scholarly journals Scene-Aware Deep Networks for Semantic Segmentation of Images

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 69184-69193 ◽  
Author(s):  
Zhike Yi ◽  
Tao Chang ◽  
Shuai Li ◽  
Ruijun Liu ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Deep Networks ◽  
Segmentation Of Images

scholarly journals A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images

2019 ◽  
Vol 19 (S9) ◽  
Author(s):  
Vitoantonio Bevilacqua ◽  
Antonio Brunetti ◽  
Giacomo Donato Cascarano ◽  
Andrea Guerriero ◽  
Francesco Pesce ◽  
...  
Keyword(s):  
Deep Learning ◽  
Magnetic Resonance ◽  
Kidney Disease ◽  
Autosomal Dominant ◽  
Automatic Segmentation ◽  
Semantic Segmentation ◽  
Magnetic Resonance Images ◽  
Polycystic Kidney ◽  
Autosomal Dominant Polycystic Kidney ◽  
Segmentation Of Images

Abstract Background The automatic segmentation of kidneys in medical images is not a trivial task when the subjects undergoing the medical examination are affected by Autosomal Dominant Polycystic Kidney Disease (ADPKD). Several works dealing with the segmentation of Computed Tomography images from pathological subjects were proposed, showing high invasiveness of the examination or requiring interaction by the user for performing the segmentation of the images. In this work, we propose a fully-automated approach for the segmentation of Magnetic Resonance images, both reducing the invasiveness of the acquisition device and not requiring any interaction by the users for the segmentation of the images. Methods Two different approaches are proposed based on Deep Learning architectures using Convolutional Neural Networks (CNN) for the semantic segmentation of images, without needing to extract any hand-crafted features. In details, the first approach performs the automatic segmentation of images without any procedure for pre-processing the input. Conversely, the second approach performs a two-steps classification strategy: a first CNN automatically detects Regions Of Interest (ROIs); a subsequent classifier performs the semantic segmentation on the ROIs previously extracted. Results Results show that even though the detection of ROIs shows an overall high number of false positives, the subsequent semantic segmentation on the extracted ROIs allows achieving high performance in terms of mean Accuracy. However, the segmentation of the entire images input to the network remains the most accurate and reliable approach showing better performance than the previous approach. Conclusion The obtained results show that both the investigated approaches are reliable for the semantic segmentation of polycystic kidneys since both the strategies reach an Accuracy higher than 85%. Also, both the investigated methodologies show performances comparable and consistent with other approaches found in literature working on images from different sources, reducing both the invasiveness of the analyses and the interaction needed by the users for performing the segmentation task.

scholarly journals A Novel Object-Based Deep Learning Framework for Semantic Segmentation of Very High-Resolution Remote Sensing Data: Comparison with Convolutional and Fully Convolutional Networks

2019 ◽  
Vol 11 (6) ◽  
pp. 684 ◽  
Author(s):  
Maria Papadomanolaki ◽  
Maria Vakalopoulou ◽  
Konstantinos Karantzalos
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Novel Object ◽  
Convolutional Networks ◽  
Learning Framework ◽  
Fully Convolutional Networks ◽  
Object Based ◽  
Deep Networks ◽  
Very High

Deep learning architectures have received much attention in recent years demonstrating state-of-the-art performance in several segmentation, classification and other computer vision tasks. Most of these deep networks are based on either convolutional or fully convolutional architectures. In this paper, we propose a novel object-based deep-learning framework for semantic segmentation in very high-resolution satellite data. In particular, we exploit object-based priors integrated into a fully convolutional neural network by incorporating an anisotropic diffusion data preprocessing step and an additional loss term during the training process. Under this constrained framework, the goal is to enforce pixels that belong to the same object to be classified at the same semantic category. We compared thoroughly the novel object-based framework with the currently dominating convolutional and fully convolutional deep networks. In particular, numerous experiments were conducted on the publicly available ISPRS WGII/4 benchmark datasets, namely Vaihingen and Potsdam, for validation and inter-comparison based on a variety of metrics. Quantitatively, experimental results indicate that, overall, the proposed object-based framework slightly outperformed the current state-of-the-art fully convolutional networks by more than 1% in terms of overall accuracy, while intersection over union results are improved for all semantic categories. Qualitatively, man-made classes with more strict geometry such as buildings were the ones that benefit most from our method, especially along object boundaries, highlighting the great potential of the developed approach.

scholarly journals Situational Awareness and Problems of Its Formation in the Tasks of UAV Behavior Control

2021 ◽  
Vol 11 (24) ◽  
pp. 11611
Author(s):  
Dmitry M. Igonin ◽  
Pavel A. Kolganov ◽  
Yury V. Tiumentsev
Keyword(s):  
Visual Information ◽  
Situational Awareness ◽  
Landing Site ◽  
Semantic Segmentation ◽  
Group Behavior ◽  
Information Support ◽  
Primary Sources ◽  
Behavior Control ◽  
Critical Elements ◽  
Segmentation Of Images

Situational awareness formation is one of the most critical elements in solving the problem of UAV behavior control. It aims to provide information support for UAV behavior control according to its objectives and tasks to be completed. We consider the UAV to be a type of controlled dynamic system. The article shows the place of UAVs in the hierarchy of dynamic systems. We introduce the concepts of UAV behavior and activity and formulate requirements for algorithms for controlling UAV behavior. We propose the concept of situational awareness as applied to the problem of behavior control of highly autonomous UAVs (HA-UAVs) and analyze the levels and types of this situational awareness. We show the specifics of situational awareness formation for UAVs and analyze its differences from situational awareness for manned aviation and remotely piloted UAVs. We propose the concept of situational awareness as applied to the problem of UAV behavior control and analyze the levels and types of this situational awareness. We highlight and discuss in more detail two crucial elements of situational awareness for HA-UAVs. The first of them is related to the analysis and prediction of the behavior of objects in the vicinity of the HA-UAV. The general considerations involved in solving this problem, including the problem of analyzing the group behavior of such objects, are discussed. As an illustrative example, the solution to the problem of tracking an aircraft maneuvering in the vicinity of a HA-UAV is given. The second element of situational awareness is related to the processing of visual information, which is one of the primary sources of situational awareness formation required for the operation of the HA-UAV control system. As an example here, we consider solving the problem of semantic segmentation of images processed when selecting a landing site for the HA-UAV in unfamiliar terrain. Both of these problems are solved using machine learning methods and tools. In the field of situational awareness for HA-UAVs, there are several problems that need to be solved. We formulate some of these problems and briefly describe them.

scholarly journals Non-Intrusive Load Disaggregation by Convolutional Neural Network and Multilabel Classification

2020 ◽  
Vol 10 (4) ◽  
pp. 1454 ◽  
Author(s):  
Luca Massidda ◽  
Marino Marrocu ◽  
Simone Manca
Keyword(s):  
Neural Network ◽  
Residential Building ◽  
Semantic Segmentation ◽  
The State ◽  
Main Method ◽  
Multilabel Classification ◽  
Energy Footprint ◽  
Load Monitoring ◽  
Segmentation Of Images ◽  
Domestic Appliances

Non-intrusive load monitoring (NILM) is the main method used to monitor the energy footprint of a residential building and disaggregate total electrical usage into appliance-related signals. The most common disaggregation algorithms are based on the Hidden Markov Model, while solutions based on deep neural networks have recently caught the attention of researchers. In this work we address the problem through the recognition of the state of activation of the appliances using a fully convolutional deep neural network, borrowing some techniques used in the semantic segmentation of images and multilabel classification. This approach has allowed obtaining high performances not only in the recognition of the activation state of the domestic appliances but also in the estimation of their consumptions, improving the state of the art for a reference dataset.

scholarly journals Panoptic Segmentation on Panoramic Radiographs: Deep Learning-Based Segmentation of Various Structures Including Maxillary Sinus and Mandibular Canal

2021 ◽  
Vol 10 (12) ◽  
pp. 2577
Author(s):  
Jun-Young Cha ◽  
Hyung-In Yoon ◽  
In-Sung Yeo ◽  
Kyung-Hoe Huh ◽  
Jung-Suk Han
Keyword(s):  
Deep Learning ◽  
Maxillary Sinus ◽  
Semantic Segmentation ◽  
Mandibular Canal ◽  
Panoramic Radiographs ◽  
Automated Method ◽  
Recognition Quality ◽  
Dental Practitioners ◽  
Set Up ◽  
Segmentation Of Images

Panoramic radiographs, also known as orthopantomograms, are routinely used in most dental clinics. However, it has been difficult to develop an automated method that detects the various structures present in these radiographs. One of the main reasons for this is that structures of various sizes and shapes are collectively shown in the image. In order to solve this problem, the recently proposed concept of panoptic segmentation, which integrates instance segmentation and semantic segmentation, was applied to panoramic radiographs. A state-of-the-art deep neural network model designed for panoptic segmentation was trained to segment the maxillary sinus, maxilla, mandible, mandibular canal, normal teeth, treated teeth, and dental implants on panoramic radiographs. Unlike conventional semantic segmentation, each object in the tooth and implant classes was individually classified. For evaluation, the panoptic quality, segmentation quality, recognition quality, intersection over union (IoU), and instance-level IoU were calculated. The evaluation and visualization results showed that the deep learning-based artificial intelligence model can perform panoptic segmentation of images, including those of the maxillary sinus and mandibular canal, on panoramic radiographs. This automatic machine learning method might assist dental practitioners to set up treatment plans and diagnose oral and maxillofacial diseases.

scholarly journals An Improved Encoder-Decoder Network Based on Strip Pool Method Applied to Segmentation of Farmland Vacancy Field

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 435
Author(s):  
Xixin Zhang ◽  
Yuhang Yang ◽  
Zhiyong Li ◽  
Xin Ning ◽  
Yilang Qin ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Signal Transmission ◽  
Semantic Segmentation ◽  
Vegetation Coverage ◽  
Data Annotation ◽  
Segmentation Algorithms ◽  
Green Vegetation ◽  
Low Altitude ◽  
Segmentation Of Images ◽  
Crop Planting

In the research of green vegetation coverage in the field of remote sensing image segmentation, crop planting area is often obtained by semantic segmentation of images taken from high altitude. This method can be used to obtain the rate of cultivated land in a region (such as a country), but it does not reflect the real situation of a particular farmland. Therefore, this paper takes low-altitude images of farmland to build a dataset. After comparing several mainstream semantic segmentation algorithms, a new method that is more suitable for farmland vacancy segmentation is proposed. Additionally, the Strip Pooling module (SPM) and the Mixed Pooling module (MPM), with strip pooling as their core, are designed and fused into the semantic segmentation network structure to better extract the vacancy features. Considering the high cost of manual data annotation, this paper uses an improved ResNet network as the backbone of signal transmission, and meanwhile uses data augmentation to improve the performance and robustness of the model. As a result, the accuracy of the proposed method in the test set is 95.6%, mIoU is 77.6%, and the error rate is 7%. Compared to the existing model, the mIoU value is improved by nearly 4%, reaching the level of practical application.

scholarly journals HC-Search: A Learning Framework for Search-based Structured Prediction

2014 ◽  
Vol 50 ◽  
pp. 369-407 ◽  
Author(s):  
J.R. Doppa ◽  
A. Fern ◽  
P. Tadepalli
Keyword(s):  
Semantic Segmentation ◽  
Structured Prediction ◽  
Training Procedure ◽  
High Quality ◽  
Structured Input ◽  
Learning Framework ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Segmentation Of Images ◽  
Structured Outputs

Structured prediction is the problem of learning a function that maps structured inputs to structured outputs. Prototypical examples of structured prediction include part-of-speech tagging and semantic segmentation of images. Inspired by the recent successes of search-based structured prediction, we introduce a new framework for structured prediction called HC-Search. Given a structured input, the framework uses a search procedure guided by a learned heuristic H to uncover high quality candidate outputs and then employs a separate learned cost function C to select a final prediction among those outputs. The overall loss of this prediction architecture decomposes into the loss due to H not leading to high quality outputs, and the loss due to C not selecting the best among the generated outputs. Guided by this decomposition, we minimize the overall loss in a greedy stage-wise manner by first training H to quickly uncover high quality outputs via imitation learning, and then training C to correctly rank the outputs generated via H according to their true losses. Importantly, this training procedure is sensitive to the particular loss function of interest and the time-bound allowed for predictions. Experiments on several benchmark domains show that our approach significantly outperforms several state-of-the-art methods.

scholarly journals Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder-decoder deep networks

2020 ◽  
Author(s):  
Samik Banerjee ◽  
Lucas Magee ◽  
Dingkang Wang ◽  
Xu Li ◽  
Bingxing Huo ◽  
...  
Keyword(s):  
Data Analysis ◽  
High Performance ◽  
Large Scale ◽  
Image Data ◽  
Semantic Segmentation ◽  
Scientific Data ◽  
Error Rates ◽  
Topological Data Analysis ◽  
Hybrid Architecture ◽  
Deep Networks

Understanding of neuronal circuitry at cellular resolution within the brain has relied on tract tracing methods which involve careful observation and interpretation by experienced neuroscientists. With recent developments in imaging and digitization, this approach is no longer feasible with the large scale (terabyte to petabyte range) images. Machine learning based techniques, using deep networks, provide an efficient alternative to the problem. However, these methods rely on very large volumes of annotated images for training and have error rates that are too high for scientific data analysis, and thus requires a significant volume of human-in-the-loop proofreading. Here we introduce a hybrid architecture combining prior structure in the form of topological data analysis methods, based on discrete Morse theory, with the best-in-class deep-net architectures for the neuronal connectivity analysis. We show significant performance gains using our hybrid architecture on detection of topological structure (e.g. connectivity of neuronal processes and local intensity maxima on axons corresponding to synaptic swellings) with precision/recall close to 90% compared with human observers. We have adapted our architecture to a high performance pipeline capable of semantic segmentation of light microscopic whole-brain image data into a hierarchy of neuronal compartments. We expect that the hybrid architecture incorporating discrete Morse techniques into deep nets will generalize to other data domains.

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