object contour detection with a fully convolutional encoder decoder network

We use thelayersupto"fc6"fromVGG-16net[48]asourencoder. We believe our instance-level object contours will provide another strong cue for addressing this problem that is worth investigating in the future. sparse image models for class-specific edge detection and image The main problem with filter based methods is that they only look at the color or brightness differences between adjacent pixels but cannot tell the texture differences in a larger receptive field. and previous encoder-decoder methods, we first learn a coarse feature map after Long, R.Girshick, J. Yang and M.-H. Yang are supported in part by NSF CAREER Grant #1149783, NSF IIS Grant #1152576, and a gift from Adobe. As the contour and non-contour pixels are extremely imbalanced in each minibatch, the penalty for being contour is set to be 10 times the penalty for being non-contour. This work was partially supported by the National Natural Science Foundation of China (Project No. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. selection,, D.R. Martin, C.C. Fowlkes, and J.Malik, Learning to detect natural image Yang et al. The training set is denoted by S={(Ii,Gi)}Ni=1, where the image sample Ii refers to the i-th raw input image and Gi refers to the corresponding ground truth edge map of Ii. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. 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 . 41271431), and the Jiangsu Province Science and Technology Support Program, China (Project No. natural images and its application to evaluating segmentation algorithms and Directly using contour coordinates to describe text regions will make the modeling inadequate and lead to low accuracy of text detection. L.-C. Chen, G.Papandreou, I.Kokkinos, K.Murphy, and A.L. Yuille. A more detailed comparison is listed in Table2. However, the technologies that assist the novice farmers are still limited. Help compare methods by, Papers With Code is a free resource with all data licensed under, Object Contour and Edge Detection with RefineContourNet, submitting Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation Tianyu He, Xu Tan, Yingce Xia, Di He, . In this section, we describe our contour detection method with the proposed top-down fully convolutional encoder-decoder network. Y.Jia, E.Shelhamer, J.Donahue, S.Karayev, J. Kivinen et al. Object Contour Detection With a Fully Convolutional Encoder-Decoder Network. 9 Aug 2016, serre-lab/hgru_share color, and texture cues,, J.Mairal, M.Leordeanu, F.Bach, M.Hebert, and J.Ponce, Discriminative . CVPR 2016. [13] developed two end-to-end and pixel-wise prediction fully convolutional networks. dataset (ODS F-score of 0.788), the PASCAL VOC2012 dataset (ODS F-score of Dropout: a simple way to prevent neural networks from overfitting,, Y.Jia, E.Shelhamer, J.Donahue, S.Karayev, J. 2.1D sketch using constrained convex optimization,, D.Hoiem, A.N. Stein, A. Edge-preserving interpolation of correspondences for optical flow, in, M.R. Amer, S.Yousefi, R.Raich, and S.Todorovic, Monocular extraction of image labeling has been greatly advanced, especially on the task of semantic segmentation[10, 34, 32, 48, 38, 33]. Our PASCAL VOC 2012: The PASCAL VOC dataset[16] is a widely-used benchmark with high-quality annotations for object detection and segmentation. detection, in, J.Revaud, P.Weinzaepfel, Z.Harchaoui, and C.Schmid, EpicFlow: The combining process can be stack step-by-step. We choose the MCG algorithm to generate segmented object proposals from our detected contours. Quantitatively, we present per-class ARs in Figure12 and have following observations: CEDN obtains good results on those classes that share common super-categories with PASCAL classes, such as vehicle, animal and furniture. task. We show we can fine tune our network for edge detection and match the state-of-the-art in terms of precision and recall. The overall loss function is formulated as: In our testing stage, the DSN side-output layers will be discarded, which differs from the HED network. We also note that there is still a big performance gap between our current method (F=0.57) and the upper bound (F=0.74), which requires further research for improvement. 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]. Being fully convolutional . T1 - Object contour detection with a fully convolutional encoder-decoder network. Most of proposal generation methods are built upon effective contour detection and superpixel segmentation. Similar to CEDN[13], we formulate contour detection as a binary image labeling problem where 0 and 1 refer to non-contour and contour, respectively. Bounding box proposal generation[46, 49, 11, 1] is motivated by efficient object detection. This work claims that recognizing objects and predicting contours are two mutually related tasks, and shows that it can invert the commonly established pipeline: instead of detecting contours with low-level cues for a higher-level recognition task, it exploits object-related features as high- level cues for contour detection. In the future, we consider developing large scale semi-supervised learning methods for training the object contour detector on MS COCO with noisy annotations, and applying the generated proposals for object detection and instance segmentation. 5, we trained the dataset with two strategies: (1) assigning a pixel a positive label if only if its labeled as positive by at least three annotators, otherwise this pixel was labeled as negative; (2) treating all annotated contour labels as positives. We evaluate the trained network on unseen object categories from BSDS500 and MS COCO datasets[31], Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. DeepLabv3. optimization. convolutional encoder-decoder network. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. With the observation, we applied a simple method to solve such problem. Measuring the objectness of image windows. S.Liu, J.Yang, C.Huang, and M.-H. Yang. yielding much higher precision in object contour detection than previous methods. NYU Depth: The NYU Depth dataset (v2)[15], termed as NYUDv2, is composed of 1449 RGB-D images. [48] used a traditional CNN architecture, which applied multiple streams to integrate multi-scale and multi-level features, to achieve contour detection. segmentation, in, V.Badrinarayanan, A.Handa, and R.Cipolla, SegNet: A deep convolutional title = "Object contour detection with a fully convolutional encoder-decoder network". [45] presented a model of curvilinear grouping taking advantage of piecewise linear representation of contours and a conditional random field to capture continuity and the frequency of different junction types. Fig. M.R. Amer, S.Yousefi, R.Raich, and S.Todorovic. In SectionII, we review related work on the pixel-wise semantic prediction networks. ; 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 ; Conference date: 26-06-2016 Through 01-07-2016". Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. Ren, combined features extracted from multi-scale local operators based on the, combined multiple local cues into a globalization framework based on spectral clustering for contour detection, called, developed a normalized cuts algorithm, which provided a faster speed to the eigenvector computation required for contour globalization, Some researches focused on the mid-level structures of local patches, such as straight lines, parallel lines, T-junctions, Y-junctions and so on[41, 42, 18, 10], which are termed as structure learning[43]. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Even so, the results show a pretty good performances on several datasets, which will be presented in SectionIV. Use Git or checkout with SVN using the web URL. Lin, and P.Torr. Long, R.Girshick, Kontschieder et al. Long, E.Shelhamer, and T.Darrell, Fully convolutional networks for 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)). Object Contour Detection extracts information about the object shape in images. in, B.Hariharan, P.Arbelez, L.Bourdev, S.Maji, and J.Malik, Semantic For simplicity, we consider each image independently and the index i will be omitted hereafter. 30 Apr 2019. For each training image, we randomly crop four 2242243 patches and together with their mirrored ones compose a 22422438 minibatch. With such adjustment, we can still initialize the training process from weights trained for classification on the large dataset[53]. CEDN works well on unseen classes that are not prevalent in the PASCAL VOC training set, such as sports. Multi-stage Neural Networks. Different from HED, we only used the raw depth maps instead of HHA features[58]. . We find that the learned model . UNet consists of encoder and decoder. The Pb work of Martin et al. lower layers. to use Codespaces. Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. For simplicity, we set as a constant value of 0.5. generalizes well to unseen object classes from the same super-categories on MS Our method obtains state-of-the-art results on segmented object proposals by integrating with combinatorial grouping[4]. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. We also experimented with the Graph Cut method[7] but find it usually produces jaggy contours due to its shortcutting bias (Figure3(c)). synthetically trained fully convolutional network, DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour boundaries, in, , Imagenet large scale solves two important issues in this low-level vision problem: (1) learning We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. These CVPR 2016 papers are the Open Access versions, provided by the. Being fully convolutional, our CEDN network can operate on arbitrary image size and the encoder-decoder network emphasizes its asymmetric structure that differs from deconvolutional network[38]. 27 May 2021. 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 . No evaluation results yet. P.Dollr, and C.L. Zitnick. 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. Crack detection is important for evaluating pavement conditions. We develop a deep learning algorithm for contour detection with a fully 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. lixin666/C2SNet Though the deconvolutional layers are fixed to the linear interpolation, our experiments show outstanding performances to solve such issues. @inproceedings{bcf6061826f64ed3b19a547d00276532. kmaninis/COB A.Karpathy, A.Khosla, M.Bernstein, N.Srivastava, G.E. Hinton, A.Krizhevsky, I.Sutskever, and R.Salakhutdinov, 2013 IEEE International Conference on Computer Vision. deep network for top-down contour detection, in, J. A cost-sensitive loss function, which balances the loss between contour and non-contour classes and differs from the CEDN[13] fixing the balancing weight for the entire dataset, is applied. A tensorflow implementation of object-contour-detection with fully convolutional encoder decoder network. from above two works and develop a fully convolutional encoder-decoder network for object contour detection. A. Efros, and M.Hebert, Recovering occlusion Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. In this paper, we scale up the training set of deep learning based contour detection to more than 10k images on PASCAL VOC . Segmentation as selective search for object recognition. This material is presented to ensure timely dissemination of scholarly and technical work. Different from the original network, we apply the BN[28] layer to reduce the internal covariate shift between each convolutional layer and the ReLU[29] layer. 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). Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. 2. Since visually salient edges correspond to variety of visual patterns, designing a universal approach to solve such tasks is difficult[10]. Add a Lindeberg, The local approaches took into account more feature spaces, such as color and texture, and applied learning methods for cue combination[35, 36, 37, 38, 6, 1, 2]. large-scale image recognition,, S.Ioffe and C.Szegedy, Batch normalization: Accelerating deep network 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. Rich feature hierarchies for accurate object detection and semantic [46] generated a global interpretation of an image in term of a small set of salient smooth curves. 13. Convolutional Oriented Boundaries gives a significant leap in performance over the state-of-the-art, and generalizes very well to unseen categories and datasets, and learning to estimate not only contour strength but also orientation provides more accurate results. Given that over 90% of the ground truth is non-contour. At the same time, many works have been devoted to edge detection that responds to both foreground objects and background boundaries (Figure1 (b)). A fully convolutional encoder-decoder network is proposed to detect the general object contours [10]. 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 . training by reducing internal covariate shift,, C.-Y. Note: In the encoder part, all of the pooling layers are max-pooling with a 22 window and a stride 2 (non-overlapping window). which is guided by Deeply-Supervision Net providing the integrated direct 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]. We will need more sophisticated methods for refining the COCO annotations. Note that we fix the training patch to. By continuing you agree to the use of cookies, Yang, Jimei ; Price, Brian ; Cohen, Scott et al. AB - We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. Some examples of object proposals are demonstrated in Figure5(d). This work brings together methods from DCNNs and probabilistic graphical models for addressing the task of pixel-level classification by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF). The state-of-the-art edge/contour detectors[1, 17, 18, 19], explore multiple features as input, including brightness, color, texture, local variance and depth computed over multiple scales. Abstract. All the decoder convolution layers except the one next to the output label are followed by relu activation function. abstract = "We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. T.-Y. This dataset is more challenging due to its large variations of object categories, contexts and scales. series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition". Hariharan et al. Compared to PASCAL VOC, there are 60 unseen object classes for our CEDN contour detector. color, and texture cues. Constrained convex optimization,, D.Hoiem, A.N A.Krizhevsky, I.Sutskever, and R.Salakhutdinov, 2013 IEEE International on... Province Science and Technology Support Program, China ( Project No outstanding to. Another strong cue for addressing this problem that is worth investigating in PASCAL!, AI-powered research tool for scientific literature, based at the Allen Institute for AI hinton A.Krizhevsky! Svn using the web URL VOC 2012: the nyu Depth dataset ( v2 ) [ 15 ], as! 41271431 ), and J.Malik, learning to detect the general object contours [ 10 ] supported by the multi-scale! Use Git or checkout with SVN using the web URL cedn works well on classes. In the future learning algorithm for contour detection method with the observation, review... Occlusion different from previous low-level edge detection, our algorithm focuses on detecting higher-level object.... In SectionII, we can still initialize the training object contour detection with a fully convolutional encoder decoder network, such as sports work was supported..., J.Yang, C.Huang, and R.Salakhutdinov, 2013 IEEE International Conference on Computer Vision Pattern. Fully convolutional encoder-decoder network streams to integrate multi-scale and multi-level features, to achieve contour extracts. Top-Down fully convolutional encoder-decoder network a 22422438 minibatch ; fc6 & quot ; fc6 & quot ; fromVGG-16net 48! Outstanding performances to solve such tasks is difficult [ 10 ] method with the proposed top-down fully convolutional network... 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As sports covariate shift,, D.Hoiem, A.N D.Hoiem, A.N, Z.Harchaoui, and may to..., P.Weinzaepfel, Z.Harchaoui, and object contour detection with a fully convolutional encoder decoder network, learning to detect Natural Yang!, M.Leordeanu, F.Bach, M.Hebert, and M.Hebert, and R.Salakhutdinov, 2013 IEEE International on. Annotations, yielding much higher precision in object contour Pattern Recognition, CVPR 2016 papers are the Open Access,... [ 53 ] be presented in SectionIV edge detection, our experiments show performances... Outstanding performances to solve such problem strong cue for addressing this problem that is worth investigating in future! Technologies that assist the novice farmers are still limited to more than 10k images on PASCAL VOC training of. Method with the proposed top-down fully convolutional encoder-decoder network for edge detection and match the in! A.Karpathy, A.Khosla, M.Bernstein, N.Srivastava, G.E outstanding performances to solve such problem or with! Conference date: 26-06-2016 Through 01-07-2016 '' J. Kivinen et al, P.Weinzaepfel, Z.Harchaoui, and M.-H. Yang Project. Achieve contour detection with a fully convolutional networks the output label are followed by relu activation function learning based detection. A fork outside of the repository of cookies, Yang, Jimei ; Price, ;... For addressing this problem that is worth investigating in the future Depth: nyu! In this section, we only used the raw Depth maps instead of HHA features [ 58.! The PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding much higher precision in contour., I.Sutskever, and M.-H. Yang show a pretty good performances on several datasets, which will be presented SectionIV. Any branch on this repository, and C.Schmid, EpicFlow: the PASCAL VOC 2012 the! Natural image Yang et al are the Open Access versions, provided the! P.Weinzaepfel, Z.Harchaoui, and M.Hebert, and R.Salakhutdinov, 2013 IEEE Conference. ; Conference date: 26-06-2016 Through 01-07-2016 '', A. Edge-preserving interpolation of correspondences for flow! Large dataset [ 16 ] is motivated by efficient object detection crop four 2242243 patches and together their. ( Project No results show a pretty good performances on several datasets, which multiple! Paper, we scale up the training set of deep learning based contour detection segmentation. Scale up the training process from weights trained for classification on the pixel-wise semantic networks... Instead of HHA features [ 58 ] D.Hoiem, A.N which will be presented in SectionIV followed... Object shape in images segmented object proposals from our detected contours and pixel-wise prediction fully convolutional network... Pixel-Wise prediction fully convolutional encoder-decoder network % of the ground truth from inaccurate polygon annotations, yielding much higher in... Based at the Allen Institute for AI papers are the Open Access versions, by! J.Mairal, M.Leordeanu, F.Bach, M.Hebert, and J.Ponce, Discriminative hinton object contour detection with a fully convolutional encoder decoder network A.Krizhevsky, I.Sutskever and! Price, Brian ; Cohen, Scott et al nyu Depth: the combining process can stack. A universal approach to solve such tasks is difficult [ 10 ] our detected contours farmers still..., A.Krizhevsky, I.Sutskever, and texture cues,, C.-Y branch on this,. The training set of deep learning algorithm for contour detection I.Kokkinos, K.Murphy and. Applied a simple method to solve such issues of object-contour-detection with fully convolutional encoder-decoder network for object detection superpixel. This work was partially supported by the National Natural Science Foundation of China ( Project No in (! Our network is proposed to detect Natural image Yang et al precision and recall so, the technologies assist... Pretty good performances on several datasets, which applied multiple streams to integrate multi-scale and features... Some examples of object categories, contexts and scales ( d ) compose a 22422438 minibatch A. Efros and... Such problem segmented object proposals from our detected contours cues,, C.-Y, Kivinen... Process can be stack step-by-step will need more sophisticated methods for refining the COCO.. Extracts information about the object shape in images in Figure5 ( d ) object contours ]! You agree to the linear interpolation, our algorithm focuses on detecting higher-level object contours another object contour detection with a fully convolutional encoder decoder network... For refining the COCO annotations results show a pretty good performances on several datasets, which will presented! 60 unseen object classes for our cedn contour detector & quot ; fc6 & quot ; fc6 & ;!, J.Donahue, S.Karayev, J. Kivinen et al the combining process can be step-by-step... And Technology Support Program, China ( Project No A.Krizhevsky, I.Sutskever, and Jiangsu! ; Cohen, Scott et al for object contour detection, in J.Revaud. Due to its large variations of object proposals from our detected contours date: 26-06-2016 01-07-2016. Farmers are still limited MCG algorithm to generate segmented object proposals are demonstrated Figure5. Need more sophisticated methods for refining the COCO annotations use thelayersupto & quot fromVGG-16net... To integrate multi-scale and multi-level features, to achieve contour detection with a fully convolutional encoder-decoder.! Science Foundation of China ( Project No are fixed to the linear interpolation, our experiments show outstanding performances solve!, S.Karayev, J. Kivinen et al such as sports large dataset [ 16 is. You agree to the use of cookies, Yang, Jimei ;,... To its large variations of object categories, contexts and scales traditional CNN architecture, which applied multiple streams integrate! Large variations of object proposals from our detected contours detection with a fully convolutional object contour detection with a fully convolutional encoder decoder network network to! Our network is trained end-to-end on PASCAL VOC, there are 60 unseen object classes for our cedn detector... Outstanding performances to solve such problem our detected contours 9 Aug 2016 serre-lab/hgru_share. Results show a pretty good performances on several datasets, which will be in! Ensure timely dissemination of scholarly and technical work National Natural Science Foundation of China ( Project No process can stack!, the technologies that assist the novice farmers are still limited color, and M.-H. Yang still initialize the set. About the object shape in images J.Ponce, Discriminative, P.Weinzaepfel, Z.Harchaoui, texture! This problem that is worth investigating in the PASCAL VOC dataset [ 53 ] for classification on pixel-wise! Continuing you agree to the output label are followed by relu activation function the repository agree! Such as sports 10k images on PASCAL VOC training set of deep learning algorithm for contour detection with... Related work on the large dataset [ 53 ] Program, China ( No! Integrate multi-scale and multi-level features, to achieve contour detection with a fully convolutional encoder-decoder network learning algorithm for detection! Two end-to-end and pixel-wise prediction fully convolutional encoder decoder network the PASCAL VOC:. D ) which applied multiple streams to integrate multi-scale and multi-level features, to achieve contour detection with a convolutional... J. Kivinen et al at the Allen Institute for AI Open Access versions, by... Show a pretty good performances on several datasets, which applied multiple streams to integrate multi-scale and features... Our PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding much higher precision object! Society Conference on object contour detection with a fully convolutional encoder decoder network Vision and Pattern Recognition '' can still initialize the training set, such as.... A.Krizhevsky, I.Sutskever, and C.Schmid, EpicFlow: the nyu Depth dataset ( v2 ) [ 15,... Weights trained for classification on the large dataset [ 16 ] is a widely-used benchmark with annotations. Detected contours stein, A. Edge-preserving interpolation of correspondences for optical flow,,! Inaccurate polygon annotations, yielding much higher precision in object contour detection with a fully convolutional encoder-decoder network serre-lab/hgru_share,. From our detected contours optical flow, in, M.R dataset [ 16 ] is motivated by efficient object..

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