Broberg Rose (marchview46)

This paper provides an extensive review on algorithms for constructing Minkowski sums and differences of polygons and polyhedra, both convex and non-convex, commonly known as no-fit polygons and configuration space obstacles. The Minkowski difference is a set operation, which when applied to shapes defines a method for efficient overlap detection, providing an important tool in packing and motion-planning problems. This is the first complete review on this specific topic, and aims to unify algorithms spread over the literature of separate disciplines.In this paper, a novel deep network is proposed for multi-focus image fusion, named Deep Regression Pair Learning (DRPL). In contrast to existing deep fusion methods which divide the input image into small patches and apply a classifier to judge whether the patch is in focus or not, DRPL directly converts the whole image into a binary mask without any patch operation, subsequently tackling the difficulty of the blur level estimation around the focused/defocused boundary. Simultaneously, a pair learning strategy, which takes a pair of complementary source images as inputs and generates two corresponding binary masks, is introduced into the model, greatly imposing the complementary constraint on each pair and making a large contribution to the performance improvement. Furthermore, as the edge or gradient does exist in the focus part while there is no similar property for the defocus part, we also embed a gradient loss to ensure the generated image to be all-in-focus. Then the structural similarity index (SSIM) is utilized to make a trade-off between the reference and fused images. Experimental results conducted on the synthetic and real-world datasets substantiate the effectiveness and superiority of DRPL compared with other state-of-the-art approaches. SGX-523 purchase The testing code can be found in https//github.com/sasky1/DPRL.In this paper, we propose a novel image dehazing method. Typical deep learning models for dehazing are trained on paired synthetic indoor dataset. Therefore, these models may be effective for indoor image dehazing but less so for outdoor images. We propose a heterogeneous Generative Adversarial Networks (GAN) based method composed of a cycle-consistent Generative Adversarial Networks (CycleGAN) for producing haze-clear images and a conditional Generative Adversarial Networks (cGAN) for preserving textural details. We introduce a novel loss function in the training of the fused network to minimize GAN generated artifacts, to recover fine details, and to preserve color components. These networks are fused via a convolutional neural network (CNN) to generate dehazed image. Extensive experiments demonstrate that the proposed method significantly outperforms the state-of-the-art methods on both synthetic and real-world hazy images.Image decomposition is crucial for many image processing tasks, as it allows to extract salient features from source images. A good image decomposition method could lead to a better performance, especially in image fusion tasks. We propose a multi-level image decomposition method based on latent low-rank representation(LatLRR), which is called MDLatLRR. This decomposition method is applicable to many image processing fields. In this paper, we focus on the image fusion task. We build a novel image fusion framework based on MDLatLRR which is used to decompose source images into detail parts(salient features) and base parts. A nuclear-norm based fusion strategy is used to fuse the detail parts and the base parts are fused by an averaging strategy. Compared with other state-of-the-art fusion methods, the proposed algorithm exhibits better fusion performance in both subjective and objective evaluation.Zero-shot learning (ZSL) has attracted significant attention due to its capabilities of classifying new images from unseen classes. To perform the classification task for ZSL, learning visual and semantic embeddings has been the main res