Background Pixel - Explore the Science & Experts | ideXlab

Scan Science and Technology

Contact Leading Edge Experts & Companies

Background Pixel

The Experts below are selected from a list of 4944 Experts worldwide ranked by ideXlab platform

Wei An – 1st expert on this subject based on the ideXlab platform

  • A Constrained Sparse Representation Model for Hyperspectral Anomaly Detection
    IEEE Transactions on Geoscience and Remote Sensing, 2019
    Co-Authors: Qiang Ling, Wei An

    Abstract:

    In this paper, we propose a novel sparsity-based algorithm for anomaly detection in hyperspectral imagery. The algorithm is based on the concept that a Background Pixel can be approximately represented as a sparse linear combination of its spatial neighbors while an anomaly Pixel cannot if the anomalies are removed from its neighborhood. To be physically meaningful, the sum-to-one and nonnegativity constraints are imposed to abundance vector based on the linear mixture model, and the upper bound constraint on sparsity level is removed for better recovery of the test Pixel. First, the proposed method utilizes the redundant Background information to automatically remove anomalies from the Background dictionary. Then, the reconstruction error obtained by the new Background dictionary is directly used for anomaly detection. Moreover, a kernel version of the proposed method is also derived to completely exploit the nonlinear feature of hyperspectral data. An important advantage of the proposed methods is their capability to adaptively model the Background even when some anomaly Pixels are involved. Extensive experiments have been conducted on three real hyperspectral data sets. It is demonstrated that the proposed detectors achieve a promising detection performance with a relatively low computational cost.

  • A Constrained Sparse-Representation-Based Binary Hypothesis Model for Target Detection in Hyperspectral Imagery
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019
    Co-Authors: Qiang Ling, Wei An

    Abstract:

    In this paper, we propose a novel constrained sparse-representation-based binary hypothesis model for target detection in hyperspectral imagery. This model is based on the concept that a target Pixel can only be linearly represented by the union dictionary combined by the Background dictionary and target dictionary, while a Background Pixel can be linearly represented by both the Background dictionary and the union dictionary. To be physically meaningful, the non-negativity constraint is imposed to the weight vector. To suppress the target signals in the Background dictionary, the upper bound constraint is also imposed to the weight vector. These upper bounds are adaptively estimated by the similarities between the atoms in the Background dictionary and target. Then, the weight vectors under different hypotheses are recovered by a fast coordinate descent method. Finally, both the residual difference and weight difference between the two hypotheses are used to perform the target detection. An important advantage of the proposed method is the robustness to varying target contamination. Extensive experiments conducted on real and synthetic hyperspectral datasets have demonstrated the superiority of the proposed detector in detection performance and computational cost. Specifically, for the Avon dataset, our method achieves the highest area under the receiver operating characteristic (ROC) curve of 99.91%, and achieves the shortest runtime of 109.76 s.

Chunheng Wang – 2nd expert on this subject based on the ideXlab platform

  • ICPR (1) – Document Image Binarization Based on Stroke Enhancement
    18th International Conference on Pattern Recognition (ICPR'06), 2006
    Co-Authors: Chunheng Wang

    Abstract:

    This paper proposes a novel document image binarization approach based on stroke neighborhood enhancement. First, foreground Pixels are initially labeled. Then, the strokes are enhanced based on their neighborhood information which include gradient information and foreground-Background Pixel distances of foreground Pixels and their neighboring Background Pixels. At last, the enhanced image is finally binarized. This approach can provide good result for those document images suffering from lighting variance, low resolution and blurring.

  • Document Image Binarization Based on Stroke Enhancement
    18th International Conference on Pattern Recognition (ICPR'06), 2006
    Co-Authors: Chunheng Wang

    Abstract:

    This paper proposes a novel document image binarization approach based on stroke neighborhood enhancement. First, foreground Pixels are initially labeled. Then, the strokes are enhanced based on their neighborhood information which include gradient information and foreground-Background Pixel distances of foreground Pixels and their neighboring Background Pixels. At last, the enhanced image is finally binarized. This approach can provide good result for those document images suffering from lighting variance, low resolution and blurring

Hugh L. Kennedy – 3rd expert on this subject based on the ideXlab platform

  • Spatio-temporal Whitening of Imaging Sensor Data Streams Using Three-Dimensional Linear Prediction
    2008 Digital Image Computing: Techniques and Applications, 2008
    Co-Authors: Hugh L. Kennedy

    Abstract:

    A three-dimensional whitening filter based on linear prediction is described. It is designed to remove textured Backgrounds, with spatial and temporal correlation, in data streams acquired from imaging electro-optic sensors operating in visible, Ultra-Violet (UV) or Infra-Red (IR) bands. The Background is modeled as an Auto-Regressive (AR) process. The operation of the filter is examined using real images of ocean waves obtained using a video camera with a synthetic, dim, sub-Pixel target, inserted. Optimal filter parameters are determined using Receiver Operating Characteristics (ROCs). The output of the whitener is filtered using a Probabilistic Data Association (PDA) filter with automatic track initiation. The whitener/tracker combination yields an average true-track confirmation delay of 0.303 s when the average false-track confirmation rate is 1.33 s-1, for a barely visible point-target that is 10% brighter than the Background Pixel that it replaces.