116,809 research outputs found
A deep level set method for image segmentation
This paper proposes a novel image segmentation approachthat integrates fully
convolutional networks (FCNs) with a level setmodel. Compared with a FCN, the
integrated method can incorporatesmoothing and prior information to achieve an
accurate segmentation.Furthermore, different than using the level set model as
a post-processingtool, we integrate it into the training phase to fine-tune the
FCN. Thisallows the use of unlabeled data during training in a
semi-supervisedsetting. Using two types of medical imaging data (liver CT and
left ven-tricle MRI data), we show that the integrated method achieves
goodperformance even when little training data is available, outperformingthe
FCN or the level set model alone
Deep Convolutional Level Set Method for Image Segmentation
Level Set Method is a popular method for image segmentation. One of the problems in Level Set Method is finding the right initial surface parameter, which implicitly affects the curve evolution and ultimately the segmentation result. By setting the initial curve too far away from the target object, Level Set Method could potentially miss the target altogether, whereas by setting the initial curve as general as possible "“ i.e. capturing the whole image "“ makes Level Set Method susceptible to noise. Recently, deep-learning methods, especially Convolutional Neural Network (CNN), have been proven to achieve state-of-the-art performance in many computer vision tasks such as image classification and detection. In this paper, a new method is proposed, called Deep Convolutional Level Set Method (DCLSM). The idea is to use the CNN object detector as a prior for Level Set Method segmentation. Using DCLSM it is possible to significantly improve the segmentation accuracy and precision of the classic Level Set Method. It was also found that the prior used in the proposed method is the lower and upper bound for DCLSM's precision and recall, respectively
Deep nested level sets: Fully automated segmentation of cardiac MR images in patients with pulmonary hypertension
In this paper we introduce a novel and accurate optimisation method for segmentation of cardiac MR (CMR) images in patients with pulmonary hypertension (PH). The proposed method explicitly takes into account the image features learned from a deep neural network. To this end, we estimate simultaneous probability maps over region and edge locations in CMR images using a fully convolutional network. Due to the distinct morphology of the heart in patients with PH, these probability maps can then be incorporated in a single nested level set optimisation framework to achieve multi-region segmentation with high efficiency. The proposed method uses an automatic way for level set initialisation and thus the whole optimisation is fully automated. We demonstrate that the proposed deep nested level set (DNLS) method outperforms existing state-of-the-art methods for CMR segmentation in PH patients
DEEP FULLY RESIDUAL CONVOLUTIONAL NEURAL NETWORK FOR SEMANTIC IMAGE SEGMENTATION
Department of Computer Science and EngineeringThe goal of semantic image segmentation is to partition the pixels of an image into semantically meaningful parts and classifying those parts according to a predefined label set. Although object recognition
models achieved remarkable performance recently and they even surpass human???s ability to recognize
objects, but semantic segmentation models are still behind. One of the reason that makes semantic
segmentation relatively a hard problem is the image understanding at pixel level by considering global
context as oppose to object recognition. One other challenge is transferring the knowledge of an object
recognition model for the task of semantic segmentation. In this thesis, we are delineating some of the
main challenges we faced approaching semantic image segmentation with machine learning algorithms.
Our main focus was how we can use deep learning algorithms for this task since they require the
least amount of feature engineering and also it was shown that such models can be applied to large scale
datasets and exhibit remarkable performance. More precisely, we worked on a variation of convolutional
neural networks (CNN) suitable for the semantic segmentation task. We proposed a model called deep
fully residual convolutional networks (DFRCN) to tackle this problem. Utilizing residual learning makes
training of deep models feasible which ultimately leads to having a rich powerful visual representation.
Our model also benefits from skip-connections which ease the propagation of information from the
encoder module to the decoder module. This would enable our model to have less parameters in the
decoder module while it also achieves better performance. We also benchmarked the effective variation
of the proposed model on a semantic segmentation benchmark.
We first make a thorough review of current high-performance models and the problems one might
face when trying to replicate such models which mainly arose from the lack of sufficient provided
information. Then, we describe our own novel method which we called deep fully residual convolutional
network (DFRCN). We showed that our method exhibits state of the art performance on a challenging
benchmark for aerial image segmentation.clos
Weakly supervised deep semantic segmentation using CNN and ELM with semantic candidate regions.
The task of semantic segmentation is to obtain strong pixel-level annotations for each pixel in the image. For fully supervised semantic segmentation, the task is achieved by a segmentation model trained using pixel-level annotations. However, the pixel-level annotation process is very expensive and time-consuming. To reduce the cost, the paper proposes a semantic candidate regions trained extreme learning machine (ELM) method with image-level labels to achieve pixel-level labels mapping. In this work, the paper casts the pixel mapping problem into a candidate region semantic inference problem. Specifically, after segmenting each image into a set of superpixels, superpixels are automatically combined to achieve segmentation of candidate region according to the number of image-level labels. Semantic inference of candidate regions is realized based on the relationship and neighborhood rough set associated with semantic labels. Finally, the paper trains the ELM using the candidate regions of the inferred labels to classify the test candidate regions. The experiment is verified on the MSRC dataset and PASCAL VOC 2012, which are popularly used in semantic segmentation. The experimental results show that the proposed method outperforms several state-of-the-art approaches for deep semantic segmentation
DFormer: Diffusion-guided Transformer for Universal Image Segmentation
This paper introduces an approach, named DFormer, for universal image
segmentation. The proposed DFormer views universal image segmentation task as a
denoising process using a diffusion model. DFormer first adds various levels of
Gaussian noise to ground-truth masks, and then learns a model to predict
denoising masks from corrupted masks. Specifically, we take deep pixel-level
features along with the noisy masks as inputs to generate mask features and
attention masks, employing diffusion-based decoder to perform mask prediction
gradually. At inference, our DFormer directly predicts the masks and
corresponding categories from a set of randomly-generated masks. Extensive
experiments reveal the merits of our proposed contributions on different image
segmentation tasks: panoptic segmentation, instance segmentation, and semantic
segmentation. Our DFormer outperforms the recent diffusion-based panoptic
segmentation method Pix2Seq-D with a gain of 3.6% on MS COCO val2017 set.
Further, DFormer achieves promising semantic segmentation performance
outperforming the recent diffusion-based method by 2.2% on ADE20K val set. Our
source code and models will be publicly on https://github.com/cp3wan/DForme
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