object contour detection with a fully convolutional encoder decoder network

Y.Jia, E.Shelhamer, J.Donahue, S.Karayev, J. A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation; Large Kernel Matters . Constrained parametric min-cuts for automatic object segmentation. optimization. Different from previous low-level edge Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. We find that the learned model . 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. This allows our model to be easily integrated with other decoders such as bounding box regression[17] and semantic segmentation[38] for joint training. 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). Contour and texture analysis for image segmentation. The encoder-decoder network is composed of two parts: encoder/convolution and decoder/deconvolution networks. 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]. feature embedding, in, L.Bottou, Large-scale machine learning with stochastic gradient descent, Given image-contour pairs, we formulate object contour detection as an image labeling problem. Note that a standard non-maximum suppression is used to clean up the predicted contour maps (thinning the contours) before evaluation. Please follow the instructions below to run the code. from RGB-D images for object detection and segmentation, in, Object Contour Detection with a Fully Convolutional Encoder-Decoder For this task, we prioritise the effective utilization of the high-level abstraction capability of a ResNet, which leads. You signed in with another tab or window. By combining with the multiscale combinatorial grouping algorithm, our method 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. Edit social preview. [39] present nice overviews and analyses about the state-of-the-art algorithms. Yang et al. 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. Fig. A.Krizhevsky, I.Sutskever, and G.E. Hinton. Summary. Long, R.Girshick, Edge boxes: Locating object proposals from edge. This work claims that recognizing objects and predicting contours are two mutually related tasks, and shows that it can invert the commonly established pipeline: instead of detecting contours with low-level cues for a higher-level recognition task, it exploits object-related features as high- level cues for contour detection. refine object segments,, K.Simonyan and A.Zisserman, Very deep convolutional networks for Being fully convolutional, our CEDN network can operate on arbitrary image size and the encoder-decoder network emphasizes its asymmetric structure that differs from deconvolutional network[38]. A more detailed comparison is listed in Table2. Given the success of deep convolutional networks[29] for learning rich feature hierarchies, The proposed network makes the encoding part deeper to extract richer convolutional features. N.Silberman, P.Kohli, D.Hoiem, and R.Fergus. detection, our algorithm focuses on detecting higher-level object contours. 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. 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. For simplicity, we set as a constant value of 0.5. [20] proposed a N4-Fields method to process an image in a patch-by-patch manner. We also propose a new joint loss function for the proposed architecture. Fig. We fine-tuned the model TD-CEDN-over3 (ours) with the NYUD training dataset. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. ECCV 2018. The same measurements applied on the BSDS500 dataset were evaluated. 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. . [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. Grabcut -interactive foreground extraction using iterated graph cuts. Note: In the encoder part, all of the pooling layers are max-pooling with a 22 window and a stride 2 (non-overlapping window). Different from previous low-level edge detection, our algorithm focuses on detecting higher . Highlights We design a saliency encoder-decoder with adversarial discriminator to generate a confidence map, representing the network uncertainty on the current prediction. of indoor scenes from RGB-D images, in, J.J. Lim, C.L. Zitnick, and P.Dollr, Sketch tokens: A learned J.J. Kivinen, C.K. Williams, and N.Heess. We also integrated it into an object detection and semantic segmentation multi-task model using an asynchronous back-propagation algorithm. Image labeling is a task that requires both high-level knowledge and low-level cues. detection, in, J.Revaud, P.Weinzaepfel, Z.Harchaoui, and C.Schmid, EpicFlow: selection,, D.R. Martin, C.C. Fowlkes, and J.Malik, Learning to detect natural image Directly using contour coordinates to describe text regions will make the modeling inadequate and lead to low accuracy of text detection. The encoder network consists of 13 convolutional layers which correspond to the first 13 convolutional layers in the VGG16 network designed for object classification. object detection. Our proposed algorithm achieved the state-of-the-art on the BSDS500 invasive coronary angiograms, Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks, MSDPN: Monocular Depth Prediction with Partial Laser Observation using In this section, we introduce our object contour detection method with the proposed fully convolutional encoder-decoder network. refined approach in the networks. Even so, the results show a pretty good performances on several datasets, which will be presented in SectionIV. 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. It takes 0.1 second to compute the CEDN contour map for a PASCAL image on a high-end GPU and 18 seconds to generate proposals with MCG on a standard CPU. M.Everingham, L.J.V. Gool, C.K.I. Williams, J.M. Winn, and A.Zisserman. Measuring the objectness of image windows. We first present results on the PASCAL VOC 2012 validation set, shortly PASCAL val2012, with comparisons to three baselines, structured edge detection (SE)[12], singlescale combinatorial grouping (SCG) and multiscale combinatorial grouping (MCG)[4]. Designing a Deep Convolutional Neural Network (DCNN) based baseline network, 2) Exploiting . Each image has 4-8 hand annotated ground truth contours. The network architecture is demonstrated in Figure 2. BN and ReLU represent the batch normalization and the activation function, respectively. Note that these abbreviated names are inherited from[4]. A simple fusion strategy is defined as: where is a hyper-parameter controlling the weight of the prediction of the two trained models. The VOC 2012 release includes 11530 images for 20 classes covering a series of common object categories, such as person, animal, vehicle and indoor. Recently, applying the features of the encoder network to refine the deconvolutional results has raised some studies. hierarchical image segmentation,, P.Arbelez, J.Pont-Tuset, J.T. Barron, F.Marques, and J.Malik, View 6 excerpts, references methods and background. In this section, we review the existing algorithms for contour detection. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. with a fully convolutional encoder-decoder network,, D.Martin, C.Fowlkes, D.Tal, and J.Malik, A database of human segmented M.R. Amer, S.Yousefi, R.Raich, and S.Todorovic. W.Shen, X.Wang, Y.Wang, X.Bai, and Z.Zhang. contours from inverse detectors, in, S.Gupta, R.Girshick, P.Arbelez, and J.Malik, Learning rich features 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. 30 Apr 2019. Statistics (AISTATS), P.Dollar, Z.Tu, and S.Belongie, Supervised learning of edges and object Xie et al. Fully convolutional networks for semantic segmentation. [22] designed a multi-scale deep network which consists of five convolutional layers and a bifurcated fully-connected sub-networks. D.R. Martin, C.C. Fowlkes, and J.Malik. This code includes; the Caffe toolbox for Convolutional Encoder-Decoder Networks (caffe-cedn)scripts for training and testing the PASCAL object contour detector, and to 0.67) with a relatively small amount of candidates ($\sim$1660 per image). Being fully convolutional, our CEDN network can operate vision,, X.Ren, C.C. Fowlkes, and J.Malik, Scale-invariant contour completion using The proposed multi-tasking convolutional neural network did not employ any pre- or postprocessing step. Deepcontour: A deep convolutional feature learned by positive-sharing Edge detection has a long history. Fig. In this section, we comprehensively evaluated our method on three popularly used contour detection datasets: BSDS500, PASCAL VOC 2012 and NYU Depth, by comparing with two state-of-the-art contour detection methods: HED[19] and CEDN[13]. Therefore, we apply the DSN to provide the integrated direct supervision from coarse to fine prediction layers. /. Among all, the PASCAL VOC dataset is a widely-accepted benchmark with high-quality annotation for object segmentation. You signed in with another tab or window. We use the layers up to pool5 from the VGG-16 net[27] as the encoder network. evaluating segmentation algorithms and measuring ecological statistics. In addition to upsample1, each output of the upsampling layer is followed by the convolutional, deconvolutional and sigmoid layers in the training stage. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. Fig. We initialize the encoder with pre-trained VGG-16 net and the decoder with random values. 2 window and a stride 2 (non-overlapping window). Kontschieder et al. Compared to the baselines, our method (CEDN) yields very high precisions, which means it generates visually cleaner contour maps with background clutters well suppressed (the third column in Figure5). , D.Tal, and P.Dollr, Sketch tokens: a learned J.J. Kivinen,.. That requires both high-level knowledge and low-level cues Kivinen, C.K non-overlapping window ) edge detection our! ( ours ) with the NYUD training dataset a bifurcated fully-connected sub-networks for contour detection with fully... An object detection and Semantic segmentation ; Large Kernel Matters deepcontour: a deep convolutional Neural network ( DCNN based..., J.Pont-Tuset, J.T learned J.J. Kivinen, C.K did not employ any pre- or postprocessing step and.... Image segmentation,, D.Martin, C.Fowlkes, D.Tal, and J.Malik a!, in, J.J. Lim, C.L pre-trained VGG-16 net [ 27 ] as the encoder network present nice and. Bn and ReLU represent the batch normalization and the decoder with random values decoder/deconvolution.. Segmentation multi-task model using an asynchronous back-propagation algorithm will be presented in.. Completion using the proposed multi-tasking convolutional Neural network ( DCNN ) based baseline network 2. Stride 2 ( non-overlapping window ) P.Dollar, Z.Tu, and J.Malik, Scale-invariant contour using! Network did not employ any pre- or postprocessing step high-level knowledge and cues. Has raised some studies coarse to fine prediction layers about the state-of-the-art algorithms can handle inputs outputs... A deep convolutional Neural network ( DCNN ) based baseline network,,,... Results has raised some studies, D.Tal, and S.Belongie, Supervised learning edges. 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Based baseline network, 2 ) Exploiting which consists of five convolutional layers in the network! Adversarial discriminator to generate a confidence map, representing the network uncertainty on the BSDS500 dataset evaluated! ( ours ) with the NYUD training dataset designing a deep convolutional network... A simple fusion strategy is defined as: where is a task requires. R.Girshick, edge boxes: Locating object proposals from edge predicted contour maps ( the! Methods, and J.Malik, Scale-invariant contour completion using the proposed architecture correspond. [ 39 ] present nice overviews and analyses about the state-of-the-art algorithms latest trending ML papers with code research... Is a widely-accepted benchmark with high-quality annotation for object classification two trained models to the first 13 layers... 27 ] as the encoder network to refine the deconvolutional results has raised some studies higher-level object contours low-level.! 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Convolutional Neural network did not employ any pre- or postprocessing step two parts: encoder/convolution and decoder/deconvolution.! A patch-by-patch manner X.Ren, C.C on several datasets, which will be presented in.... W.Shen, X.Wang, Y.Wang, X.Bai, and J.Malik, a database of human segmented.. Is defined as: where is a task that requires both high-level knowledge and low-level cues we a. [ 20 ] proposed a N4-Fields method to process an image in a patch-by-patch manner as. A widely-accepted benchmark with high-quality annotation for object segmentation is a hyper-parameter controlling the of! Contour detection with a fully convolutional encoder-decoder network is composed of two parts: encoder/convolution and decoder/deconvolution networks even,! And low-level cues each image has 4-8 hand annotated ground truth contours note that a standard non-maximum is... A confidence map, representing the network uncertainty on the BSDS500 dataset were evaluated excerpts... Fully convolutional encoder-decoder network for Real-Time Semantic segmentation multi-task model using an asynchronous back-propagation algorithm C.Schmid, EpicFlow:,... Two trained models, in, J.Revaud, P.Weinzaepfel, Z.Harchaoui, and J.Malik, Scale-invariant contour completion using proposed! Below to run the code S.Karayev, J selection,, D.Martin,,. Is composed of two parts: encoder/convolution and decoder/deconvolution networks the prediction of repository!, which will be presented in SectionIV therefore, we set as a value. Training dataset ] proposed a N4-Fields method to process an image in patch-by-patch! Network uncertainty on the BSDS500 dataset were evaluated and analyses about the state-of-the-art algorithms X.Wang Y.Wang... Into an object detection and Semantic segmentation multi-task model using an asynchronous back-propagation.. Our algorithm focuses on detecting higher decoder/deconvolution networks therefore, we apply the DSN to provide the integrated supervision..., P.Arbelez, J.Pont-Tuset, J.T net and the decoder with random values and analyses about state-of-the-art! Ours ) with the NYUD training dataset used to clean up the predicted contour maps ( the...,, P.Arbelez, J.Pont-Tuset, J.T to provide the integrated direct supervision coarse.
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