Object Instance Annotation With Deep Extreme Level Set Evolution

Wang, Zian; Acuna, David; Ling, Huan; Kar, Amlan; Fidler, Sanja

doi:10.1109/cvpr.2019.00768

Cited by 84 publications

(85 citation statements)

References 28 publications

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“…For example, the region-proposals component in R-CNN can be clearly understood as a generator to predict the regions of the objects; • Strategy 3: Improve the robustness of AI models by integration of multiple algorithms and results. Ensemble learning is a good solution [15,244], which can improve the accuracy of the final result by using the results of multiple models; • Strategy 4: Improve the practicality of the AI model results by integrating post-processing algorithms, such as the Markov random field [245], the conditional random field [246], and level set evolution [247], which can help remove noise points and provide accurate boundaries. This is critical for some cartographic applications; • Strategy 5: Improve the fineness of change maps through refined detection units.…”

Section: Reliability Of Aimentioning

confidence: 99%

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

et al. 2020

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Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring, agriculture investigation, disaster assessment, and map revision. In recent years, integrated artificial intelligence (AI) technology has become a research focus in developing new change detection methods. Although some researchers claim that AI-based change detection approaches outperform traditional change detection approaches, it is not immediately obvious how and to what extent AI can improve the performance of change detection. This review focuses on the state-of-the-art methods, applications, and challenges of AI for change detection. Specifically, the implementation process of AI-based change detection is first introduced. Then, the data from different sensors used for change detection, including optical RS data, synthetic aperture radar (SAR) data, street view images, and combined heterogeneous data, are presented, and the available open datasets are also listed. The general frameworks of AI-based change detection methods are reviewed and analyzed systematically, and the unsupervised schemes used in AI-based change detection are further analyzed. Subsequently, the commonly used networks in AI for change detection are described. From a practical point of view, the application domains of AI-based change detection methods are classified based on their applicability. Finally, the major challenges and prospects of AI for change detection are discussed and delineated, including (a) heterogeneous big data processing, (b) unsupervised AI, and (c) the reliability of AI. This review will be beneficial for researchers in understanding this field.

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Section: Reliability Of Aimentioning

confidence: 99%

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

et al. 2020

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“…This speeds up annotation since the annotators are only required to perform very coarse labeling. [34] combines CNN feature learning with level set optimization in an end-to-end fashion, and exploits extreme points as a form of user interaction. While most level set-based methods were not interactive, [11] proposed to incorporate user clicks into the energy function.…”

Section: Related Workmentioning

confidence: 99%

Fast Interactive Object Annotation With Curve-GCN

Ling

Gao

Kar

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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260

193

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Manually labeling objects by tracing their boundaries is a laborious process. In [7,2], the authors proposed Polygon-RNN that produces polygonal annotations in a recurrent manner using a CNN-RNN architecture, allowing interactive correction via humans-in-the-loop. We propose a new framework that alleviates the sequential nature of Polygon-RNN, by predicting all vertices simultaneously using a Graph Convolutional Network (GCN). Our model is trained endto-end. It supports object annotation by either polygons or splines, facilitating labeling efficiency for both line-based and curved objects. We show that Curve-GCN outperforms all existing approaches in automatic mode, including the powerful PSP-DeepLab [8,23] and is significantly more efficient in interactive mode than Polygon-RNN++. Our model runs at 29.3ms in automatic, and 2.6ms in interactive mode, making it 10x and 100x faster than Polygon-RNN++.

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“…In recent years, CNN models have been extensively used for interactive segmentation (Xu et al (2017(Xu et al ( , 2016; Agustsson et al (2019); Papadopoulos et al (2017); Maninis et al (2018); ; Castrejon et al (2017); Acuna et al (2018); Wang et al (2019)). A well-known example is DEX-TRE (Maninis et al (2018)) which utilizes extreme points as an auxiliary input to the network.…”

Section: Interactive Segmentationmentioning

confidence: 99%

“…Also, there are some hybrid methods which are based on the level sets (Caselles et al (1997)). and Wang et al (2019) embedded the level set optimization strategy in deep network to achieve precise boundary prediction from coarse annotations.…”

Section: Interactive Segmentationmentioning

confidence: 99%

NuClick: A deep learning framework for interactive segmentation of microscopic images

Koohbanani

Jahanifar²,

Tajadin

et al. 2020

Medical Image Analysis

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Object segmentation is an important step in the workflow of computational pathology. Deep learning based models generally require large amount of labeled data for precise and reliable prediction. However, collecting labeled data is expensive because it often requires expert knowledge, particularly in medical imaging domain where labels are the result of a time-consuming analysis made by one or more human experts. As nuclei, cells and glands are fundamental objects for downstream analysis in computational pathology/cytology, in this paper we propose NuClick, a CNN-based approach to speed up collecting annotations for these objects requiring minimum interaction from the annotator. We show that for nuclei and cells in histology and cytology images, one click inside each object is enough for NuClick to yield a precise annotation. For multicellular structures such as glands, we propose a novel approach to provide the NuClick with a squiggle as a guiding signal, enabling it to segment the glandular boundaries. These supervisory signals are fed to the network as auxiliary inputs along with RGB channels. With detailed experiments, we show that NuClick is applicable to a wide range of object scales, robust against variations in the user input, adaptable to new domains, and delivers reliable annotations. An instance segmentation model trained on masks generated by NuClick achieved the first rank in LYON19 challenge. As exemplar outputs of our framework, we are releasing two datasets: 1) a dataset of lymphocyte annotations within IHC images, and 2) a dataset of segmented WBCs in blood smear images.

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Object Instance Annotation With Deep Extreme Level Set Evolution

Cited by 84 publications

References 28 publications

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

Fast Interactive Object Annotation With Curve-GCN

NuClick: A deep learning framework for interactive segmentation of microscopic images

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