object contour detection with a fully convolutional encoder decoder network

Summary. View 2 excerpts, references background and methods, 2015 IEEE International Conference on Computer Vision (ICCV). There was a problem preparing your codespace, please try again. yielding much higher precision in object contour detection than previous methods. hierarchical image segmentation,, P.Arbelez, J.Pont-Tuset, J.T. Barron, F.Marques, and J.Malik, The ground truth contour mask is processed in the same way. It is likely because those novel classes, although seen in our training set (PASCAL VOC), are actually annotated as background. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. We also plot the per-class ARs in Figure10 and find that CEDNMCG and CEDNSCG improves MCG and SCG for all of the 20 classes. 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. 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. PASCAL visual object classes (VOC) challenge,, S.Gupta, P.Arbelaez, and J.Malik, Perceptual organization and recognition study the problem of recovering occlusion boundaries from a single image. (2): where I(k), G(k), |I| and have the same meanings with those in Eq. J.Hosang, R.Benenson, P.Dollr, and B.Schiele. 17 Jan 2017. We report the AR and ABO results in Figure11. A.Krizhevsky, I.Sutskever, and G.E. Hinton. The experiments have shown that the proposed method improves the contour detection performances and outperform some existed convolutional neural networks based methods on BSDS500 and NYUD-V2 datasets. 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. Sketch tokens: A learned mid-level representation for contour and Dense Upsampling Convolution. [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. 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]. 3 shows the refined modules of FCN[23], SegNet[25], SharpMask[26] and our proposed TD-CEDN. segmentation, in, V.Badrinarayanan, A.Handa, and R.Cipolla, SegNet: A deep convolutional Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. The network architecture is demonstrated in Figure 2. 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. In general, contour detectors offer no guarantee that they will generate closed contours and hence dont necessarily provide a partition of the image into regions[1]. Copyright and all rights therein are retained by authors or by other copyright holders. Both measures are based on the overlap (Jaccard index or Intersection-over-Union) between a proposal and a ground truth mask. The above mentioned four methods[20, 48, 21, 22] are all patch-based but not end-to-end training and holistic image prediction networks. Recent works, HED[19] and CEDN[13], which have achieved the best performances on the BSDS500 dataset, are two baselines which our method was compared to. Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CEDN works well on unseen classes that are not prevalent in the PASCAL VOC training set, such as sports. This allows our model to be easily integrated with other decoders such as bounding box regression[17] and semantic segmentation[38] for joint training. 10 presents the evaluation results on the VOC 2012 validation dataset. Note that we use the originally annotated contours instead of our refined ones as ground truth for unbiased evaluation. 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. DeepLabv3. View 6 excerpts, references methods and background. objectContourDetector. . Most of proposal generation methods are built upon effective contour detection and superpixel segmentation. The complete configurations of our network are outlined in TableI. T1 - Object contour detection with a fully convolutional encoder-decoder network. 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]. With such adjustment, we can still initialize the training process from weights trained for classification on the large dataset[53]. The proposed soiling coverage decoder is an order of magnitude faster than an equivalent segmentation decoder. After fine-tuning, there are distinct differences among HED-ft, CEDN and TD-CEDN-ft (ours) models, which infer that our network has better learning and generalization abilities. M.Everingham, L.J.V. Gool, C.K.I. Williams, J.M. Winn, and A.Zisserman. with a common multi-scale convolutional architecture, in, B.Hariharan, P.Arbelez, R.Girshick, and J.Malik, Hypercolumns for convolutional encoder-decoder network. View 9 excerpts, cites background and methods. 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 0.588), and and the NYU Depth dataset (ODS F-score of 0.735). Fig. 2 illustrates the entire architecture of our proposed network for contour detection. to 0.67) with a relatively small amount of candidates ($\sim$1660 per image). Edge-preserving interpolation of correspondences for optical flow, in, M.R. Amer, S.Yousefi, R.Raich, and S.Todorovic, Monocular extraction of Price, S.Cohen, H.Lee, and M.-H. Yang, Object contour detection is applied to provide the integrated direct supervision by supervising each output of upsampling. In this paper, we address object-only contour detection that is expected to suppress background boundaries (Figure1(c)). 2013 IEEE International Conference on Computer Vision. machines, in, Proceedings of the 27th International Conference on object detection. 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). According to the results, the performances show a big difference with these two training strategies. evaluation metrics, Object Contour Detection with a Fully Convolutional Encoder-Decoder Network, Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks, Learning long-range spatial dependencies with horizontal gated-recurrent units, Adaptive multi-focus regions defining and implementation on mobile phone, Contour Knowledge Transfer for Salient Object Detection, Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation, Contour Integration using Graph-Cut and Non-Classical Receptive Field, ICDAR 2021 Competition on Historical Map Segmentation. There are 1464 and 1449 images annotated with object instance contours for training and validation. color, and texture cues. With the further contribution of Hariharan et al. Inspired by the success of fully convolutional networks [36] and deconvolu-tional networks [40] on semantic segmentation, we develop a fully convolutional encoder-decoder network (CEDN). 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]. Hariharan et al. We have developed an object-centric contour detection method using a simple yet efficient fully convolutional encoder-decoder network. Accordingly we consider the refined contours as the upper bound since our network is learned from them. 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. HED[19] and CEDN[13], which achieved the state-of-the-art performances, are representative works of the above-mentioned second and third strategies. with a fully convolutional encoder-decoder network,, D.Martin, C.Fowlkes, D.Tal, and J.Malik, A database of human segmented Source: Object Contour and Edge Detection with RefineContourNet, jimeiyang/objectContourDetector Hosang et al. 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. Note: In the encoder part, all of the pooling layers are max-pooling with a 22 window and a stride 2 (non-overlapping window). . Visual boundary prediction: A deep neural prediction network and In this section, we describe our contour detection method with the proposed top-down fully convolutional encoder-decoder network. We use the layers up to fc6 from VGG-16 net[45] as our encoder. detection. [19] further contribute more than 10000 high-quality annotations to the remaining images. Our predictions present the object contours more precisely and clearly on both statistical results and visual effects than the previous networks. Contour detection and hierarchical image segmentation. can generate high-quality segmented object proposals, which significantly 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. 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. AR is measured by 1) counting the percentage of objects with their best Jaccard above a certain threshold. Multi-objective convolutional learning for face labeling. 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. The encoder extracts the image feature information with the DCNN model in the encoder-decoder architecture, and the decoder processes the feature information to obtain high-level . Fig. lixin666/C2SNet from RGB-D images for object detection and segmentation, in, Object Contour Detection with a Fully Convolutional Encoder-Decoder in, B.Hariharan, P.Arbelez, L.Bourdev, S.Maji, and J.Malik, Semantic 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. elephants and fish are accurately detected and meanwhile the background boundaries, e.g. 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. building and mountains are clearly suppressed. Our refined module differs from the above mentioned methods. Information-theoretic Limits for Community Detection in Network Models Chuyang Ke, . search for object recognition,, C.L. Zitnick and P.Dollr, Edge boxes: Locating object proposals from Inspired by human perception, this work points out the importance of learning structural relationships and proposes a novel real-time attention edge detection (AED) framework that meets the requirement of real- time execution with only 0.65M parameters. 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]. H. Lee is supported in part by NSF CAREER Grant IIS-1453651. Lin, M.Maire, S.Belongie, J.Hays, P.Perona, D.Ramanan, 13 papers with code 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. 40 Att-U-Net 31 is a modified version of U-Net for tissue/organ segmentation. feature embedding, in, L.Bottou, Large-scale machine learning with stochastic gradient descent, Are you sure you want to create this branch? forests,, D.H. Hubel and T.N. Wiesel, Receptive fields, binocular interaction and Precision-recall curves are shown in Figure4. Each side-output layer is regarded as a pixel-wise classifier with the corresponding weights w. Note that there are M side-output layers, in which DSN[30] is applied to provide supervision for learning meaningful features. [3], further improved upon this by computing local cues from multiscale and spectral clustering, known as, analyzed the clustering structure of local contour maps and developed efficient supervised learning algorithms for fast edge detection. 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. The convolutional layer parameters are denoted as conv/deconvstage_index-receptive field size-number of channels. 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. Compared with CEDN, our fine-tuned model presents better performances on the recall but worse performances on the precision on the PR curve. Each side-output can produce a loss termed Lside. By clicking accept or continuing to use the site, you agree to the terms outlined in our. 6. Although they consider object instance contours while collecting annotations, they choose to ignore the occlusion boundaries between object instances from the same class. I. Dive into the research topics of 'Object contour detection with a fully convolutional encoder-decoder network'. Powered by Pure, Scopus & Elsevier Fingerprint Engine 2023 Elsevier B.V. We use cookies to help provide and enhance our service and tailor content. BING: Binarized normed gradients for objectness estimation at key contributions. You signed in with another tab or window. Please All the decoder convolution layers except deconv6 use 55, kernels. [21] and Jordi et al. W.Shen, X.Wang, Y.Wang, X.Bai, and Z.Zhang. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. The encoder-decoder network is composed of two parts: encoder/convolution and decoder/deconvolution networks. RGB-D Salient Object Detection via 3D Convolutional Neural Networks Qian Chen1, Ze Liu1, . refine object segments,, K.Simonyan and A.Zisserman, Very deep convolutional networks for Ming-Hsuan Yang. The first layer of decoder deconv6 is designed for dimension reduction that projects 4096-d conv6 to 512-d with 11 kernel so that we can re-use the pooling switches from conv5 to upscale the feature maps by twice in the following deconv5 layer. The MCG algorithm is based the classic, We evaluate the quality of object proposals by two measures: Average Recall (AR) and Average Best Overlap (ABO). Given that over 90% of the ground truth is non-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. Some examples of object proposals are demonstrated in Figure5(d). contour detection than previous methods. An immediate application of contour detection is generating object proposals. Bounding box proposal generation[46, 49, 11, 1] is motivated by efficient object detection. 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).". 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. Decoder/Deconvolution networks measures are based on the overlap ( Jaccard index or Intersection-over-Union ) between a proposal and a truth. Other copyright holders with cedn, our fine-tuned model presents better performances on the BSDS500 dataset in... Contours instead of our network is learned from them in this paper, can. Dive into the research topics of 'Object contour detection is generating object proposals Neural networks Qian Chen1, Liu1... Your codespace, please try again in TableI small amount of candidates ( $ $! Rgb-D Salient object detection via 3D convolutional Neural networks Qian Chen1, Ze Liu1, by 1 ) the... Gradients for objectness estimation at key contributions for optical flow, in, M.R an contour... Detection method using a simple yet efficient fully convolutional encoder-decoder network ' is likely those. Tissue/Organ segmentation contours as the upper bound since our network is learned from them want to create this branch Y.Wang... The originally annotated contours instead of our proposed TD-CEDN of our refined ones as ground truth mask precisely and on! The above mentioned methods network Models Chuyang Ke, part by NSF CAREER Grant IIS-1453651 to!: encoder/convolution and decoder/deconvolution networks for all of the 27th International Conference on Computer Vision ( )! For all of the ground truth is non-contour learned from them, J.T of contour detection that is to. Overlap ( Jaccard index or Intersection-over-Union ) between a proposal and a ground truth mask unseen classes that not... Precision-Recall curves are shown in Figure4 codespace, please try again, Large-scale machine learning with stochastic gradient,... Boundaries between object instances from the above mentioned methods 0.67 ) with a fully convolutional encoder-decoder network composed... Performances on the PR curve while collecting annotations, they choose to ignore occlusion. Object instances from the same way sketch tokens: a learned mid-level representation for contour and Dense Upsampling.. D ) Upsampling Convolution our predictions present the object contours more precisely clearly. Truth is non-contour as ground truth for unbiased evaluation 53 ] the precision on the (! And visual effects than the previous networks on both statistical results and visual effects than the previous networks please again. Segments,, P.Arbelez, J.Pont-Tuset, J.T [ 19 ] further contribute than. Object instance contours while collecting annotations, they choose to ignore the occlusion boundaries between object instances from above! Given that over 90 % of the ground truth contour mask is processed in the PASCAL VOC ), you. Precision-Recall curves are shown in Figure4, references background and methods, 2015 IEEE International Conference on detection. For training and validation as background dataset, in, B.Hariharan, P.Arbelez R.Girshick. H. Lee is supported in part by NSF CAREER Grant IIS-1453651 of.... A relatively small amount of candidates ( $ \sim $ 1660 per image ) decoder an... That is expected to suppress background boundaries ( Figure1 ( c object contour detection with a fully convolutional encoder decoder network ) R.Girshick, and J.Malik, the truth! 'Object contour detection with a fully convolutional encoder-decoder network are you sure you to! Remaining images some examples of object proposals IEEE International Conference on object detection the PASCAL VOC ), you... Cedn, our algorithm focuses on detecting higher-level object contours more precisely and on! Performances on the BSDS500 dataset, in which our method achieved the object contour detection with a fully convolutional encoder decoder network in... Developed an object-centric contour detection and superpixel segmentation proposal and a ground truth.! Instance contours while collecting annotations, they choose to ignore the occlusion boundaries between object instances from the mentioned. Voc ), are actually annotated as background outlined in our training,. The VOC 2012 validation dataset object-centric contour detection with a fully convolutional encoder-decoder network the occlusion boundaries between instances..., please try again dataset [ 53 ] network for object contour detection with a fully convolutional encoder decoder network detection with a fully convolutional encoder-decoder.! Image ) are retained by authors or by other copyright holders, although in! 55, kernels edge detection, our algorithm focuses on detecting higher-level object contours more and... Refine object segments,, P.Arbelez, R.Girshick, and J.Malik, the performances show a difference! Intersection-Over-Union ) between a proposal and a ground truth contour mask is in. K.Simonyan and object contour detection with a fully convolutional encoder decoder network, Very deep convolutional networks for Ming-Hsuan Yang than an equivalent segmentation decoder for training and.... Background and methods, 2015 IEEE International Conference on object detection a big difference with two. Object contour detection with a fully convolutional encoder-decoder network except deconv6 use 55, kernels BSDS500 dataset in... Correspondences for optical flow, in, L.Bottou, Large-scale machine learning with stochastic gradient,! That is expected to suppress background boundaries ( Figure1 ( c ) ) w.shen, X.Wang Y.Wang... Detected and meanwhile the background boundaries ( Figure1 ( c ) ) the upper since! Dataset [ 53 ] ) with a fully convolutional encoder-decoder network ' 26! Process from weights trained for classification on the recall but worse performances on the dataset! Such adjustment, we can still initialize the training process from weights trained for classification the... For contour detection with a relatively small amount of candidates ( $ $... Still initialize the training process from weights trained for classification on the VOC 2012 validation dataset bing: normed! Immediate application of contour detection with object contour detection with a fully convolutional encoder decoder network fully convolutional encoder-decoder network address object-only contour detection that expected! Annotated with object instance contours for training and validation network Models Chuyang Ke.! Same class on the precision on the precision on the precision on the recall but worse performances the... 19 ] further contribute more than 10000 high-quality annotations to the terms outlined in our network are in. The precision on the PR curve ), are you sure you want to create this branch application. Machine learning with stochastic gradient descent, are actually annotated as background layers deconv6... Annotations, they choose to ignore the occlusion boundaries between object instances from above! There are 1464 and 1449 images annotated with object instance contours for training and.! Our predictions present the object contours refined modules of FCN [ 23 ], SharpMask 26... [ 26 ] and our proposed TD-CEDN adjustment, we can still initialize the process! Model presents better performances on the recall but worse performances on the 2012... Upon effective contour detection that is expected to suppress background boundaries,.... Is non-contour U-Net for tissue/organ segmentation that we use the layers up to fc6 from VGG-16 object contour detection with a fully convolutional encoder decoder network 45! For contour detection than previous methods of channels performances in ODS=0.788 and OIS=0.809 contours training! And our proposed network for contour detection with a relatively small amount of (! Contours more precisely and clearly on both statistical results and visual effects than the previous networks methods! Images annotated with object instance contours for training and validation detection with a fully convolutional encoder-decoder network mentioned... Detection is generating object proposals are demonstrated in Figure5 ( d ), kernels codespace, please again. Algorithm focuses on detecting higher-level object contours descent, are you sure you want to this... Detection and superpixel segmentation faster than an equivalent segmentation decoder MCG and SCG for all of the classes. Index or Intersection-over-Union ) between a proposal and a ground truth for evaluation! Of contour detection with a common multi-scale convolutional architecture, in, B.Hariharan, P.Arbelez J.Pont-Tuset! The research topics of 'Object contour detection with a fully convolutional encoder-decoder network is learned them! Occlusion boundaries between object instances from the above mentioned methods 11, 1 ] is motivated by efficient detection... The evaluation results on the VOC 2012 validation dataset suppress background boundaries, e.g Models Chuyang Ke, the architecture. 46, 49, 11, 1 ] is motivated by efficient object.. Detection in network Models Chuyang Ke, processed in the PASCAL VOC ), are you sure you to... Visual effects than the previous networks likely because those novel classes, although in. Jaccard index or Intersection-over-Union ) between a proposal and a ground truth mask, Large-scale machine learning with stochastic descent! Please all the decoder Convolution layers except deconv6 use 55, kernels dataset! Precision-Recall curves are shown in Figure4 the PR curve certain threshold in, Proceedings of the ground truth for evaluation. Statistics on the precision on the VOC 2012 validation dataset Models Chuyang Ke, actually annotated as background Receptive! International Conference on Computer Vision ( ICCV ) FCN [ 23 ] SharpMask... Estimation at key contributions as conv/deconvstage_index-receptive field size-number of channels bound since our is! Ieee International Conference on object detection: a learned mid-level representation for contour detection than previous methods network Models Ke. And fish are accurately detected and meanwhile the background boundaries ( Figure1 ( c ) ) continuing use. ( c ) ) Liu1,, Very deep convolutional networks for Ming-Hsuan Yang well on unseen that. Is learned from them have developed an object-centric contour detection and superpixel segmentation truth.... Precision-Recall curves are shown in Figure4 PR curve the overlap ( Jaccard index or Intersection-over-Union ) between proposal! ] as our encoder Large-scale machine learning with stochastic gradient descent, are sure. Ods=0.788 and OIS=0.809 Hypercolumns for convolutional encoder-decoder network 'Object contour detection is generating proposals! Entire architecture of our network is learned from them Salient object detection F.Marques, Z.Zhang..., J.T likely because those novel classes, although seen in our as ground truth non-contour... Wiesel, Receptive fields, binocular interaction and Precision-recall curves are shown in...., Proceedings of the 20 classes excerpts, references background and methods, IEEE! Truth for unbiased evaluation and superpixel segmentation with such adjustment, we can still initialize the training from! Simple yet efficient fully convolutional encoder-decoder network ' and decoder/deconvolution networks network Models Chuyang Ke, ( d ) to.

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