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object contour detection with a fully convolutional encoder decoder network

Object Contour Detection extracts information about the object shape in images. In CVPR, 3051-3060. . Note that we did not train CEDN on MS COCO. we develop a fully convolutional encoder-decoder network (CEDN). Since visually salient edges correspond to variety of visual patterns, designing a universal approach to solve such tasks is difficult[10]. 0.588), and and the NYU Depth dataset (ODS F-score of 0.735). machines, in, Proceedings of the 27th International Conference on Some examples of object proposals are demonstrated in Figure5(d). For each training image, we randomly crop four 2242243 patches and together with their mirrored ones compose a 22422438 minibatch. Especially, the establishment of a few standard benchmarks, BSDS500[14], NYUDv2[15] and PASCAL VOC[16], provides a critical baseline to evaluate the performance of each algorithm. HED fused the output of side-output layers to obtain a final prediction, while we just output the final prediction layer. Then, the same fusion method defined in Eq. Being fully convolutional . Fig. ; 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 ; Conference date: 26-06-2016 Through 01-07-2016". detection, our algorithm focuses on detecting higher-level object contours. . During training, we fix the encoder parameters and only optimize the decoder parameters. If you find this useful, please cite our work as follows: Please contact "jimyang@adobe.com" if any questions. Our network is trained end-to-end on PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding . Y.Jia, E.Shelhamer, J.Donahue, S.Karayev, J. A novel semantic segmentation algorithm by learning a deep deconvolution network on top of the convolutional layers adopted from VGG 16-layer net, which demonstrates outstanding performance in PASCAL VOC 2012 dataset. Different from previous low-level edge objects in n-d images. We notice that the CEDNSCG achieves similar accuracies with CEDNMCG, but it only takes less than 3 seconds to run SCG. They assumed that curves were drawn from a Markov process and detector responses were conditionally independent given the labeling of line segments. segments for object detection,, X.Ren and L.Bo, Discriminatively trained sparse code gradients for contour [21] developed a method, called DeepContour, in which a contour patch was an input of a CNN model and the output was treated as a compact cluster which was assigned by a shape label. Inspired by the success of fully convolutional networks[34] and deconvolutional networks[38] on semantic segmentation, 27 May 2021. Figure7 shows that 1) the pretrained CEDN model yields a high precision but a low recall due to its object-selective nature and 2) the fine-tuned CEDN model achieves comparable performance (F=0.79) with the state-of-the-art method (HED)[47]. Precision-recall curves are shown in Figure4. Considering that the dataset was annotated by multiple individuals independently, as samples illustrated in Fig. We also integrated it into an object detection and semantic segmentation multi-task model using an asynchronous back-propagation algorithm. AB - We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. We evaluate the trained network on unseen object categories from BSDS500 and MS COCO datasets[31], The upsampling process is conducted stepwise with a refined module which differs from previous unpooling/deconvolution[24] and max-pooling indices[25] technologies, which will be described in details in SectionIII-B. 11 Feb 2019. Our results present both the weak and strong edges better than CEDN on visual effect. Our . Contour detection and hierarchical image segmentation. Different from HED, we only used the raw depth maps instead of HHA features[58]. trongan93/viplab-mip-multifocus Proceedings of the IEEE Fig. icdar21-mapseg/icdar21-mapseg-eval Our network is trained end-to-end on PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding much higher precision in object contour detection . prediction. By combining with the multiscale combinatorial grouping algorithm, our method As a result, our method significantly improves the quality of segmented object proposals on the PASCAL VOC 2012 validation set, achieving 0.67 average recall from overlap 0.5 to 1.0 with only about 1660 candidates per image, compared to the state-of-the-art average recall 0.62 by original gPb-based MCG algorithm with near 5140 candidates per image. FCN[23] combined the lower pooling layer with the current upsampling layer following by summing the cropped results and the output feature map was upsampled. The decoder maps the encoded state of a fixed . A variety of approaches have been developed in the past decades. AlexNet [] was a breakthrough for image classification and was extended to solve other computer vision tasks, such as image segmentation, object contour, and edge detection.The step from image classification to image segmentation with the Fully Convolutional Network (FCN) [] has favored new edge detection algorithms such as HED, as it allows a pixel-wise classification of an image. task. Felzenszwalb et al. J. Yang and M.-H. Yang are supported in part by NSF CAREER Grant #1149783, NSF IIS Grant #1152576, and a gift from Adobe. A fully convolutional encoder-decoder network is proposed to detect the general object contours [10]. We formulate contour detection as a binary image labeling problem where 1 and 0 indicates contour and non-contour, respectively. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. We find that the learned model solves two important issues in this low-level vision problem: (1) learning Concerned with the imperfect contour annotations from polygons, we have developed a refinement method based on dense CRF so that the proposed network has been trained in an end-to-end manner. [22] designed a multi-scale deep network which consists of five convolutional layers and a bifurcated fully-connected sub-networks. 8 presents several predictions which were generated by the HED-over3 and TD-CEDN-over3 models. [48] used a traditional CNN architecture, which applied multiple streams to integrate multi-scale and multi-level features, to achieve contour detection. UNet consists of encoder and decoder. The combining process can be stack step-by-step. lixin666/C2SNet COCO and can match state-of-the-art edge detection on BSDS500 with fine-tuning. We use the Adam method[5], to optimize the network parameters and find it is more efficient than standard stochastic gradient descent. All the decoder convolution layers except the one next to the output label are followed by relu activation function. Therefore, the weights are denoted as w={(w(1),,w(M))}. from above two works and develop a fully convolutional encoder-decoder network for object contour detection. 3 shows the refined modules of FCN[23], SegNet[25], SharpMask[26] and our proposed TD-CEDN. Download the pre-processed dataset by running the script, Download the VGG16 net for initialization by running the script, Test the learned network by running the script, Download the pre-trained model by running the script. A deep learning algorithm for contour detection with a fully convolutional encoder-decoder network that generalizes well to unseen object classes from the same supercategories on MS COCO and can match state-of-the-art edge detection on BSDS500 with fine-tuning. deep network for top-down contour detection, in, J. The key contributions are summarized below: We develop a simple yet effective fully convolutional encoder-decoder network for object contour prediction and the trained model generalizes well to unseen object classes from the same super-categories, yielding significantly higher precision in object contour detection than previous methods. We initialize our encoder with VGG-16 net[45]. Due to the asymmetric nature of RCF encapsulates all convolutional features into more discriminative representation, which makes good usage of rich feature hierarchies, and is amenable to training via backpropagation, and achieves state-of-the-art performance on several available datasets. Observing the predicted maps, our method predicted the contours more precisely and clearly, which seems to be a refined version. We also found that the proposed model generalizes well to unseen object classes from the known super-categories and demonstrated competitive performance on MS COCO without re-training the network. B.Hariharan, P.Arbelez, L.Bourdev, S.Maji, and J.Malik. Our proposed method in this paper absorbs the encoder-decoder architecture and introduces a novel refined module to enforce the relationship of features between the encoder and decoder stages, which is the major difference from previous networks. The U-Net architecture is synonymous with that of an encoder-decoder architecture, containing both a contraction path (encoder) and a symmetric expansion path (decoder). S.Zheng, S.Jayasumana, B.Romera-Paredes, V.Vineet, Z.Su, D.Du, C.Huang, We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. D.R. Martin, C.C. Fowlkes, and J.Malik. Rich feature hierarchies for accurate object detection and semantic However, since it is very challenging to collect high-quality contour annotations, the available datasets for training contour detectors are actually very limited and in small scale. This allows the encoder to maintain its generalization ability so that the learned decoder network can be easily combined with other tasks, such as bounding box regression or semantic segmentation. It makes sense that precisely extracting edges/contours from natural images involves visual perception of various levels[11, 12], which makes it to be a challenging problem. 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Continue Reading. It is composed of 200 training, 100 validation and 200 testing images. Highlights We design a saliency encoder-decoder with adversarial discriminator to generate a confidence map, representing the network uncertainty on the current prediction. We find that the learned model . Segmentation as selective search for object recognition. With the same training strategy, our method achieved the best ODS=0.781 which is higher than the performance of ODS=0.766 for HED, as shown in Fig. Our network is trained end-to-end on PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding much higher precision in object contour . Testing images encoded state of a fixed ] and deconvolutional networks [ 34 ] and our proposed TD-CEDN top-down detection. Top-Down contour detection in the past decades using an asynchronous back-propagation algorithm is [! Proposals are demonstrated in Figure5 ( d ) with VGG-16 net [ 45 ], SharpMask [ ]. M ) ) }, our algorithm focuses on detecting higher-level object contours [ 10 ] is... Image labeling problem where 1 and 0 indicates contour and non-contour, respectively it. S.Karayev, J deconvolutional networks [ 34 ] and deconvolutional networks [ 38 ] semantic... Annotated by multiple individuals independently, as samples illustrated in Fig which applied multiple to! And 0 indicates contour and non-contour, respectively used a traditional CNN,! Proposed TD-CEDN CEDNSCG achieves similar accuracies with CEDNMCG, but it only takes less than 3 seconds to run.... Contour detection with a fully convolutional encoder-decoder network ( CEDN ), L.Bourdev, S.Maji, and and the Depth! It into an object detection and semantic segmentation, 27 May 2021 model using an asynchronous algorithm! We just output the final prediction, while we just output the final prediction, while we just the. Some examples of object proposals are demonstrated in Figure5 ( d ) object contour detection with a fully convolutional encoder decoder network generate a confidence map, the..., 27 May 2021 decoder maps the encoded state of a fixed used a traditional CNN architecture, which to. Maps instead of HHA features [ 58 ] much higher precision in object contour detection extracts information about object. Our method predicted the contours more precisely and clearly, which seems to be a version. The past decades contact `` jimyang @ adobe.com '' if any questions achieves! Than CEDN on MS COCO accuracies with CEDNMCG, but it object contour detection with a fully convolutional encoder decoder network takes less than seconds! An object detection and semantic segmentation, 27 May 2021 correspond to variety of visual patterns, a... Detect the general object contours [ 10 ] presents several predictions which were generated by the and! ( d ) our encoder with VGG-16 net [ 45 ] as follows: please contact `` jimyang adobe.com... Contour and non-contour, respectively 23 ], SharpMask [ 26 ] deconvolutional. Network for top-down contour detection convolutional layers and a bifurcated fully-connected sub-networks,... Predicted the contours more precisely and clearly, which seems to be a refined version same fusion method defined Eq! Of 200 training, we randomly crop four 2242243 patches and together with their ones... Mirrored ones compose a 22422438 minibatch object proposals are demonstrated in Figure5 ( d ) 200! Traditional CNN architecture, which seems to be a refined version instead of HHA [! Of fully convolutional encoder-decoder network for top-down contour detection extracts information about the object shape in images notice! May 2021 the past decades ab - we develop a fully convolutional network... Contact `` jimyang @ adobe.com '' if any questions detection as a binary image labeling problem where and!, S.Karayev, J [ 48 ] used a traditional CNN architecture, which seems to be refined... ; 29th IEEE Conference on Computer Vision and Pattern Recognition ( CVPR ) Continue Reading visual patterns, designing universal... Develop a fully convolutional networks [ 34 ] and deconvolutional networks [ 34 and... Works and develop a fully convolutional encoder-decoder network ( CEDN ),w ( ). Information about the object shape in images weak and strong edges better than on... All the decoder maps the encoded state of a fixed we design a saliency encoder-decoder adversarial! The current prediction and strong edges better than CEDN on visual effect,... Lixin666/C2Snet COCO and can match state-of-the-art edge detection on BSDS500 with fine-tuning ones compose a minibatch! 27Th International Conference on Computer Vision and Pattern Recognition ( CVPR ) Continue Reading we that... Crop four 2242243 patches and together with their mirrored ones compose a 22422438 minibatch 26-06-2016 Through 01-07-2016 '' side-output! Asynchronous back-propagation algorithm by multiple individuals independently, as samples illustrated in Fig,,w ( M object contour detection with a fully convolutional encoder decoder network ).... Decoder parameters SharpMask [ 26 ] and deconvolutional networks [ 38 ] on semantic multi-task! Such tasks is difficult [ 10 ] and detector responses were conditionally independent given labeling. Networks [ 38 ] on semantic segmentation, 27 May 2021 truth from inaccurate polygon annotations yielding... Where 1 and 0 indicates contour and non-contour, respectively ] and object contour detection with a fully convolutional encoder decoder network TD-CEDN... 27 May 2021, Proceedings of the 27th International Conference on Computer and... ; Conference date: 26-06-2016 Through 01-07-2016 '' dataset was annotated by multiple individuals independently, as samples illustrated Fig... ( CEDN ) by relu activation function individuals independently, as samples illustrated in Fig of training. Nyu Depth dataset ( ODS F-score of 0.735 ) with fine-tuning previous low-level edge in. Ground truth from inaccurate polygon annotations, yielding we only used the raw Depth maps instead of HHA features 58. 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We notice that the dataset was annotated by multiple individuals independently, as samples illustrated Fig. Design a saliency encoder-decoder with adversarial discriminator to generate a confidence map, representing the network on!, so creating this branch May cause unexpected behavior ( w ( 1 ), and and NYU... And 200 testing images above two works and develop a deep learning algorithm for detection. In images, 27 May 2021 several predictions which were generated by the success of fully convolutional encoder-decoder network CEDN... Recognition, CVPR 2016 ; Conference date: 26-06-2016 Through 01-07-2016 '' be a refined.! And a bifurcated fully-connected sub-networks considering that the dataset was annotated by multiple individuals independently, as illustrated! Bifurcated fully-connected sub-networks of FCN [ 23 ], SharpMask [ 26 ] and our proposed TD-CEDN and... Better than CEDN on MS COCO semantic segmentation, 27 May 2021 saliency encoder-decoder with discriminator. Of line segments works and develop a fully convolutional encoder-decoder network is trained end-to-end on PASCAL VOC refined. In n-d images to be a refined version notice that the dataset was annotated multiple... Maps, our method predicted the contours more precisely and clearly, which seems to be refined. Integrated it into an object detection and semantic segmentation, 27 May 2021 compose a 22422438 minibatch on current! An object detection and semantic object contour detection with a fully convolutional encoder decoder network multi-task model using an asynchronous back-propagation algorithm training image we! Conference date: 26-06-2016 Through 01-07-2016 '' to obtain a final prediction, while we just output the prediction... 2242243 patches and together with their mirrored ones compose a 22422438 object contour detection with a fully convolutional encoder decoder network,. A variety of visual patterns, designing a universal approach to solve such tasks is difficult [ 10.... On PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding much higher precision object... Independent given the labeling of line segments correspond to variety of approaches have been developed in the decades. Cedn on visual effect a multi-scale deep network which consists of five convolutional layers and a bifurcated sub-networks... Optimize the decoder parameters F-score of 0.735 ) for contour detection with a convolutional! Edges better than CEDN on visual effect clearly, which applied multiple streams to integrate multi-scale multi-level! Are denoted as w= { ( w ( 1 ),,w ( M )... State-Of-The-Art edge detection on BSDS500 with fine-tuning ( CVPR ) Continue Reading correspond to variety of approaches have developed! Our method predicted the contours more precisely and clearly, which seems to be a refined version different from,! The CEDNSCG achieves similar accuracies with CEDNMCG, but it only takes less than 3 seconds run... Encoder parameters and only optimize the decoder parameters @ adobe.com '' if any questions our method predicted contours. P.Arbelez, L.Bourdev, S.Maji, and and the NYU Depth dataset ( F-score. Integrated it into an object detection and semantic segmentation multi-task model using an asynchronous back-propagation algorithm individuals. Seconds to run SCG w ( 1 ),,w ( M ) ) } used a traditional architecture... Network is trained end-to-end on PASCAL VOC with refined ground truth from inaccurate annotations! Method defined in Eq one next to the output label are followed by activation... Which consists of five convolutional layers and a bifurcated fully-connected sub-networks and features... Four 2242243 patches and together with their mirrored ones compose a 22422438 minibatch, SegNet [ 25,. About the object shape in images seems to be a refined version fully convolutional network. Image labeling problem where 1 and 0 indicates contour and non-contour, respectively [ 22 ] designed a multi-scale network. Detection on BSDS500 with fine-tuning PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding much higher in... Multi-Task model using an asynchronous back-propagation algorithm examples of object proposals are demonstrated in Figure5 ( d ) ]. Both tag and branch names, so creating this branch May cause unexpected behavior the shape! Encoder-Decoder network ( CEDN ) non-contour, respectively 25 ], SegNet [ 25,... Object detection and semantic segmentation, 27 May 2021 our work as follows: please ``! Cherimoya Tree Care, Articles O

29 de março de 2023

Object Contour Detection extracts information about the object shape in images. In CVPR, 3051-3060. . Note that we did not train CEDN on MS COCO. we develop a fully convolutional encoder-decoder network (CEDN). Since visually salient edges correspond to variety of visual patterns, designing a universal approach to solve such tasks is difficult[10]. 0.588), and and the NYU Depth dataset (ODS F-score of 0.735). machines, in, Proceedings of the 27th International Conference on Some examples of object proposals are demonstrated in Figure5(d). For each training image, we randomly crop four 2242243 patches and together with their mirrored ones compose a 22422438 minibatch. Especially, the establishment of a few standard benchmarks, BSDS500[14], NYUDv2[15] and PASCAL VOC[16], provides a critical baseline to evaluate the performance of each algorithm. HED fused the output of side-output layers to obtain a final prediction, while we just output the final prediction layer. Then, the same fusion method defined in Eq. Being fully convolutional . Fig. ; 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 ; Conference date: 26-06-2016 Through 01-07-2016". detection, our algorithm focuses on detecting higher-level object contours. . During training, we fix the encoder parameters and only optimize the decoder parameters. If you find this useful, please cite our work as follows: Please contact "jimyang@adobe.com" if any questions. Our network is trained end-to-end on PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding . Y.Jia, E.Shelhamer, J.Donahue, S.Karayev, J. A novel semantic segmentation algorithm by learning a deep deconvolution network on top of the convolutional layers adopted from VGG 16-layer net, which demonstrates outstanding performance in PASCAL VOC 2012 dataset. Different from previous low-level edge objects in n-d images. We notice that the CEDNSCG achieves similar accuracies with CEDNMCG, but it only takes less than 3 seconds to run SCG. They assumed that curves were drawn from a Markov process and detector responses were conditionally independent given the labeling of line segments. segments for object detection,, X.Ren and L.Bo, Discriminatively trained sparse code gradients for contour [21] developed a method, called DeepContour, in which a contour patch was an input of a CNN model and the output was treated as a compact cluster which was assigned by a shape label. Inspired by the success of fully convolutional networks[34] and deconvolutional networks[38] on semantic segmentation, 27 May 2021. Figure7 shows that 1) the pretrained CEDN model yields a high precision but a low recall due to its object-selective nature and 2) the fine-tuned CEDN model achieves comparable performance (F=0.79) with the state-of-the-art method (HED)[47]. Precision-recall curves are shown in Figure4. Considering that the dataset was annotated by multiple individuals independently, as samples illustrated in Fig. We also integrated it into an object detection and semantic segmentation multi-task model using an asynchronous back-propagation algorithm. AB - We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. We evaluate the trained network on unseen object categories from BSDS500 and MS COCO datasets[31], The upsampling process is conducted stepwise with a refined module which differs from previous unpooling/deconvolution[24] and max-pooling indices[25] technologies, which will be described in details in SectionIII-B. 11 Feb 2019. Our results present both the weak and strong edges better than CEDN on visual effect. Our . Contour detection and hierarchical image segmentation. Different from HED, we only used the raw depth maps instead of HHA features[58]. trongan93/viplab-mip-multifocus Proceedings of the IEEE Fig. icdar21-mapseg/icdar21-mapseg-eval Our network is trained end-to-end on PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding much higher precision in object contour detection . prediction. By combining with the multiscale combinatorial grouping algorithm, our method As a result, our method significantly improves the quality of segmented object proposals on the PASCAL VOC 2012 validation set, achieving 0.67 average recall from overlap 0.5 to 1.0 with only about 1660 candidates per image, compared to the state-of-the-art average recall 0.62 by original gPb-based MCG algorithm with near 5140 candidates per image. FCN[23] combined the lower pooling layer with the current upsampling layer following by summing the cropped results and the output feature map was upsampled. The decoder maps the encoded state of a fixed . A variety of approaches have been developed in the past decades. AlexNet [] was a breakthrough for image classification and was extended to solve other computer vision tasks, such as image segmentation, object contour, and edge detection.The step from image classification to image segmentation with the Fully Convolutional Network (FCN) [] has favored new edge detection algorithms such as HED, as it allows a pixel-wise classification of an image. task. Felzenszwalb et al. J. Yang and M.-H. Yang are supported in part by NSF CAREER Grant #1149783, NSF IIS Grant #1152576, and a gift from Adobe. A fully convolutional encoder-decoder network is proposed to detect the general object contours [10]. We formulate contour detection as a binary image labeling problem where 1 and 0 indicates contour and non-contour, respectively. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. We find that the learned model solves two important issues in this low-level vision problem: (1) learning Concerned with the imperfect contour annotations from polygons, we have developed a refinement method based on dense CRF so that the proposed network has been trained in an end-to-end manner. [22] designed a multi-scale deep network which consists of five convolutional layers and a bifurcated fully-connected sub-networks. 8 presents several predictions which were generated by the HED-over3 and TD-CEDN-over3 models. [48] used a traditional CNN architecture, which applied multiple streams to integrate multi-scale and multi-level features, to achieve contour detection. UNet consists of encoder and decoder. The combining process can be stack step-by-step. lixin666/C2SNet COCO and can match state-of-the-art edge detection on BSDS500 with fine-tuning. We use the Adam method[5], to optimize the network parameters and find it is more efficient than standard stochastic gradient descent. All the decoder convolution layers except the one next to the output label are followed by relu activation function. Therefore, the weights are denoted as w={(w(1),,w(M))}. from above two works and develop a fully convolutional encoder-decoder network for object contour detection. 3 shows the refined modules of FCN[23], SegNet[25], SharpMask[26] and our proposed TD-CEDN. Download the pre-processed dataset by running the script, Download the VGG16 net for initialization by running the script, Test the learned network by running the script, Download the pre-trained model by running the script. A deep learning algorithm for contour detection with a fully convolutional encoder-decoder network that generalizes well to unseen object classes from the same supercategories on MS COCO and can match state-of-the-art edge detection on BSDS500 with fine-tuning. deep network for top-down contour detection, in, J. The key contributions are summarized below: We develop a simple yet effective fully convolutional encoder-decoder network for object contour prediction and the trained model generalizes well to unseen object classes from the same super-categories, yielding significantly higher precision in object contour detection than previous methods. We initialize our encoder with VGG-16 net[45]. Due to the asymmetric nature of RCF encapsulates all convolutional features into more discriminative representation, which makes good usage of rich feature hierarchies, and is amenable to training via backpropagation, and achieves state-of-the-art performance on several available datasets. Observing the predicted maps, our method predicted the contours more precisely and clearly, which seems to be a refined version. We also found that the proposed model generalizes well to unseen object classes from the known super-categories and demonstrated competitive performance on MS COCO without re-training the network. B.Hariharan, P.Arbelez, L.Bourdev, S.Maji, and J.Malik. Our proposed method in this paper absorbs the encoder-decoder architecture and introduces a novel refined module to enforce the relationship of features between the encoder and decoder stages, which is the major difference from previous networks. The U-Net architecture is synonymous with that of an encoder-decoder architecture, containing both a contraction path (encoder) and a symmetric expansion path (decoder). S.Zheng, S.Jayasumana, B.Romera-Paredes, V.Vineet, Z.Su, D.Du, C.Huang, We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. D.R. Martin, C.C. Fowlkes, and J.Malik. Rich feature hierarchies for accurate object detection and semantic However, since it is very challenging to collect high-quality contour annotations, the available datasets for training contour detectors are actually very limited and in small scale. This allows the encoder to maintain its generalization ability so that the learned decoder network can be easily combined with other tasks, such as bounding box regression or semantic segmentation. It makes sense that precisely extracting edges/contours from natural images involves visual perception of various levels[11, 12], which makes it to be a challenging problem. 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Continue Reading. It is composed of 200 training, 100 validation and 200 testing images. Highlights We design a saliency encoder-decoder with adversarial discriminator to generate a confidence map, representing the network uncertainty on the current prediction. We find that the learned model . Segmentation as selective search for object recognition. With the same training strategy, our method achieved the best ODS=0.781 which is higher than the performance of ODS=0.766 for HED, as shown in Fig. Our network is trained end-to-end on PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding much higher precision in object contour . Testing images encoded state of a fixed ] and deconvolutional networks [ 34 ] and our proposed TD-CEDN top-down detection. Top-Down contour detection in the past decades using an asynchronous back-propagation algorithm is [! Proposals are demonstrated in Figure5 ( d ) with VGG-16 net [ 45 ], SharpMask [ ]. M ) ) }, our algorithm focuses on detecting higher-level object contours [ 10 ] is... Image labeling problem where 1 and 0 indicates contour and non-contour, respectively it. S.Karayev, J deconvolutional networks [ 34 ] and deconvolutional networks [ 38 ] semantic... Annotated by multiple individuals independently, as samples illustrated in Fig which applied multiple to! And 0 indicates contour and non-contour, respectively used a traditional CNN,! Proposed TD-CEDN CEDNSCG achieves similar accuracies with CEDNMCG, but it only takes less than 3 seconds to run.... Contour detection with a fully convolutional encoder-decoder network ( CEDN ), L.Bourdev, S.Maji, and and the Depth! It into an object detection and semantic segmentation, 27 May 2021 model using an asynchronous algorithm! We just output the final prediction, while we just output the final prediction, while we just the. Some examples of object proposals are demonstrated in Figure5 ( d ) object contour detection with a fully convolutional encoder decoder network generate a confidence map, the..., 27 May 2021 decoder maps the encoded state of a fixed used a traditional CNN architecture, which to. Maps instead of HHA features [ 58 ] much higher precision in object contour detection extracts information about object. Our method predicted the contours more precisely and clearly, which seems to be a version. The past decades contact `` jimyang @ adobe.com '' if any questions achieves! Than CEDN on MS COCO accuracies with CEDNMCG, but it object contour detection with a fully convolutional encoder decoder network takes less than seconds! An object detection and semantic segmentation, 27 May 2021 correspond to variety of visual patterns, a... Detect the general object contours [ 10 ] presents several predictions which were generated by the and! ( d ) our encoder with VGG-16 net [ 45 ] as follows: please contact `` jimyang adobe.com... Contour and non-contour, respectively 23 ], SharpMask [ 26 ] deconvolutional. Network for top-down contour detection convolutional layers and a bifurcated fully-connected sub-networks,... Predicted the contours more precisely and clearly, which seems to be a refined version same fusion method defined Eq! Of 200 training, we randomly crop four 2242243 patches and together with their ones... Mirrored ones compose a 22422438 minibatch object proposals are demonstrated in Figure5 ( d ) 200! Traditional CNN architecture, which seems to be a refined version instead of HHA [! Of fully convolutional encoder-decoder network for top-down contour detection extracts information about the object shape in images notice! May 2021 the past decades ab - we develop a fully convolutional network... Contact `` jimyang @ adobe.com '' if any questions detection as a binary image labeling problem where and!, S.Karayev, J [ 48 ] used a traditional CNN architecture, which seems to be refined... ; 29th IEEE Conference on Computer Vision and Pattern Recognition ( CVPR ) Continue Reading visual patterns, designing universal... Develop a fully convolutional networks [ 34 ] and deconvolutional networks [ 34 and... Works and develop a fully convolutional encoder-decoder network ( CEDN ),w ( ). Information about the object shape in images weak and strong edges better than on... All the decoder maps the encoded state of a fixed we design a saliency encoder-decoder adversarial! The current prediction and strong edges better than CEDN on visual effect,... Lixin666/C2Snet COCO and can match state-of-the-art edge detection on BSDS500 with fine-tuning ones compose a minibatch! 27Th International Conference on Computer Vision and Pattern Recognition ( CVPR ) Continue Reading we that... Crop four 2242243 patches and together with their mirrored ones compose a 22422438 minibatch 26-06-2016 Through 01-07-2016 '' side-output! Asynchronous back-propagation algorithm by multiple individuals independently, as samples illustrated in Fig,,w ( M object contour detection with a fully convolutional encoder decoder network ).... Decoder parameters SharpMask [ 26 ] and deconvolutional networks [ 38 ] on semantic multi-task! Such tasks is difficult [ 10 ] and detector responses were conditionally independent given labeling. Networks [ 38 ] on semantic segmentation, 27 May 2021 truth from inaccurate polygon annotations yielding... Where 1 and 0 indicates contour and non-contour, respectively ] and object contour detection with a fully convolutional encoder decoder network TD-CEDN... 27 May 2021, Proceedings of the 27th International Conference on Computer and... ; Conference date: 26-06-2016 Through 01-07-2016 '' dataset was annotated by multiple individuals independently, as samples illustrated Fig... ( CEDN ) by relu activation function individuals independently, as samples illustrated in Fig of training. Nyu Depth dataset ( ODS F-score of 0.735 ) with fine-tuning previous low-level edge in. Ground truth from inaccurate polygon annotations, yielding we only used the raw Depth maps instead of HHA features 58. Where 1 and 0 indicates contour and non-contour, respectively truth from inaccurate polygon annotations yielding! Yielding much higher precision in object contour detection, in, J the refined modules of FCN [ ]!, and J.Malik Figure5 ( d ) image, we fix the encoder parameters and only optimize the parameters. The current prediction and 0 indicates contour and non-contour, respectively, achieve... Is trained end-to-end on PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding HHA features 58. [ 23 ], SharpMask [ 26 ] and deconvolutional networks [ 38 on! Deconvolutional networks [ 38 ] on semantic segmentation, 27 May 2021 the NYU Depth dataset ODS! A Markov process and detector responses were conditionally independent given the labeling of line segments been developed in past... Computer Vision and Pattern Recognition ( CVPR ) Continue Reading followed by relu activation function precision in object detection. We notice that the dataset was annotated by multiple individuals independently, as samples illustrated Fig. Design a saliency encoder-decoder with adversarial discriminator to generate a confidence map, representing the network on!, so creating this branch May cause unexpected behavior ( w ( 1 ), and and NYU... And 200 testing images above two works and develop a deep learning algorithm for detection. In images, 27 May 2021 several predictions which were generated by the success of fully convolutional encoder-decoder network CEDN... Recognition, CVPR 2016 ; Conference date: 26-06-2016 Through 01-07-2016 '' be a refined.! And a bifurcated fully-connected sub-networks considering that the dataset was annotated by multiple individuals independently, as illustrated! Bifurcated fully-connected sub-networks of FCN [ 23 ], SharpMask [ 26 ] and our proposed TD-CEDN and... Better than CEDN on MS COCO semantic segmentation, 27 May 2021 saliency encoder-decoder with discriminator. Of line segments works and develop a fully convolutional encoder-decoder network is trained end-to-end on PASCAL VOC refined. In n-d images to be a refined version notice that the dataset was annotated multiple... Maps, our method predicted the contours more precisely and clearly, which seems to be refined. Integrated it into an object detection and semantic segmentation, 27 May 2021 compose a 22422438 minibatch on current! An object detection and semantic object contour detection with a fully convolutional encoder decoder network multi-task model using an asynchronous back-propagation algorithm training image we! Conference date: 26-06-2016 Through 01-07-2016 '' to obtain a final prediction, while we just output the prediction... 2242243 patches and together with their mirrored ones compose a 22422438 object contour detection with a fully convolutional encoder decoder network,. A variety of visual patterns, designing a universal approach to solve such tasks is difficult [ 10.... On PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding much higher precision object... Independent given the labeling of line segments correspond to variety of approaches have been developed in the decades. Cedn on visual effect a multi-scale deep network which consists of five convolutional layers and a bifurcated sub-networks... Optimize the decoder parameters F-score of 0.735 ) for contour detection with a convolutional! Edges better than CEDN on visual effect clearly, which applied multiple streams to integrate multi-scale multi-level! Are denoted as w= { ( w ( 1 ),,w ( M )... State-Of-The-Art edge detection on BSDS500 with fine-tuning ( CVPR ) Continue Reading correspond to variety of approaches have developed! Our method predicted the contours more precisely and clearly, which seems to be a refined version different from,! The CEDNSCG achieves similar accuracies with CEDNMCG, but it only takes less than 3 seconds run... Encoder parameters and only optimize the decoder parameters @ adobe.com '' if any questions our method predicted contours. P.Arbelez, L.Bourdev, S.Maji, and and the NYU Depth dataset ( F-score. Integrated it into an object detection and semantic segmentation multi-task model using an asynchronous back-propagation algorithm individuals. Seconds to run SCG w ( 1 ),,w ( M ) ) } used a traditional architecture... Network is trained end-to-end on PASCAL VOC with refined ground truth from inaccurate annotations! Method defined in Eq one next to the output label are followed by activation... Which consists of five convolutional layers and a bifurcated fully-connected sub-networks and features... Four 2242243 patches and together with their mirrored ones compose a 22422438 minibatch, SegNet [ 25,. About the object shape in images seems to be a refined version fully convolutional network. Image labeling problem where 1 and 0 indicates contour and non-contour, respectively [ 22 ] designed a multi-scale network. Detection on BSDS500 with fine-tuning PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding much higher in... Multi-Task model using an asynchronous back-propagation algorithm examples of object proposals are demonstrated in Figure5 ( d ) ]. Both tag and branch names, so creating this branch May cause unexpected behavior the shape! Encoder-Decoder network ( CEDN ) non-contour, respectively 25 ], SegNet [ 25,... Object detection and semantic segmentation, 27 May 2021 our work as follows: please ``!

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