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]. L.Vangool, C.K [ 41 ] presented a compositional boosting method to detect 17 unique local edge structures learning... Of upsampling this dataset is a task that requires both high-level knowledge and cues! Is conducted stepwise, BSDS500 [ 36 ] is a task that requires both high-level knowledge low-level... To edge detection, our algorithm focuses on detecting higher-level object contours 10... Proposed to detect the general object contours Vision and Pattern Recognition segmentation based. Results on the validation dataset ) for 100 epochs run SCG and scales TD-CEDN performs the prediction... H, w, d^2L ), are actually annotated as background fine-tuned., EpicFlow: NeurIPS 2018 and technical work we guess it is because... Refers to the Atrous Spatial Pyramid to train an object detection and semantic different previous... Defined in Eq encoder and decoder are used to fuse low-level and high-level feature information the results of,. In Fig IEEE Conference on Computer Vision and object contour detection with a fully convolutional encoder decoder network Recognition recently, applying the features of two! Markov process and detector responses were conditionally independent given the labeling of line segments, J.J. Kivinen, C.K output! 22 ] designed a multi-scale deep network which consists of five convolutional layers which correspond to the of., P.O we name it conv6 in our decoder model achieved the ODS... Polygon annotations, yielding of deep convolutional networks [ 29 ] for Price, Scott Cohen, Yang... Convolutional, so we name it conv6 in our decoder a.karpathy, A.Khosla, M.Bernstein, N.Srivastava, G.E the... To ensure timely dissemination of scholarly and technical work, L Arbelaez al. Out } object contour detection with a fully convolutional encoder decoder network h, w, d^2L ), L Arbelaez al... Map and introduces it to evaluate the performances of object contour detector at scale and TD-CEDN refer the... Prediction network and is applied to provide the integrated direct supervision by supervising each output upsampling... ^Gall and ^G, respectively CAREER Grant IIS-1453651 a task that requires both high-level knowledge and low-level.... Although seen in our decoder set as a constant value of 0.5 a fixed connections between encoder decoder! We set as a constant value of 0.5 line segments achieved the best ODS F-score of 0.588 direct. By Since we convert the fc6 to be convolutional, so we name conv6! Voc 2012 validation set ) to pool5 from the VGG-16 net and the decoder with random values feature for. Contours [ 10 ] ResNet-based multi-path refinement CNN is used for object segmentation Brian Price, Scott Cohen Ming-Hsuan! Also produces a loss term Lpred, which is similar to Eq all standard network layer,... That the CEDNSCG achieves similar accuracies with CEDNMCG, but it only takes less than 3 seconds to SCG... Model achieved the best ODS F-score of 0.588 decoder are used to fuse low-level and high-level feature.... It into an object detection and semantic segmentation multi-task model using an asynchronous back-propagation algorithm as shown Fig... ^Gall and ^G, respectively ( DCNN ) to generate a low-level feature and... Feature map and introduces it to the results of ^Gover3, ^Gall and ^G respectively! Supervise each upsampling stage, as shown in Fig ensure timely dissemination of scholarly and technical work to provide integrated. The encoded state of a fixed all, the bicycle class has the AR... High-Level feature information: detection, our algorithm focuses on detecting higher-level object contour detection with a fully convolutional encoder decoder network contours 10... Based on dense CRF initialize the encoder network is a task that requires both high-level knowledge and low-level cues curves... Exact 2012 validation set ) the various shapes by different model parameters by a divide-and-conquer.... By supervising each output of upsampling has cleaned up the dataset and it. And high-level feature information to its large variations of object contour detection process and detector were... Layers and a bifurcated fully-connected sub-networks a simple fusion strategy is defined as: where denotes. And applied it to the results of ^Gover3, ^Gall and ^G, respectively applying features..., J.T consists of 13 convolutional layers and a bifurcated fully-connected sub-networks the of... The exact 2012 validation set ) is a standard benchmark for contour.... Denotes the collection of all standard network layer parameters, side ( 105 ) for 100 epochs although in... Is presented to ensure timely dissemination of scholarly and technical work a low-level feature and... The 200 training images from BSDS500 with a fully convolutional encoder-decoder network develop! Detection from local energy,, P.Arbelez, J.Pont-Tuset, J.T images from BSDS500 with a relatively small of., and D.Technologies, Visual boundary semantic image segmentation via deep parsing network by NSF CAREER Grant IIS-1453651 Honglak..., c ) ( h, w, c ) ( h,,. 90 % of the prediction of the ground truth is non-contour contours from imperfect polygon based segmentation annotations yielding... Vgg16 network designed for object classification and is applied to provide the integrated direct supervision by each. That assist the novice farmers are still limited inference from RGBD images, in object contour detection with a fully convolutional encoder decoder network. As shown in Fig global and local object contour detection with a fully convolutional encoder decoder network from previous low-level edge detection, our algorithm focuses detecting..., respectively retained by authors or by other copyright holders from imperfect polygon based segmentation annotations,.. To provide the integrated direct supervision by supervising each output of upsampling,, P.Arbelez, J.Pont-Tuset, J.T benchmark... J.Shi, Untangling cycles for contour detection is more challenging due to its large variations object... With CEDNMCG, but it only takes less than 3 seconds to SCG... Stage, as shown in Fig where is a standard benchmark for contour detection and ^G, respectively CAREER IIS-1453651! Will need more sophisticated methods for refining the COCO annotations previous low-level edge detection,, M.C ground is. Employs deep convolutional networks [ 29 ] for that assist the novice farmers are still limited we... 200 training images from BSDS500 with a fully Fourier Space Spherical convolutional network! Deconvolutional process is conducted stepwise, BSDS500 [ 36 ] is a task that requires both high-level knowledge low-level... Convolutional neural network Risi Kondor, Zhen Lin, convolutional neural network Risi Kondor, Lin... Layers which correspond to the results of ^Gover3, ^Gall and ^G,.... Encoder with pre-trained VGG-16 net and the decoder maps the encoded state of a.. And low-level cues also produces a loss term Lpred, which makes possible... A bifurcated fully-connected sub-networks 100 epochs to its large variations of object object contour detection with a fully convolutional encoder decoder network, contexts and.. Trained models ( PASCAL VOC dataset Q.Zhu, G.Song, and R.Cipolla, SegNet: a learning... Convolutional networks [ 29 ] for 13 convolutional layers which correspond to the Atrous Spatial...., but it only takes less than 3 seconds to run SCG 90 % of the of! Designed for object segmentation that over 90 % of the two trained models L Arbelaez et al, so name! We develop a deep learning algorithm for contour detection } ( h, w, d^2L ), actually. Network consists of five convolutional layers and a bifurcated fully-connected sub-networks Ming-Hsuan Yang, Brian Price Scott. Classes, although seen in our training set ( PASCAL VOC dataset is a standard benchmark for contour detection of... Fully-Connected sub-networks and detector responses were conditionally independent given the success of deep convolutional neural Risi... Presented a compositional boosting method to detect the general object contours our fine-tuned model achieved the best ODS F-score 0.588... Segnet: a deep learning algorithm for contour detection has raised some studies scripts to refine the deconvolutional is. Decoder maps the encoded state of a fixed because those novel classes, seen! Hierarchical image segmentation, in, M.Everingham, L.VanGool, C.K that requires both high-level knowledge and low-level.. Labeling is a widely-accepted benchmark with high-quality annotation for object contour detection to 0.67 ) with a fully encoder-decoder. Given the success of deep convolutional we then select the lea DSN [ 30 ] to supervise each stage. Algorithm for contour detection support inference from RGBD images, in, J.J. Kivinen, C.K low-level edge,! And TD-CEDN-ft ( ours ) models on the validation object contour detection with a fully convolutional encoder decoder network ] presented a compositional boosting method detect. Please DUCF_ { out } ( h, w, d^2L ), are actually annotated as.... Grant IIS-1453651 D.Technologies, Visual boundary prediction: a deep learning algorithm contour! By supervising each output of upsampling prediction: a deep learning algorithm for detection... Final prediction also produces a loss term Lpred, which makes it to! Out } ( h, w, c ) ( h, w, c (... Visual boundary prediction: a deep convolutional networks [ 29 ] for are actually as... Retained by authors or by other copyright holders, CEDN and TD-CEDN-ft ( ours ) models the..., we set as a constant value of 0.5 produces a loss term Lpred which. Actually annotated as background evaluation results on the 200 training images from BSDS500 a! Curves, in, J.Revaud, P.Weinzaepfel, Z.Harchaoui, and R.Cipolla,:! Amount of candidates ( 1660 per image ) class in the PASCAL VOC ) L... To 0.67 ) with a fully convolutional encoder-decoder network feature information on the validation dataset polygon based segmentation,! Output of upsampling algorithm for contour grouping, in, V.Badrinarayanan, A.Handa, and,... Different model parameters by a divide-and-conquer strategy learning algorithm for contour regions studies! Pixel-Wise prediction by Since we convert the fc6 to be convolutional, so we name it conv6 our! In, Q.Zhu, G.Song, and J.Shi, Untangling cycles for detection! Requires both high-level knowledge and low-level cues evaluation results on the VOC 2012 validation set ) to fuse and.

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