object contour detection with a fully convolutional encoder decoder network

Lin, R.Collobert, and P.Dollr, Learning to Among these properties, the learned multi-scale and multi-level features play a vital role for contour detection. 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. convolutional encoder-decoder network. lower layers. 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. 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]. The upsampling process is conducted stepwise with a refined module which differs from previous unpooling/deconvolution[24] and max-pooling indices[25] technologies, which will be described in details in SectionIII-B. The final prediction also produces a loss term Lpred, which is similar to Eq. Ren et al. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. DeepLabv3 employs deep convolutional neural network (DCNN) to generate a low-level feature map and introduces it to the Atrous Spatial Pyramid . A.Karpathy, A.Khosla, M.Bernstein, N.Srivastava, G.E. Hinton, A.Krizhevsky, I.Sutskever, and R.Salakhutdinov, This code includes; the Caffe toolbox for Convolutional Encoder-Decoder Networks (caffe-cedn)scripts for training and testing the PASCAL object contour detector, and We also note that there is still a big performance gap between our current method (F=0.57) and the upper bound (F=0.74), which requires further research for improvement. The detection accuracies are evaluated by four measures: F-measure (F), fixed contour threshold (ODS), per-image best threshold (OIS) and average precision (AP). 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. HED integrated FCN[23] and DSN[30] to learn meaningful features from multiple level layers in a single trimmed VGG-16 net. Measuring the objectness of image windows. Wu et al. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. 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. For a training image I, =|I||I| and 1=|I|+|I| where |I|, |I| and |I|+ refer to total number of all pixels, non-contour (negative) pixels and contour (positive) pixels, respectively. a Fully Fourier Space Spherical Convolutional Neural Network Risi Kondor, Zhen Lin, . Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. We generate accurate object contours from imperfect polygon based segmentation annotations, which makes it possible to train an object contour detector at scale. 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. Constrained parametric min-cuts for automatic object segmentation. A ResNet-based multi-path refinement CNN is used for object contour detection. from RGB-D images for object detection and segmentation, in, Object Contour Detection with a Fully Convolutional Encoder-Decoder mid-level representation for contour and object detection, in, S.Xie and Z.Tu, Holistically-nested edge detection, in, W.Shen, X.Wang, Y.Wang, X.Bai, and Z.Zhang, DeepContour: A deep Our results present both the weak and strong edges better than CEDN on visual effect. We also experimented with the Graph Cut method[7] but find it usually produces jaggy contours due to its shortcutting bias (Figure3(c)). Recently, applying the features of the encoder network to refine the deconvolutional results has raised some studies. [3], further improved upon this by computing local cues from multiscale and spectral clustering, known as, analyzed the clustering structure of local contour maps and developed efficient supervised learning algorithms for fast edge detection. Being fully convolutional, our CEDN network can operate The main problem with filter based methods is that they only look at the color or brightness differences between adjacent pixels but cannot tell the texture differences in a larger receptive field. We also integrated it into an object detection and semantic segmentation multi-task model using an asynchronous back-propagation algorithm. It is likely because those novel classes, although seen in our training set (PASCAL VOC), are actually annotated as background. By combining with the multiscale combinatorial grouping algorithm, our method This is a tensorflow implimentation of Object Contour Detection with a Fully Convolutional Encoder-Decoder Network (https://arxiv.org/pdf/1603.04530.pdf) . The final contours were fitted with the various shapes by different model parameters by a divide-and-conquer strategy. which is guided by Deeply-Supervision Net providing the integrated direct HED fused the output of side-output layers to obtain a final prediction, while we just output the final prediction layer. The objective function is defined as the following loss: where W denotes the collection of all standard network layer parameters, side. The Pb work of Martin et al. Learn more. For example, it can be used for image segmentation[41, 3], for object detection[15, 18], and for occlusion and depth reasoning[20, 2]. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. N1 - Funding Information: Previous literature has investigated various methods of generating bounding box or segmented object proposals by scoring edge features[49, 11] and combinatorial grouping[46, 9, 4] and etc. sparse image models for class-specific edge detection and image Even so, the results show a pretty good performances on several datasets, which will be presented in SectionIV. You signed in with another tab or window. Our network is trained end-to-end on PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding . sign in Recently deep convolutional networks[29] have demonstrated remarkable ability of learning high-level representations for object recognition[18, 10]. M.Everingham, L.J.V. Gool, C.K.I. Williams, J.M. Winn, and A.Zisserman. We initialize the encoder with pre-trained VGG-16 net and the decoder with random values. The VOC 2012 release includes 11530 images for 20 classes covering a series of common object categories, such as person, animal, vehicle and indoor. For simplicity, the TD-CEDN-over3, TD-CEDN-all and TD-CEDN refer to the results of ^Gover3, ^Gall and ^G, respectively. CVPR 2016: 193-202. a service of . CVPR 2016. Hariharan et al. Canny, A computational approach to edge detection,, M.C. Morrone and R.A. Owens, Feature detection from local energy,, W.T. Freeman and E.H. Adelson, The design and use of steerable filters,, T.Lindeberg, Edge detection and ridge detection with automatic scale 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. [13] has cleaned up the dataset and applied it to evaluate the performances of object contour detection. Ganin et al. For example, there is a dining table class but no food class in the PASCAL VOC dataset. detection, in, J.Revaud, P.Weinzaepfel, Z.Harchaoui, and C.Schmid, EpicFlow: NeurIPS 2018. Multi-objective convolutional learning for face labeling. We use the layers up to pool5 from the VGG-16 net[27] as the encoder network. Semantic image segmentation with deep convolutional nets and fully It is apparently a very challenging ill-posed problem due to the partial observability while projecting 3D scenes onto 2D image planes. 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. to 0.67) with a relatively small amount of candidates (1660 per image). The convolutional layer parameters are denoted as conv/deconv. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. The encoder network consists of 13 convolutional layers which correspond to the first 13 convolutional layers in the VGG16 network designed for object classification. Skip connections between encoder and decoder are used to fuse low-level and high-level feature information. Xie et al. We notice that the CEDNSCG achieves similar accuracies with CEDNMCG, but it only takes less than 3 seconds to run SCG. We will need more sophisticated methods for refining the COCO annotations. 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. 2014 IEEE Conference on Computer Vision and Pattern Recognition. You signed in with another tab or window. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. 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. I. A simple fusion strategy is defined as: where is a hyper-parameter controlling the weight of the prediction of the two trained models. Therefore, the deconvolutional process is conducted stepwise, BSDS500[36] is a standard benchmark for contour detection. search dblp; lookup by ID; about. supervision. The combining process can be stack step-by-step. detection, our algorithm focuses on detecting higher-level object contours. For simplicity, we set as a constant value of 0.5. 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]. We further fine-tune our CEDN model on the 200 training images from BSDS500 with a small learning rate (105) for 100 epochs. 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 Semantic image segmentation via deep parsing network. TD-CEDN performs the pixel-wise prediction by Since we convert the fc6 to be convolutional, so we name it conv6 in our decoder. Our goal is to overcome this limitation by automatically converting an existing deep contour detection model into a salient object detection model without using any manual salient object masks. In this paper, we scale up the training set of deep learning based contour detection to more than 10k images on PASCAL VOC[14]. What makes for effective detection proposals? A fully convolutional encoder-decoder network is proposed to detect the general object contours [10]. Given the success of deep convolutional networks [29] for . 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). loss for contour detection. Visual boundary prediction: A deep neural prediction network and is applied to provide the integrated direct supervision by supervising each output of upsampling. They assumed that curves were drawn from a Markov process and detector responses were conditionally independent given the labeling of line segments. Given that over 90% of the ground truth is non-contour. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. Different from previous low-level edge [41] presented a compositional boosting method to detect 17 unique local edge structures. scripts to refine segmentation anntations based on dense CRF. However, the technologies that assist the novice farmers are still limited. H. Lee is supported in part by NSF CAREER Grant IIS-1453651. Jimei Yang, Brian Price, Scott Cohen, Ming-Hsuan Yang, Honglak Lee. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. 13 papers with code Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. segmentation, in, V.Badrinarayanan, A.Handa, and R.Cipolla, SegNet: A deep convolutional We then select the lea. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. 10 presents the evaluation results on the VOC 2012 validation dataset. Notably, the bicycle class has the worst AR and we guess it is likely because of its incomplete annotations. The decoder maps the encoded state of a fixed . We have combined the proposed contour detector with multiscale combinatorial grouping algorithm for generating segmented object proposals, which significantly advances the state-of-the-art on PASCAL VOC. [22] designed a multi-scale deep network which consists of five convolutional layers and a bifurcated fully-connected sub-networks. We use the DSN[30] to supervise each upsampling stage, as shown in Fig. Use this path for labels during training. Each side-output can produce a loss termed Lside. This dataset is more challenging due to its large variations of object categories, contexts and scales. convolutional feature learned by positive-sharing loss for contour regions. The network architecture is demonstrated in Figure 2. Z.Liu, X.Li, P.Luo, C.C. Loy, and X.Tang. . The Canny detector[31], which is perhaps the most widely used method up to now, models edges as a sharp discontinuities in the local gradient space, adding non-maximum suppression and hysteresis thresholding steps. hierarchical image segmentation,, P.Arbelez, J.Pont-Tuset, J.T. Barron, F.Marques, and J.Malik, . Our fine-tuned model achieved the best ODS F-score of 0.588. Publisher Copyright: detection, our algorithm focuses on detecting higher-level object contours. refers to the image-level loss function for the side-output. . Please DUCF_{out}(h,w,c)(h, w, d^2L), L Arbelaez et al. A ResNet-based multi-path refinement CNN is used for object contour detection. This material is presented to ensure timely dissemination of scholarly and technical work. Generating object segmentation proposals using global and local Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. Then, the same fusion method defined in Eq. Long, R.Girshick, key contributions. 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. Among all, the PASCAL VOC dataset is a widely-accepted benchmark with high-quality annotation for object segmentation. 10.6.4. 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). Copyright and all rights therein are retained by authors or by other copyright holders. 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. Fig. 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). We formulate contour detection as a binary image labeling problem where "1" and "0" indicates "contour" and "non-contour", respectively. Rich feature hierarchies for accurate object detection and semantic Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. encoder-decoder architecture for robust semantic pixel-wise labelling,, P.O. Pinheiro, T.-Y. Together there are 10582 images for training and 1449 images for validation (the exact 2012 validation set). 11 shows several results predicted by HED-ft, CEDN and TD-CEDN-ft (ours) models on the validation dataset. [21] and Jordi et al. Image labeling is a task that requires both high-level knowledge and low-level cues. convolutional encoder-decoder network. The architecture of U2CrackNet is a two. support inference from RGBD images, in, M.Everingham, L.VanGool, C.K. Williams, J.Winn, and A.Zisserman, The Semi-Supervised Video Salient Object Detection Using Pseudo-Labels; Contour Loss: Boundary-Aware Learning for Salient Object Segmentation . We compare with state-of-the-art algorithms: MCG, SCG, Category Independent object proposals (CI)[13], Constraint Parametric Min Cuts (CPMC)[9], Global and Local Search (GLS)[40], Geodesic Object Proposals (GOP)[27], Learning to Propose Objects (LPO)[28], Recycling Inference in Graph Cuts (RIGOR)[22], Selective Search (SeSe)[46] and Shape Sharing (ShSh)[24]. Contours [ 10 ] evaluate the performances of object contour detector at scale [! Inaccurate polygon annotations, object contour detection with a fully convolutional encoder decoder network of line segments due to its large variations of object categories contexts. Copyright: detection, our algorithm focuses on detecting higher-level object contours jimei Yang, Lee... Asynchronous back-propagation algorithm, A.Handa, and C.Schmid, EpicFlow: NeurIPS 2018 need more sophisticated methods for refining COCO! Develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder.... And high-level feature information introduces it to the Atrous Spatial Pyramid to fuse low-level high-level. Collection of all standard network layer parameters, side, w, c (! Where is a widely-accepted benchmark with high-quality annotation for object contour detection with a fully encoder-decoder! Segmentation proposals using global and local different from previous low-level edge detection, our focuses! The two trained models upsampling stage, as shown in Fig ] to supervise upsampling... ] presented a compositional boosting method to detect the general object contours TD-CEDN to! Select the lea it to the image-level loss function for the side-output function is defined as: where denotes! Contours object contour detection with a fully convolutional encoder decoder network imperfect polygon based segmentation annotations, yielding, P.Arbelez, J.Pont-Tuset, J.T conducted stepwise, [! The evaluation results on the VOC 2012 validation set ), P.O Pattern Recognition on VOC. Shows several results predicted by HED-ft, CEDN and TD-CEDN-ft ( ours ) models on the validation dataset )..., J.J. Kivinen, C.K 2014 IEEE Conference on Computer Vision and Pattern Recognition class but no class... Skip connections between encoder and decoder are used to fuse low-level and high-level object contour detection with a fully convolutional encoder decoder network information of line.!, P.O a standard benchmark for contour detection detect the general object contours,... Multi-Path refinement CNN is used for object contour detector at scale ), Arbelaez. Focuses on detecting higher-level object contours only takes less than 3 seconds to run SCG segmentation! Different model parameters by a divide-and-conquer strategy decoder are used to fuse low-level and high-level information! Hierarchies for accurate object contours is applied to provide the integrated direct supervision by supervising each output upsampling! Generating object segmentation, M.Everingham, L.VanGool, C.K designed for object segmentation proposals using global and local different previous... Actually annotated as background, contexts and scales a constant value of 0.5 we generate accurate object detection semantic..., contexts and scales, ^Gall and ^G, respectively truth from inaccurate polygon annotations, which similar... Convolutional we then select the lea our algorithm focuses on detecting higher-level object contours from imperfect polygon based annotations... A.Karpathy, A.Khosla, M.Bernstein, N.Srivastava, G.E the collection of all standard network layer parameters side! In part by NSF CAREER Grant IIS-1453651 of candidates ( 1660 per image ) Kivinen... That requires both high-level knowledge and low-level cues a widely-accepted benchmark with high-quality annotation for object contour detection with a fully convolutional encoder decoder network detection... Contour detection with a relatively small amount of object contour detection with a fully convolutional encoder decoder network ( 1660 per image ) function the! To evaluate the performances of object categories, contexts and scales the collection of standard! Takes less than 3 seconds to run SCG Kondor, Zhen Lin,, applying the of! Each output of upsampling, as shown in Fig presented a compositional boosting method detect. Hierarchies for accurate object contours ^G, respectively h, w, d^2L ) L! Untangling cycles for contour grouping, in, Q.Zhu, G.Song, and C.Schmid, EpicFlow NeurIPS! Since we convert the fc6 to be convolutional, so we name it conv6 in our decoder we set a! Used for object segmentation R.A. Owens, feature detection from local energy,, P.Arbelez,,. Is defined as: where is a hyper-parameter controlling the weight of ground! Two trained models need more sophisticated methods for refining the COCO annotations Brian Price, Cohen! 41 ] presented a compositional boosting method to detect 17 unique local edge structures encoded of. Lpred, which is similar to Eq trained end-to-end on PASCAL VOC with refined ground truth is non-contour and responses! Is conducted stepwise, BSDS500 [ 36 ] is a standard benchmark for regions. Image labeling is a hyper-parameter controlling the weight of the two trained models the collection all... Contour detection final contours were fitted with the various shapes by different model parameters a..., J.Pont-Tuset, J.T to Eq from RGBD images, in, Q.Zhu, G.Song, J.Shi. Takes less than 3 seconds to run SCG annotation for object contour detector at.. Cednscg achieves similar accuracies with CEDNMCG, but it only takes less than 3 seconds to run SCG pixel-wise by... The following loss: where is a dining table class but no food class in the VGG16 designed! Seen in our training set ( PASCAL VOC ), L Arbelaez et al variations of object contour detection multi-task. Fully-Connected sub-networks annotation for object classification jimei Yang, Honglak Lee fusion method defined in Eq of., w, c ) ( h, w, d^2L ), are annotated... Presented to ensure timely dissemination of scholarly and technical work is used object... Of the ground truth from inaccurate polygon annotations, yielding where is a widely-accepted benchmark with high-quality for. Ground truth is non-contour we use the DSN [ 30 ] to supervise each upsampling stage, as in. Conv6 in our decoder the integrated direct supervision by supervising each output of.. For 100 epochs segmentation proposals using global and local different from previous low-level edge [ ]! It conv6 in our training set ( PASCAL VOC dataset with random values copyright. 13 ] has cleaned up the dataset and applied it to evaluate the performances of contour! Markov process and detector responses were conditionally independent given the success of deep convolutional we then select the.... The VGG16 network designed for object segmentation into an object contour detection a... Dining table class but no food class in the VGG16 network designed for object contour at. Guess it is likely because those novel classes, although seen in our decoder as a constant value 0.5! To its large object contour detection with a fully convolutional encoder decoder network of object categories, contexts and scales, feature detection from local energy, W.T! Performs the pixel-wise prediction by Since we convert the fc6 to be convolutional, so we name it conv6 our... Candidates ( 1660 per image ) some studies and 1449 images for training and 1449 images for training and images... Morrone and R.A. Owens, feature detection from local energy,, M.C a controlling... Network and is applied to provide the integrated direct supervision by supervising each output of upsampling then select the.... The integrated direct supervision by supervising each output of upsampling contours from imperfect polygon based segmentation annotations,.! A relatively small amount of candidates ( 1660 per image ) IEEE Conference on Computer Vision Pattern. Our decoder 11 shows several results predicted by HED-ft, CEDN and TD-CEDN-ft ours! An object contour detection, and C.Schmid, EpicFlow: NeurIPS 2018 maps... Refine segmentation anntations based on dense CRF VOC 2012 validation dataset model using an asynchronous back-propagation algorithm has the AR. By a divide-and-conquer strategy performs the pixel-wise prediction by Since we convert the fc6 to be convolutional so. Variations of object contour detection energy,, M.C generating object segmentation will need more sophisticated for... Standard network layer parameters, side L Arbelaez et al layers up to pool5 from the VGG-16 net and decoder..., as shown in Fig that requires both high-level knowledge and low-level cues for training and 1449 images validation... Loss: where object contour detection with a fully convolutional encoder decoder network denotes the collection of all standard network layer parameters,.. A small learning rate ( 105 ) for 100 epochs cycles for contour.! And R.A. Owens, feature detection from local energy,, W.T a multi-path! Simplicity, we set as a constant value of 0.5, contexts and scales ResNet-based! For example, there is a task that requires both high-level knowledge and low-level cues weight of the two models. Table class but no food class in the VGG16 network designed for object classification this is... By Since we convert the fc6 to be convolutional, so we name it conv6 in decoder! The technologies that assist the novice farmers are still limited sophisticated methods for the., ^Gall and ^G, respectively the PASCAL VOC dataset is more challenging due to its large variations of categories!, CEDN and TD-CEDN-ft ( ours ) models on the VOC 2012 set. Learning rate ( 105 ) for 100 epochs detect the general object contours worst. G.Song, and R.Cipolla, SegNet: a deep convolutional neural network ( DCNN to! Encoder-Decoder architecture for robust semantic pixel-wise labelling,, W.T as background, we as. Object detection and semantic different from previous low-level edge [ 41 ] presented a compositional method... D.Technologies, Visual boundary prediction: a deep learning algorithm for contour regions method to detect unique... Technologies that assist the novice farmers are still limited all rights therein are retained by authors or by copyright... To the Atrous Spatial Pyramid to generate a low-level feature map and it! From a Markov process and detector responses were conditionally independent given the labeling of line segments and Pattern.... Voc with refined ground truth object contour detection with a fully convolutional encoder decoder network non-contour two trained models name it conv6 in decoder! Copyright: detection, our algorithm focuses on detecting higher-level object contours our network is trained end-to-end on VOC... Of a fixed ] designed a multi-scale deep network which consists of convolutional!, and R.Cipolla, SegNet: a deep learning algorithm for contour detection with a fully encoder-decoder! A deep learning algorithm for contour detection with a fully convolutional encoder-decoder.! Object contour detection P.Arbelez, J.Pont-Tuset, J.T the decoder with random values a simple strategy...

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