Specularities often confound algorithms designed to solve computer vision tasks such as image segmentation , object detection , and tracking . These tasks usually require color image segmentation to partition an image into regions , where each region corresponds to a particular material . These flows help avoid a number of problems with traditional flows including helping in avoiding undesirable local minima . We demonstrate practical applications of these flows for the object detection problem , including applications to both image segmentation and visual object tracking . One popular solution is the R-CNN framework [ 15 ] and its fast versions [ 14 , 27 ] . They decompose the object detection problem into two cascaded easier tasks : 1 ) generating object proposals from images , 2 ) classifying proposals into various object categories . Despite that we are handling with two relatively easier tasks , they are not solved perfectly and there 's still room for improvement . Object proposal algorithms have been demonstrated to be very successful in accelerating object detection process . High object localization quality and detection recall can be obtained using thousands of proposals . In this paper we present novel methods for automatically annotating images with relationship and position tags that are derived using mask and bounding box data . A Mask Region-based Convolutional Neural Network ( Mask R-CNN ) is used as the foundation for the object detection process . The relationships are found by manipulating the bounding box and mask segmentation outputs of a Mask R-CNN . Finally , the feature points on the labeled road region are used to detect the objects on the road . Experimental results show that the method demonstrates significant object detecting performance without further restrictions , and performs effectively in complex detecting environment . The background model affects object detecting algorithm tolerating changes in background scene and the granularity of the detected foreground objects . An algorithm using a subtracted background modeling based on Cauchy statistical distribution the purpose of object detecting is presented . The paper concludes that the ratios of the intensity values between background image and current image are fitted to a Cauchy distribution . subsection : Temporal Feature Aggregation Temporal cues could be incorporated for feature reinforcement in object detection tasks . For example , TCNN uses optical flow to map detections to neighboring frames and suppresses low - confidence predictions while incorporating tracking algorithms . It can be observed that the DPN obtains the mAP of , which makes large improvements , i.e. compared with ResNet - 101 and compared with ResNeXt - 101 ( d ) . The better results shown in this experiment demonstrate that the Dual Path Network is also capable of learning better feature representations for detecting objects and benefiting the object detection task . subsection : Experiments on the semantic segmentation task section : Abstract Feature pyramids are a basic component in recognition systems for detecting objects at different scales . But recent deep learning object detectors have avoided pyramid representations , in part because they are compute and memory intensive . Object skeleton detection is a challenging problem with wide application . Recently , deep Convolutional Neural Networks ( CNNs ) have substantially improved the performance of the state-of-the-art in this task . They are complementary to the object contour , and provide extra information , such as how object scale ( thickness ) varies among object parts . But object skeleton extraction from natural images is very challenging , because it requires the extractor to be able to capture both local and non-local image context in order to determine the scale of each skeleton pixel . In this paper , we present a novel fully convolutional network with multiple scale-associated side outputs to address this problem . This paper presents a novel method for extracting road networks from VHR remotely sensed images in complex urban scenes . Inspired by image segmentation , edge detection , and object skeleton extraction , we develop a multitask convolutional neural network ( CNN ) , called RoadNet , to simultaneously predict road surfaces , edges , and centerlines , which is the first work in such field . The RoadNet solves seven important issues in this vision problem : 1 ) automatically learning multiscale and multilevel features [ gained by the deeply supervised nets ( DSN ) providing integrated direct supervision ] to cope with the roads in various scenes and scales ; 2 ) holistically training the mentioned tasks in a cascaded end-to-end CNN model ; 3 ) correlating the predictions of road surfaces , edges , and centerlines in a network model to improve the multitask prediction ; 4 ) designing elaborate architecture and loss function , by which the well-trained model produces approximately single-pixel width road edges/centerlines without nonmaximum suppression postprocessing ; 5 ) cropping and bilinear blending to deal with the large VHR images with finite-computing resources ; 6 ) introducing rough and simple user interaction to obtain desired predictions in the challenging regions ; and 7 ) establishing a benchmark data set which consists of a series of VHR remote sensing images with pixelwise annotation . Different from the existing works geared towards co-segmentation or co-localization , in this paper , we explore a new joint processing topic : image co-skeletonization , which is defined as joint skeleton extraction of objects in an image collection . Object skeletonization in a single natural image is a challenging problem because there is hardly any prior knowledge about the object . Therefore , we resort to the idea of object co-skeletonization , hoping that the commonness prior that exists across the images may help , just as it does for other joint processing problems such as co-segmentation . Different from the existing works geared towards co-segmentation or co-localization , in this paper , we explore a new joint processing topic : co-skeletonization , which is defined as joint skeleton extraction of common objects in a set of semantically similar images . Object skeletonization in real world images is a challenging problem , because there is no prior knowledge of the objects shape if we consider only a single image . This motivates us to resort to the idea of object co-skeletonization hoping that the commonness prior existing across the similar images may help , just as it does for other joint processing problems such as co-segmentation . It consists of 12,500 printed text words in 50 paragraphs and 15,000 words written by 100 different writers , males and females aged 5 to 60 years . Pre-processing operations are conducted including segmenting paragraphs into lines , segmenting line into words , segmenting words into characters , detecting skeleton , and determining baseline and other horizontal zones . The experimental results have shown that the proposed method has achieved promising accuracy recognition rate with 90.46 % for printed text and handwritten characters . The main application aims at feeding a pore network approach to simulate unsaturated hydraulic properties from soil core images . Beyond the application example , the successive algorithms presented in the paper allow , from any 3D object image , the extraction of the object skeleton , the measurement of the distribution of local apertures and the partition of the object into a set of interconnected subobjects according to different size classes . The resulting , specific library DXSoil is available under the OpenDX environment more generally dedicated to data exploration and visualization . Also inability to be used in mobile devices further limits its applicability . We create a system using OpenCV to be used as a library which will be used for human body skeleton detection and tracking . The proposed system uses video stream input through an integrated webcam and processes it to obtain human skeleton . This study aims the tiny chip size package ( CSP ) components to develop a Kinect-based inspection system . The proposed system applied Kinect sensor to perform human skeleton detection . With our developed software , the operator outside the clean room can use his hand to directly control the important inspection modules such as lighting module and translation module , which are located in the clean room , through the glass window by applying somatosensory technology . Thus , robust skeleton detection requires more powerful multi-scale feature integration ability than other vision tasks . In this paper , we present a new convolutional neural network ( CNN ) architecture by introducing a novel hierarchical feature integration mechanism , named Hi-Fi , to address the object skeleton detection problem . CNNs can capture high-level semantics from deeper layers as well as low-level details from shallower layers . The RGB camera and the infrared light module aim to do distance estimation of a body or several bodies . The module not only provides image perception but also objective skeleton detection . In which , a laser source in the infrared light module emits invisible infrared light which passes through a filter and is scattered into a semi-random but constant pattern of small dots which is projected onto the environment in front of the sensor . Robust object skeleton detection requires to explore rich representative visual features and effective feature fusion strategies . In this paper , we first re-visit the implementation of HED , the essential principle of which can be ideally described with a linear reconstruction model . Computing object skeletons in natural images is challenging , owing to large variations in object appearance and scale , and the complexity of handling background clutter . Many recent methods frame object skeleton detection as a binary pixel classification problem , which is similar in spirit to learning - based edge detection , as well as to semantic segmentation methods . In the present article , we depart from this strategy by training a CNN to predict a two - dimensional vector field , which maps each scene point to a candidate skeleton pixel , in the spirit of flux - based skeletonization algorithms . Most such approaches focus on designing an appropriate network and leveraging better multi - level features for capturing skeletons across a range of spatial scales . Object skeleton computation using CNNs from natural images is inherently different from the problem of edge detection . As illustrated in Figure [ reference ] , edges associated with object boundaries can typically be detected locally , due to a local appearance change or a change in texture . 2 ) Based on the context flux , we develop a method which we dub DeepFlux , that accurately and efficiently detects object skeletons in an image . 3 ) DeepFlux consistently outperforms state - of - the - art methods on five public benchmarks . To our knowledge this is the first application of flux concepts , which have been successfully used for skeletonization of binary objects , to the detection of object skeletons in natural images . It is also the first attempt at learning such flux - based representations directly from natural images . section : Related Work Object skeletonization has been widely studied in recent decades . In our review , we contrast traditional methods with those based on deep learning . A beneficial side - effect is that our method does not rely directly on the coarse responses produced by deeper CNN layers for localizing skeletons at larger scales , which further reduces localization errors . As we show in our experiments , these properties make our method more robust to the localization of skeleton points , especially around ligature regions , and less prone to gaps , discontinuities , and irregularities caused by local mispredictions . Finally , it is easy to accurately recover a binary object skeleton using the magnitude and direction of the predicted flux , as explained in Section [ reference ] . The good news , however , is that DeepFlux is not sensitive to the value of . Finally , one may argue that simply using a dilated skeleton ground truth is sufficient to make a baseline model more robust in accurately localizing skeleton points . To examine if this is the case , we retrained our baseline model using binary cross - entropy on the same dilated ground truth we used for DeepFlux . section : Conclusion We have proposed DeepFlux , a novel approach for accurate detection of object skeletons in the wild . Departing from the usual view of learning - based skeleton detection as a binary classification problem , we have recast it as the regression problem of predicting a 2D vector field of “ context flux ” . We have developed a simple convolutional neural network to compute such a flux , followed by a simple post - processing scheme that can accurately recover object skeletons in Our approach steers clear of many limitations related to poor localization , commonly shared by previous methods , and particularly shines in handling ligature points , and skeletons at large scales . The resulting belief functions are used as initial affordance hypotheses , which are subject to further investigation and validation . As pure visual affordance detection can fail to properly estimate important preconditions , e.g. , the stability of environmental structures , validation experiments are conducted in order to incrementally improve the system belief and the reliability of detected affordances . The proposed formalism is implemented and evaluated in the context of loco-manipulation affordances for humanoid robots using the simulated robots ARMAR-III and ARMAR-4 . Haptic object recognition by a robot hand is essential for adapting to human environment because of its role in multimodal sensing and detecting affordance . An intrinsic difficulty of such recognition is due to its locality . Haptic object recognition by a robot hand is essential for adapting to human environment because of its role in multimodal sensing and detecting affordance . An intrinsic difficulty of such recognition is due to its locality . Leveraging Deep 3D saliency and geometry for fast and scalable multiple affordance detection . This paper develops and evaluates a novel method that allows for the detection of affordances in a scalable and multiple-instance manner on visually recovered pointclouds . Our approach has many advantages over alternative methods , as it is based on highly parallelizable , one-shot learning that is fast in commodity hardware . We train and validate these models on a manually curated dataset labeled with leisure ontology classes for the city of Zwolle , and discuss their potential for urban leisure and tourism research and related city policies and planning . We found that multi-label affordance estimation is not straightforward but can be made to work using both official webtexts and user-generated content on a medium semantic level . This opens up new opportunities for data-driven approaches to urban leisure and tourism studies . Object affordance detection , which aims to understand functional attributes of objects , is of great significance for an autonomous robot to achieve a humanoid object manipulation . In this paper , we propose a novel relationship-aware convolutional neural network , which takes the symbiotic relationship between multiple affordances and the combinational relationship between the affordance and objectness into consideration , to predict the most probable affordance label for each pixel in the object . Opposed to Norman 's proposal , processing of affordance is likely to occur not solely in the dorsal stream but also in the ventral stream . Moreover , the dorsal stream might do more than just serve an important role in motor actions . End-to-end learning in deep reinforcement learning based on raw visual input has shown great promise in various tasks involving sensorimotor control . However , complex tasks such as tool use require recognition of affordance and a series of non-trivial subtasks such as reaching the tool , grasping the tool , and wielding the tool . In such tasks , end-to-end approaches with only the raw input ( e.g. pixel-wise images ) may fail to learn to perform the task or may take too long to converge . The 203 workshop papers were carefully reviewed and selected for inclusion in the proceedings . They where presented at workshops with the following themes : where computer vision meets art ; computer vision in vehicle technology ; spontaneous facial behavior analysis ; consumer depth cameras for computer vision ; " chalearn " looking at people : pose , recovery , action/interaction , gesture recognition ; video event categorization , tagging and retrieval towards big data ; computer vision with local binary pattern variants ; visual object tracking challenge ; computer vision + ontology applies cross-disciplinary technologies ; visual perception of affordance and functional visual primitives for scene analysis ; graphical models in computer vision ; light fields for computer vision ; computer vision for road scene understanding and autonomous driving ; soft biometrics ; transferring and adapting source knowledge in computer vision ; surveillance and re-identification ; color and photometry in computer vision ; assistive computer vision and robotics ; computer vision problems in plant phenotyping ; and non-rigid shape analysis and deformable image alignment . Additionally , a panel discussion on video segmentation is included In this paper , we introduce the Flexible Space Subdivision framework ( FSS ) that allows to automatically identify the spaces that can be used for indoor navigation purpose . We propose a classification of indoor objects based on their ability to autonomously change location and we define a spatial subdivision of the indoor environment based on the classified objects and their functions . The framework can consider any 3D indoor configuration , the static and dynamic activities it hosts and it enables the possibility to consider all types of locomotion ( e.g. walking , flying , etc . ) . The research is hoping to continue in the following areas : taking into account the varying lighting/weather conditions and the blurring effect , and detecting objects at a lower height such as bricks/stones . The research will also be looking at other issues like detecting indoor objects , DSP based implementation issues , rendering the visual information and the usability issues . The last stage of the method ensures interframe consistency based on a cost function between regions from subsequent frames . The planar segmentation can be used both for identifying indoor objects , which in general have planar faces , as well as for estimating the camera motion and then reconstructing the environment . While deep convolutional neural networks have shown a remarkable success in image classification , the problems of inter-class similarities , intra-class variances , the effective combination of multi-modal data , and the spatial variability in images of objects remain to be major challenges . To address these problems , this paper proposes a novel framework to learn a discriminative and spatially invariant classification model for object and indoor scene recognition using multi-modal RGB-D imagery . This is achieved through three postulates : 1 ) spatial invariance < inline-formula><tex-math notation="LaTeX">$-$</tex-math><alternatives > < inline-graphic xlink : href="asif-ieq1 - 2747134.gif"/></alternatives></inline-formula > this is achieved by combining a spatial transformer network with a deep convolutional neural network to learn features which are invariant to spatial translations , rotations , and scale changes , 2 ) high discriminative capability < inline-formula > < tex-math notation="LaTeX">$-$</tex-math><alternatives><inline-graphic xlink : href="asif-ieq2 - 2747134.gif"/ > < /alternatives></inline-formula > this is achieved by introducing Fisher encoding within the CNN architecture to learn features which have small inter-class similarities and large intra-class compactness , and 3 ) multi-modal hierarchical fusion < inline-formula><tex-math notation="LaTeX">$-$</tex-math><alternatives > < inline-graphic xlink : href="asif-ieq3 - 2747134.gif"/></alternatives></inline-formula > this is achieved through the regularization of semantic segmentation to a multi-modal CNN architecture , where class probabilities are estimated at different hierarchical levels ( i.e. , image- and pixel-levels ) , and fused into a Conditional Random Field (CRF)-based inference hypothesis , the optimization of which produces consistent class labels in RGB-D images . Object detection of indoor scene is widely used in the field of service robot . State-of-art object detectors rely heavily on large-scale datasets like PASCAL VOC2007 , VOC2012 . Then by using improved depth information , the algorithm extracts the foreground and implements the object segmentation of color image at the resolution of 640 × 480 from Kinect camera . Finally , the segmentation results are applied into the object recognition in indoor scene to validate the effectiveness of scene segmentation . The results of indoor segmentation demonstrate the real-time performance and robustness of the proposed method . This paper presents a 3-D object recognition method and its implementation on a robotic navigation aid to allow real-time detection of indoor structural objects for the navigation of a blind person . The method segments a point cloud into numerous planar patches and extracts The hierarchical description enables easy navigation in the formerly unstructured data , measurement takings , as well as carrying out retrieval tasks that incorporate geometric , topological , and also functional building properties describing e.g. the designated use of single rooms according to the objects it contains . In contrast to previous methods which either focus on the segmentation into rooms or on the recognition of indoor objects , our holistic approach incorporates a rather large spectrum of entities on different semantic levels that are inherent to 3D building representations . In our evaluation we show the feasibility of our method for extraction of hierarchical building descriptions for various tasks using synthetic as well as real world data . Recognizing indoor scene and objects and estimating their poses present a wide range of applications in robotic field . This task becomes more challenging especially in cluttered environments like the indoor scenery .
Specularities often confound algorithms designed to solve computer vision tasks such as image segmentation , object detection , and tracking . These tasks usually require color image segmentation to partition an image into regions , where each region corresponds to a particular material .
These flows help avoid a number of problems with traditional flows including helping in avoiding undesirable local minima . We demonstrate practical applications of these flows for the object detection problem , including applications to both image segmentation and visual object tracking .
One popular solution is the R-CNN framework [ 15 ] and its fast versions [ 14 , 27 ] . They decompose the object detection problem into two cascaded easier tasks : 1 ) generating object proposals from images , 2 ) classifying proposals into various object categories . Despite that we are handling with two relatively easier tasks , they are not solved perfectly and there 's still room for improvement .
Object proposal algorithms have been demonstrated to be very successful in accelerating object detection process . High object localization quality and detection recall can be obtained using thousands of proposals .
In this paper we present novel methods for automatically annotating images with relationship and position tags that are derived using mask and bounding box data . A Mask Region-based Convolutional Neural Network ( Mask R-CNN ) is used as the foundation for the object detection process . The relationships are found by manipulating the bounding box and mask segmentation outputs of a Mask R-CNN .
Finally , the feature points on the labeled road region are used to detect the objects on the road . Experimental results show that the method demonstrates significant object detecting performance without further restrictions , and performs effectively in complex detecting environment .
The background model affects object detecting algorithm tolerating changes in background scene and the granularity of the detected foreground objects . An algorithm using a subtracted background modeling based on Cauchy statistical distribution the purpose of object detecting is presented . The paper concludes that the ratios of the intensity values between background image and current image are fitted to a Cauchy distribution .
subsection : Temporal Feature Aggregation Temporal cues could be incorporated for feature reinforcement in object detection tasks . For example , TCNN uses optical flow to map detections to neighboring frames and suppresses low - confidence predictions while incorporating tracking algorithms .
It can be observed that the DPN obtains the mAP of , which makes large improvements , i.e. compared with ResNet - 101 and compared with ResNeXt - 101 ( d ) . The better results shown in this experiment demonstrate that the Dual Path Network is also capable of learning better feature representations for detecting objects and benefiting the object detection task . subsection : Experiments on the semantic segmentation task
section : Abstract Feature pyramids are a basic component in recognition systems for detecting objects at different scales . But recent deep learning object detectors have avoided pyramid representations , in part because they are compute and memory intensive .
Object skeleton detection is a challenging problem with wide application . Recently , deep Convolutional Neural Networks ( CNNs ) have substantially improved the performance of the state-of-the-art in this task .
They are complementary to the object contour , and provide extra information , such as how object scale ( thickness ) varies among object parts . But object skeleton extraction from natural images is very challenging , because it requires the extractor to be able to capture both local and non-local image context in order to determine the scale of each skeleton pixel . In this paper , we present a novel fully convolutional network with multiple scale-associated side outputs to address this problem .
This paper presents a novel method for extracting road networks from VHR remotely sensed images in complex urban scenes . Inspired by image segmentation , edge detection , and object skeleton extraction , we develop a multitask convolutional neural network ( CNN ) , called RoadNet , to simultaneously predict road surfaces , edges , and centerlines , which is the first work in such field . The RoadNet solves seven important issues in this vision problem : 1 ) automatically learning multiscale and multilevel features [ gained by the deeply supervised nets ( DSN ) providing integrated direct supervision ] to cope with the roads in various scenes and scales ; 2 ) holistically training the mentioned tasks in a cascaded end-to-end CNN model ; 3 ) correlating the predictions of road surfaces , edges , and centerlines in a network model to improve the multitask prediction ; 4 ) designing elaborate architecture and loss function , by which the well-trained model produces approximately single-pixel width road edges/centerlines without nonmaximum suppression postprocessing ; 5 ) cropping and bilinear blending to deal with the large VHR images with finite-computing resources ; 6 ) introducing rough and simple user interaction to obtain desired predictions in the challenging regions ; and 7 ) establishing a benchmark data set which consists of a series of VHR remote sensing images with pixelwise annotation .
Different from the existing works geared towards co-segmentation or co-localization , in this paper , we explore a new joint processing topic : image co-skeletonization , which is defined as joint skeleton extraction of objects in an image collection . Object skeletonization in a single natural image is a challenging problem because there is hardly any prior knowledge about the object . Therefore , we resort to the idea of object co-skeletonization , hoping that the commonness prior that exists across the images may help , just as it does for other joint processing problems such as co-segmentation .
Different from the existing works geared towards co-segmentation or co-localization , in this paper , we explore a new joint processing topic : co-skeletonization , which is defined as joint skeleton extraction of common objects in a set of semantically similar images . Object skeletonization in real world images is a challenging problem , because there is no prior knowledge of the objects shape if we consider only a single image . This motivates us to resort to the idea of object co-skeletonization hoping that the commonness prior existing across the similar images may help , just as it does for other joint processing problems such as co-segmentation .
It consists of 12,500 printed text words in 50 paragraphs and 15,000 words written by 100 different writers , males and females aged 5 to 60 years . Pre-processing operations are conducted including segmenting paragraphs into lines , segmenting line into words , segmenting words into characters , detecting skeleton , and determining baseline and other horizontal zones . The experimental results have shown that the proposed method has achieved promising accuracy recognition rate with 90.46 % for printed text and handwritten characters .
The main application aims at feeding a pore network approach to simulate unsaturated hydraulic properties from soil core images . Beyond the application example , the successive algorithms presented in the paper allow , from any 3D object image , the extraction of the object skeleton , the measurement of the distribution of local apertures and the partition of the object into a set of interconnected subobjects according to different size classes . The resulting , specific library DXSoil is available under the OpenDX environment more generally dedicated to data exploration and visualization .
Also inability to be used in mobile devices further limits its applicability . We create a system using OpenCV to be used as a library which will be used for human body skeleton detection and tracking . The proposed system uses video stream input through an integrated webcam and processes it to obtain human skeleton .
This study aims the tiny chip size package ( CSP ) components to develop a Kinect-based inspection system . The proposed system applied Kinect sensor to perform human skeleton detection . With our developed software , the operator outside the clean room can use his hand to directly control the important inspection modules such as lighting module and translation module , which are located in the clean room , through the glass window by applying somatosensory technology .
Thus , robust skeleton detection requires more powerful multi-scale feature integration ability than other vision tasks . In this paper , we present a new convolutional neural network ( CNN ) architecture by introducing a novel hierarchical feature integration mechanism , named Hi-Fi , to address the object skeleton detection problem . CNNs can capture high-level semantics from deeper layers as well as low-level details from shallower layers .
The RGB camera and the infrared light module aim to do distance estimation of a body or several bodies . The module not only provides image perception but also objective skeleton detection . In which , a laser source in the infrared light module emits invisible infrared light which passes through a filter and is scattered into a semi-random but constant pattern of small dots which is projected onto the environment in front of the sensor .
Robust object skeleton detection requires to explore rich representative visual features and effective feature fusion strategies . In this paper , we first re-visit the implementation of HED , the essential principle of which can be ideally described with a linear reconstruction model .
Computing object skeletons in natural images is challenging , owing to large variations in object appearance and scale , and the complexity of handling background clutter . Many recent methods frame object skeleton detection as a binary pixel classification problem , which is similar in spirit to learning - based edge detection , as well as to semantic segmentation methods . In the present article , we depart from this strategy by training a CNN to predict a two - dimensional vector field , which maps each scene point to a candidate skeleton pixel , in the spirit of flux - based skeletonization algorithms .
Most such approaches focus on designing an appropriate network and leveraging better multi - level features for capturing skeletons across a range of spatial scales . Object skeleton computation using CNNs from natural images is inherently different from the problem of edge detection . As illustrated in Figure [ reference ] , edges associated with object boundaries can typically be detected locally , due to a local appearance change or a change in texture .
2 ) Based on the context flux , we develop a method which we dub DeepFlux , that accurately and efficiently detects object skeletons in an image . 3 ) DeepFlux consistently outperforms state - of - the - art methods on five public benchmarks . To our knowledge this is the first application of flux concepts , which have been successfully used for skeletonization of binary objects , to the detection of object skeletons in natural images . It is also the first attempt at learning such flux - based representations directly from natural images .
section : Related Work Object skeletonization has been widely studied in recent decades . In our review , we contrast traditional methods with those based on deep learning .
A beneficial side - effect is that our method does not rely directly on the coarse responses produced by deeper CNN layers for localizing skeletons at larger scales , which further reduces localization errors . As we show in our experiments , these properties make our method more robust to the localization of skeleton points , especially around ligature regions , and less prone to gaps , discontinuities , and irregularities caused by local mispredictions . Finally , it is easy to accurately recover a binary object skeleton using the magnitude and direction of the predicted flux , as explained in Section [ reference ] .
The good news , however , is that DeepFlux is not sensitive to the value of . Finally , one may argue that simply using a dilated skeleton ground truth is sufficient to make a baseline model more robust in accurately localizing skeleton points . To examine if this is the case , we retrained our baseline model using binary cross - entropy on the same dilated ground truth we used for DeepFlux .
section : Conclusion We have proposed DeepFlux , a novel approach for accurate detection of object skeletons in the wild . Departing from the usual view of learning - based skeleton detection as a binary classification problem , we have recast it as the regression problem of predicting a 2D vector field of “ context flux ” . We have developed a simple convolutional neural network to compute such a flux , followed by a simple post - processing scheme that can accurately recover object skeletons in Our approach steers clear of many limitations related to poor localization , commonly shared by previous methods , and particularly shines in handling ligature points , and skeletons at large scales .
The resulting belief functions are used as initial affordance hypotheses , which are subject to further investigation and validation . As pure visual affordance detection can fail to properly estimate important preconditions , e.g. , the stability of environmental structures , validation experiments are conducted in order to incrementally improve the system belief and the reliability of detected affordances . The proposed formalism is implemented and evaluated in the context of loco-manipulation affordances for humanoid robots using the simulated robots ARMAR-III and ARMAR-4 .
Haptic object recognition by a robot hand is essential for adapting to human environment because of its role in multimodal sensing and detecting affordance . An intrinsic difficulty of such recognition is due to its locality .
Haptic object recognition by a robot hand is essential for adapting to human environment because of its role in multimodal sensing and detecting affordance . An intrinsic difficulty of such recognition is due to its locality .
Leveraging Deep 3D saliency and geometry for fast and scalable multiple affordance detection . This paper develops and evaluates a novel method that allows for the detection of affordances in a scalable and multiple-instance manner on visually recovered pointclouds . Our approach has many advantages over alternative methods , as it is based on highly parallelizable , one-shot learning that is fast in commodity hardware .
We train and validate these models on a manually curated dataset labeled with leisure ontology classes for the city of Zwolle , and discuss their potential for urban leisure and tourism research and related city policies and planning . We found that multi-label affordance estimation is not straightforward but can be made to work using both official webtexts and user-generated content on a medium semantic level . This opens up new opportunities for data-driven approaches to urban leisure and tourism studies .
Object affordance detection , which aims to understand functional attributes of objects , is of great significance for an autonomous robot to achieve a humanoid object manipulation . In this paper , we propose a novel relationship-aware convolutional neural network , which takes the symbiotic relationship between multiple affordances and the combinational relationship between the affordance and objectness into consideration , to predict the most probable affordance label for each pixel in the object .
Opposed to Norman 's proposal , processing of affordance is likely to occur not solely in the dorsal stream but also in the ventral stream . Moreover , the dorsal stream might do more than just serve an important role in motor actions .
End-to-end learning in deep reinforcement learning based on raw visual input has shown great promise in various tasks involving sensorimotor control . However , complex tasks such as tool use require recognition of affordance and a series of non-trivial subtasks such as reaching the tool , grasping the tool , and wielding the tool . In such tasks , end-to-end approaches with only the raw input ( e.g. pixel-wise images ) may fail to learn to perform the task or may take too long to converge .
The 203 workshop papers were carefully reviewed and selected for inclusion in the proceedings . They where presented at workshops with the following themes : where computer vision meets art ; computer vision in vehicle technology ; spontaneous facial behavior analysis ; consumer depth cameras for computer vision ; " chalearn " looking at people : pose , recovery , action/interaction , gesture recognition ; video event categorization , tagging and retrieval towards big data ; computer vision with local binary pattern variants ; visual object tracking challenge ; computer vision + ontology applies cross-disciplinary technologies ; visual perception of affordance and functional visual primitives for scene analysis ; graphical models in computer vision ; light fields for computer vision ; computer vision for road scene understanding and autonomous driving ; soft biometrics ; transferring and adapting source knowledge in computer vision ; surveillance and re-identification ; color and photometry in computer vision ; assistive computer vision and robotics ; computer vision problems in plant phenotyping ; and non-rigid shape analysis and deformable image alignment . Additionally , a panel discussion on video segmentation is included
In this paper , we introduce the Flexible Space Subdivision framework ( FSS ) that allows to automatically identify the spaces that can be used for indoor navigation purpose . We propose a classification of indoor objects based on their ability to autonomously change location and we define a spatial subdivision of the indoor environment based on the classified objects and their functions . The framework can consider any 3D indoor configuration , the static and dynamic activities it hosts and it enables the possibility to consider all types of locomotion ( e.g. walking , flying , etc . ) .
The research is hoping to continue in the following areas : taking into account the varying lighting/weather conditions and the blurring effect , and detecting objects at a lower height such as bricks/stones . The research will also be looking at other issues like detecting indoor objects , DSP based implementation issues , rendering the visual information and the usability issues .
The last stage of the method ensures interframe consistency based on a cost function between regions from subsequent frames . The planar segmentation can be used both for identifying indoor objects , which in general have planar faces , as well as for estimating the camera motion and then reconstructing the environment .
While deep convolutional neural networks have shown a remarkable success in image classification , the problems of inter-class similarities , intra-class variances , the effective combination of multi-modal data , and the spatial variability in images of objects remain to be major challenges . To address these problems , this paper proposes a novel framework to learn a discriminative and spatially invariant classification model for object and indoor scene recognition using multi-modal RGB-D imagery . This is achieved through three postulates : 1 ) spatial invariance < inline-formula><tex-math notation="LaTeX">$-$</tex-math><alternatives > < inline-graphic xlink : href="asif-ieq1 - 2747134.gif"/></alternatives></inline-formula > this is achieved by combining a spatial transformer network with a deep convolutional neural network to learn features which are invariant to spatial translations , rotations , and scale changes , 2 ) high discriminative capability < inline-formula > < tex-math notation="LaTeX">$-$</tex-math><alternatives><inline-graphic xlink : href="asif-ieq2 - 2747134.gif"/ > < /alternatives></inline-formula > this is achieved by introducing Fisher encoding within the CNN architecture to learn features which have small inter-class similarities and large intra-class compactness , and 3 ) multi-modal hierarchical fusion < inline-formula><tex-math notation="LaTeX">$-$</tex-math><alternatives > < inline-graphic xlink : href="asif-ieq3 - 2747134.gif"/></alternatives></inline-formula > this is achieved through the regularization of semantic segmentation to a multi-modal CNN architecture , where class probabilities are estimated at different hierarchical levels ( i.e. , image- and pixel-levels ) , and fused into a Conditional Random Field (CRF)-based inference hypothesis , the optimization of which produces consistent class labels in RGB-D images .
Object detection of indoor scene is widely used in the field of service robot . State-of-art object detectors rely heavily on large-scale datasets like PASCAL VOC2007 , VOC2012 .
Then by using improved depth information , the algorithm extracts the foreground and implements the object segmentation of color image at the resolution of 640 × 480 from Kinect camera . Finally , the segmentation results are applied into the object recognition in indoor scene to validate the effectiveness of scene segmentation . The results of indoor segmentation demonstrate the real-time performance and robustness of the proposed method .
This paper presents a 3-D object recognition method and its implementation on a robotic navigation aid to allow real-time detection of indoor structural objects for the navigation of a blind person . The method segments a point cloud into numerous planar patches and extracts
The hierarchical description enables easy navigation in the formerly unstructured data , measurement takings , as well as carrying out retrieval tasks that incorporate geometric , topological , and also functional building properties describing e.g. the designated use of single rooms according to the objects it contains . In contrast to previous methods which either focus on the segmentation into rooms or on the recognition of indoor objects , our holistic approach incorporates a rather large spectrum of entities on different semantic levels that are inherent to 3D building representations . In our evaluation we show the feasibility of our method for extraction of hierarchical building descriptions for various tasks using synthetic as well as real world data .
Recognizing indoor scene and objects and estimating their poses present a wide range of applications in robotic field . This task becomes more challenging especially in cluttered environments like the indoor scenery .
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