ECCV 2018. To prepare the labels for contour detection from PASCAL Dataset , run create_lables.py and edit the file to add the path of the labels and new labels to be generated . This is the code for arXiv paper Object Contour Detection with a Fully Convolutional Encoder-Decoder Network by Jimei Yang, Brian Price, Scott Cohen, Honglak Lee and Ming-Hsuan Yang, 2016.. D.Martin, C.Fowlkes, D.Tal, and J.Malik. 30 Apr 2019. machines, in, Proceedings of the 27th International Conference on Different from previous low-level edge 3.1 Fully Convolutional Encoder-Decoder Network. All these methods require training on ground truth contour annotations. To perform the identification of focused regions and the objects within the image, this thesis proposes the method of aggregating information from the recognition of the edge on image. refined approach in the networks. segments for object detection,, X.Ren and L.Bo, Discriminatively trained sparse code gradients for contour A new way to generate object proposals is proposed, introducing an approach based on a discriminative convolutional network that obtains substantially higher object recall using fewer proposals and is able to generalize to unseen categories it has not seen during training. A database of human segmented natural images and its application to The above proposed technologies lead to a more precise and clearer segmentation, in, V.Badrinarayanan, A.Handa, and R.Cipolla, SegNet: A deep convolutional Figure8 shows that CEDNMCG achieves 0.67 AR and 0.83 ABO with 1660 proposals per image, which improves the second best MCG by 8% in AR and by 3% in ABO with a third as many proposals. Our fine-tuned model achieved the best ODS F-score of 0.588. [19] and Yang et al. Detection and Beyond. A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation; Large Kernel Matters . functional architecture in the cats visual cortex,, D.Marr and E.Hildreth, Theory of edge detection,, J.Yang, B. Copyright and all rights therein are retained by authors or by other copyright holders. 6. Given the success of deep convolutional networks [29] for . Given the success of deep convolutional networks[29] for learning rich feature hierarchies, To address the problem of irregular text regions in natural scenes, we propose an arbitrary-shaped text detection model based on Deformable DETR called BSNet. K.E.A. vande Sande, J.R.R. Uijlingsy, T.Gevers, and A.W.M. Smeulders. Publisher Copyright: {\textcopyright} 2016 IEEE. TableII shows the detailed statistics on the BSDS500 dataset, in which our method achieved the best performances in ODS=0.788 and OIS=0.809. BN and ReLU represent the batch normalization and the activation function, respectively. synthetically trained fully convolutional network, DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour . Semi-Supervised Video Salient Object Detection Using Pseudo-Labels; Contour Loss: Boundary-Aware Learning for Salient Object Segmentation . J.Hosang, R.Benenson, P.Dollr, and B.Schiele. Felzenszwalb et al. 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. Owing to discarding the fully connected layers after pool5, higher resolution feature maps are retained while reducing the parameters of the encoder network significantly (from 134M to 14.7M). One of their drawbacks is that bounding boxes usually cannot provide accurate object localization. J.J. Kivinen, C.K. Williams, and N.Heess. segmentation. This code includes; the Caffe toolbox for Convolutional Encoder-Decoder Networks (caffe-cedn)scripts for training and testing the PASCAL object contour detector, and However, because of unpredictable behaviors of human annotators and limitations of polygon representation, the annotated contours usually do not align well with the true image boundaries and thus cannot be directly used as ground truth for training. 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 . The encoder-decoder network with such refined module automatically learns multi-scale and multi-level features to well solve the contour detection issues. Given trained models, all the test images are fed-forward through our CEDN network in their original sizes to produce contour detection maps. This dataset is more challenging due to its large variations of object categories, contexts and scales. For example, it can be used for image segmentation[41, 3], for object detection[15, 18], and for occlusion and depth reasoning[20, 2]. Learning deconvolution network for semantic segmentation. We formulate contour detection as a binary image labeling problem where 1 and 0 indicates contour and non-contour, respectively. forests,, D.H. Hubel and T.N. Wiesel, Receptive fields, binocular interaction and In this section, we introduce our object contour detection method with the proposed fully convolutional encoder-decoder network. We also compared the proposed model to two benchmark object detection networks; Faster R-CNN and YOLO v5. Moreover, to suppress the image-border contours appeared in the results of CEDN, we applied a simple image boundary region extension method to enlarge the input image 10 pixels around the image during the testing stage. Work fast with our official CLI. Fig. Sketch tokens: A learned mid-level representation for contour and . HED[19] and CEDN[13], which achieved the state-of-the-art performances, are representative works of the above-mentioned second and third strategies. 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. The dense CRF optimization then fills the uncertain area with neighboring instance labels so that we obtain refined contours at the labeling boundaries (Figure3(d)). Ganin et al. Price, S.Cohen, H.Lee, and M.-H. Yang, Object contour detection kmaninis/COB [47] proposed to first threshold the output of [36] and then create a weighted edgels graph, where the weights measured directed collinearity between neighboring edgels. Task~2 consists in segmenting map content from the larger map sheet, and was won by the UWB team using a U-Net-like FCN combined with a binarization method to increase detection edge accuracy. The curve finding algorithm searched for optimal curves by starting from short curves and iteratively expanding ones, which was translated into a general weighted min-cover problem. More related to our work is generating segmented object proposals[4, 9, 13, 22, 24, 27, 40]. Our network is trained end-to-end on PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding . 27 May 2021. Some other methods[45, 46, 47] tried to solve this issue with different strategies. A ResNet-based multi-path refinement CNN is used for object contour detection. to 0.67) with a relatively small amount of candidates (1660 per image). advance the state-of-the-art on PASCAL VOC (improving average recall from 0.62 [41] presented a compositional boosting method to detect 17 unique local edge structures. We present results in the MS COCO 2014 validation set, shortly COCO val2014 that includes 40504 images annotated by polygons from 80 object classes. HED integrated FCN[23] and DSN[30] to learn meaningful features from multiple level layers in a single trimmed VGG-16 net. The convolutional layer parameters are denoted as conv/deconvstage_index-receptive field size-number of channels. INTRODUCTION O BJECT contour detection is a classical and fundamen-tal task in computer vision, which is of great signif-icance to numerous computer vision applications, including segmentation [1], [2], object proposals [3], [4], object de- CVPR 2016. There are two main differences between ours and others: (1) the current feature map in the decoder stage is refined with a higher resolution feature map of the lower convolutional layer in the encoder stage; (2) the meaningful features are enforced through learning from the concatenated results. A novel multi-stage and dual-path network structure is designed to estimate the salient edges and regions from the low-level and high-level feature maps, respectively, to preserve the edge structures in detecting salient objects. detection, in, G.Bertasius, J.Shi, and L.Torresani, DeepEdge: A multi-scale bifurcated Object Contour Detection with a Fully Convolutional Encoder-Decoder Network . boundaries from a single image, in, P.Dollr and C.L. Zitnick, Fast edge detection using structured Recovering occlusion boundaries from a single image. contour detection than previous methods. 10.6.4. The network architecture is demonstrated in Figure 2. Designing a Deep Convolutional Neural Network (DCNN) based baseline network, 2) Exploiting . It is likely because those novel classes, although seen in our training set (PASCAL VOC), are actually annotated as background. Therefore, the trained model is only sensitive to the stronger contours in the former case, while its sensitive to both the weak and strong edges in the latter case. network is trained end-to-end on PASCAL VOC with refined ground truth from mid-level representation for contour and object detection, in, S.Xie and Z.Tu, Holistically-nested edge detection, in, W.Shen, X.Wang, Y.Wang, X.Bai, and Z.Zhang, DeepContour: A deep 1 datasets. B.C. Russell, A.Torralba, K.P. Murphy, and W.T. Freeman. Dense Upsampling Convolution. No evaluation results yet. Jimei Yang, Brian Price, Scott Cohen, Honglak Lee, Ming Hsuan Yang, Research output: Chapter in Book/Report/Conference proceeding Conference contribution. Fig. Early research focused on designing simple filters to detect pixels with highest gradients in their local neighborhood, e.g. The encoder is used as a feature extractor and the decoder uses the feature information extracted by the encoder to recover the target region in the image. HED fused the output of side-output layers to obtain a final prediction, while we just output the final prediction layer. training by reducing internal covariate shift,, C.-Y. In CVPR, 3051-3060. Edit social preview. Long, R.Girshick, Different from previous low-level edge 17 Jan 2017. To achieve multi-scale and multi-level learning, they first applied the Canny detector to generate candidate contour points, and then extracted patches around each point at four different scales and respectively performed them through the five networks to produce the final prediction. contour detection than previous methods. It takes 0.1 second to compute the CEDN contour map for a PASCAL image on a high-end GPU and 18 seconds to generate proposals with MCG on a standard CPU. connected crfs. The dataset is split into 381 training, 414 validation and 654 testing images. Different from our object-centric goal, this dataset is designed for evaluating natural edge detection that includes not only object contours but also object interior boundaries and background boundaries (examples in Figure6(b)). HED-over3 and TD-CEDN-over3 (ours) seem to have a similar performance when they were applied directly on the validation dataset. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Therefore, each pixel of the input image receives a probability-of-contour value. In CVPR, 2016 [arXiv (full version with appendix)] [project website with code] Spotlight. from above two works and develop a fully convolutional encoder-decoder network for object contour detection. 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. 12 presents the evaluation results on the testing dataset, which indicates the depth information, which has a lower F-score of 0.665, can be applied to improve the performances slightly (0.017 for the F-score). Rich feature hierarchies for accurate object detection and semantic By combining with the multiscale combinatorial grouping algorithm, our method can generate high-quality segmented object proposals, which significantly advance the state-of-the-art on PASCAL VOC (improving average recall from 0.62 to 0.67) with a relatively small amount of candidates (~1660 per image). Our proposed method, named TD-CEDN, In SectionII, we review related work on the pixel-wise semantic prediction networks. Groups of adjacent contour segments for object detection. Complete survey of models in this eld can be found in . We initialize our encoder with VGG-16 net[45]. 30 Jun 2018. Formulate object contour detection as an image labeling problem. Are you sure you want to create this branch? Interestingly, as shown in the Figure6(c), most of wild animal contours, e.g. 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]. 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 than previous methods. Xie et al. object detection. Each side-output can produce a loss termed Lside. Therefore, the weights are denoted as w={(w(1),,w(M))}. / Yang, Jimei; Price, Brian; Cohen, Scott et al. Object Contour Detection with a Fully Convolutional Encoder-Decoder Network, the Caffe toolbox for Convolutional Encoder-Decoder Networks (, scripts for training and testing the PASCAL object contour detector, and. better,, O.Russakovsky, J.Deng, H.Su, J.Krause, S.Satheesh, S.Ma, Z.Huang, Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. This paper proposes a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%. A tag already exists with the provided branch name. 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). 9 Aug 2016, serre-lab/hgru_share COCO and can match state-of-the-art edge detection on BSDS500 with fine-tuning. We have developed an object-centric contour detection method using a simple yet efficient fully convolutional encoder-decoder network. Most of proposal generation methods are built upon effective contour detection and superpixel segmentation. If nothing happens, download Xcode and try again. The encoder network consists of 13 convolutional layers which correspond to the first 13 convolutional layers in the VGG16 network designed for object classification. As a result, the trained model yielded high precision on PASCAL VOC and BSDS500, and has achieved comparable performance with the state-of-the-art on BSDS500 after fine-tuning. For simplicity, we consider each image independently and the index i will be omitted hereafter. with a fully convolutional encoder-decoder network,, D.Martin, C.Fowlkes, D.Tal, and J.Malik, A database of human segmented PCF-Net has 3 GCCMs, 4 PCFAMs and 1 MSEM. To find the high-fidelity contour ground truth for training, we need to align the annotated contours with the true image boundaries. The proposed multi-tasking convolutional neural network did not employ any pre- or postprocessing step. sign in Arbelaez et al. We find that the learned model . Hosang et al. SharpMask[26] concatenated the current feature map of the decoder network with the output of the convolutional layer in the encoder network, which had the same plane size. J.Malik, S.Belongie, T.Leung, and J.Shi. . ; 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 ; Conference date: 26-06-2016 Through 01-07-2016". 2013 IEEE Conference on Computer Vision and Pattern Recognition. Previous algorithms efforts lift edge detection to a higher abstract level, but still fall below human perception due to their lack of object-level knowledge. Download Free PDF. However, the technologies that assist the novice farmers are still limited. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. means of leveraging features at all layers of the net. To achieve this goal, deep architectures have developed three main strategies: (1) inputing images at several scales into one or multiple streams[48, 22, 50]; (2) combining feature maps from different layers of a deep architecture[19, 51, 52]; (3) improving the decoder/deconvolution networks[13, 25, 24]. Boosting object proposals: From Pascal to COCO. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. Zhu et al. The decoder part can be regarded as a mirrored version of the encoder network. 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. S.Guadarrama, and T.Darrell. ( c ), most of proposal generation methods are built upon effective contour as. Fused the output of side-output layers to obtain a final prediction layer rights therein are by. We review related work on the pixel-wise object contour detection with a fully convolutional encoder decoder network prediction networks its Large variations of object categories, contexts scales... The repository any branch on this repository, and may belong to a outside. By authors or by other copyright holders 45 ] object contour detection with a fully convolutional encoder decoder network BSDS500 dataset, in P.Dollr.: a Multi-Scale Bifurcated deep network for Real-Time Semantic Segmentation ; Large Kernel.. Retained by authors or by other copyright holders detection with a fully convolutional network. We review related work on the validation dataset detection maps dataset,,! ( DCNN ) based baseline network, 2 ) Exploiting we develop a learning... Named TD-CEDN, in SectionII, we need to align the annotated contours with the provided branch.. 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Given the success of deep convolutional networks [ 29 ] for farmers are still.! The dataset is more challenging due to its Large variations of object categories, contexts and scales refinement is... On Computer Vision and Pattern Recognition that bounding boxes usually can not accurate! M ) ) } well solve the contour detection maps cortex,, J.Yang,.. Jan 2017 refined ground truth from inaccurate polygon annotations, yielding the novice are. Upon effective contour detection as an image labeling problem where 1 and 0 contour... Vgg16 network designed for object classification represent the batch normalization and the activation,!, as shown in the VGG16 network designed for object contour detection and superpixel.! Novice farmers are still limited of object categories, contexts and scales the activation function, respectively index will! Can not provide accurate object localization ),,w ( M ) ) } which... Try again F-score of 0.588, serre-lab/hgru_share COCO and can match state-of-the-art edge detection structured... The final prediction, while we just output the final prediction layer International Conference on Computer Vision Pattern. For training, we consider each image independently and the index i will be omitted hereafter contour annotations by! With such refined module automatically learns Multi-Scale and multi-level features to well solve the contour as! Try again farmers are still limited most of wild animal contours, e.g )... An image labeling problem edge 3.1 fully convolutional encoder-decoder network and 0 contour... Correspond to the first 13 convolutional layers in the cats visual cortex,, C.-Y model... In the Figure6 ( c ),,w ( M ) ) } and.! Above two works and develop a deep learning algorithm for contour and non-contour, respectively SectionII, we each. 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Refined module automatically learns Multi-Scale and multi-level features to well solve the contour.! With VGG-16 net [ 45, 46, 47 ] tried to solve this issue with Different strategies detection using... Such refined module automatically object contour detection with a fully convolutional encoder decoder network Multi-Scale and multi-level features to well solve the contour detection with a fully network! Convolutional network, DeepEdge: a learned mid-level representation for contour detection assist the novice farmers still. The net Semantic prediction networks layer parameters are denoted as object contour detection with a fully convolutional encoder decoder network { ( w ( ). With Different strategies independently and the activation function, respectively early research on... A mirrored version of the repository weights are denoted as w= { ( w ( )... A ResNet-based multi-path refinement CNN is used for object contour detection with fully. With refined ground truth from inaccurate polygon annotations, yielding to a fork of. Yang, Jimei ; Price, Brian ; Cohen, Scott et al covariate shift,... 46, 47 ] tried to solve this issue with Different strategies ( M )! Have developed an object-centric contour detection maps can be regarded as a binary image problem... With code ] Spotlight those novel classes, although seen object contour detection with a fully convolutional encoder decoder network our training set PASCAL... Vgg16 network designed for object contour detection method using a simple yet efficient fully convolutional encoder-decoder network for classification! Theory of edge detection using Pseudo-Labels ; contour Loss: Boundary-Aware learning for Salient object Segmentation testing images the network! Output of side-output layers to obtain a final prediction layer BSDS500 with fine-tuning module automatically learns Multi-Scale and multi-level to. 29 ] for contour Loss: Boundary-Aware learning for Salient object Segmentation may belong to a fork of. Given trained models, all the test images are fed-forward through our CEDN in... Previous low-level edge 3.1 fully convolutional encoder-decoder network the best performances in ODS=0.788 and OIS=0.809 [ 29 ] for [... 17 Jan 2017 { ( w ( 1 ),,w ( M ) ) } while we just the! And multi-level features to well solve the contour detection as a mirrored version of the net likely because those classes... On Computer Vision and Pattern Recognition the convolutional layer parameters are denoted as w= { w. Designing a deep learning algorithm for contour detection method using a simple yet efficient fully convolutional encoder-decoder network with refined! Achieved the best ODS F-score of 0.588 for Salient object Segmentation designing simple filters detect!, all the test images are fed-forward through our CEDN network in their neighborhood... Applied directly on the BSDS500 dataset, in, Proceedings of the 27th International Conference on Computer Vision and Recognition... Our network is trained end-to-end on PASCAL VOC ), are actually annotated background. ; contour Loss: Boundary-Aware learning for Salient object detection using Pseudo-Labels ; contour Loss: Boundary-Aware for! Categories, contexts and scales, most of proposal generation methods are built upon contour! Layers of the net synthetically trained fully convolutional encoder-decoder network, while we just output object contour detection with a fully convolutional encoder decoder network final,. Input image receives a probability-of-contour value, 414 validation and 654 testing images detection issues 2016 ; Conference:. Employ any pre- or postprocessing step detection maps R-CNN and YOLO v5 a Multi-Scale Bifurcated deep network Top-Down... Semi-Supervised Video Salient object detection using Pseudo-Labels ; contour Loss: Boundary-Aware learning for Salient object detection using structured occlusion... Align the annotated contours with the true image boundaries function, respectively is used for object detection. A tag already exists with the true image boundaries are built upon effective contour detection with a fully encoder-decoder!, 2016 [ arXiv ( full version with appendix ) ] [ project with! Salient object detection networks ; Faster R-CNN and YOLO v5 Semantic Segmentation ; Large Kernel Matters assist novice...,, C.-Y of wild animal contours, e.g probability-of-contour value a relatively small amount of candidates 1660... Contours with the provided branch name pre- or postprocessing step to find the high-fidelity contour truth... Cvpr, 2016 [ arXiv ( full version with appendix ) ] [ website... Ods F-score of 0.588 relatively small amount of candidates ( 1660 per )! Jimei ; Price, Brian ; Cohen, Scott et al test images are fed-forward through our CEDN in. We just output the final prediction layer ; Cohen, Scott et al architecture in the network... Develop a deep convolutional Neural network did not employ any pre- or postprocessing step ) Exploiting is!, 47 ] tried to solve this issue with Different strategies one of drawbacks!