scholarly journals EFFICIENT SEMANTIC SEGMENTATION OF MAN-MADE SCENES USING FULLY-CONNECTED CONDITIONAL RANDOM FIELD

2016 ◽  
Vol XLI-B3 ◽  
pp. 633-640
Author(s):  
Weihao Li ◽  
Michael Ying Yang
Keyword(s):  
Random Field ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Contextual Information ◽  
Mean Field ◽  
Semantic Segmentation ◽  
Gaussian Kernels ◽  
Previous State ◽  
Fully Connected ◽  
Better Than

In this paper we explore semantic segmentation of man-made scenes using fully connected conditional random field (CRF). Images of man-made scenes display strong contextual dependencies in the spatial structures. Fully connected CRFs can model long-range connections within the image of man-made scenes and make use of contextual information of scene structures. The pairwise edge potentials of fully connected CRF models are defined by a linear combination of Gaussian kernels. Using filter-based mean field algorithm, the inference is very efficient. Our experimental results demonstrate that fully connected CRF performs better than previous state-of-the-art approaches on both eTRIMS dataset and LabelMeFacade dataset.

scholarly journals EFFICIENT SEMANTIC SEGMENTATION OF MAN-MADE SCENES USING FULLY-CONNECTED CONDITIONAL RANDOM FIELD

2016 ◽  
Vol XLI-B3 ◽  
pp. 633-640 ◽  
Author(s):  
Weihao Li ◽  
Michael Ying Yang
Keyword(s):  
Random Field ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Contextual Information ◽  
Mean Field ◽  
Semantic Segmentation ◽  
Gaussian Kernels ◽  
Previous State ◽  
Fully Connected ◽  
Better Than

In this paper we explore semantic segmentation of man-made scenes using fully connected conditional random field (CRF). Images of man-made scenes display strong contextual dependencies in the spatial structures. Fully connected CRFs can model long-range connections within the image of man-made scenes and make use of contextual information of scene structures. The pairwise edge potentials of fully connected CRF models are defined by a linear combination of Gaussian kernels. Using filter-based mean field algorithm, the inference is very efficient. Our experimental results demonstrate that fully connected CRF performs better than previous state-of-the-art approaches on both eTRIMS dataset and LabelMeFacade dataset.

scholarly journals Boundary Perception Guidance: A Scribble-Supervised Semantic Segmentation Approach

2019 ◽  
Author(s):  
Bin Wang ◽  
Guojun Qi ◽  
Sheng Tang ◽  
Tianzhu Zhang ◽  
Yunchao Wei ◽  
...  
Keyword(s):  
Random Field ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Semantic Segmentation ◽  
Semantic Feature ◽  
Feature Maps ◽  
Feature Enhancement ◽  
Pascal Voc ◽  
Segmentation Approach ◽  
Fully Connected

Semantic segmentation suffers from the fact that densely annotated masks are expensive to obtain. To tackle this problem, we aim at learning to segment by only leveraging scribbles that are much easier to collect for supervision. To fully explore the limited pixel-level annotations from scribbles, we present a novel Boundary Perception Guidance (BPG) approach, which consists of two basic components, i.e., prediction refinement and boundary regression. Specifically, the prediction refinement progressively makes a better segmentation by adopting an iterative upsampling and a semantic feature  enhancement strategy. In the boundary regression, we employ class-agnostic edge maps for supervision to effectively guide the segmentation network in localizing the boundaries between different semantic regions, leading to producing finer-grained representation of feature maps for semantic segmentation. The experiment results on the PASCAL VOC 2012 demonstrate the proposed BPG achieves mIoU of 73.2% without fully connected Conditional Random Field (CRF) and 76.0% with CRF, setting up the new state-of-the-art in literature.

scholarly journals Nested Named Entity Recognition via Second-best Sequence Learning and Decoding

2020 ◽  
Vol 8 ◽  
pp. 605-620 ◽  
Author(s):  
Takashi Shibuya ◽  
Eduard Hovy
Keyword(s):  
Random Field ◽  
Sequence Learning ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Neural Model ◽  
Entity Recognition ◽  
Named Entities ◽  
Second Best ◽  
Named Entity ◽  
Better Than

When an entity name contains other names within it, the identification of all combinations of names can become difficult and expensive. We propose a new method to recognize not only outermost named entities but also inner nested ones. We design an objective function for training a neural model that treats the tag sequence for nested entities as the second best path within the span of their parent entity. In addition, we provide the decoding method for inference that extracts entities iteratively from outermost ones to inner ones in an outside-to-inside way. Our method has no additional hyperparameters to the conditional random field based model widely used for flat named entity recognition tasks. Experiments demonstrate that our method performs better than or at least as well as existing methods capable of handling nested entities, achieving F1-scores of 85.82%, 84.34%, and 77.36% on ACE-2004, ACE-2005, and GENIA datasets, respectively.

scholarly journals WATERBODIES EXTRACTION FROM LANDSAT8-OLI IMAGERY USING AWATER INDEXS-GUIED STOCHASTIC FULLY-CONNECTED CONDITIONAL RANDOM FIELD MODEL AND THE SUPPORT VECTOR MACHINE

2018 ◽  
Vol XLII-3 ◽  
pp. 1789-1794
Author(s):  
X. Wang ◽  
L. Xu
Keyword(s):  
Support Vector Machine ◽  
Random Field ◽  
Extraction Method ◽  
Spatial Information ◽  
Conditional Random Field ◽  
Water Extraction ◽  
Spectral Information ◽  
Support Vector ◽  
Water Index ◽  
Fully Connected

One of the most important applications of remote sensing classification is water extraction. The water index (WI) based on Landsat images is one of the most common ways to distinguish water bodies from other land surface features. But conventional WI methods take into account spectral information only form a limited number of bands, and therefore the accuracy of those WI methods may be constrained in some areas which are covered with snow/ice, clouds, etc. An accurate and robust water extraction method is the key to the study at present. The support vector machine (SVM) using all bands spectral information can reduce for these classification error to some extent. Nevertheless, SVM which barely considers spatial information is relatively sensitive to noise in local regions. Conditional random field (CRF) which considers both spatial information and spectral information has proven to be able to compensate for these limitations. Hence, in this paper, we develop a systematic water extraction method by taking advantage of the complementarity between the SVM and a water index-guided stochastic fully-connected conditional random field (SVM-WIGSFCRF) to address the above issues. In addition, we comprehensively evaluate the reliability and accuracy of the proposed method using Landsat-8 operational land imager (OLI) images of one test site. We assess the method’s performance by calculating the following accuracy metrics: Omission Errors (OE) and Commission Errors (CE); Kappa coefficient (KP) and Total Error (TE). Experimental results show that the new method can improve target detection accuracy under complex and changeable environments.

Quantify pixel-level detection of dam surface crack using deep learning

2022 ◽  
Author(s):  
Bo Chen ◽  
Hua Zhang ◽  
Yonglong Li ◽  
Shuang Wang ◽  
Huaifang Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Surface Crack ◽  
Crack Detection ◽  
State Of The Art ◽  
Contextual Information ◽  
Semantic Segmentation ◽  
Quantitative Information ◽  
Cross Entropy ◽  
Detection Methods ◽  
Water Conservancy

Abstract An increasing number of detection methods based on computer vision are applied to detect cracks in water conservancy infrastructure. However, most studies directly use existing feature extraction networks to extract cracks information, which are proposed for open-source datasets. As the cracks distribution and pixel features are different from these data, the extracted cracks information is incomplete. In this paper, a deep learning-based network for dam surface crack detection is proposed, which mainly addresses the semantic segmentation of cracks on the dam surface. Particularly, we design a shallow encoding network to extract features of crack images based on the statistical analysis of cracks. Further, to enhance the relevance of contextual information, we introduce an attention module into the decoding network. During the training, we use the sum of Cross-Entropy and Dice Loss as the loss function to overcome data imbalance. The quantitative information of cracks is extracted by the imaging principle after using morphological algorithms to extract the morphological features of the predicted result. We built a manual annotation dataset containing 1577 images to verify the effectiveness of the proposed method. This method achieves the state-of-the-art performance on our dataset. Specifically, the precision, recall, IoU, F1_measure, and accuracy achieve 90.81%, 81.54%, 75.23%, 85.93%, 99.76%, respectively. And the quantization error of cracks is less than 4%.

scholarly journals CRF-Based Model for Instrument Detection and Pose Estimation in Retinal Microsurgery

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Mohamed Alsheakhali ◽  
Abouzar Eslami ◽  
Hessam Roodaki ◽  
Nassir Navab
Keyword(s):  
Random Field ◽  
Pose Estimation ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Recovery Process ◽  
New Method ◽  
Ophthalmic Surgery ◽  
Illumination Changes ◽  
Cluttered Background

Detection of instrument tip in retinal microsurgery videos is extremely challenging due to rapid motion, illumination changes, the cluttered background, and the deformable shape of the instrument. For the same reason, frequent failures in tracking add the overhead of reinitialization of the tracking. In this work, a new method is proposed to localize not only the instrument center point but also its tips and orientation without the need of manual reinitialization. Our approach models the instrument as a Conditional Random Field (CRF) where each part of the instrument is detected separately. The relations between these parts are modeled to capture the translation, rotation, and the scale changes of the instrument. The tracking is done via separate detection of instrument parts and evaluation of confidence via the modeled dependence functions. In case of low confidence feedback an automatic recovery process is performed. The algorithm is evaluated on in vivo ophthalmic surgery datasets and its performance is comparable to the state-of-the-art methods with the advantage that no manual reinitialization is needed.

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