations. Pixel-wise methods usually generate a number of false samples and outlier skin pixels, which can make it difficult for morphological operations to provide satisfactory results in complex scenarios. Furthermore, in many cases only a coarse region is required (e.g., the bounding-box of the face) rather than detailed pixel-wise labeling. A patch-wise skin segmentation method is proposed based on deep neural networks. Our method treats image patches as processing units instead of pixels, which directly exploits the spatial information of pixels in the detection stage rather than using morphological operations on isolated pixels after detection. An image patch dataset is built and deep skin models (DSMs) are trained based on the new dataset. Trained DSMs are then integrated into a sliding window framework to segment skin regions of the human body parts. Experiments on standard benchmarks demonstrate that DSMs provide more explicit skin region of interest candidates than pixel-wise methods in complex scenarios, and achieve competitive performance on pixel-wise skin detection.

An Enhanced Histogram of Oriented Gradients for Pedestrian Detection

The outstanding Histogram-of-Oriented-Gradients (HOG) feature proposed by Dalal and Triggs is a state-of-art technique for pedestrian detection, and it is usually applied with a linear support vector machine (SVM) in a sliding-window framework. Most other algorithms for pedestrian detection use HOG as the basic feature, and combine other features with HOG to form the feature set. Hence, the HOG feature is actually the most efficient and fundamental feature for pedestrian detection. However, the HOG feature cannot adequately handle scale variation of pedestrians. In addition, simply downsampling an image into a different scale, or decomposing via wavelet into multi-resolution subimages, calculating their HOG feature and combining them cannot enhance performance. Therefore, in this paper, based on the idea of multi-resolution feature descriptors, we propose a new robust edge feature referred to as Enhanced HOG (eHOG). It is a complementary descriptor for the histograms-of-oriented-gradients feature. Though the extraction process of the eHOG descriptor is derived only from HOG itself, similar to the process of extracting edge information from the downscaling image, it retains much more information for the edge gradient than that of the original HOG, without significantly increasing the complexity of computation. In the INRIA pedestrian dataset, many experiments have been conducted with eHOG and HOG, and the results show that the proposed new feature consistently improves the detection rate more than the original HOG feature detector. Particularly, eHOG with a Histogram Intersection Kernel SVM (HIKSVM) classifier has greatly improved performance. These results suggest that eHOG may be a better substitute for HOG for pedestrian detection in many applications.

Approximate inference for dynamic Bayesian networks: sliding window approach

Dynamic Bayesian networks (DBNs) are probabilistic graphical models that have become a ubiquitous tool for compactly describing statistical relationships among a group of stochastic processes. A suite of elaborately designed inference algorithms makes it possible for intelligent systems to use a DBN to make inferences in uncertain conditions. Unfortunately, exact inference or even approximation in a DBN has been proved to be NP-hard and is generally computationally prohibitive. In this paper, we investigate a sliding window framework for approximate inference in DBNs to reduce the computational burden. By introducing a sliding window that moves forward as time progresses, inference at any time is restricted to a quite narrow region of the network. The main contributions to the sliding window framework include an exploration of its foundations, explication of how it operates, and the proposal of two strategies for adaptive window size selection. To make this framework available as an inference engine, the interface algorithm widely used in exact inference is then integrated with the framework for approximate inference in DBNs. After analyzing its computational complexity, further empirical work is presented to demonstrate the validity of the proposed algorithms.

Detecting susceptibility genes for rheumatoid arthritis based on a novel sliding-window approach

With the recent rapid improvements in high-throughout genotyping techniques, researchers are facing a very challenging task of large-scale genetic association analysis, especially at the whole-genome level, without an optimal solution. In this study, we propose a new approach for genetic association analysis based on a variable-sized sliding-window framework. This approach employs principal component analysis to find the optimal window size. Using the bisection algorithm in window size searching, the proposed method tackles the exhaustive computation problem. It is more efficient and effective than currently available approaches. We conduct the genome-wide association study in Genetic Analysis Workshop 16 (GAW16) Problem 1 data using the proposed method. Our method successfully identified several susceptibility genes that have been reported by other researchers and additional candidate genes for follow-up studies.

A Variable‐Sized Sliding‐Window Approach for Genetic Association Studies via Principal Component Analysis

Recently with the rapid improvements in high-throughout genotyping techniques, researchers are facing the very challenging task of analysing large-scale genetic associations, especially at the whole-genome level, without an optimal solution. In this study, we propose a new approach for genetic association analysis that is based on a variable-sized sliding-window framework and employs principal component analysis to find the optimum window size. With the help of the bisection algorithm in window-size searching, our method is more computationally efficient than available approaches. We evaluate the performance of the proposed method by comparing it with two other methods-a single-marker method and a variable-length Markov chain method. We demonstrate that, in most cases, the proposed method out-performs the other two methods. Furthermore, since the proposed method is based on genotype data, it does not require any computationally intensive phasing program to account for uncertain haplotype phase.

Target tracking with incomplete detection

In this paper, we address the multiple target tracking problem as a maximum a posteriori problem. We adopt a graph representation of all observations over time. To make full use of the visual observations from the image sequence, we introduce both motion and appearance likelihood. The multiple target tracking problem is formulated as finding multiple optimal paths in the graph. Due to the noisy foreground segmentation, an object may be represented by several foreground regions and similarly one foreground region may correspond to multiple objects. To deal with this problem, we propose merge, split and mean shift operations to generate new hypotheses to the measurement graph. The proposed approach uses a sliding window framework, that aggregates information across a fixed number of frames. Experimental results on both indoor and outdoor data sets are reported. Furthermore, we provide a comparison between the proposed approach with the existing methods that do not merge/split detected blobs.

Genome‐wide autozygosity mapping in human populations

Individuals are frequently observed to have long segments of uninterrupted sequences of homozygous markers. One of the major mechanisms that gives rise to such long homozygous segments is consanguineous marriages, where parents pass shared chromosomal segments to their child. Such chromosomal segments are also known as autozygous segments. The clinical evidence that progeny from inbred individuals may have reduced health and fitness because of homozygosity of recessive alleles is well known. As the length of such homozygous segments depends on the degree of parental consanguinity, it would be logical to observe shorter homozygous segments in more outbred populations. However, a recent study identified long homozygous regions, thus likely to be autozygous segments in the HapMap populations. While an abundance of homozygous segments may significantly reduce the ability to fine map disease genes using association studies, detecting tracts of extended homozygosity related to disease status seems the natural next step in genome-wide association studies beyond allele, genotype and haplotype association analyses. In this study, we propose a new algorithm to map disease-related segments based on autozygosity using case-control data. The underlying rationale for the proposed method is that shared autozygosity regions that differ between diseased and healthy individuals may harbor mutations underlying diseases. Specifically, our algorithm uses a sliding-window framework and employs a logarithm of the odds score measure of autozygosity coupled with permutation-based methods to identify disease-related regions. We illustrate the advantage of the algorithm with its application to a genome-wide association study on Parkinson's disease.

Association Mapping via Regularized Regression Analysis of Single-Nucleotide–Polymorphism Haplotypes in Variable-Sized Sliding Windows

Large-scale haplotype association analysis, especially at the whole-genome level, is still a very challenging task without an optimal solution. In this study, we propose a new approach for haplotype association analysis that is based on a variable-sized sliding-window framework and employs regularized regression analysis to tackle the problem of multiple degrees of freedom in the haplotype test. Our method can handle a large number of haplotypes in association analyses more efficiently and effectively than do currently available approaches. We implement a procedure in which the maximum size of a sliding window is determined by local haplotype diversity and sample size, an attractive feature for large-scale haplotype analyses, such as a whole-genome scan, in which linkage disequilibrium patterns are expected to vary widely. We compare the performance of our method with that of three other methods--a test based on a single-nucleotide polymorphism, a cladistic analysis of haplotypes, and variable-length Markov chains--with use of both simulated and experimental data. By analyzing data sets simulated under different disease models, we demonstrate that our method consistently outperforms the other three methods, especially when the region under study has high haplotype diversity. Built on the regression analysis framework, our method can incorporate other risk-factor information into haplotype-based association analysis, which is becoming an increasingly necessary step for studying common disorders to which both genetic and environmental risk factors contribute.