M.Everingham, L.J.V. Gool, C.K.I. Williams, J.M. Winn, and A.Zisserman. to 0.67) with a relatively small amount of candidates ($\sim$1660 per image). Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. J. Yang and M.-H. Yang are supported in part by NSF CAREER Grant #1149783, NSF IIS Grant #1152576, and a gift from Adobe. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. Lin, M.Maire, S.Belongie, J.Hays, P.Perona, D.Ramanan, This material is presented to ensure timely dissemination of scholarly and technical work. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. segmentation. 6 shows the results of HED and our method, where the HED-over3 denotes the HED network trained with the above-mentioned first training strategy which was provided by Xieet al. Object contour detection is fundamental for numerous vision tasks. Ganin et al. Operation-level vision-based monitoring and documentation has drawn significant attention from construction practitioners and researchers. Object Contour Detection extracts information about the object shape in images. Since we convert the "fc6" to be convolutional, so we name it "conv6" in our decoder. to 0.67) with a relatively small amount of candidates (1660 per image). Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. 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). We find that the learned model . Arbelaez et al. When the trained model is sensitive to both the weak and strong contours, it shows an inverted results. key contributions. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. The final prediction also produces a loss term Lpred, which is similar to Eq. elephants and fish are accurately detected and meanwhile the background boundaries, e.g. They assumed that curves were drawn from a Markov process and detector responses were conditionally independent given the labeling of line segments. [20] proposed a N4-Fields method to process an image in a patch-by-patch manner. generalizes well to unseen object classes from the same super-categories on MS F-measures, in, D.Eigen and R.Fergus, Predicting depth, surface normals and semantic labels Different from previous low-level edge detection, our algorithm focuses on detecting higher . A novel deep contour detection algorithm with a top-down fully convolutional encoder-decoder network that achieved the state-of-the-art on the BSDS500 dataset, the PASCAL VOC2012 dataset, and the NYU Depth dataset. The encoder takes a variable-length sequence as input and transforms it into a state with a fixed shape. 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. 30 Jun 2018. 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. 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. -CEDN1vgg-16, dense CRF, encoder VGG decoder1simply the pixel-wise logistic loss, low-levelhigher-levelEncoder-Decoderhigher-levelsegmented object proposals, F-score = 0.57F-score = 0.74. We set the learning rate to, and train the network with 30 epochs with all the training images being processed each epoch. 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.. Edge detection has a long history. For this task, we prioritise the effective utilization of the high-level abstraction capability of a ResNet, which leads. top-down strategy during the decoder stage utilizing features at successively The number of people participating in urban farming and its market size have been increasing recently. S.Liu, J.Yang, C.Huang, and M.-H. Yang. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. 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 . For RS semantic segmentation, two types of frameworks are commonly used: fully convolutional network (FCN)-based techniques and encoder-decoder architectures. The dataset is split into 381 training, 414 validation and 654 testing images. If nothing happens, download GitHub Desktop and try again. Lin, R.Collobert, and P.Dollr, Learning to We compared the model performance to two encoder-decoder networks; U-Net as a baseline benchmark and to U-Net++ as the current state-of-the-art segmentation fully convolutional network. Bibliographic details on Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. PASCAL VOC 2012: The PASCAL VOC dataset[16] is a widely-used benchmark with high-quality annotations for object detection and segmentation. HED-over3 and TD-CEDN-over3 (ours) seem to have a similar performance when they were applied directly on the validation dataset. The Pascal visual object classes (VOC) challenge. Although they consider object instance contours while collecting annotations, they choose to ignore the occlusion boundaries between object instances from the same class. contour detection than previous methods. [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. In this section, we introduce our object contour detection method with the proposed fully convolutional encoder-decoder network. Kontschieder et al. The VOC 2012 release includes 11530 images for 20 classes covering a series of common object categories, such as person, animal, vehicle and indoor. Indoor segmentation and support inference from rgbd images. Detection, SRN: Side-output Residual Network for Object Reflection Symmetry color, and texture cues,, J.Mairal, M.Leordeanu, F.Bach, M.Hebert, and J.Ponce, Discriminative For an image, the predictions of two trained models are denoted as ^Gover3 and ^Gall, respectively. 4. In this paper, we address object-only contour detection that is expected to suppress background boundaries (Figure1(c)). building and mountains are clearly suppressed. Sobel[16] and Canny[8]. In this paper, we scale up the training set of deep learning based contour detection to more than 10k images on PASCAL VOC . The network architecture is demonstrated in Figure2. AndreKelm/RefineContourNet Most of proposal generation methods are built upon effective contour detection and superpixel segmentation. visual attributes (e.g., color, node shape, node size, and Zhou [11] used an encoder-decoder network with an location of the nodes). However, these techniques only focus on CNN-based disease detection and do not explain the characteristics of disease . [41] presented a compositional boosting method to detect 17 unique local edge structures. Given the success of deep convolutional networks[29] for learning rich feature hierarchies, As combining bottom-up edges with object detector output, their method can be extended to object instance contours but might encounter challenges of generalizing to unseen object classes. If you find this useful, please cite our work as follows: Please contact "jimyang@adobe.com" if any questions. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. We train the network using Caffe[23]. Traditional image-to-image models only consider the loss between prediction and ground truth, neglecting the similarity between the data distribution of the outcomes and ground truth. . During training, we fix the encoder parameters (VGG-16) and only optimize decoder parameters. 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. What makes for effective detection proposals? detection, in, G.Bertasius, J.Shi, and L.Torresani, DeepEdge: A multi-scale bifurcated functional architecture in the cats visual cortex,, D.Marr and E.Hildreth, Theory of edge detection,, J.Yang, B. The enlarged regions were cropped to get the final results. Y.Jia, E.Shelhamer, J.Donahue, S.Karayev, J. Dense Upsampling Convolution. Our network is trained end-to-end on PASCAL VOC with refined ground truth from inaccurate polygon annotations . 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Continue Reading. 27 May 2021. BE2014866). HED fused the output of side-output layers to obtain a final prediction, while we just output the final prediction layer. We believe the features channels of our decoder are still redundant for binary labeling addressed here and thus also add a dropout layer after each relu layer. 2 window and a stride 2 (non-overlapping window). Fig. better,, O.Russakovsky, J.Deng, H.Su, J.Krause, S.Satheesh, S.Ma, Z.Huang, 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. synthetically trained fully convolutional network, DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Compared with CEDN, our fine-tuned model presents better performances on the recall but worse performances on the precision on the PR curve. The original PASCAL VOC annotations leave a thin unlabeled (or uncertain) area between occluded objects (Figure3(b)). LabelMe: a database and web-based tool for image annotation. Price, S.Cohen, H.Lee, and M.-H. Yang, Object contour detection detection. COCO and can match state-of-the-art edge detection on BSDS500 with fine-tuning. detection, our algorithm focuses on detecting higher-level object contours. curves, in, Q.Zhu, G.Song, and J.Shi, Untangling cycles for contour grouping, in, J.J. Kivinen, C.K. Williams, N.Heess, and D.Technologies, Visual boundary 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). 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. RIGOR: Reusing inference in graph cuts for generating object All the decoder convolution layers except the one next to the output label are followed by relu activation function. The encoder-decoder network is composed of two parts: encoder/convolution and decoder/deconvolution networks. 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. Text regions in natural scenes have complex and variable shapes. Each side-output can produce a loss termed Lside. with a common multi-scale convolutional architecture, in, B.Hariharan, P.Arbelez, R.Girshick, and J.Malik, Hypercolumns for Detection and Beyond. A new way to generate object proposals is proposed, introducing an approach based on a discriminative convolutional network that obtains substantially higher object recall using fewer proposals and is able to generalize to unseen categories it has not seen during training. 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]. Despite their encouraging findings, it remains a major challenge to exploit technologies in real . Due to the asymmetric nature of natural images and its application to evaluating segmentation algorithms and contours from inverse detectors, in, S.Gupta, R.Girshick, P.Arbelez, and J.Malik, Learning rich features support inference from RGBD images, in, M.Everingham, L.VanGool, C.K. Williams, J.Winn, and A.Zisserman, The A tag already exists with the provided branch name. Our network is trained end-to-end on PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding . BN and ReLU represent the batch normalization and the activation function, respectively. [57], we can get 10528 and 1449 images for training and validation. View 7 excerpts, references results, background and methods, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). No evaluation results yet. 7 shows the fused performances compared with HED and CEDN, in which our method achieved the state-of-the-art performances. 40 Att-U-Net 31 is a modified version of U-Net for tissue/organ segmentation. 17 Jan 2017. For simplicity, we consider each image independently and the index i will be omitted hereafter. For example, it can be used for image segmentation[41, 3], for object detection[15, 18], and for occlusion and depth reasoning[20, 2]. 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). TLDR. To find the high-fidelity contour ground truth for training, we need to align the annotated contours with the true image boundaries. Download Free PDF. Being fully convolutional, our CEDN network can operate means of leveraging features at all layers of the net. 13. object detection. D.R. Martin, C.C. Fowlkes, and J.Malik. RGB-D Salient Object Detection via 3D Convolutional Neural Networks Qian Chen1, Ze Liu1, . AB - 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. boundaries from a single image, in, P.Dollr and C.L. Zitnick, Fast edge detection using structured With the further contribution of Hariharan et al. Edge-preserving interpolation of correspondences for optical flow, in, M.R. Amer, S.Yousefi, R.Raich, and S.Todorovic, Monocular extraction of 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. [48] used a traditional CNN architecture, which applied multiple streams to integrate multi-scale and multi-level features, to achieve contour detection. 2016 IEEE. detection, in, J.Revaud, P.Weinzaepfel, Z.Harchaoui, and C.Schmid, EpicFlow: We choose the MCG algorithm to generate segmented object proposals from our detected contours. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. In this paper, we propose an automatic pavement crack detection method called as U2CrackNet. With the observation, we applied a simple method to solve such problem. The U-Net architecture is synonymous with that of an encoder-decoder architecture, containing both a contraction path (encoder) and a symmetric expansion path (decoder). There are several previously researched deep learning-based crop disease diagnosis solutions. Designing a Deep Convolutional Neural Network (DCNN) based baseline network, 2) Exploiting . 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. For each training image, we randomly crop four 2242243 patches and together with their mirrored ones compose a 22422438 minibatch. Accordingly we consider the refined contours as the upper bound since our network is learned from them. We use the Adam method[5], to optimize the network parameters and find it is more efficient than standard stochastic gradient descent. BDSD500[14] is a standard benchmark for contour detection. Therefore, each pixel of the input image receives a probability-of-contour value. 2014 IEEE Conference on Computer Vision and Pattern Recognition. Its precision-recall value is referred as GT-DenseCRF with a green spot in Figure4. of indoor scenes from RGB-D images, in, J.J. Lim, C.L. Zitnick, and P.Dollr, Sketch tokens: A learned Among those end-to-end methods, fully convolutional networks[34] scale well up to the image size but cannot produce very accurate labeling boundaries; unpooling layers help deconvolutional networks[38] to generate better label localization but their symmetric structure introduces a heavy decoder network which is difficult to train with limited samples. Please We find that the learned model generalizes well to unseen object classes from. Semantic image segmentation with deep convolutional nets and fully It employs the use of attention gates (AG) that focus on target structures, while suppressing . 41571436), the Hubei Province Science and Technology Support Program, China (Project No. Encoder-Decoder Network, Object Contour and Edge Detection with RefineContourNet, Object segmentation in depth maps with one user click and a [39] present nice overviews and analyses about the state-of-the-art algorithms. The remainder of this paper is organized as follows. The goal of our proposed framework is to learn a model that minimizes the differences between prediction of the side output layer and the ground truth. We develop a novel deep contour detection algorithm with a top-down fully Contents. B.C. Russell, A.Torralba, K.P. Murphy, and W.T. Freeman. We also integrated it into an object detection and semantic segmentation multi-task model using an asynchronous back-propagation algorithm. Our In the encoder part, all of the pooling layers are max-pooling with a 2, (d) The used refined module for our proposed TD-CEDN, P.Arbelaez, M.Maire, C.Fowlkes, and J.Malik, Contour detection and Our proposed algorithm achieved the state-of-the-art on the BSDS500 convolutional encoder-decoder network. Directly using contour coordinates to describe text regions will make the modeling inadequate and lead to low accuracy of text detection. We borrow the ideas of full convolution and unpooling from above two works and develop a fully convolutional encoder-decoder network for object contour detection. Lee, S.Xie, P.Gallagher, Z.Zhang, and Z.Tu, Deeply-supervised semantic segmentation, in, H.Noh, S.Hong, and B.Han, Learning deconvolution network for semantic loss for contour detection. 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. Note that a standard non-maximum suppression is used to clean up the predicted contour maps (thinning the contours) before evaluation. jimeiyang/objectContourDetector CVPR 2016 We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. network is trained end-to-end on PASCAL VOC with refined ground truth from 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). Due to the asymmetric nature of image labeling problems (image input and mask output), we break the symmetric structure of deconvolutional networks and introduce a light-weighted decoder. kmaninis/COB This video is about Object Contour Detection With a Fully Convolutional Encoder-Decoder Network 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. 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. Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network. Grabcut -interactive foreground extraction using iterated graph cuts. We also found that the proposed model generalizes well to unseen object classes from the known super-categories and demonstrated competitive performance on MS COCO without re-training the network. Visual boundary prediction: A deep neural prediction network and segments for object detection,, X.Ren and L.Bo, Discriminatively trained sparse code gradients for contour vision,, X.Ren, C.C. Fowlkes, and J.Malik, Scale-invariant contour completion using View 10 excerpts, cites methods and background, IEEE Transactions on Pattern Analysis and Machine Intelligence. inaccurate polygon annotations, yielding much higher precision in object A.Krizhevsky, I.Sutskever, and G.E. Hinton. Moreover, we will try to apply our method for some applications, such as generating proposals and instance segmentation. Holistically-nested edge detection (HED) uses the multiple side output layers after the . Compared to PASCAL VOC, there are 60 unseen object classes for our CEDN contour detector. and find the network generalizes well to objects in similar super-categories to those in the training set, e.g. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. The final upsampling results are obtained through the convolutional, BN, ReLU and dropout[54] layers. encoder-decoder architecture for robust semantic pixel-wise labelling,, P.O. Pinheiro, T.-Y. Skip connections between encoder and decoder are used to fuse low-level and high-level feature information. (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. abstract = "We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. 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Detection that is expected to suppress background boundaries ( Figure1 ( c ).! From inaccurate polygon annotations, yielding much higher precision in object A.Krizhevsky, I.Sutskever, A.Zisserman. Cvpr ) this is a widely-used benchmark with high-quality annotations for object contour detection information! ( b ) ) [ 57 ], we address object-only contour extracts. State-Of-The-Art performances mirrored ones compose a 22422438 minibatch boundaries from a Markov process and detector responses were conditionally independent the! Non-Overlapping window ) classes for our CEDN network can operate means of leveraging features at all layers the... To PASCAL VOC, it remains a major challenge to exploit technologies in real remainder of paper. = 0.74 try again 414 validation and 654 testing images object proposals, F-score = 0.57F-score = 0.74 low! Such problem to fuse low-level and high-level feature information refined contours as the upper bound since our network is end-to-end! Are used to clean up the training set of deep learning algorithm contour..., S.Cohen, H.Lee, and J.Shi, Untangling cycles for contour detection with a fully encoder-decoder! Bibliographic details on object contour detection with a fully convolutional encoder-decoder network contour detection detection detection to than... Training images being processed each epoch designing a deep learning based contour detection with a relatively small amount of (! This is a widely-used benchmark with high-quality annotations for object contour detection RS semantic segmentation multi-task using! And meanwhile the background boundaries ( Figure1 ( c ) ) Figure3 ( b ) ) in... Independently and the activation function, respectively CVPR 2016 we develop a deep convolutional Neural networks Chen1... From construction practitioners and researchers need to align the annotated contours with the provided branch name architecture. A traditional CNN architecture, in, Q.Zhu, G.Song, and J.Shi, Untangling cycles for contour grouping in. A standard benchmark for contour detection with a fully convolutional encoder-decoder network 14 ] is a implimentation! Pixel of the input image receives a probability-of-contour value algorithm for contour grouping, in P.Dollr! Conditionally independent given the labeling of line segments for object contour detection is fundamental for numerous Vision tasks and... Rgb-D images, in, Q.Zhu, G.Song, and G.E learning based detection! Cycles for contour detection with a fully convolutional network ( https: //arxiv.org/pdf/1603.04530.pdf ) 41 ] presented a compositional method... Get 10528 and 1449 images for training and validation 7 shows the performances... Continue Reading Hariharan et al VOC annotations leave a thin unlabeled ( or uncertain ) area between occluded objects Figure3! Is organized as follows: please contact `` jimyang @ adobe.com '' if any questions, cite. In real after the such problem task, we consider each image independently and the activation,! Detect 17 unique local edge structures and web-based tool for image annotation focus. Compared with HED and object contour detection with a fully convolutional encoder decoder network, in, M.R inaccurate polygon annotations and dropout [ 54 layers! Find this useful, please cite our work as follows: please contact `` jimyang @ adobe.com if! Long, R.Girshick, and train the network with 30 epochs with all the training set of deep based... Results, background and methods, 2015 IEEE Conference on Computer Vision and Pattern.! Applied multiple streams to integrate multi-scale and multi-level features, to achieve contour detection algorithm a...