Yuehuan Wang

Integrable pressure gradients via harmonics-based orthogonal projection

In the past, several methods based on iterative solution of pressure-Poisson equation have been developed for measurement of pressure from phase-contrast magnetic resonance (PC-MR) data. In this paper, a non-iterative harmonics-based orthogonal projection method is discussed which can keep the pressures measured based on the Navier-Stokes equation independent of the path of integration. The gradient of pressure calculated with Navier-Stokes equation is expanded with a series of orthogonal basis functions, and is subsequently projected onto an integrable subspace. Before the projection step however, a scheme is devised to eliminate the discontinuity at the vessel boundaries. The approach was applied to velocities obtained from computational fluid dynamics (CFD) simulations of stenotic flow and compared with pressures independently obtained by CFD. Additionally, MR velocity data measured in in-vitro phantom models with different degree of stenoses and different flow rates were used to test the algorithm and results were compared with CFD simulations. The pressure results obtained from the new method were also compared with pressures calculated by an iterative solution to the pressure-Poisson equation. Experiments have shown that the proposed approach is faster and is less sensitive to noise.

Moving cast shadow detection using online sub-scene shadow modeling and object inner-edges analysis

Abstract In this paper, we propose an adaptive and accurate moving cast shadow detection method employing online sub-scene shadow modeling and object inner-edges analysis for applications of static-camera video surveillance. To describe shadow appearance more accurately, the proposed method builds adaptive online shadow models for sub-scenes with different conditions of irradiance and reflectance. The online shadow models are learned by utilizing Gaussian functions to fit the significant peaks of accumulating histograms, which are calculated from Hue, Saturation and Intensity (HSI) difference of moving objects between background and foreground. Additionally, object inner-edges analysis is adopted to reject camouflages, which are misclassified foreground regions that are highly similar to shadows. Finally, the main shadow regions are expanded to recycle the misclassified shadow pixels based on local color constancy. The proposed algorithm can adaptively handle the shadow appearance changes and camouflages without prior information about illuminations and scenarios. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods.

Pulsatile pressure measurements via harmonics-based orthogonal projection of noisy pressure gradients

In the past, several methods based on iterative solution of pressure-Poisson equation have been developed for measurement of pressure from phase-contrast magnetic resonance (PC-MR) data. We have developed a novel non-iterative harmonics-based orthogonal projection method which can keep the pressures measured based on the Navier-Stokes equation independent of the path of integration. The gradient of pressure calculated with Navier-Stokes equation is expanded with a series of orthogonal basis functions, and is subsequently projected onto an integrable subspace. Before the projection step however, a scheme is devised to eliminate the discontinuity at the vessel boundaries. The approach was applied to noise-added velocities obtained for both steady and pulsatile stenotic flows from computational fluid dynamics (CFD) simulations and compared with pressures independently obtained by CFD. Additionally, MR velocity data for steady flows measured in in-vitro phantom models with different degree of stenoses and different flow rates were used to test the algorithm and results were compared with CFD simulations.

Salient object detection via boosting object-level distinctiveness and saliency refinement☆

Abstract Many salient object detection approaches share the common drawback that they cannot uniformly highlight heterogeneous regions of salient objects, and thus, parts of the salient objects are not discriminated from background regions in a saliency map. In this paper, we focus on this drawback and accordingly propose a novel algorithm that more uniformly highlights the entire salient object as compared to many approaches. Our method consists of two stages: boosting the object-level distinctiveness and saliency refinement. In the first stage, a coarse object-level saliency map is generated based on boosting the distinctiveness of the object proposals in the test images, using a set of object-level features and the Modest AdaBoost algorithm. In the second stage, several saliency refinement steps are executed to obtain a final saliency map in which the boundaries of salient objects are preserved. Quantitative and qualitative comparisons with state-of-the-art approaches demonstrate the superior performance of our approach.

Salient region detection via color spatial distribution determined global contrasts

In this paper, we propose a novel salient region detection method via color spatial distribution determined global contrasts. First, original image is preprocessed by a texture suppression approach, and segmented into superpixels. After that, the color spatial distribution of all superpixels is computed. Then, based on values of the distribution in whole image and boundaries of image, some superpixels are determined as foreground and background queries. Next, two global contrasts based on these queries are computed respectively to produce two different saliency maps. Ultimately, color spatial distribution and the two saliency maps are accumulated to generate final saliency map. Our approach is evaluated on M-SRA 1000 dataset, and the experimental results demonstrate superior performance of our method to eight state-of-the-art approaches.

Salient object detection by multi-level features learning determined sparse reconstruction

We propose a salient object detection algorithm via multilevel features learning determined sparse reconstruction. There are three stages in our method. First, the test image are successively processed by a segmentation and semantic information generation procedures. Second, three kinds of features are extracted from semantic, global, and local levels for each superpixel to train a random forest regressor, the learned regression model is then used to generate an initial saliency map. Third, the ultimate detection result is produced using sparse reconstruction determined by the initial saliency map. Compared with most approaches, the proposed method has two obvious advantages. First, the heterogeneous regions inside salient object are often allocated similar saliency values in saliency map. Second, there are much fewer false positives in our detection results. The superior performance of our method were evaluated on four datasets with 12 state-of-the-art approaches.

Remote Sensing Images Stripe Noise Removal by Double Sparse Regulation and Region Separation

Stripe noise removal continues to be an active field of research for remote image processing. Most existing approaches are prone to generating artifacts in extreme areas and removing the stripe-like details. In this paper, a weighted double sparsity unidirectional variation (WDSUV) model is constructed to reduce this phenomenon. The WDSUV takes advantage of both the spatial domain and the gradient domain’s sparse property of stripe noise, and processes the heavy stripe area, extreme area and regular noise corrupted areas using different strategies. The proposed model consists of two variation terms and two sparsity terms that can well exploit the intrinsic properties of stripe noise. Then, the alternating direction method of multipliers (ADMM) optimal solver is employed to solve the optimization model in an alternating minimization scheme. Compared with the state-of-the-art approaches, the experimental results on both the synthetic and real remote sensing data demonstrate that the proposed model has a better destriping performance in terms of the preservation of small details, stripe noise estimation and in the mean time for artifacts’ reduction.

Patch similarity based edge-preserving background estimation for single frame infrared small target detection

Edges in infrared image usually cause serious false alarms in single frame infrared small target detection. So a novel edge-preserving background estimation method is proposed for small target detection to attenuate this problem. First we will introduce the patch similarity feature of infrared image. Then, patch similarity of infrared image is utilized to formulate edge-preserving infrared background estimation. At last, estimated background will be eliminated from original infrared image to suppress edges. The effective edge-preserving ability of our approach will be shown through experiments and comparisons with state-of-the-art background estimation methods.

Multi-period visual tracking via online DeepBoost learning

In this paper, we propose a novel accurate and robust boosting-style tracking-by-detection method. The proposed algorithm adopts a flexible and capacity-conscious object appearance model, which combines the strengths of both local and global visual representations. We firstly propose a joint local-global visual representation, in which main local and global spatial structure information of the target is flexibly embedded in the candidate classifier set with members from multiple complexity families. In addition, to avoid over-fitting our tracker adopts an effective online DeepBoost learning method (ODB). The key capacity-conscious ability of ODB helps to avoid over-fitting and generate a more adaptive and robust tracker. Furthermore, we propose a multi-period tracking framework (MPTF) to enhance the trackers recovery ability for tracking failures. The proposed Multi-period DeepBoost-Tracker (MPDBT) can well encode the object spatial structures and excellently handle object appearance variations, and it can also recover from tracking failures with the help of the proposed MPTF. The experimental results demonstrate that our tracker outperforms the state-of-the-art trackers. HighlightsWe propose an adaptive visual tracking algorithm via online DeepBoost learning.We propose a combining-local-global visual representation.The capacity-conscious ability of online DeepBoost learning can avoid over-fitting.We propose a multi-period tracking framework to enhance the recovery ability.Our method can achieve higher level of accuracy and robustness.The proposed tracker outperforms the state-of-the-art trackers.

An independent sequential maximum likelihood approach to simultaneous track-to-track association and bias removal

In this paper we propose an independent sequential maximum likelihood approach to address the joint track-to-track association and bias removal in multi-sensor information fusion systems. First, we enumerate all kinds of association situation following by estimating a bias for each association. Then we calculate the likelihood of each association after bias compensated. Finally we choose the maximum likelihood of all association situations as the association result and the corresponding bias estimation is the registration result. Considering the high false alarm and interference, we adopt the independent sequential association to calculate the likelihood. Simulation results show that our proposed method can give out the right association results and it can estimate the bias precisely simultaneously for small number of targets in multi-sensor fusion system.