MOTIVATION: Accurate detection of copy number variations (CNVs) from targeted panel sequencing remains challenging due to limited genomic coverage and pronounced sample-specific biases. Existing normalization strategies, including baseline-cohort, matched-control, and single-sample approaches, often struggle to balance noise suppression with adaptability, leading to inconsistent performance across heterogeneous samples. RESULTS: We present PScnv, a personalized self-normalizing framework for robust CNV detection from panel sequencing data. PScnv integrates a pre-built panel-of-normals (PoN) with sample-intrinsic stable chromosomes through ridge-regression normalization to generate individualized log2 ratio profiles with reduced systematic variation. CNVs are then identified using a hierarchical multi-phase segmentation pipeline incorporating z-score pre-partitioning, kernel-based correction, and circular binary segmentation. In 139 clinical tumor samples with orthogonal FISH validation at MET, ERBB2, and MTAP, PScnv showed improved accuracy and robustness over existing methods that do not require patient-matched normal samples, provided that a pre-built PoN cohort is available. AVAILABILITY: Source code is available for academic use at https://github.com/lvws/PScnv.

We propose a novel tracking algorithm by minimizing the sum-of-squared differences (SSD) between the normalized image gradients of the template image and the input image from the test image sequence. The proposed tracking algorithm is efficient to implement since it is based on the framework of the inverse compositional algorithm, a computationally efficient tracking technique. The experiments show that the proposed tracking algorithm is superior to the intensity-based inverse compositional algorithm in tracking objects under varying illumination conditions.

UAV videos are difficult to process because of fast abrupt motion, low resolution, noisy imagery, cluttered background, low contrast, and small target size. In particular, object size varies from several to thousands of pixels in different videos, which is difficult to handle well using a single algorithm. The paper proposes a switching/combined approach for object tracking in UAV videos, where small objects are tracked using a spatiotemporal segmentation method and larger objects are tracked using a modified statistical deformable model. The proposed snake model addresses the problems in existing statistical snakes that require a good initialization, manual parameter tuning and high computational complexity. It detects clutter, high gradient noise and partial occlusion, and corrects the object bounding box by automatically adjusting the snake parameters. The computational complexity is significantly reduced by performing the operations only on a small image region and using a fast snake deformation method. The effectiveness of the proposed method is demonstrated using real UAV video sequences.

A new impulse noise detection technique for switching median filters is presented, which is based on the minimum absolute value of four convolutions obtained using one-dimensional Laplacian operators. Extensive simulations show that the proposed filter provides better performance than many of the existing switching median filters with comparable computational complexity. In particular, the proposed filter is directed toward improved line preservation.

Euclidean distance transform (EDT) using a gray-scale morphological erosion with a large distance structuring element is difficult to implement in real-time. Herein, a stack filtering method that permits the implementation of EDT in real-time using only binary logic gates is proposed. This letter reports a fast errorless method for EDT implementation.