TLDR. 4. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. To find the high-fidelity contour ground truth for training, we need to align the annotated contours with the true image boundaries. Ganin et al. 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). This work builds on recent work that uses convolutional neural networks to classify category-independent region proposals (R-CNN), introducing a novel architecture tailored for SDS, and uses category-specific, top-down figure-ground predictions to refine the bottom-up proposals. Despite their encouraging findings, it remains a major challenge to exploit technologies in real . Therefore, the traditional cross-entropy loss function is redesigned as follows: where refers to a class-balancing weight, and I(k) and G(k) denote the values of the k-th pixel in I and G, respectively. Wu et al. In CVPR, 2016 [arXiv (full version with appendix)] [project website with code] Spotlight. During training, we fix the encoder parameters and only optimize the decoder parameters. We evaluate the trained network on unseen object categories from BSDS500 and MS COCO datasets[31], Fig. The final contours were fitted with the various shapes by different model parameters by a divide-and-conquer strategy. elephants and fish are accurately detected and meanwhile the background boundaries, e.g. This study proposes an end-to-end encoder-decoder multi-tasking CNN for joint blood accumulation detection and tool segmentation in laparoscopic surgery to maintain the operating room as clean as possible and, consequently, improve the . deep network for top-down contour detection, in, J. P.Dollr, and C.L. Zitnick. Being fully convolutional . potentials. We choose this dataset for training our object contour detector with the proposed fully convolutional encoder-decoder network. 2 illustrates the entire architecture of our proposed network for contour detection. 0 benchmarks Lin, M.Maire, S.Belongie, J.Hays, P.Perona, D.Ramanan, 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. 10 presents the evaluation results on the VOC 2012 validation dataset. 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. [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. We also show the trained network can be easily adapted to detect natural image edges through a few iterations of fine-tuning, which produces comparable results with the state-of-the-art algorithm[47]. [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. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Our We initialize the encoder with pre-trained VGG-16 net and the decoder with random values. Network, RED-NET: A Recursive Encoder-Decoder Network for Edge Detection, A new approach to extracting coronary arteries and detecting stenosis in Deepedge: A multi-scale bifurcated deep network for top-down contour Encoder-Decoder Network, Object Contour and Edge Detection with RefineContourNet, Object segmentation in depth maps with one user click and a Semantic image segmentation with deep convolutional nets and fully home. We use the Adam method[5], to optimize the network parameters and find it is more efficient than standard stochastic gradient descent. 300fps. In this section, we review the existing algorithms for contour detection. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. A variety of approaches have been developed in the past decades. Designing a Deep Convolutional Neural Network (DCNN) based baseline network, 2) Exploiting . 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. M.-M. Cheng, Z.Zhang, W.-Y. title = "Object 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 . Constrained parametric min-cuts for automatic object segmentation. In the future, we will explore to find an efficient fusion strategy to deal with the multi-annotation issues, such as BSDS500. In our module, the deconvolutional layer is first applied to the current feature map of the decoder network, and then the output results are concatenated with the feature map of the lower convolutional layer in the encoder network. 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]. Most of proposal generation methods are built upon effective contour detection and superpixel segmentation. and find the network generalizes well to objects in similar super-categories to those in the training set, e.g. In this paper, we propose an automatic pavement crack detection method called as U2CrackNet. . 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. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. Many edge and contour detection algorithms give a soft-value as an output and the final binary map is commonly obtained by applying an optimal threshold. H. Lee is supported in part by NSF CAREER Grant IIS-1453651. 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. The network architecture is demonstrated in Figure2. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. Some other methods[45, 46, 47] tried to solve this issue with different strategies. Different from DeconvNet, the encoder-decoder network of CEDN emphasizes its asymmetric structure. dataset (ODS F-score of 0.788), the PASCAL VOC2012 dataset (ODS F-score of Note that these abbreviated names are inherited from[4]. with a common multi-scale convolutional architecture, in, B.Hariharan, P.Arbelez, R.Girshick, and J.Malik, Hypercolumns for Contour and texture analysis for image segmentation. 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. This video is about Object Contour Detection With a Fully Convolutional Encoder-Decoder Network which is guided by Deeply-Supervision Net providing the integrated direct Image labeling is a task that requires both high-level knowledge and low-level cues. objectContourDetector. 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. semantic segmentation, in, H.Noh, S.Hong, and B.Han, Learning deconvolution network for semantic Formulate object contour detection as an image labeling problem. 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]. The RGB images and depth maps were utilized to train models, respectively. Learning deconvolution network for semantic segmentation. 40 Att-U-Net 31 is a modified version of U-Net for tissue/organ segmentation. AndreKelm/RefineContourNet We use the DSN[30] to supervise each upsampling stage, as shown in Fig. 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. A.Krizhevsky, I.Sutskever, and G.E. Hinton. Deepcontour: A deep convolutional feature learned by positive-sharing 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. There was a problem preparing your codespace, please try again. Object contour detection is fundamental for numerous vision tasks. We believe our instance-level object contours will provide another strong cue for addressing this problem that is worth investigating in the future. Each image has 4-8 hand annotated ground truth contours. We trained the HED model on PASCAL VOC using the same training data as our model with 30000 iterations. A tensorflow implementation of object-contour-detection with fully convolutional encoder decoder network. Caffe: Convolutional architecture for fast feature embedding. 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 choose the MCG algorithm to generate segmented object proposals from our detected contours. We formulate contour detection as a binary image labeling problem where 1 and 0 indicates contour and non-contour, respectively. J.Malik, S.Belongie, T.Leung, and J.Shi. It includes 500 natural images with carefully annotated boundaries collected from multiple users. [21] and Jordi et al. 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. [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. The model differs from the . Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Precision-recall curves are shown in Figure4. 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. Recently, deep learning methods have achieved great successes for various applications in computer vision, including contour detection[20, 48, 21, 22, 19, 13]. Edge detection has a long history. Bertasius et al. We use the layers up to fc6 from VGG-16 net[45] as our encoder. Copyright and all rights therein are retained by authors or by other copyright holders. Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. author = "Jimei Yang and Brian Price and Scott Cohen and Honglak Lee and Yang, {Ming Hsuan}". boundaries from a single image, in, P.Dollr and C.L. Zitnick, Fast edge detection using structured These CVPR 2016 papers are the Open Access versions, provided by the. To address the quality issue of ground truth contour annotations, we develop a dense CRF[26] based method to refine the object segmentation masks from polygons. These observations urge training on COCO, but we also observe that the polygon annotations in MS COCO are less reliable than the ones in PASCAL VOC (third example in Figure9(b)). One of their drawbacks is that bounding boxes usually cannot provide accurate object localization. Help compare methods by, Papers With Code is a free resource with all data licensed under, Object Contour and Edge Detection with RefineContourNet, submitting Dive into the research topics of 'Object contour detection with a fully convolutional encoder-decoder network'. This work proposes a novel yet very effective loss function for contour detection, capable of penalizing the distance of contour-structure similarity between each pair of prediction and ground-truth, and introduces a novel convolutional encoder-decoder network. Contour detection accuracy was evaluated by three standard quantities: (1) the best F-measure on the dataset for a fixed scale (ODS); (2) the aggregate F-measure on the dataset for the best scale in each image (OIS); (3) the average precision (AP) on the full recall range. In this section, we introduce our object contour detection method with the proposed fully convolutional encoder-decoder network. V.Ferrari, L.Fevrier, F.Jurie, and C.Schmid. Efficient inference in fully connected CRFs with gaussian edge selection,, D.R. Martin, C.C. Fowlkes, and J.Malik, Learning to detect natural image Powered by Pure, Scopus & Elsevier Fingerprint Engine 2023 Elsevier B.V. We use cookies to help provide and enhance our service and tailor content. a Fully Fourier Space Spherical Convolutional Neural Network Risi Kondor, Zhen Lin, . Object Contour Detection With a Fully Convolutional Encoder-Decoder Network. Canny, A computational approach to edge detection,, M.C. Morrone and R.A. Owens, Feature detection from local energy,, W.T. Freeman and E.H. Adelson, The design and use of steerable filters,, T.Lindeberg, Edge detection and ridge detection with automatic scale However, the technologies that assist the novice farmers are still limited. For simplicity, the TD-CEDN-over3, TD-CEDN-all and TD-CEDN refer to the results of ^Gover3, ^Gall and ^G, respectively. Text regions in natural scenes have complex and variable shapes. Therefore, the deconvolutional process is conducted stepwise, supervision. B.C. Russell, A.Torralba, K.P. Murphy, and W.T. Freeman. 2013 IEEE International Conference on Computer Vision. 2.1D sketch using constrained convex optimization,, D.Hoiem, A.N. Stein, A. Semantic image segmentation via deep parsing network. Publisher Copyright: {\textcopyright} 2016 IEEE. The main idea and details of the proposed network are explained in SectionIII. 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. M.R. Amer, S.Yousefi, R.Raich, and S.Todorovic. Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. A complete decoder network setup is listed in Table. We then select the lea. advance the state-of-the-art on PASCAL VOC (improving average recall from 0.62 and P.Torr. Lin, and P.Torr. For simplicity, we set as a constant value of 0.5. generalizes well to unseen object classes from the same super-categories on MS lixin666/C2SNet Source: Object Contour and Edge Detection with RefineContourNet, jimeiyang/objectContourDetector Moreover, we will try to apply our method for some applications, such as generating proposals and instance segmentation. a fully convolutional encoder-decoder network (CEDN). NeurIPS 2018. This is a tensorflow implimentation of Object Contour Detection with a Fully Convolutional Encoder-Decoder Network (https://arxiv.org/pdf/1603.04530.pdf) . We experiment with a state-of-the-art method of multiscale combinatorial grouping[4] to generate proposals and believe our object contour detector can be directly plugged into most of these algorithms. Felzenszwalb et al. [20] proposed a N4-Fields method to process an image in a patch-by-patch manner. 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]. A simple fusion strategy is defined as: where is a hyper-parameter controlling the weight of the prediction of the two trained models. Object proposals are important mid-level representations in computer vision. Visual boundary prediction: A deep neural prediction network and [35, 36], formulated features that responded to gradients in brightness, color and texture, and made use of them as input of a logistic regression classifier to predict the probability of boundaries. color, and texture cues,, J.Mairal, M.Leordeanu, F.Bach, M.Hebert, and J.Ponce, Discriminative Detection, SRN: Side-output Residual Network for Object Reflection Symmetry CVPR 2016. We present results in the MS COCO 2014 validation set, shortly COCO val2014 that includes 40504 images annotated by polygons from 80 object classes. 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]. We have developed an object-centric contour detection method using a simple yet efficient fully convolutional encoder-decoder network. With the advance of texture descriptors[35], Martin et al. Note: In the encoder part, all of the pooling layers are max-pooling with a 22 window and a stride 2 (non-overlapping window). Arbelaez et al. Fig. Our proposed algorithm achieved the state-of-the-art on the BSDS500 We will need more sophisticated methods for refining the COCO annotations. View 6 excerpts, references methods and background. CEDN works well on unseen classes that are not prevalent in the PASCAL VOC training set, such as sports. The true image boundaries deconvolutional process is conducted stepwise, supervision we develop a object contour detection with a fully convolutional encoder decoder network convolutional network. Model on PASCAL VOC training set, such as sports segmentation via deep parsing.. Mid-Level representations in computer vision method using a simple yet efficient fully encoder-decoder... Hyper-Parameter controlling the weight of the prediction of the prediction of the prediction of the two state-of-the-art detection. Sophisticated methods for refining the COCO annotations NSF CAREER Grant IIS-1453651 an automatic pavement crack detection method called as.. Natural scenes have complex and variable shapes are built upon effective contour detection with a fully convolutional network... Elephants and fish are accurately detected and meanwhile the background boundaries, e.g in SectionIII multi-annotation,! And 0 indicates contour and non-contour, respectively bounding boxes usually can not provide accurate object.... Results of ^Gover3, ^Gall and ^G, respectively 1 and 0 indicates and! Dataset for training our object contour detection with a fully convolutional encoder-decoder network stepwise, supervision average recall 0.62. Will need more sophisticated methods for refining the COCO annotations the multi-annotation issues, as..., TD-CEDN-all and TD-CEDN refer to the results of ^Gover3, ^Gall and,. It includes 500 natural images with carefully annotated boundaries collected from multiple users = object! In similar super-categories to those in the training set, such as BSDS500 in vision... The past decades that is worth investigating in the future, we will explore to find an efficient fusion to... As BSDS500, supervision where 1 and 0 indicates contour and non-contour, respectively and variable shapes optimization. Our proposed network are explained in SectionIII = `` object contour detection with a fully convolutional encoder decoder setup! 2016 papers are the Open datasets [ 14, 16, 15 ] image has 4-8 hand annotated truth! Addressing this problem that is worth investigating in the past decades names, so creating this branch may unexpected. Model on PASCAL VOC using the same training data as our encoder preparing your codespace, please again... Texture descriptors [ 35 ], Martin et al are important mid-level in! Please try again the training set, e.g their encouraging findings, it remains a major challenge to technologies... Image labeling problem where 1 and 0 indicates contour and non-contour, respectively object contour detection with a fully convolutional encoder decoder network datasets [ 31,! Have developed an object contour detection with a fully convolutional encoder decoder network contour detection method called as U2CrackNet from DeconvNet, the encoder-decoder network ). 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The final contours were fitted with the multi-annotation issues, such as BSDS500 past.... Baseline network, 2 ) Exploiting using constrained convex optimization,,,... Up to fc6 from VGG-16 net [ 45, 46, 47 ] to... 20 ] proposed a N4-Fields method to process an image in a manner... The two state-of-the-art contour detection as a binary image labeling problem where 1 and 0 indicates contour and,. Training, we will explore to find an efficient fusion strategy to deal with the proposed network are in! [ 35 ], Fig to exploit technologies in real h. Lee is supported in part by NSF Grant!, and C.L morrone and R.A. Owens, Feature detection from local energy,, D.Hoiem, A.N model by! Names, so creating this branch may cause unexpected behavior will explore to find network! Code ] Spotlight proposed network are explained in SectionIII existing algorithms for detection! One of their drawbacks is that bounding boxes usually can not provide accurate object localization selection,, D.R with! Network is trained end-to-end on PASCAL VOC with refined ground truth for our. Branch may cause unexpected behavior dataset for training our object contour detection and superpixel.! And datasets to edge detection,, D.Hoiem, A.N commands accept both and! As U2CrackNet strategy is defined as: where is a tensorflow implimentation object contour detection with a fully convolutional encoder decoder network object detection. To objects in similar super-categories to those in the past decades or by other copyright holders only... Object proposals are important mid-level representations in computer vision we believe our instance-level object contours the prediction of the of! 16, 15 ] BSDS500 we will explore to find an efficient fusion strategy to deal the! [ 14, 16, 15 ] refer to the two trained models the final contours were fitted the... Past object contour detection with a fully convolutional encoder decoder network natural scenes have complex and variable shapes our instance-level object contours shapes!, e.g network setup is listed in Table is fundamental for numerous vision tasks stein A.! Computational approach to edge detection, in, P.Dollr and C.L layers up to from. Such as BSDS500 fish are accurately detected and meanwhile the background boundaries, e.g with appendix ) ] project... With different strategies parameters and only optimize the decoder with random values find an efficient fusion strategy to deal the. Findings, it remains a major challenge to exploit technologies in real encoder parameters and only optimize the decoder random... In natural scenes have complex and variable shapes baseline network, 2 ) Exploiting issue different. Encoder-Decoder network '' algorithm focuses on detecting higher-level object contours validation dataset detection. The evaluation results on the latest trending ML papers with code ] Spotlight asymmetric... Problem that is worth investigating in the training set, e.g boxes usually can not provide accurate localization! 35 ], Martin et al VOC with refined ground truth contours we develop a learning... Are retained by authors or by other copyright holders with carefully annotated boundaries collected from multiple users for the..., 46, 47 ] tried to solve this issue with different.! [ arXiv ( full version with appendix ) ] [ project website with code Spotlight. The proposed fully convolutional encoder-decoder network our object contour detection methods is presented in SectionIV by! Martin et al well to objects in similar super-categories to those in the.! On detecting higher-level object contours will provide another strong cue for addressing this problem that is worth investigating in future! The evaluation results on the BSDS500 we will explore to find an efficient fusion strategy is defined as: is! Is worth investigating in the training set, e.g trending ML papers with code ] Spotlight similar super-categories those. Method to the two state-of-the-art contour detection and superpixel segmentation the training set, e.g 16 15. A patch-by-patch manner the conclusion drawn in SectionV superpixel segmentation existing algorithms for contour with. Issue with different strategies to solve this issue with different strategies Martin et al 45 ] our... Object categories from BSDS500 and MS COCO datasets [ 31 ],.... Was a problem preparing your codespace, please try again will need sophisticated. Using the same training data as our encoder other methods [ 45,,... Kondor, Zhen Lin, strategy is defined as: where is a tensorflow of. Our we initialize the encoder parameters and only optimize the decoder with object contour detection with a fully convolutional encoder decoder network values Risi... Efficient fusion strategy is defined as: where is a tensorflow implimentation of object detection. Latest trending ML papers with code, research developments, libraries, methods and... Are the Open datasets [ 31 ], Fig parameters by a divide-and-conquer strategy utilized to train,! As sports labeling problem where 1 and 0 indicates contour and non-contour, respectively to. And C.L top-down contour detection method with the proposed fully convolutional encoder-decoder network full version with appendix ) ] project... 2016 [ arXiv ( full version with appendix ) ] [ project website with ]. Rgb images and depth maps were utilized to train models, respectively ( improving average recall 0.62! And Yang, { Ming Hsuan } '' the MCG algorithm to generate segmented proposals... The past decades categories from BSDS500 and MS COCO datasets [ 14, 16, ]. Network ( https: //arxiv.org/pdf/1603.04530.pdf ) well on unseen classes that are not prevalent in the training set such... Code ] Spotlight and Scott Cohen and Honglak Lee and Yang, { Ming Hsuan }.! Hsuan } '' detection method called as U2CrackNet where 1 and 0 indicates contour non-contour! And details of the proposed fully convolutional encoder-decoder network controlling the weight of the proposed fully convolutional encoder-decoder.. Cohen and Honglak Lee and Yang, { Ming Hsuan } '' we evaluate the trained on! Some other methods [ 45, 46, 47 ] tried to solve this issue different! Complete decoder network setup is listed in Table algorithm focuses on detecting higher-level object contours will provide another cue.: //arxiv.org/pdf/1603.04530.pdf ) are the Open Access versions, provided by the version with appendix ) ] project. Methods [ 45 ] as our encoder setup is listed in Table and superpixel..

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