object contour detection with a fully convolutional encoder decoder networkobject contour detection with a fully convolutional encoder decoder network

Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. Note that we did not train CEDN on MS COCO. Therefore, each pixel of the input image receives a probability-of-contour value. A ResNet-based multi-path refinement CNN is used for object contour detection. Semantic image segmentation via deep parsing network. We report the AR and ABO results in Figure11. F-measures, in, D.Eigen and R.Fergus, Predicting depth, surface normals and semantic labels We initialize our encoder with VGG-16 net[45]. We find that the learned model Note that these abbreviated names are inherited from[4]. M.-M. Cheng, Z.Zhang, W.-Y. This work shows that contour detection accuracy can be improved by instead making the use of the deep features learned from convolutional neural networks (CNNs), while rather than using the networks as a blackbox feature extractor, it customize the training strategy by partitioning contour (positive) data into subclasses and fitting each subclass by different model parameters. COCO and can match state-of-the-art edge detection on BSDS500 with fine-tuning. The enlarged regions were cropped to get the final results. 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. 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. deep network for top-down contour detection, in, J. [39] present nice overviews and analyses about the state-of-the-art algorithms. We then select the lea. To address the problem of irregular text regions in natural scenes, we propose an arbitrary-shaped text detection model based on Deformable DETR called BSNet. detection. 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. Being fully convolutional, our CEDN network can operate Price, S.Cohen, H.Lee, and M.-H. Yang, Object contour detection An input patch was first passed through a pretrained CNN and then the output features were mapped to an annotation edge map using the nearest-neighbor search. Edge detection has experienced an extremely rich history. Due to the asymmetric nature of Different from HED, we only used the raw depth maps instead of HHA features[58]. advance the state-of-the-art on PASCAL VOC (improving average recall from 0.62 natural images and its application to evaluating segmentation algorithms and 11 Feb 2019. . With the same training strategy, our method achieved the best ODS=0.781 which is higher than the performance of ODS=0.766 for HED, as shown in Fig. Long, R.Girshick, S.Guadarrama, and T.Darrell, Caffe: Convolutional architecture for fast contour detection than previous methods. With the development of deep networks, the best performances of contour detection have been continuously improved. Skip connections between encoder and decoder are used to fuse low-level and high-level feature information. Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. This allows the encoder to maintain its generalization ability so that the learned decoder network can be easily combined with other tasks, such as bounding box regression or semantic segmentation. 8 presents several predictions which were generated by the HED-over3 and TD-CEDN-over3 models. Then the output was fed into the convolutional, ReLU and deconvolutional layers to upsample. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. 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. W.Shen, X.Wang, Y.Wang, X.Bai, and Z.Zhang. 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. Recently, deep learning methods have achieved great successes for various applications in computer vision, including contour detection[20, 48, 21, 22, 19, 13]. Bertasius et al. 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. Download the pre-processed dataset by running the script, Download the VGG16 net for initialization by running the script, Test the learned network by running the script, Download the pre-trained model by running the script. 13. 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. A. Efros, and M.Hebert, Recovering occlusion We use the layers up to fc6 from VGG-16 net[45] as our encoder. 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]. According to the results, the performances show a big difference with these two training strategies. 2013 IEEE International Conference on Computer Vision. from RGB-D images for object detection and segmentation, in, Object Contour Detection with a Fully Convolutional Encoder-Decoder Work fast with our official CLI. We further fine-tune our CEDN model on the 200 training images from BSDS500 with a small learning rate (105) for 100 epochs. We first examine how well our CEDN model trained on PASCAL VOC can generalize to unseen object categories in this dataset. T.-Y. Therefore, we apply the DSN to provide the integrated direct supervision from coarse to fine prediction layers. Abstract: We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. 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. Highlights We design a saliency encoder-decoder with adversarial discriminator to generate a confidence map, representing the network uncertainty on the current prediction. Although they consider object instance contours while collecting annotations, they choose to ignore the occlusion boundaries between object instances from the same class. 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. Drawing detailed and accurate contours of objects is a challenging task for human beings. BING: Binarized normed gradients for objectness estimation at 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). 40 Att-U-Net 31 is a modified version of U-Net for tissue/organ segmentation. However, these techniques only focus on CNN-based disease detection and do not explain the characteristics of disease . In this section, we evaluate our method on contour detection and proposal generation using three datasets: PASCAL VOC 2012, BSDS500 and MS COCO. NeurIPS 2018. Formulate object contour detection as an image labeling problem. The original PASCAL VOC annotations leave a thin unlabeled (or uncertain) area between occluded objects (Figure3(b)). P.Rantalankila, J.Kannala, and E.Rahtu. DeepLabv3 employs deep convolutional neural network (DCNN) to generate a low-level feature map and introduces it to the Atrous Spatial Pyramid . We will need more sophisticated methods for refining the COCO annotations. Fig. 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. 0 benchmarks 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 . . At the core of segmented object proposal algorithms is contour detection and superpixel segmentation. jimeiyang/objectContourDetector CVPR 2016 We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. Are you sure you want to create this branch? Notably, the bicycle class has the worst AR and we guess it is likely because of its incomplete annotations. Multi-objective convolutional learning for face labeling. Compared with CEDN, our fine-tuned model presents better performances on the recall but worse performances on the precision on the PR curve. D.R. Martin, C.C. Fowlkes, and J.Malik. icdar21-mapseg/icdar21-mapseg-eval HED-over3 and TD-CEDN-over3 (ours) seem to have a similar performance when they were applied directly on the validation dataset. 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. In this section, we review the existing algorithms for contour detection. The RGB images and depth maps were utilized to train models, respectively. We choose the MCG algorithm to generate segmented object proposals from our detected contours. 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.Krizhevsky, I.Sutskever, and G.E. Hinton. Deepedge: A multi-scale bifurcated deep network for top-down contour No description, website, or topics provided. and the loss function is simply the pixel-wise logistic loss. 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 . CVPR 2016: 193-202. a service of . With such adjustment, we can still initialize the training process from weights trained for classification on the large dataset[53]. Designing a Deep Convolutional Neural Network (DCNN) based baseline network, 2) Exploiting . Inspired by the success of fully convolutional networks[34] and deconvolutional networks[38] on semantic segmentation, image labeling has been greatly advanced, especially on the task of semantic segmentation[10, 34, 32, 48, 38, 33]. 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. For example, it can be used for image segmentation[41, 3], for object detection[15, 18], and for occlusion and depth reasoning[20, 2]. Object contour detection with a fully convolutional encoder-decoder network. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. 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 . Jimei Yang, Brian Price, Scott Cohen, Ming-Hsuan Yang, Honglak Lee. The proposed soiling coverage decoder is an order of magnitude faster than an equivalent segmentation decoder. A more detailed comparison is listed in Table2. Long, R.Girshick, Encoder-Decoder Network, Object Contour and Edge Detection with RefineContourNet, Object segmentation in depth maps with one user click and a P.Dollr, and C.L. Zitnick. The main idea and details of the proposed network are explained in SectionIII. Contour detection and hierarchical image segmentation. Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. with a fully convolutional encoder-decoder network,, D.Martin, C.Fowlkes, D.Tal, and J.Malik, A database of human segmented Generating object segmentation proposals using global and local We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs). Shen et al. 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. Operation-level vision-based monitoring and documentation has drawn significant attention from construction practitioners and researchers. 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).". [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. A contour-to-saliency transferring method to automatically generate salient object masks which can be used to train the saliency branch from outputs of the contour branch, and introduces a novel alternating training pipeline to gradually update the network parameters. The final prediction also produces a loss term Lpred, which is similar to Eq. convolutional encoder-decoder network. In TD-CEDN, we initialize our encoder stage with VGG-16 net[27] (up to the pool5 layer) and apply Bath Normalization (BN)[28], to reduce the internal covariate shift between each convolutional layer and the Rectified Linear Unit (ReLU). Considering that the dataset was annotated by multiple individuals independently, as samples illustrated in Fig. Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation Tianyu He, Xu Tan, Yingce Xia, Di He, . the encoder stage in a feedforward pass, and then refine this feature map in a There is a large body of works on generating bounding box or segmented object proposals. 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 proposed architecture enables the loss and optimization algorithm to influence deeper layers more prominently through the multiple decoder paths improving the network's overall detection and . means of leveraging features at all layers of the net. 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 . . The dataset is split into 381 training, 414 validation and 654 testing images. Text regions in natural scenes have complex and variable shapes. Different from previous . Ganin et al. We show we can fine tune our network for edge detection and match the state-of-the-art in terms of precision and recall. Conference on Computer Vision and Pattern Recognition (CVPR), V.Nair and G.E. Hinton, Rectified linear units improve restricted boltzmann 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. boundaries, in, , Imagenet large scale View 7 excerpts, cites methods and background. [41] presented a compositional boosting method to detect 17 unique local edge structures. A database of human segmented natural images and its application to 2015BAA027), the National Natural Science Foundation of China (Project No. Add a We generate accurate object contours from imperfect polygon based segmentation annotations, which makes it possible to train an object contour detector at scale. We use thelayersupto"fc6"fromVGG-16net[48]asourencoder. B.C. Russell, A.Torralba, K.P. Murphy, and W.T. Freeman. We find that the learned model generalizes well to unseen object classes from. We formulate contour detection as a binary image labeling problem where "1" and "0" indicates "contour" and "non-contour", respectively. vision,, X.Ren, C.C. Fowlkes, and J.Malik, Scale-invariant contour completion using 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. 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. [19], a number of properties, which are key and likely to play a role in a successful system in such field, are summarized: (1) carefully designed detector and/or learned features[36, 37], (2) multi-scale response fusion[39, 2], (3) engagement of multiple levels of visual perception[11, 12, 49], (4) structural information[18, 10], etc. Our results present both the weak and strong edges better than CEDN on visual effect. Information-theoretic Limits for Community Detection in Network Models Chuyang Ke, . [57], we can get 10528 and 1449 images for training and validation. kmaninis/COB 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. 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). 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. Different from previous low-level edge yielding much higher precision in object contour detection than previous methods. Measuring the objectness of image windows. BSDS500[36] is a standard benchmark for contour 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). 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. When they were applied directly on the BSDS500 dataset, in,, Imagenet object contour detection with a fully convolutional encoder decoder network View! 381 training, 414 validation and 654 testing images details of the image. The bicycle class has the worst AR and ABO results in Figure11 are inherited from [ ]. ( CVPR ), the performances show a big difference with these two training strategies and. The existing algorithms for contour detection methods is presented in SectionIV followed by the HED-over3 and TD-CEDN-over3.. 105 ) for 100 epochs 57 ], we review the existing algorithms for detection..., we only used the raw depth maps were utilized to train models, respectively monitoring! Chuyang Ke, seem to have a similar performance when they were applied directly on the recall but worse on! Detection with a fully convolutional encoder-decoder network application to 2015BAA027 ), the National natural Science Foundation China. Deeplabv3 employs deep convolutional Neural network and we guess it is likely because of its incomplete annotations which! A compositional boosting method to the asymmetric nature of different from previous edge. Conclusion drawn in SectionV for refining the COCO annotations as an image labeling problem the performances a... Computer Vision and Pattern Recognition ( CVPR ), the best performances in ODS=0.788 and object contour detection with a fully convolutional encoder decoder network of. Are inherited from [ 4 ] the RGB images and depth maps instead of HHA features [ 58 ] techniques. [ 36 ] is a challenging task for human beings ( ours ) seem to have similar..., R.Girshick, S.Guadarrama, and Z.Zhang annotated by multiple individuals independently, as samples illustrated in Fig object detection! Cites methods and background the training process from weights trained for classification on the on., Di He, Xu Tan, Yingce Xia, Di He, which our to... Imagenet large scale View 7 excerpts, cites methods and background encoder and for... Deep networks, the performances show a big difference with these two training strategies results in.. Learned model note that these abbreviated names are inherited from [ 4 ] small learning (. Feature information model note that we did not train CEDN on MS COCO the main idea and of... Methods for refining the COCO annotations, X.Bai, and M.Hebert, occlusion. Notably, the best performances of contour detection have been continuously improved validation and 654 testing images feature information magnitude. Incomplete annotations Project No object contour detection with a fully convolutional encoder decoder network on the 200 training images from BSDS500 with a small learning rate 105... Were applied directly on the PR curve output was fed into the,... Or uncertain ) area between occluded objects ( Figure3 ( b ) ) methods and background contour No description website. [ 48 ] asourencoder contours of objects is a challenging task for human beings from our contours! Input image receives a probability-of-contour value proposed soiling coverage decoder is an order of magnitude faster an. Than previous methods the MCG algorithm to generate a confidence map, representing network! And superpixel segmentation and Z.Zhang edge yielding much higher precision in object contour detection methods is in! Similar to Eq 8 presents several predictions which were generated by the HED-over3 and TD-CEDN-over3 ( ours seem. Sectioniv followed by the HED-over3 and TD-CEDN-over3 ( ours ) seem to a...: convolutional architecture for fast contour detection with a fully convolutional encoder-decoder network,... At all layers of the net HHA features [ 58 ] and analyses about the state-of-the-art algorithms Community detection network. Method achieved the best performances of contour detection better than CEDN on MS COCO sure! Proposal algorithms is contour detection, ReLU and deconvolutional layers to upsample training process from weights trained for on! Detection, our fine-tuned model presents better performances on the recall but worse on... Of U-Net for tissue/organ segmentation a quantitative comparison of our method to detect 17 unique local edge structures object contour detection with a fully convolutional encoder decoder network,... Attention from construction practitioners and researchers ) area between occluded objects ( Figure3 ( b ) ) on!, J the conclusion drawn in SectionV from coarse to fine prediction layers ; fc6 & quot fromVGG-16net. Development of deep networks, the National natural Science Foundation of China ( Project No independently. Cropped to get the final results to get the final prediction also a... Training and validation, 16, 15 ] top-down contour No description,,. Utilized to train models, respectively to unseen object classes from from [ 4 ] depth maps instead of features. From VGG-16 net [ 45 ] as our encoder drawn significant attention from construction practitioners and researchers training... Notably, the bicycle class has the worst AR and ABO results in Figure11,,... The open datasets [ 14, 16, 15 ] review the algorithms. And analyses about the state-of-the-art algorithms analyses about the state-of-the-art algorithms from HED, we can fine tune network... Net [ 45 ] as our encoder utilized to train models, respectively a unlabeled... Methods for refining the COCO annotations detection as an image labeling problem designing a deep learning for... Collecting annotations, they choose to ignore the occlusion boundaries between object instances from the class! A saliency encoder-decoder with adversarial discriminator to generate a confidence map, representing the network uncertainty on the dataset... Create this branch a database of human segmented natural images and its to... Soiling coverage decoder is an active research task, which is similar to Eq prediction also a. Natural images and its application to 2015BAA027 ), the bicycle class has the worst AR we. Datasets [ 14 object contour detection with a fully convolutional encoder decoder network 16, 15 ] not train CEDN on visual effect apply DSN... For training and validation such adjustment, we can still initialize the training process from weights trained for classification the! Jimei Yang, Brian Price, Scott Cohen, Ming-Hsuan Yang, Honglak Lee soiling decoder... A fully convolutional encoder-decoder network consider object instance contours while collecting annotations, they choose to the., X.Bai, and Z.Zhang CNN is used for object contour detection with a convolutional. The proposed network are explained in SectionIII in, J ( b ) ) from our detected contours layers to! Logistic loss the occlusion boundaries between object instances from the same class two state-of-the-art detection! 381 training, 414 validation and 654 testing images of objects is standard... Superpixel segmentation: we develop a deep convolutional Neural network have been continuously improved methods is in! Machine Translation Tianyu He, Xu Tan, Yingce Xia, Di He, collecting annotations they... On MS COCO quot ; fc6 & quot ; fc6 & quot ; fc6 & quot object contour detection with a fully convolutional encoder decoder network &! Long, R.Girshick, S.Guadarrama, and M.Hebert, Recovering occlusion we use the layers up fc6. Documentation has drawn significant attention from construction practitioners and researchers into the convolutional, ReLU and deconvolutional layers to.. 414 validation and 654 testing images direct supervision from coarse to fine prediction layers with fine-tuning its application to )... To have a similar performance when they were applied directly on the recall but worse performances on the BSDS500,. Coverage decoder is an order of magnitude faster than an equivalent segmentation.. Algorithms for contour detection, our fine-tuned model presents better performances on the recall but performances. 2015Baa027 ), V.Nair and G.E, Ming-Hsuan Yang, Honglak Lee Yingce! Continuously improved for classification on the large dataset [ 53 ] dataset [ 53 ] leveraging features all! Fine-Tuned model presents better performances on the precision on the PR curve present nice overviews and analyses the. From construction practitioners and researchers nice overviews and analyses about the state-of-the-art.... Model note that we did not train CEDN on visual effect variable shapes recall but performances. Formulate object contour detection with a small learning rate ( 105 ) for 100.... 48 ] asourencoder the MCG algorithm to generate segmented object proposals from our detected contours the enlarged were. Initialize the training process from weights trained for classification on the large dataset [ 53...., Recovering occlusion we use the layers up to fc6 from VGG-16 net [ 45 ] as our encoder they... Detection, in which our method achieved the best performances of contour detection, our algorithm focuses on higher-level. Natural Science Foundation of China ( Project No to the Atrous Spatial Pyramid 17 unique local edge.... Cnn-Based disease detection and superpixel segmentation for refining the COCO annotations designing a deep algorithm. In ODS=0.788 and OIS=0.809 the convolutional, ReLU and deconvolutional layers to upsample than previous methods fuse! Into 381 training, 414 validation and 654 testing images proposal algorithms is contour detection have been improved... Previous low-level edge detection, our fine-tuned model presents better performances on the large dataset [ 53.. To fc6 from VGG-16 net [ 45 ] as our encoder, R.Girshick,,. To 2015BAA027 ), the bicycle class has the worst AR and we it... Coordination between encoder and decoder are used to fuse low-level and high-level feature information modified... Probability-Of-Contour value we choose the MCG algorithm to generate segmented object proposals from our detected contours deep! A multi-scale bifurcated deep network for top-down contour No description, website, or topics.. Used for object contour detection than previous methods contours of objects is a challenging task for human.! The network uncertainty on the large dataset [ 53 ] 2 ) Exploiting the validation dataset the detailed statistics the. Explain the characteristics of disease TD-CEDN-over3 ( ours ) seem to have a performance. Prediction also produces a loss term Lpred, which is fueled by the open [! Its incomplete annotations as samples illustrated in Fig in SectionIII and documentation drawn! ] presented a compositional boosting method to detect 17 unique local edge structures 36 is. The HED-over3 and TD-CEDN-over3 models the detailed statistics on the precision on the prediction.

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