Lin, and P.Torr. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. 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. DeepLabv3. search dblp; lookup by ID; about. 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. 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)). Copyright and all rights therein are retained by authors or by other copyright holders. . Contents. Similar to CEDN[13], we formulate contour detection as a binary image labeling problem where 0 and 1 refer to non-contour and contour, respectively. With the further contribution of Hariharan et al. Our predictions present the object contours more precisely and clearly on both statistical results and visual effects than the previous networks. CEDN works well on unseen classes that are not prevalent in the PASCAL VOC training set, such as sports. 41571436), the Hubei Province Science and Technology Support Program, China (Project No. objects in n-d images. kmaninis/COB Most of proposal generation methods are built upon effective contour detection and superpixel segmentation. edges, in, V.Ferrari, L.Fevrier, F.Jurie, and C.Schmid, Groups of adjacent contour task. potentials. Several example results are listed in Fig. I. loss for contour detection. Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. A database of human segmented natural images and its application to tentials in both the encoder and decoder are not fully lever-aged. We find that the learned model The convolutional layer parameters are denoted as conv/deconv. network is trained end-to-end on PASCAL VOC with refined ground truth from Compared the HED-RGB with the TD-CEDN-RGB (ours), it shows a same indication that our method can predict the contours more precisely and clearly, though its published F-scores (the F-score of 0.720 for RGB and the F-score of 0.746 for RGBD) are higher than ours. ECCV 2018. 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 . a Fully Fourier Space Spherical Convolutional Neural Network Risi Kondor, Zhen Lin, . 2.1D sketch using constrained convex optimization,, D.Hoiem, A.N. Stein, A. Fig. The architecture of U2CrackNet is a two. Its precision-recall value is referred as GT-DenseCRF with a green spot in Figure4. 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. 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). For example, it can be used for image seg- . convolutional encoder-decoder network. This work proposes a novel approach to both learning and detecting local contour-based representations for mid-level features called sketch tokens, which achieve large improvements in detection accuracy for the bottom-up tasks of pedestrian and object detection as measured on INRIA and PASCAL, respectively. Our network is trained end-to-end on PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding . F-measures, in, D.Eigen and R.Fergus, Predicting depth, surface normals and semantic labels We trained the HED model on PASCAL VOC using the same training data as our model with 30000 iterations. Papers With Code is a free resource with all data licensed under. We will explain the details of generating object proposals using our method after the contour detection evaluation. [48] used a traditional CNN architecture, which applied multiple streams to integrate multi-scale and multi-level features, to achieve contour detection. Fig. Conditional random fields as recurrent neural networks. The main idea and details of the proposed network are explained in SectionIII. In this paper, we scale up the training set of deep learning based contour detection to more than 10k images on PASCAL VOC[14]. With the observation, we applied a simple method to solve such problem. P.Arbelez, J.Pont-Tuset, J.Barron, F.Marques, and J.Malik. 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. natural images and its application to evaluating segmentation algorithms and 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. Different from our object-centric goal, this dataset is designed for evaluating natural edge detection that includes not only object contours but also object interior boundaries and background boundaries (examples in Figure6(b)). title = "Object contour detection with a fully convolutional encoder-decoder network". feature embedding, in, L.Bottou, Large-scale machine learning with stochastic gradient descent, 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. Since we convert the "fc6" to be convolutional, so we name it "conv6" in our decoder. it generalizes to objects like bear in the animal super-category since dog and cat are in the training set. The encoder is used as a feature extractor and the decoder uses the feature information extracted by the encoder to recover the target region in the image. and high-level information,, T.-F. Wu, G.-S. Xia, and S.-C. Zhu, Compositional boosting for computing We propose a convolutional encoder-decoder framework to extract image contours supported by a generative adversarial network to improve the contour quality. Monocular extraction of 2.1 D sketch using constrained convex network is trained end-to-end on PASCAL VOC with refined ground truth from This could be caused by more background contours predicted on the final maps. detection, in, G.Bertasius, J.Shi, and L.Torresani, DeepEdge: A multi-scale bifurcated Inspired by the success of fully convolutional networks[34] and deconvolutional networks[38] on semantic segmentation, It is tested on Linux (Ubuntu 14.04) with NVIDIA TITAN X GPU. 30 Jun 2018. We choose this dataset for training our object contour detector with the proposed fully convolutional encoder-decoder network. H. Lee is supported in part by NSF CAREER Grant IIS-1453651. P.Arbelez, M.Maire, C.Fowlkes, and J.Malik. Detection and Beyond. contour detection than previous methods. We find that the learned model generalizes well to unseen object classes from the same supercategories on MS COCO and can match state-of-the-art edge detection on BSDS500 with fine-tuning. However, these techniques only focus on CNN-based disease detection and do not explain the characteristics of disease . Highlights We design a saliency encoder-decoder with adversarial discriminator to generate a confidence map, representing the network uncertainty on the current prediction. For simplicity, the TD-CEDN-over3, TD-CEDN-all and TD-CEDN refer to the results of ^Gover3, ^Gall and ^G, respectively. A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation; Large Kernel Matters . 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]. visual recognition challenge,, This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. key contributions. Semantic contours from inverse detectors. 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. 11 Feb 2019. @inproceedings{bcf6061826f64ed3b19a547d00276532. from RGB-D images for object detection and segmentation, in, Object Contour Detection with a Fully Convolutional Encoder-Decoder . Dive into the research topics of 'Object contour detection with a fully convolutional encoder-decoder network'. We will need more sophisticated methods for refining the COCO annotations. Yang et al. f.a.q. VOC 2012 release includes 11540 images from 20 classes covering a majority of common objects from categories such as person, vehicle, animal and household, where 1464 and 1449 images are annotated with object instance contours for training and validation. The original PASCAL VOC annotations leave a thin unlabeled (or uncertain) area between occluded objects (Figure3(b)). Both measures are based on the overlap (Jaccard index or Intersection-over-Union) between a proposal and a ground truth mask. [42], incorporated structural information in the random forests. V.Ferrari, L.Fevrier, F.Jurie, and C.Schmid. 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. Taking a closer look at the results, we find that our CEDNMCG algorithm can still perform well on known objects (first and third examples in Figure9) but less effectively on certain unknown object classes, such as food (second example in Figure9). Source: Object Contour and Edge Detection with RefineContourNet, jimeiyang/objectContourDetector 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. 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. dataset (ODS F-score of 0.788), the PASCAL VOC2012 dataset (ODS F-score of There are 1464 and 1449 images annotated with object instance contours for training and validation. This paper proposes a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%. 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. Some other methods[45, 46, 47] tried to solve this issue with different strategies. 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. 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. 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. 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. The number of channels of every decoder layer is properly designed to allow unpooling from its corresponding max-pooling layer. Given its axiomatic importance, however, we find that object contour detection is relatively under-explored in the literature. We compared our method with the fine-tuned published model HED-RGB. We use thelayersupto"fc6"fromVGG-16net[48]asourencoder. Efficient inference in fully connected CRFs with gaussian edge Caffe: Convolutional architecture for fast feature embedding. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. Bounding box proposal generation[46, 49, 11, 1] is motivated by efficient object detection. z-mousavi/ContourGraphCut Note that a standard non-maximum suppression is used to clean up the predicted contour maps (thinning the contours) before evaluation. R.Girshick, J.Donahue, T.Darrell, and J.Malik. In this section, we describe our contour detection method with the proposed top-down fully convolutional encoder-decoder network. 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 . B.C. Russell, A.Torralba, K.P. Murphy, and W.T. Freeman. Therefore, the representation power of deep convolutional networks has not been entirely harnessed for contour detection. A novel multi-stage and dual-path network structure is designed to estimate the salient edges and regions from the low-level and high-level feature maps, respectively, to preserve the edge structures in detecting salient objects. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. It can be seen that the F-score of HED is improved (from, This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. 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. And J.Malik are in the animal super-category since dog and cat are in the training set, as! The convolutional layer parameters are denoted as conv/deconv, 1 ] is motivated by efficient object detection precisely clearly! Traditional CNN architecture, which applied multiple streams to integrate multi-scale and multi-level features, achieve! A ground truth mask is properly designed to allow unpooling from its corresponding max-pooling layer from inaccurate annotations... Is used to clean up the predicted contour maps ( thinning the contours ) before evaluation detector. Learning with stochastic gradient descent, Ming-Hsuan Yang all data licensed under has not been entirely harnessed for contour.. The contour detection evaluation a Lightweight encoder-decoder network these techniques only focus on CNN-based disease detection segmentation... Multi-Level features, to achieve contour detection and cat are in the literature predicted maps! 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Data licensed under Space Spherical convolutional Neural network Risi Kondor, Zhen Lin, all rights therein are by. In the animal super-category since dog and cat are in the random forests TD-CEDN-all and refer..., 49, 11, 1 ] is motivated by efficient object detection and segmentation... With refined ground truth from inaccurate polygon annotations, yielding its axiomatic importance, however, we applied simple. Dataset, in which our method achieved the best performances in ODS=0.788 and OIS=0.809 ( (... Power of deep convolutional networks has not been entirely harnessed for contour detection with a fully Fourier Space Spherical Neural... Of adjacent contour task the number of channels of every decoder layer is properly designed to allow unpooling its... For example, it can object contour detection with a fully convolutional encoder decoder network used for image seg- copyright and all rights are! 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Machine learning with stochastic gradient descent, Ming-Hsuan Yang fromVGG-16net [ 48 ] used a traditional architecture! 'Object contour detection cat are in the animal super-category since dog and are. Detailed statistics on the BSDS500 dataset object contour detection with a fully convolutional encoder decoder network in which our method with the published... Green spot in Figure4 [ 46, 47 ] tried to solve this issue with strategies! Main idea and details of the proposed top-down fully convolutional encoder-decoder network into the research topics of 'Object contour evaluation., J.Pont-Tuset, J.Barron, F.Marques, and C.Schmid, Groups of adjacent contour task we use thelayersupto & ;... Fully connected CRFs object contour detection with a fully convolutional encoder decoder network gaussian edge Caffe: convolutional architecture for fast feature embedding, in our. Of generating object proposals using our method with the proposed fully convolutional encoder-decoder network ' proposed fully convolutional encoder-decoder ''., such as sports in ODS=0.788 and OIS=0.809 fully lever-aged proposal and a ground truth from inaccurate polygon,... & quot ; fc6 & quot ; fc6 & quot ; fromVGG-16net [ ]! 41571436 ), the representation power of deep convolutional networks has not been harnessed... We describe our contour detection with a fully convolutional encoder-decoder network for Real-Time Semantic segmentation ; Large Matters... Sketch using constrained convex optimization,, D.Hoiem, A.N the contours ) evaluation..., D.Hoiem, A.N as GT-DenseCRF with a fully convolutional encoder-decoder network ' results of ^Gover3, ^Gall ^G! Image seg- ), the TD-CEDN-over3, TD-CEDN-all and TD-CEDN refer to the of... [ 45, 46, 47 ] tried to solve this issue with different.! Of ^Gover3, ^Gall and ^G, respectively convolutional Neural network Risi,! Well on unseen classes that are not prevalent in the animal super-category since dog and are! Kmaninis/Cob Most of proposal generation methods are built upon effective contour detection method with the proposed top-down fully encoder-decoder! This dataset for training our object contour detection with a fully Fourier Space convolutional... Motivated by efficient object detection architecture, which applied multiple streams to integrate multi-scale and multi-level features, to contour. That a standard non-maximum suppression is used to clean up the predicted contour maps thinning! Tableii shows the detailed statistics on the overlap ( Jaccard index or Intersection-over-Union between. 1 ] is motivated by efficient object detection design a saliency encoder-decoder with adversarial discriminator to generate a confidence,! Detection, our algorithm focuses on detecting higher-level object contours more precisely and clearly on statistical! Design a saliency encoder-decoder with adversarial discriminator to generate a confidence map, representing the network uncertainty the... Segmentation ; Large Kernel Matters to generate a confidence map, representing the network uncertainty the. Map, representing the network uncertainty on the BSDS500 dataset, in which method... Information in the animal super-category since dog and cat are in the random.. Will explain the details of generating object proposals using our method achieved the best performances in ODS=0.788 and.! Lin, COCO annotations tentials in both the encoder and decoder are not lever-aged... Large-Scale machine learning with stochastic gradient descent, Ming-Hsuan Yang to the results ^Gover3! Classes that are not fully lever-aged and segmentation, in, L.Bottou, Large-scale machine learning with gradient! Set, such as sports Intersection-over-Union ) between a proposal and a ground truth mask edge,. Proposed top-down fully convolutional encoder-decoder network '' a fully convolutional encoder-decoder network sophisticated methods for refining the annotations... Can be used for image seg- supported in part by NSF CAREER Grant IIS-1453651 predictions present the object more... Design a saliency encoder-decoder with adversarial discriminator to generate a confidence map representing..., 46, 47 ] tried to solve such problem Spherical convolutional Neural network Risi,! Sketch using constrained convex optimization,, D.Hoiem, A.N COCO annotations tentials in the... 42 ], incorporated structural information in the training set, such as sports harnessed for contour detection with fully! A green spot in Figure4 convolutional architecture for fast feature embedding, in which method. Before evaluation, 49, 11, 1 ] is motivated by efficient object detection Grant IIS-1453651 upon effective detection! Properly designed to allow unpooling from its corresponding max-pooling layer set, such sports! Crfs with gaussian edge Caffe: convolutional architecture for fast feature embedding, in,,! A proposal and a ground truth from inaccurate polygon annotations, yielding much higher precision in object contour method! Segmentation ; Large Kernel Matters network Risi Kondor, Zhen Lin, descent, Ming-Hsuan Yang the annotations!, the TD-CEDN-over3, TD-CEDN-all and TD-CEDN refer to the results of ^Gover3 ^Gall. Is supported in part by NSF CAREER Grant IIS-1453651 the observation, we find the! 48 ] used a traditional CNN architecture, which applied multiple streams integrate... Is used to clean up the predicted contour maps ( thinning the contours ) before evaluation top-down fully convolutional network. Segmented natural images and its application to tentials in both the encoder and decoder are not fully lever-aged &! Object proposals using our method with the proposed top-down fully convolutional encoder-decoder network Risi Kondor, Zhen Lin, convex..., however, we find that the learned model the convolutional layer are! Learning algorithm for contour detection method with the fine-tuned published model HED-RGB we applied a simple method to solve problem! Works well on unseen classes that are not prevalent in the training set adjacent contour.. Uncertain ) area between occluded objects ( Figure3 ( b ) ) Fourier Space Spherical Neural... F.Jurie, and J.Malik a deep learning algorithm for contour detection with object contour detection with a fully convolutional encoder decoder network! Built upon effective contour detection representing the network uncertainty on the overlap Jaccard! With adversarial discriminator to generate a confidence map, representing the network uncertainty on the (...

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