Fig. Our According to the results, the performances show a big difference with these two training strategies. dataset (ODS F-score of 0.788), the PASCAL VOC2012 dataset (ODS F-score of 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. This could be caused by more background contours predicted on the final maps. We also propose a new joint loss function for the proposed architecture. search for object recognition,, C.L. Zitnick and P.Dollr, Edge boxes: Locating object proposals from Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation Tianyu He, Xu Tan, Yingce Xia, Di He, . Long, R.Girshick, can generate high-quality segmented object proposals, which significantly . contour detection than previous methods. We find that the learned model 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. [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. detection. When the trained model is sensitive to both the weak and strong contours, it shows an inverted results. In this section, we introduce our object contour detection method with the proposed fully convolutional encoder-decoder network. . NYU Depth: The NYU Depth dataset (v2)[15], termed as NYUDv2, is composed of 1449 RGB-D images. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. Powered by Pure, Scopus & Elsevier Fingerprint Engine 2023 Elsevier B.V. We use cookies to help provide and enhance our service and tailor content. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. For simplicity, we consider each image independently and the index i will be omitted hereafter. Ganin et al. 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. Precision-recall curves are shown in Figure4. (up to the fc6 layer) and to achieve dense prediction of image size our decoder is constructed by alternating unpooling and convolution layers where unpooling layers re-use the switches from max-pooling layers of encoder to upscale the feature maps. CVPR 2016. Contents. [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. optimization. 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)). No evaluation results yet. Please follow the instructions below to run the code. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. Fig. We develop a simple yet effective fully convolutional encoder-decoder network for object contour detection and the trained model generalizes well to unseen object classes from the same super-categories, yielding significantly higher precision than previous methods. [19] further contribute more than 10000 high-quality annotations to the remaining images. Our network is trained end-to-end on PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding . In the future, we will explore to find an efficient fusion strategy to deal with the multi-annotation issues, such as BSDS500. Being fully convolutional, our CEDN network can operate Several example results are listed in Fig. The remainder of this paper is organized as follows. we develop a fully convolutional encoder-decoder network (CEDN). kmaninis/COB We formulate contour detection as a binary image labeling problem where "1" and "0" indicates "contour" and "non-contour", respectively. We find that the learned model Different from DeconvNet, the encoder-decoder network of CEDN emphasizes its asymmetric structure. TLDR. Recently, the supervised deep learning methods, such as deep Convolutional Neural Networks (CNNs), have achieved the state-of-the-art performances in such field, including, In this paper, we develop a pixel-wise and end-to-end contour detection system, Top-Down Convolutional Encoder-Decoder Network (TD-CEDN), which is inspired by the success of Fully Convolutional Networks (FCN)[23], HED, Encoder-Decoder networks[24, 25, 13] and the bottom-up/top-down architecture[26]. 9 presents our fused results and the CEDN published predictions. Quantitatively, we evaluate both the pretrained and fine-tuned models on the test set in comparisons with previous methods. Contour detection and hierarchical image segmentation. BN and ReLU represent the batch normalization and the activation function, respectively. We will explain the details of generating object proposals using our method after the contour detection evaluation. Source: Object Contour and Edge Detection with RefineContourNet, jimeiyang/objectContourDetector [20] proposed a N4-Fields method to process an image in a patch-by-patch manner. /. This dataset is more challenging due to its large variations of object categories, contexts and scales. Bertasius et al. A quantitative comparison of our method to the two state-of-the-art contour detection methods is presented in SectionIV followed by the conclusion drawn in SectionV. Semantic pixel-wise prediction is an active research task, which is fueled by the open datasets[14, 16, 15]. We fine-tuned the model TD-CEDN-over3 (ours) with the NYUD training dataset. We use thelayersupto"fc6"fromVGG-16net[48]asourencoder. f.a.q. generalizes well to unseen object classes from the same super-categories on MS A tag already exists with the provided branch name. We generate accurate object contours from imperfect polygon based segmentation annotations, which makes it possible to train an object contour detector at scale. large-scale image recognition,, S.Ioffe and C.Szegedy, Batch normalization: Accelerating deep network By clicking accept or continuing to use the site, you agree to the terms outlined in our. Note that our model is not deliberately designed for natural edge detection on BSDS500, and we believe that the techniques used in HED[47] such as multiscale fusion, carefully designed upsampling layers and data augmentation could further improve the performance of our model. For example, there is a dining table class but no food class in the PASCAL VOC dataset. yielding much higher precision in object contour detection than previous methods. Unlike skip connections In addition to the structural at- prevented target discontinuity in medical images, such tribute (topological relationship), DNGs also have other as those of the pancreas, and achieved better results. The RGB images and depth maps were utilized to train models, respectively. from RGB-D images for object detection and segmentation, in, Object Contour Detection with a Fully Convolutional Encoder-Decoder Please K.E.A. vande Sande, J.R.R. Uijlingsy, T.Gevers, and A.W.M. Smeulders. ECCV 2018. 2 window and a stride 2 (non-overlapping window). This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Thus the improvements on contour detection will immediately boost the performance of object proposals. 1 datasets. 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. We find that the learned model . A more detailed comparison is listed in Table2. 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. BSDS500[36] is a standard benchmark for contour detection. Therefore, each pixel of the input image receives a probability-of-contour value. CEDN focused on applying a more complicated deconvolution network, which was inspired by DeconvNet[24] and was composed of deconvolution, unpooling and ReLU layers, to improve upsampling results. boundaries using brightness and texture, in, , Learning to detect natural image boundaries using local brightness, Each image has 4-8 hand annotated ground truth contours. There is a large body of works on generating bounding box or segmented object proposals. 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. (2): where I(k), G(k), |I| and have the same meanings with those in Eq. An immediate application of contour detection is generating object proposals. 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. Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. search. I. Though the deconvolutional layers are fixed to the linear interpolation, our experiments show outstanding performances to solve such issues. feature embedding, in, L.Bottou, Large-scale machine learning with stochastic gradient descent, If you find this useful, please cite our work as follows: Please contact "jimyang@adobe.com" if any questions. Summary. B.Hariharan, P.Arbelez, L.Bourdev, S.Maji, and J.Malik. 520 - 527. These learned features have been adopted to detect natural image edges[25, 6, 43, 47] and yield a new state-of-the-art performance[47]. 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. Wu et al. Note that the occlusion boundaries between two instances from the same class are also well recovered by our method (the second example in Figure5). SegNet[25] used the max pooling indices to upsample (without learning) the feature maps and convolved with a trainable decoder network. Our results present both the weak and strong edges better than CEDN on visual effect. J.Hosang, R.Benenson, P.Dollr, and B.Schiele. 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). Our predictions present the object contours more precisely and clearly on both statistical results and visual effects than the previous networks. with a fully convolutional encoder-decoder network,, D.Martin, C.Fowlkes, D.Tal, and J.Malik, A database of human segmented The above proposed technologies lead to a more precise and clearer A fully convolutional encoder-decoder network is proposed to detect the general object contours [10]. in, B.Hariharan, P.Arbelez, L.Bourdev, S.Maji, and J.Malik, Semantic Kivinen et al. HED performs image-to-image prediction by means of a deep learning model that leverages fully convolutional neural networks and deeply-supervised nets, and automatically learns rich hierarchical representations that are important in order to resolve the challenging ambiguity in edge and object boundary detection. Our network is trained end-to-end on PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding . We choose this dataset for training our object contour detector 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. 41271431), and the Jiangsu Province Science and Technology Support Program, China (Project No. 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. supervision. As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching -- and sometimes even surpassing -- human accuracy on a variety of visual recognition tasks. 3.1 Fully Convolutional Encoder-Decoder Network. [21] and Jordi et al. Bounding box proposal generation[46, 49, 11, 1] is motivated by efficient object detection. [39] present nice overviews and analyses about the state-of-the-art algorithms. Fully convolutional networks for semantic segmentation. task. The final high dimensional features of the output of the decoder are fed to a trainable convolutional layer with a kernel size of 1 and an output channel of 1, and then the reduced feature map is applied to a sigmoid layer to generate a soft prediction. 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. 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. from above two works and develop a fully convolutional encoder-decoder network for object contour detection. In addition to upsample1, each output of the upsampling layer is followed by the convolutional, deconvolutional and sigmoid layers in the training stage. HED integrated FCN[23] and DSN[30] to learn meaningful features from multiple level layers in a single trimmed VGG-16 net. Learn more. J.J. Kivinen, C.K. Williams, and N.Heess. 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 . convolutional encoder-decoder network. A tensorflow implementation of object-contour-detection with fully convolutional encoder decoder network - GitHub - Raj-08/tensorflow-object-contour-detection: A tensorflow implementation of object-contour-detection with fully convolutional encoder decoder network D.Hoiem, A.N. Stein, A.Efros, and M.Hebert. Compared with CEDN, our fine-tuned model presents better performances on the recall but worse performances on the precision on the PR curve. loss for contour detection. S.Liu, J.Yang, C.Huang, and M.-H. Yang. S.Zheng, S.Jayasumana, B.Romera-Paredes, V.Vineet, Z.Su, D.Du, C.Huang, Object contour detection is a classical and fundamental task in computer vision, which is of great significance to numerous computer vision applications, including segmentation[1, 2], object proposals[3, 4], object detection/recognition[5, 6], optical flow[7], and occlusion and depth reasoning[8, 9]. Price, S.Cohen, H.Lee, and M.-H. Yang, Object contour detection The final contours were fitted with the various shapes by different model parameters by a divide-and-conquer strategy. P.Arbelez, J.Pont-Tuset, J.Barron, F.Marques, and J.Malik. All these methods require training on ground truth contour annotations. 6. Generating object segmentation proposals using global and local kmaninis/COB Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. Being fully convolutional, the developed TD-CEDN can operate on an arbitrary image size and the encoder-decoder network emphasizes its symmetric structure which is similar to the SegNet[25] and DeconvNet[24] but not the same, as shown in Fig. For example, it can be used for image seg- . DeepLabv3. Encoder-decoder architectures can handle inputs and outputs that both consist of variable-length sequences and thus are suitable for seq2seq problems such as machine translation. advance the state-of-the-art on PASCAL VOC (improving average recall from 0.62 We consider contour alignment as a multi-class labeling problem and introduce a dense CRF model[26] where every instance (or background) is assigned with one unique label. Given its axiomatic importance, however, we find that object contour detection is relatively under-explored in the literature. The network architecture is demonstrated in Figure 2. Given the success of deep convolutional networks [29] for . The architecture of U2CrackNet is a two. We use the layers up to pool5 from the VGG-16 net[27] as the encoder network. To adhere to the terms and constraints invoked by each author 's copyright as the encoder network model TD-CEDN-over3 ours... Vgg-16 net [ 27 ] as the encoder network can operate Several results... Overviews and analyses about the state-of-the-art algorithms algorithm focuses on detecting higher-level object contours a tag exists! We introduce our object contour detection method with the proposed fully convolutional please! Generating bounding box proposal generation [ 46, 49, 11, 1 ] is by. Though the deconvolutional layers are fixed to the linear interpolation, our CEDN network can Several... This repository, and may belong to a fork outside of the input image a. Author 's copyright contour detection than previous methods with these two training strategies the performance object! All these methods require training on ground truth contour annotations, it can be for... Presents our fused results and the CEDN published predictions annotations to the interpolation! Encoder-Decoder please K.E.A higher precision in object contour detector with the proposed fully convolutional encoder-decoder network of CEDN emphasizes asymmetric!, b.hariharan, P.Arbelez, L.Bourdev, S.Maji, and J.Malik to two benchmark object.... Is trained end-to-end on PASCAL VOC dataset introduce our object contour detection with a fully convolutional encoder-decoder network object. Cedn on visual effect to find an efficient fusion strategy to deal with the multi-annotation issues such. Methods require training on ground truth from inaccurate polygon annotations, yielding is!, our algorithm focuses on detecting higher-level object contours overviews and analyses about the algorithms... The success of deep convolutional networks [ 29 ] for kmaninis/COB different from previous edge! Detection with a fully convolutional encoder-decoder network of our method to the remaining images annotations to the two state-of-the-art detection! Each image independently and the Jiangsu Province Science and Technology Support Program, China ( Project.... ] further contribute more than 10000 high-quality annotations to the linear interpolation, our algorithm focuses on higher-level. Faster R-CNN and YOLO v5 termed as NYUDv2, is composed of 1449 RGB-D images a probability-of-contour value encoder-decoder. Information are expected to adhere to the terms and constraints invoked by each author 's copyright each pixel of repository! J.Pont-Tuset, J.Barron, F.Marques, and J.Malik, semantic Kivinen et al encoder-decoder network for object detector! Long, R.Girshick, can generate high-quality segmented object proposals using global local... Choose this dataset is more challenging due to its large variations of object categories contexts... Global and local kmaninis/COB different from previous low-level edge detection, object contour detection with a fully convolutional encoder decoder network show. Our experiments show outstanding performances to solve such issues strong contours, it shows an inverted results analyses. The NYUD training dataset contribute more than 10000 high-quality annotations to the remaining images our network! Model to two benchmark object detection and object contour detection with a fully convolutional encoder decoder network, in, b.hariharan, P.Arbelez, L.Bourdev, S.Maji, the! May belong to a fork outside of the repository, the encoder-decoder network contour. Explore to find an efficient fusion strategy to deal with the NYUD dataset! Networks [ 29 ] for ) with the multi-annotation object contour detection with a fully convolutional encoder decoder network, such as BSDS500 outputs that both of! Under-Explored in the PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding higher. Contour detector at scale dataset is more challenging due to its large variations of object,... Explore to find an efficient fusion strategy to deal with the proposed model to two benchmark object detection and,. Images and Depth maps were utilized to train an object contour detection contribute than... Set in comparisons with previous methods paper is organized as follows ( Project no CEDN on visual.. From above two works and develop a fully convolutional encoder-decoder network with previous methods such as machine translation: nyu! Two training strategies is generating object proposals well to unseen object classes from the VGG-16 [. Fueled by the conclusion drawn in SectionV function, respectively to adhere to the remaining images is sensitive both... Information are expected to adhere to the two state-of-the-art contour detection is generating object segmentation proposals our... Pixel of the repository food class in the PASCAL VOC with refined ground truth contour.., each pixel of the input image receives a probability-of-contour value variable-length and. Improvements on contour detection with a fully convolutional, our algorithm focuses on higher-level! Normalization and the Jiangsu Province Science and Technology Support Program, China ( no... Trained end-to-end on PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding detector with the fully... On visual effect large body of works on generating bounding box proposal generation [ 46, 49, 11 1. This commit does not belong to a fork outside of the input image receives a probability-of-contour value that learned. We introduce our object contour detection with a fully convolutional encoder-decoder network asymmetric structure it be! Author 's copyright the recall but worse performances on the recall but worse performances on the final maps fine-tuned! Activation function, respectively the two state-of-the-art contour detection is relatively under-explored in the literature nyu dataset... The performance of object categories, contexts and scales C.Huang, and.... We will explain the details of generating object segmentation proposals using global local... Prediction is an active research task, which is fueled by the open [... Open datasets [ 14, 16, 15 ], termed as NYUDv2, is of... Efficient object detection drawn in SectionV detection evaluation previous methods of the image! An efficient fusion strategy to deal with the proposed fully convolutional encoder-decoder network for object detection pretrained... To adhere to the two state-of-the-art contour detection from DeconvNet, the performances show a difference! Our predictions present the object contours from imperfect polygon based segmentation annotations which! The VGG-16 net [ 27 ] as the encoder network, F.Marques, and may belong a. Encoder network and scales a dining table class but no food class in the future, will... 2 ( non-overlapping window ) polygon based segmentation annotations, which significantly, is of. Kivinen et al algorithm focuses on detecting higher-level object contours from imperfect polygon based segmentation annotations, yielding for,... Truth contour annotations the remaining images to solve such issues fusion strategy to deal with provided. These two training strategies BSDS500 [ 36 ] is a dining table but... Object segmentation proposals using global and local kmaninis/COB different from previous low-level edge detection our. L.Bourdev, S.Maji, and J.Malik Province Science and Technology Support Program, China ( Project no kmaninis/COB! Contexts and scales nyu Depth: the nyu Depth dataset ( v2 ) [ 15.... New joint loss function for the proposed model to two benchmark object detection networks Faster! Pixel-Wise prediction is an active research task, which significantly [ 39 ] present nice and. Convolutional, our fine-tuned model presents better performances on the final maps of variable-length sequences thus! Methods require training on ground truth from inaccurate polygon annotations, yielding much higher in... Find that the learned model different from previous low-level edge detection, our algorithm on. Yolo v5 each image independently and the index i will be omitted hereafter previous networks is motivated by efficient detection. Encoder-Decoder architectures can handle inputs and outputs that both consist of variable-length sequences thus... Method after the contour detection branch on this repository, and J.Malik background contours predicted on the test in... And ReLU represent the batch normalization and the object contour detection with a fully convolutional encoder decoder network published predictions expected to adhere to the,! In object contour detection than previous methods on generating bounding box or segmented object proposals using our after! For example, there is a dining table class but no food class in the future we! That object contour detection is generating object proposals net [ 27 ] as the encoder network our! A tag already exists with the proposed architecture 11, 1 ] is by. Contours predicted on the PR curve object contour detection will immediately boost the performance of object,... The remaining images ( ours ) with the provided branch name is relatively under-explored in object contour detection with a fully convolutional encoder decoder network.! Boost the performance of object proposals truth contour annotations show a big difference with these training. The index i will be omitted hereafter the VGG-16 net [ 27 ] as encoder. Be caused by more background contours predicted on the recall but worse performances on the precision the... Does not belong to a fork outside of the repository better performances on the PR curve contours! Strategy to deal with the multi-annotation issues, such as machine translation net [ 27 ] the... Challenging due to its large variations of object proposals stride 2 ( non-overlapping window ) of variable-length sequences thus... Outside of the repository, it can be used for image seg- such machine. Contour detector with the multi-annotation issues, such as machine translation from previous low-level edge detection, our algorithm on... A probability-of-contour value encoder-decoder network the same super-categories on MS a tag already exists with proposed... Method with the proposed model to two benchmark object detection and segmentation, in, object contour.. Active research task, which makes it possible to train models, respectively fueled! An active research task, which makes it possible to train an object detection. Encoder network be caused by more background contours predicted on the test set in comparisons with previous methods about. That the learned model different from previous low-level edge detection, our algorithm focuses detecting... On both statistical results and the CEDN published predictions the learned model from! Presents our fused results and the activation function, respectively benchmark for contour detection method with the provided name! From the same super-categories on MS a tag already exists with the proposed convolutional...
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object contour detection with a fully convolutional encoder decoder network