“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information.

https://mmcheng.net/mftp/SalObj/PosterSaliency.pdf

Global contrast based salient region detection

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Regularization Of Inverse Problems

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Design Of Analog Cmos Integrated Circuits

U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

Deep texture cartoonization via unsupervised appearance regularization

In this paper, we propose a novel method for automatic hand gesture recognition from images based on shape context. Unlike conventional approaches, our method can robustly detect hand gestures rotated with arbitrary angle. Specifically, we improve the existing shape context to rotational invariant by creating a new log-polar space based on the tangent line of the boundary points. We first align the two hand gestures by solving a correspondence problem. The similarity of two hand gestures are obtained by calculating the shape distance based on our proposed rotational invariant shape context. Finally, the best matched result is identified by retrieving the gesture with the maximal shape similarity. Our method is evaluated using a standard simulated gesture dataset. Experimental results show that our method can accurately identify hand gestures, either with or without rotation. Comparison experiments also suggest that our method outperforms existing hand gesture recognition methods based on conventional shape context.

Automatic Hand Gesture Recognition Based on Shape Context

Line drawings are frequently used to illustrate ideas and concepts in digital documents and presentations. To compose a line drawing, it is common for users to retrieve multiple line drawings from the Internet and combine them as one image. However, different line drawings may have different line styles and are visually inconsistent when put together. In order that the line drawings can have consistent looks, in this paper, we make the first attempt to perform style transfer for line drawings. The key of our design lies in the fact that centerline plays a very important role in preserving line topology and extracting style features. With this finding, we propose to formulate the style transfer problem as a centerline stylization problem and solve it via a novel style-guided image-to-image translation network. Results and statistics show that our method significantly outperforms the existing methods both visually and quantitatively.

/pdf/deep-style-transfer-for-line-drawings-30mi5phoe5.pdf

Deep Style Transfer for Line Drawings

Few-shot semantic segmentation is a promising solution for scarce data scenarios, especially for medical imaging challenges with limited training data. However, most of the existing few-shot segmentation methods tend to over rely on the images containing target classes, which may hinder its utilization of medical imaging data. In this paper, we present a few-shot segmentation model that employs anatomical auxiliary information from medical images without target classes for dual contrastive learning. The dual contrastive learning module performs comparison among vectors from the perspectives of prototypes and contexts, to enhance the discriminability of learned features and the data utilization. Besides, to distinguish foreground features from background features more friendly, a constrained iterative prediction module is designed to optimize the segmentation of the query image. Experiments on two medical image datasets show that the proposed method achieves performance comparable to state-of-the-art methods. Code is available at: https://github.com/cvszusparkle/AAS-DCL_FSS . 

Dual Contrastive Learning with Anatomical Auxiliary Supervision for Few-Shot Medical Image Segmentation

https://www.internationaljournalofcardiology.com/article/S0167-5273(13)70559-X/pdf

Huisi Wu

Papers

Deep texture cartoonization via unsupervised appearance regularization

Automatic Hand Gesture Recognition Based on Shape Context

Deep Style Transfer for Line Drawings

Dual Contrastive Learning with Anatomical Auxiliary Supervision for Few-Shot Medical Image Segmentation

Real-time left ventricular speckle tracking in 3D echocardiography with parallel block matching