The proliferation of modern image editing tools has raised concerns about image manipulation, particularly regarding the potential to mislead the public and compromise privacy and security. Consequently, detecting and localizing tampered regions has become a critical research challenge. Traditional methods struggle with subtle manipulations, such as splicing, copy-move, and removal, which are often more discernible in the frequency domain than in the spatial domain. Additionally, the size imbalance between the tampered and background regions further complicates the detection process. To address these challenges, we propose DFFormer, an end-to-end network that leverages frequency feature differences and a dynamic token strategy for precise manipulation localization. DFFormer combines the Conventional Neural Network (CNN) and Transformer in a hybrid architecture with three key modules: the Adaptive Frequency Transformer (AFT), the Prototype Learning Module (PLM), and the Cascaded Progressive Token Fusion Head (CPTF-Head). AFT integrates high- and low-frequency components into self-attention via the Parallel Adaptive Frequency Attention (PAFA) block, enhancing tampering feature representation while preserving fine details. PLM employs KNN-based density peak clustering (DPC-KNN) and weighted token aggregation to optimize dynamic token reduction. The CPTF-Head adopts a hierarchical coarse-to-fine strategy to integrate multiscale features, thereby improving localization accuracy and edge refinement. Experiments demonstrate that DFFormer outperforms state-of-the-art models across four benchmark datasets and one real-world dataset, exhibiting superior generalization and robustness. The source code is publicly available at https://github.com/XiangGD/DFFormer.git.

Multi-Agent Path Finding (MAPF) involves finding collision-free paths for multiple agents while minimizing a cost function—an NP-hard problem. Bounded suboptimal methods like Enhanced Conflict-Based Search (ECBS) and Explicit Estimation CBS (EECBS) balance solution quality with computational efficiency using focal search mechanisms. While effective, traditional focal search faces a limitation: the lower bound (LB) value determining which nodes enter the FOCAL list often increases slowly in early search stages, resulting in a constrained search space that delays finding valid solutions. In this paper, we propose a novel bounded suboptimal algorithm, double-ECBS (DECBS), to address this issue by first determining the maximum LB value and then employing a best-first search guided by this LB to find a collision-free path. Experimental results demonstrate that DECBS outperforms ECBS in most test cases and is compatible with existing optimization techniques. DECBS can reduce nearly 30% high-level Constraint Tree (CT) nodes and 50% low-level focal search nodes. When agent density is high, DECBS achieves a 23.5% average runtime improvement over ECBS with identical suboptimality bounds and optimizations.

The datasets collected by people are always just a sampling of the real world. In this letter, we explore the possibility of achieving high-quality domain adaptation (DA) without explicit adaptation. As a baseline, we implemented the significantly improved second-generation version of TasselLFANet, TasselLFANetV2. This model, with indicators reaching AP50of 0.981 and R2of 0.9684, demonstrates leading performance in two typical cross-domain settings of data distribution scenarios, agriculture and remote sensing (RS), exhibiting strong domain adaptation and generalization, surpassing advanced methods such as YOLOv8-UAV, PlantBiCNet, SLA, etc. We further studied the combination of regularization techniques and feature re-mapping modules can effectively alleviate the domain invariance of the model. What’s more, when the training set and validation set are set the same, the training performance of the model is better, but the premise is that there must be a proper data transformation strategy. This work provides a new perspective for understanding and solving the problem of domain difference in deep learning. The code, datasets can be accessed at https://github.com/Ye-Sk/TasselLFANetV2.

In this paper, we propose a novel closed-form transformation estimation method based on moving regularized least squares optimization with thin-plate spline (MRLS-TPS) for non-rigid image deformation. The method takes the user-controlled point-offset-vectors as the input data, and estimates the spatial transformation about the two control point sets for each pixel. To achieve a realistic deformation, we formulates the transformation estimation as a vector-field interpolation problem by a moving regularized least squares method. Unlike MLS, the mapping function is modeled by a non-rigid function thin-plate spline with regularization technique, such that the deformation can satisfy both global linear affine motion and local non-rigid warping. We derive a closed-form solution of the transformation and achieve a fast implementation. In addition, the proposed method can give a wonderful user experience, fast and convenient manipulating. Extensive experiments on real images demonstrated the proposed method outperforms other state-of-the-art methods and the commercial software Adobe PhotoShop CS 6, especially in case of flexible object motion.

The Microsoft Kinect sensor has been widely used in many applications, but it suffers from the drawback of low depth accuracy. In this paper, we present a unified depth modification model to improve the Kinect depth accuracy by registering depth and color images in an iterative manner. Specifically, in each iteration, we first establish a coarse correspondence based on the feature descriptor of the canny edge. Then, we estimate the fine correspondence using a robust estimator called the L2E with the nonparametric model. Finally, we correct the depth data according to the correspondence results. In order to evaluate the effectiveness of our approach, we have performed extensive experiments and then analyzed the experimental results from the following respects: the accuracy of depth data, the accuracy of correspondence between color and depth images as well as the measurement error in the 3D reconstruction by our method. The experimental results show that our approach greatly improves the depth accuracy.

In this paper, a novel closed-form transformation estimation method based on feature guided moving least squares together with manifold regularization is proposed for nonrigid image/surface deformation. The method takes the user-controlled point-offset-vectors and the feature points of the image/surface as input, and estimates the spatial transformation between the two control point sets for each pixel/voxel. To achieve a detail-preserving and realistic deformation, the transformation estimation is formulated as a vector-field interpolation problem using a feature guided moving least squares method, where a manifold regularization is imposed as a prior on the transformation to capture the underlying intrinsic geometry of the input image/surface. The non-rigid transformation is specified in a reproducing kernel Hilbert space. We derive a closed-form solution of the transformation and adopt a sparse approximation to achieve a fast implementation, which largely reduces the computation complexity without performance sacrifice. In addition, the proposed method can give a wonderful user experience, fast and convenient manipulating. Extensive experiments on both 2D and 3D data demonstrate that the proposed method can produce more natural deformations compared with other state-of-the-art methods.

Automatic extraction of crop organ from images is a crucial step for quantitatively acquiring crop growth information in precision agriculture. There has been some attempt on this task, but the performance is not satisfactory. In this paper, we proposed an image-based method based on low-rank matrix recovery to extract organ accurately. In our method, a crop image is considered to be compose of two factors: background and organ. In a certain feature space, the image is represented as a low-rank matrix plus sparse noises. The organ is then extracted by identifying the sparse noises when using low-rank matrix recovery algorithm. In order to ensure the rank of background is low, a linear transform for the feature space is introduced and needs to be learned from historical data. Dynamic threshold segmentation followed by vegetation removing techniques are ultimately adopted in the final step. The experimental results on the benchmark farmland dataset show that our method achieve competitive performance, compared with the other well-established methods, yielding the highest performance of 93.9% with the lowest standard deviation of 2.86%, which means our method is more robust and not sensitive to the complex environmental elements and different cultivars.