In comparison with other point-by-point extraction and ANN-based classification techniques , the same success rates have been obtained for road surfaces and fences , and better results have been obtained for guardrails . Semantic segmentation with PointNet is suitable when segmenting the scene as a whole , however , if certain classes have more interest , there are other alternatives that do not need a high training cost . Semantic segmentation is a hot topic in computer vision where the most challenging tasks of object detection and recognition have been handling by the success of semantic segmentation approaches . We propose a concept of objectby-object learning technique to detect 11 types of facial skin lesions using semantic segmentation methods . This paper explores novel approaches for improving the spatial codification for the pooling of local descriptors to solve the semantic segmentation problem . We propose to partition the image into three regions for each object to be described : Figure , Border and Ground . document : Learning a Discriminative Feature Network for Semantic Segmentation Most existing methods of semantic segmentation still suffer from two aspects of challenges : intra - class inconsistency and inter - class indistinction . Importantly , our system fully integrates CRF modelling with CNNs , making it possible to train the whole deep network end - to - end with the usual back - propagation algorithm , avoiding offline post - processing methods for object delineation . We apply the proposed method to the problem of semantic image segmentation , obtaining top results on the challenging Pascal VOC 2012 segmentation benchmark . section : Introduction The experimental results showed that the identification accuracy of the structure landmark was about 90 % and the matching accuracy was 92 % . The mean off-line localization error was about 1.2 m. Compared with the AMM-based method , this approach is robust to the random turning activities of people and can realize indoor localization with a faster convergence speed . The major problem of indoor localization is the presence of Non-line-of-sight ( NLOS ) channels . In order to perform the NLOS identification , this paper proposes a new NLOS identification approach . Determining the pose of a mobile device based on visual information is a promising approach to solve the indoor localization problem . We present an approach that transforms localized images along a mapping trajectory into virtual viewpoints that cover a set of densely sampled camera positions and orientations in a confined environment . Furthermore , accuracy of position determination is calculated and evaluated . Results show advantage of BLE technology for indoor localization purposes ( without calibration measurement ) . However , its performance highly depends on the number of considered BLE nodes and on applied evaluation method ( e.g. number of considered sectors ) . Geometric-Semantic Pose Verification for Indoor Visual Localization . Visual localization in large and complex indoor scenes , dominated by weakly textured rooms and repeating geometric patterns , is a challenging problem with high practical relevance for applications such as Augmented Reality and robotics . Visual features take up a dominant part of the information that helps human and robotics understand the environment , and many visual localization systems have been proposed . However , the problem of indoor visual localization has not been well settled due to the tough trade-off of accuracy and cost . To better address this problem , a localization method based on image retrieval is proposed in this paper , which mainly consists of two parts . The updated clock skew from the Kalman filter is incorporated into the calibration of the difference of arrival time to achieve highly accurate localization of moving targets . Experiments show that our method provides a nanosecond level synchronization resolution and has good robustness to clock synchronization period and non-line-of-sight error , which is quite suitable for indoor localization scenario . Different approaches have been proposed to reduce workload , including fingerprinting deployment and update efforts , but the performance degrades greatly when the workload is reduced below a certain level . In this paper , we propose an indoor localization scenario that applies metric learning and manifold alignment to realize direct mapping localization ( DML ) using a low resolution radio map with single sample of RSS that reduces the fingerprinting workload by up to 87\% . Compared to previous work . Indoor localization estimation has become an attractive research topic due to growing interest in location-aware services . Many research works have proposed solving this problem by using wireless communication systems based on radiofrequency . We will later reveal that by employing such region-based scheme , the location estimation can become more accurate . The steps that we have used in our indoor localization estimation follow a two-level approach whereby the first step aims at determining the region where the target device resides . The second step narrows down further to determining the exact location of the device based on the magnetic flux reading obtained earlier . It is a key problem in indoor localization accuracy that the received signal strength indicator ( RSSI ) is affected by changing environment such as diffraction , obstacles , and multi-path effect . To improve the accuracy , we propose an indoor localization algorithm based on ELMAN Neural Network . We deploy these three technologies in multiple line-of-sight and non-line-of-sight indoor scenarios including corridors , open spaces , spaces with a varying number of walls , and across floors of multi-storied buildings . Considering the coverage , stability and regularity of signals , accuracy of localization , responsiveness , power , and cost – we conclude that LoRa is a feasible choice for indoor localization solution , especially in wide and tall indoor environments like warehouses and multi-storied buildings . Abstract A novel approach toward the indoor position localization of a mobile target is presented in this paper . It is based on the reception quality of infrared patterns that are sent by a small number of transmitters that are placed around the covered area . Now the visual-based indoor localization have become a popular research in recent years . We proposed an improvement of the original RANSAC method and apply it in the indoor visual-based localization . In our improving proposed method , we define a matching quality function to measure the matching quality of a matching pair of images . Video data are composed of multimodal information streams including visual , auditory and textual streams , so an approach of story segmentation for news video using multimodal analysis is described in this paper . The proposed approach detects the topic-caption frames , and integrates them with silence clips detection results , as well as shot segmentation results to locate the news story boundaries . The integration of audio-visual features and text information overcomes the weakness of the approach using only image analysis techniques . One-shot semantic segmentation is a new challenging task extended from traditional semantic segmentation , which aims to predict unseen object categories for each pixel given only one annotated sample . Previous works employ oversimplified operations to fuse the features from query image and support image , while neglecting to incorporate multi-scale information that is essential for the one-shot segmentation task . In this paper , we propose a novel one-shot based architecture , Deep Similarity Fusion Network ( DSFN ) to tackle this issue . Face/Gesture/Action Detection and Recognition.- Palmprint Recognition Under Unconstrained Scenes.- Comparative Studies on Multispectral Palm Image Fusion for Biometrics.- Learning Gabor Magnitude Features for Palmprint Recognition.- Sign Recognition Using Constrained Optimization.- Poster Session 4 : Image and Video Processing.- Depth from Stationary Blur with Adaptive Filtering.- Three-Stage Motion Deblurring from a Video.- Near-Optimal Mosaic Selection for Rotating and Zooming Video Cameras.- Video Mosaicing Based on Structure from Motion for Distortion-Free Document Digitization.- Super Resolution of Images of 3D Scenecs.- Learning-Based Super-Resolution System Using Single Facial Image and Multi-resolution Wavelet Synthesis.- Poster Session 4 : Segmentation and Classification.- Statistical Framework for Shot Segmentation and Classification in Sports Video.- Sports Classification Using Cross-Ratio Histograms.- A Bayesian Network for Foreground Segmentation in Region Level.- Efficient Graph Cuts for Multiclass Interactive Image Segmentation.- Feature Subset Selection for Multi-class SVM Based Image Classification.- Evaluating Multi-class Multiple-Instance Learning for Image Categorization.- Poster Session 4 : Shape.- TransforMesh : A Topology-Adaptive Mesh-Based Approach to Surface Evolution.- Microscopic Surface Shape Estimation of a Transparent Plate Using a Complex Image.- Shape Recovery from Turntable Image Sequence.- Shape from Contour for the Digitization of Curved Documents.- Improved Space Carving Method for Merging and Interpolating Multiple Range Images Using Information of Light Sources of Active Stereo.- Shape Representation and Classification Using Boundary Radius Function.- A Convex Programming Approach to the Trace Quotient Problem.- Learning a Fast Emulator of a Binary Decision Process.- Multiplexed Illumination for Measuring BRDF Using an Ellipsoidal Mirror and a Projector.- Analyzing the Influences of Camera Warm-Up Effects on Image Acquisition.- Simultaneous Plane Extraction and 2D Homography Estimation Using Local Feature Transformations.- A Fast Optimal Algorithm for L 2 Triangulation.- Adaptively Determining Degrees of Implicit Polynomial Curves and Surfaces.- Determining Relative Geometry of Cameras from Normal Flows.- Poster Session 5 : Geometry.- Highest Accuracy Fundamental Matrix Computation.- Sequential L ? ? ? In this paper , a novel statistical framework is proposed for shot segmentation and classification . The proposed framework segments and classifies shots simultaneously using same difference features based on statistical inference . For the first time , we show that the complexity of instance segmentation , in terms of both design and computation complexity , can be the same as bounding box object detection and this much simpler and flexible instance segmentation framework can achieve competitive accuracy . We hope that the proposed PolarMask framework can serve as a fundamental and strong baseline for single shot instance segmentation task . From shot based to extraction of video object planes ( VOPs ) , video segmentation develops to extract video object(VO ) . In fact , shot based segmentation is one of the primary steps in the true object based video segmentation . Currently , segmentation technology for content based representation is still premature . On the one hand , shot-based segmentation can dramatically facilitate and enhance object-based segmentation by using key-frame extraction to select a few key-frames for statistical model training . On the other hand , object-based segmentation can be used to improve shot-based segmentation results by using model-based key-frame refinement . The proposed approach is able to integrate advantages of these two segmentation methods and provide a new combined shot-based and object-based framework for a variety of advanced video analysis tasks . Prior knowledge about video structure can be used both as a means to improve the performance of content analysis and to extract features that allow semantic classification . We introduce statistical models for two important components of this structure , shot duration and activity , and demonstrate the usefulness of these models by introducing a Bayesian formulation for the shot segmentation problem . The new formulations is shown to extend standard thresholding methods in an adaptive and intuitive way , leading to improved segmentation accuracy . Semantic segmentation is an important visual perception module of automated driving . Most of the progress has been focused on single-frame image segmentation with an optional temporal post-processing . In this paper , we propose a novel algorithm to utilize the temporal information in the semantic segmentation model using convolutional gated recurrent networks . In the segmentation network , apart from the pyramid backbone network , an extra decoder network is often employed to recover the spatial detail information . In this paper , we put forward a method for single-shot segmentation in a feature residual pyramid network ( RPNet ) , which learns the coarse results and residuals of segmentations by decomposing the label at different levels of residual blocks . Specifically speaking , we use the residual features to learn the edges and details , and we also use the top-level feature to learn the coarse segmentation result . An Alternative Method for Room Layout Estimation . In this paper , we propose an alternative method to estimate room layouts of cluttered indoor scenes . The goal of room layout estimation is to predict the three-dimensional box that represents the room spatial structure from a monocular image . In this paper , a deconvolution network is trained first ... The speakers are assumed to lie on a two-dimensional plane and the multipath channel is characterized using the image model . We propose an algorithm for room geometry estimation relying on localization of the early images of the speakers by sparse approximation of the spatial spectrum of the virtual sources in a free-space model . The images are then clustered exploiting the low-rank structure of the spectro-temporal components belonging to each source . The results describe the potential of HybLoc to provide the hybrid location of user viz a viz the reported literature for both levels of prediction . For room estimation , HybLoc has demonstrated mean 85 % accuracy , 89 % precision as compared with frequently used k Nearest Neighbors ( kNN ) and Artificial Neural Network (ANN)-based approaches with 56 % accuracy , 60 % precision and 42 % accuracy , 48 % precision , respectively , averaged over all buildings . We also compared HybLoc performance with baseline Random Forest providing 79 % accuracy and 82 % precision which clearly demonstrates the enhanced performance by HybLoc . The indoor localization system is based on the processing of Received Signal Strength Indicator ( RSSI ) measurements by a tracking device , from Bluetooth Beacons , using a fingerprint-based procedure . The system has been tested in realistic settings achieving accuracy above 93 % in room estimation . The proposed system was used in 271 houses collecting data for 1–7-day sessions . Both results are in perfect agreement that verifies the theoretical analysis . In addition , room occupancy estimation is performed by using two methods , namely Kullback-Leibler(KL)-divergence and Euclidean distance . In these methods , the stored PDF of the received power in the database is compared with the measured received power PDF , which reveals the estimated room occupancy . This process of embedding intelligence into end-devices enables more efficient energy consumption and network load distribution . In this work , we present an embedded algorithm for room occupancy estimation based on a thermal sensor with accuracy over the state-of-the-art . We study the performance of a variety of deep learning models on different embedded processors . Estimation of room layout suffers from heavy occlusions and clutters in indoor scenes . In this paper , we propose a deep network that combines textures and geometric hints to predict the surface layout from a single image . Such information can be used to calibrate the drift of encounter position . The calibrated encounter position is beneficial to most of the floorplan generation steps , such as trace drift elimination , landmark positioning , hallway assembling , and room area estimation . Our experiments in three shopping malls show that CrowdX achieves an average F-measure around 89.4 % . We introduce a new layout algorithm , along with its underlying growth rules , and propose its use in generating room layout plans . We apply it to room layout planning , investigate its characteristics by observing the generated room layout plans , and discuss our results . We also introduce a framework for a room layout planning support system consisting of the layout algorithm , evolutionary multi-objective optimization ( EMO ) , interactive evolutionary computation ( IEC ) , and a graphical user interface . This paper investigates how to estimate the room size from acoustic impulse responses . The proposed approach contains two phases , impulse response estimation and room size estimation . In the former , we utilize the adaptive signal processing , namely least mean square algorithm , to iteratively approximate the acoustic impulse response ( AIR ) , which indicates the room geometry with multipath . This paper presents a novel method of reconstructing indoor scenes , both structures and objects , by a single panorama photo . The method combines room structure estimation , furniture detection , models selection , as well as 3D positions reasoning . Compare with others , our preliminary results show this method could get almost the same performance with a simpler procedure . Spatial layout estimation of indoor rooms plays an important role in many visual analysis applications such as robotics and human-computer interaction . While many methods have been proposed for recovering spatial layout of rooms in recent years , their performance is still far from satisfactory due to high occlusion caused by the presence of objects that clutter the scene .
In comparison with other point-by-point extraction and ANN-based classification techniques , the same success rates have been obtained for road surfaces and fences , and better results have been obtained for guardrails . Semantic segmentation with PointNet is suitable when segmenting the scene as a whole , however , if certain classes have more interest , there are other alternatives that do not need a high training cost .
Semantic segmentation is a hot topic in computer vision where the most challenging tasks of object detection and recognition have been handling by the success of semantic segmentation approaches . We propose a concept of objectby-object learning technique to detect 11 types of facial skin lesions using semantic segmentation methods .
This paper explores novel approaches for improving the spatial codification for the pooling of local descriptors to solve the semantic segmentation problem . We propose to partition the image into three regions for each object to be described : Figure , Border and Ground .
document : Learning a Discriminative Feature Network for Semantic Segmentation Most existing methods of semantic segmentation still suffer from two aspects of challenges : intra - class inconsistency and inter - class indistinction .
Importantly , our system fully integrates CRF modelling with CNNs , making it possible to train the whole deep network end - to - end with the usual back - propagation algorithm , avoiding offline post - processing methods for object delineation . We apply the proposed method to the problem of semantic image segmentation , obtaining top results on the challenging Pascal VOC 2012 segmentation benchmark . section : Introduction
The experimental results showed that the identification accuracy of the structure landmark was about 90 % and the matching accuracy was 92 % . The mean off-line localization error was about 1.2 m. Compared with the AMM-based method , this approach is robust to the random turning activities of people and can realize indoor localization with a faster convergence speed .
The major problem of indoor localization is the presence of Non-line-of-sight ( NLOS ) channels . In order to perform the NLOS identification , this paper proposes a new NLOS identification approach .
Determining the pose of a mobile device based on visual information is a promising approach to solve the indoor localization problem . We present an approach that transforms localized images along a mapping trajectory into virtual viewpoints that cover a set of densely sampled camera positions and orientations in a confined environment .
Furthermore , accuracy of position determination is calculated and evaluated . Results show advantage of BLE technology for indoor localization purposes ( without calibration measurement ) . However , its performance highly depends on the number of considered BLE nodes and on applied evaluation method ( e.g. number of considered sectors ) .
Geometric-Semantic Pose Verification for Indoor Visual Localization . Visual localization in large and complex indoor scenes , dominated by weakly textured rooms and repeating geometric patterns , is a challenging problem with high practical relevance for applications such as Augmented Reality and robotics .
Visual features take up a dominant part of the information that helps human and robotics understand the environment , and many visual localization systems have been proposed . However , the problem of indoor visual localization has not been well settled due to the tough trade-off of accuracy and cost . To better address this problem , a localization method based on image retrieval is proposed in this paper , which mainly consists of two parts .
The updated clock skew from the Kalman filter is incorporated into the calibration of the difference of arrival time to achieve highly accurate localization of moving targets . Experiments show that our method provides a nanosecond level synchronization resolution and has good robustness to clock synchronization period and non-line-of-sight error , which is quite suitable for indoor localization scenario .
Different approaches have been proposed to reduce workload , including fingerprinting deployment and update efforts , but the performance degrades greatly when the workload is reduced below a certain level . In this paper , we propose an indoor localization scenario that applies metric learning and manifold alignment to realize direct mapping localization ( DML ) using a low resolution radio map with single sample of RSS that reduces the fingerprinting workload by up to 87\% . Compared to previous work .
Indoor localization estimation has become an attractive research topic due to growing interest in location-aware services . Many research works have proposed solving this problem by using wireless communication systems based on radiofrequency .
We will later reveal that by employing such region-based scheme , the location estimation can become more accurate . The steps that we have used in our indoor localization estimation follow a two-level approach whereby the first step aims at determining the region where the target device resides . The second step narrows down further to determining the exact location of the device based on the magnetic flux reading obtained earlier .
It is a key problem in indoor localization accuracy that the received signal strength indicator ( RSSI ) is affected by changing environment such as diffraction , obstacles , and multi-path effect . To improve the accuracy , we propose an indoor localization algorithm based on ELMAN Neural Network .
We deploy these three technologies in multiple line-of-sight and non-line-of-sight indoor scenarios including corridors , open spaces , spaces with a varying number of walls , and across floors of multi-storied buildings . Considering the coverage , stability and regularity of signals , accuracy of localization , responsiveness , power , and cost – we conclude that LoRa is a feasible choice for indoor localization solution , especially in wide and tall indoor environments like warehouses and multi-storied buildings .
Abstract A novel approach toward the indoor position localization of a mobile target is presented in this paper . It is based on the reception quality of infrared patterns that are sent by a small number of transmitters that are placed around the covered area .
Now the visual-based indoor localization have become a popular research in recent years . We proposed an improvement of the original RANSAC method and apply it in the indoor visual-based localization . In our improving proposed method , we define a matching quality function to measure the matching quality of a matching pair of images .
Video data are composed of multimodal information streams including visual , auditory and textual streams , so an approach of story segmentation for news video using multimodal analysis is described in this paper . The proposed approach detects the topic-caption frames , and integrates them with silence clips detection results , as well as shot segmentation results to locate the news story boundaries . The integration of audio-visual features and text information overcomes the weakness of the approach using only image analysis techniques .
One-shot semantic segmentation is a new challenging task extended from traditional semantic segmentation , which aims to predict unseen object categories for each pixel given only one annotated sample . Previous works employ oversimplified operations to fuse the features from query image and support image , while neglecting to incorporate multi-scale information that is essential for the one-shot segmentation task . In this paper , we propose a novel one-shot based architecture , Deep Similarity Fusion Network ( DSFN ) to tackle this issue .
Face/Gesture/Action Detection and Recognition.- Palmprint Recognition Under Unconstrained Scenes.- Comparative Studies on Multispectral Palm Image Fusion for Biometrics.- Learning Gabor Magnitude Features for Palmprint Recognition.- Sign Recognition Using Constrained Optimization.- Poster Session 4 : Image and Video Processing.- Depth from Stationary Blur with Adaptive Filtering.- Three-Stage Motion Deblurring from a Video.- Near-Optimal Mosaic Selection for Rotating and Zooming Video Cameras.- Video Mosaicing Based on Structure from Motion for Distortion-Free Document Digitization.- Super Resolution of Images of 3D Scenecs.- Learning-Based Super-Resolution System Using Single Facial Image and Multi-resolution Wavelet Synthesis.- Poster Session 4 : Segmentation and Classification.- Statistical Framework for Shot Segmentation and Classification in Sports Video.- Sports Classification Using Cross-Ratio Histograms.- A Bayesian Network for Foreground Segmentation in Region Level.- Efficient Graph Cuts for Multiclass Interactive Image Segmentation.- Feature Subset Selection for Multi-class SVM Based Image Classification.- Evaluating Multi-class Multiple-Instance Learning for Image Categorization.- Poster Session 4 : Shape.- TransforMesh : A Topology-Adaptive Mesh-Based Approach to Surface Evolution.- Microscopic Surface Shape Estimation of a Transparent Plate Using a Complex Image.- Shape Recovery from Turntable Image Sequence.- Shape from Contour for the Digitization of Curved Documents.- Improved Space Carving Method for Merging and Interpolating Multiple Range Images Using Information of Light Sources of Active Stereo.- Shape Representation and Classification Using Boundary Radius Function.- A Convex Programming Approach to the Trace Quotient Problem.- Learning a Fast Emulator of a Binary Decision Process.- Multiplexed Illumination for Measuring BRDF Using an Ellipsoidal Mirror and a Projector.- Analyzing the Influences of Camera Warm-Up Effects on Image Acquisition.- Simultaneous Plane Extraction and 2D Homography Estimation Using Local Feature Transformations.- A Fast Optimal Algorithm for L 2 Triangulation.- Adaptively Determining Degrees of Implicit Polynomial Curves and Surfaces.- Determining Relative Geometry of Cameras from Normal Flows.- Poster Session 5 : Geometry.- Highest Accuracy Fundamental Matrix Computation.- Sequential L ? ? ?
In this paper , a novel statistical framework is proposed for shot segmentation and classification . The proposed framework segments and classifies shots simultaneously using same difference features based on statistical inference .
For the first time , we show that the complexity of instance segmentation , in terms of both design and computation complexity , can be the same as bounding box object detection and this much simpler and flexible instance segmentation framework can achieve competitive accuracy . We hope that the proposed PolarMask framework can serve as a fundamental and strong baseline for single shot instance segmentation task .
From shot based to extraction of video object planes ( VOPs ) , video segmentation develops to extract video object(VO ) . In fact , shot based segmentation is one of the primary steps in the true object based video segmentation . Currently , segmentation technology for content based representation is still premature .
On the one hand , shot-based segmentation can dramatically facilitate and enhance object-based segmentation by using key-frame extraction to select a few key-frames for statistical model training . On the other hand , object-based segmentation can be used to improve shot-based segmentation results by using model-based key-frame refinement . The proposed approach is able to integrate advantages of these two segmentation methods and provide a new combined shot-based and object-based framework for a variety of advanced video analysis tasks .
Prior knowledge about video structure can be used both as a means to improve the performance of content analysis and to extract features that allow semantic classification . We introduce statistical models for two important components of this structure , shot duration and activity , and demonstrate the usefulness of these models by introducing a Bayesian formulation for the shot segmentation problem . The new formulations is shown to extend standard thresholding methods in an adaptive and intuitive way , leading to improved segmentation accuracy .
Semantic segmentation is an important visual perception module of automated driving . Most of the progress has been focused on single-frame image segmentation with an optional temporal post-processing . In this paper , we propose a novel algorithm to utilize the temporal information in the semantic segmentation model using convolutional gated recurrent networks .
In the segmentation network , apart from the pyramid backbone network , an extra decoder network is often employed to recover the spatial detail information . In this paper , we put forward a method for single-shot segmentation in a feature residual pyramid network ( RPNet ) , which learns the coarse results and residuals of segmentations by decomposing the label at different levels of residual blocks . Specifically speaking , we use the residual features to learn the edges and details , and we also use the top-level feature to learn the coarse segmentation result .
An Alternative Method for Room Layout Estimation . In this paper , we propose an alternative method to estimate room layouts of cluttered indoor scenes .
The goal of room layout estimation is to predict the three-dimensional box that represents the room spatial structure from a monocular image . In this paper , a deconvolution network is trained first ...
The speakers are assumed to lie on a two-dimensional plane and the multipath channel is characterized using the image model . We propose an algorithm for room geometry estimation relying on localization of the early images of the speakers by sparse approximation of the spatial spectrum of the virtual sources in a free-space model . The images are then clustered exploiting the low-rank structure of the spectro-temporal components belonging to each source .
The results describe the potential of HybLoc to provide the hybrid location of user viz a viz the reported literature for both levels of prediction . For room estimation , HybLoc has demonstrated mean 85 % accuracy , 89 % precision as compared with frequently used k Nearest Neighbors ( kNN ) and Artificial Neural Network (ANN)-based approaches with 56 % accuracy , 60 % precision and 42 % accuracy , 48 % precision , respectively , averaged over all buildings . We also compared HybLoc performance with baseline Random Forest providing 79 % accuracy and 82 % precision which clearly demonstrates the enhanced performance by HybLoc .
The indoor localization system is based on the processing of Received Signal Strength Indicator ( RSSI ) measurements by a tracking device , from Bluetooth Beacons , using a fingerprint-based procedure . The system has been tested in realistic settings achieving accuracy above 93 % in room estimation . The proposed system was used in 271 houses collecting data for 1–7-day sessions .
Both results are in perfect agreement that verifies the theoretical analysis . In addition , room occupancy estimation is performed by using two methods , namely Kullback-Leibler(KL)-divergence and Euclidean distance . In these methods , the stored PDF of the received power in the database is compared with the measured received power PDF , which reveals the estimated room occupancy .
This process of embedding intelligence into end-devices enables more efficient energy consumption and network load distribution . In this work , we present an embedded algorithm for room occupancy estimation based on a thermal sensor with accuracy over the state-of-the-art . We study the performance of a variety of deep learning models on different embedded processors .
Estimation of room layout suffers from heavy occlusions and clutters in indoor scenes . In this paper , we propose a deep network that combines textures and geometric hints to predict the surface layout from a single image .
Such information can be used to calibrate the drift of encounter position . The calibrated encounter position is beneficial to most of the floorplan generation steps , such as trace drift elimination , landmark positioning , hallway assembling , and room area estimation . Our experiments in three shopping malls show that CrowdX achieves an average F-measure around 89.4 % .
We introduce a new layout algorithm , along with its underlying growth rules , and propose its use in generating room layout plans . We apply it to room layout planning , investigate its characteristics by observing the generated room layout plans , and discuss our results . We also introduce a framework for a room layout planning support system consisting of the layout algorithm , evolutionary multi-objective optimization ( EMO ) , interactive evolutionary computation ( IEC ) , and a graphical user interface .
This paper investigates how to estimate the room size from acoustic impulse responses . The proposed approach contains two phases , impulse response estimation and room size estimation . In the former , we utilize the adaptive signal processing , namely least mean square algorithm , to iteratively approximate the acoustic impulse response ( AIR ) , which indicates the room geometry with multipath .
This paper presents a novel method of reconstructing indoor scenes , both structures and objects , by a single panorama photo . The method combines room structure estimation , furniture detection , models selection , as well as 3D positions reasoning . Compare with others , our preliminary results show this method could get almost the same performance with a simpler procedure .
Spatial layout estimation of indoor rooms plays an important role in many visual analysis applications such as robotics and human-computer interaction . While many methods have been proposed for recovering spatial layout of rooms in recent years , their performance is still far from satisfactory due to high occlusion caused by the presence of objects that clutter the scene .
Road Environment Semantic Segmentation with Deep Learning from MLS Point Cloud Data RethNet: Object-by-Object Learning for Detecting Facial Skin Problems Improving spatial codification in semantic segmentation Learning a Discriminative Feature Network for Semantic Segmentation Conditional Random Fields as Recurrent Neural Networks Modeling of Structure Landmark for Indoor Pedestrian Localization A new low complexity NLOS identification approach based on UWB energy detection Virtual reference view generation for CBIR-based visual pose estimation BLE device indoor localization based on RSS fingerprinting mapped by propagation modes Is This the Right Place? Geometric-Semantic Pose Verification for Indoor Visual Localization Indoor Visual Positioning Aided by CNN-Based Image Retrieval: Training-Free, 3D Modeling-Free A Kalman-filter-based wireless clock synchronization method in indoor localization Fast Radio Map Construction and Position Estimation via Direct Mapping for WLAN Indoor Localization System Discrete Indoor Three-Dimensional Localization System Based on Neural Networks Using Visible Light Communication A region-to-point indoor localization approach via RSS-magnetic fingerprinting Optimization and Application of Indoor Localization Algorithm Based on ELMAN Neural Network LoRaIn: Making a Case for LoRa in Indoor Localization Estimation of a Target Position Based on Infrared Pattern Reception Quality An improvement algorithm on RANSAC for image-based indoor localization Integrating audio-visual features and text information for story segmentation of news video Deep Similarity Fusion Networks for One-Shot Semantic Segmentation Computer Vision - ACCV 2007, 8th Asian Conference on Computer Vision, Tokyo, Japan, November 18-22, 2007, Proceedings, Part I Statistical Framework for Shot Segmentation and Classification in Sports Video PolarMask: Single Shot Instance Segmentation With Polar Representation Object-based Video Segmentation Technology Combined key-frame extraction and object-based video segmentation Bayesian Video Shot Segmentation Convolutional gated recurrent networks for video semantic segmentation in automated driving Residual Pyramid Learning for Single-Shot Semantic Segmentation Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation Learning to Predict High-Quality Edge Maps for Room Layout Estimation Structured Sparsity Models for Reverberant Speech Separation HybLoc: Hybrid Indoor Wi-Fi Localization Using Soft Clustering-Based Random Decision Forest Ensembles A Low-Cost Indoor Activity Monitoring System for Detecting Frailty in Older Adults Indoor Occupancy Estimation Using Visible Light Sensing (VLS) System Edge Computing with Embedded AI: Thermal Image Analysis for Occupancy Estimation in Intelligent Buildings Robust Room Layout Estimation from a Single Image with Geometric Hints CrowdX: Enhancing Automatic Construction of Indoor Floorplan with Opportunistic Encounters Layout algorithm for an EC-based room layout planning support system Room size estimation from acoustic impulse responses Indoor Scene Reconstruction: From Panorama Images to CAD Models Estimating spatial layout of rooms from RGB-D videos
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ICCV Workshops ICIP 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2015 IEEE International Conference on Computer Vision (ICCV) ACM Multimedia PerCom Workshops IWCMC ACPR (1) Computer Vision – ACCV 2007 ACCV (2) NIPS ITSC VTC-Fall ICIP GCCE ISMAR Adjunct MMSP
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