We formulate contour detection as a binary image labeling problem where "1" and "0" indicates "contour" and "non-contour", respectively. Zhu et al. A tag already exists with the provided branch name. advance the state-of-the-art on PASCAL VOC (improving average recall from 0.62 Are you sure you want to create this branch? All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. For example, there is a dining table class but no food class in the PASCAL VOC dataset. 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. The model differs from the . We consider contour alignment as a multi-class labeling problem and introduce a dense CRF model[26] where every instance (or background) is assigned with one unique label. 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. This work brings together methods from DCNNs and probabilistic graphical models for addressing the task of pixel-level classification by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF). 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. In the future, we will explore to find an efficient fusion strategy to deal with the multi-annotation issues, such as BSDS500. We report the AR and ABO results in Figure11. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. In the work of Xie et al. Proceedings of the IEEE Contour detection and hierarchical image segmentation. Semantic contours from inverse detectors. We use the layers up to fc6 from VGG-16 net[45] as our encoder. Long, R.Girshick, S.Liu, J.Yang, C.Huang, and M.-H. Yang. 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. B.C. Russell, A.Torralba, K.P. Murphy, and W.T. Freeman. 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. Note that our model is not deliberately designed for natural edge detection on BSDS500, and we believe that the techniques used in HED[47] such as multiscale fusion, carefully designed upsampling layers and data augmentation could further improve the performance of our model. The decoder part can be regarded as a mirrored version of the encoder network. Papers With Code is a free resource with all data licensed under. 0 benchmarks Traditional image-to-image models only consider the loss between prediction and ground truth, neglecting the similarity between the data distribution of the outcomes and ground truth. 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 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]. PASCAL visual object classes (VOC) challenge,, S.Gupta, P.Arbelaez, and J.Malik, Perceptual organization and recognition We also found that the proposed model generalizes well to unseen object classes from the known super-categories and demonstrated competitive performance on MS COCO without re-training the network. According to the results, the performances show a big difference with these two training strategies. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. Learn more. We generate accurate object contours from imperfect polygon based segmentation annotations, which makes it possible to train an object contour detector at scale. Especially, the establishment of a few standard benchmarks, BSDS500[14], NYUDv2[15] and PASCAL VOC[16], provides a critical baseline to evaluate the performance of each algorithm. An input patch was first passed through a pretrained CNN and then the output features were mapped to an annotation edge map using the nearest-neighbor search. 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. selection,, D.R. Martin, C.C. Fowlkes, and J.Malik, Learning to detect natural image In this section, we review the existing algorithms for contour detection. Constrained parametric min-cuts for automatic object segmentation. Precision-recall curves are shown in Figure4. Our fine-tuned model achieved the best ODS F-score of 0.588. Fully convolutional networks for semantic segmentation. J. Yang and M.-H. Yang are supported in part by NSF CAREER Grant #1149783, NSF IIS Grant #1152576, and a gift from Adobe. 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. 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. 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. Operation-level vision-based monitoring and documentation has drawn significant attention from construction practitioners and researchers. search. We believe the features channels of our decoder are still redundant for binary labeling addressed here and thus also add a dropout layer after each relu layer. Sobel[16] and Canny[8]. In addition to upsample1, each output of the upsampling layer is followed by the convolutional, deconvolutional and sigmoid layers in the training stage. Microsoft COCO: Common objects in context. It makes sense that precisely extracting edges/contours from natural images involves visual perception of various levels[11, 12], which makes it to be a challenging problem. Different from previous low-level edge Text regions in natural scenes have complex and variable shapes. They assumed that curves were drawn from a Markov process and detector responses were conditionally independent given the labeling of line segments. During training, we fix the encoder parameters (VGG-16) and only optimize decoder parameters. We find that the learned model . For example, it can be used for image segmentation[41, 3], for object detection[15, 18], and for occlusion and depth reasoning[20, 2]. Despite their encouraging findings, it remains a major challenge to exploit technologies in real . NeurIPS 2018. LabelMe: a database and web-based tool for image annotation. It indicates that multi-scale and multi-level features improve the capacities of the detectors. convolutional encoder-decoder network. a Fully Fourier Space Spherical Convolutional Neural Network Risi Kondor, Zhen Lin, . The convolutional layer parameters are denoted as conv/deconvstage_index-receptive field size-number of channels. conditional random fields, in, P.Felzenszwalb and D.McAllester, A min-cover approach for finding salient Our network is trained end-to-end on PASCAL VOC with refined ground truth from inaccurate polygon annotations . Inspired by the success of fully convolutional networks [36] and deconvolu-tional networks [40] on semantic segmentation, we develop a fully convolutional encoder-decoder network (CEDN). [39] present nice overviews and analyses about the state-of-the-art algorithms. When the trained model is sensitive to both the weak and strong contours, it shows an inverted results. 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 . Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. to 0.67) with a relatively small amount of candidates (1660 per image). Abstract In this paper, we propose a novel semi-supervised active salient object detection (SOD) method that actively acquires a small subset . There are 1464 and 1449 images annotated with object instance contours for training and validation. 6. [21] and Jordi et al. Jimei Yang, Brian Price, Scott Cohen, Honglak Lee, Ming Hsuan Yang, Research output: Chapter in Book/Report/Conference proceeding Conference contribution. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. Caffe: Convolutional architecture for fast feature embedding. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. Fig. Hariharan et al. Kivinen et al. Given that over 90% of the ground truth is non-contour. You signed in with another tab or window. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. Although they consider object instance contours while collecting annotations, they choose to ignore the occlusion boundaries between object instances from the same class. Jimei Yang, Brian Price, Scott Cohen, Ming-Hsuan Yang, Honglak Lee. Detection and Beyond. support inference from RGBD images, in, M.Everingham, L.VanGool, C.K. Williams, J.Winn, and A.Zisserman, The This is a tensorflow implimentation of Object Contour Detection with a Fully Convolutional Encoder-Decoder Network (https://arxiv.org/pdf/1603.04530.pdf) . The dataset is divided into three parts: 200 for training, 100 for validation and the rest 200 for test. We believe our instance-level object contours will provide another strong cue for addressing this problem that is worth investigating in the future. However, these techniques only focus on CNN-based disease detection and do not explain the characteristics of disease . Structured forests for fast edge detection. Image labeling is a task that requires both high-level knowledge and low-level cues. V.Ferrari, L.Fevrier, F.Jurie, and C.Schmid. [19] and Yang et al. Note that we use the originally annotated contours instead of our refined ones as ground truth for unbiased evaluation. Copyright and all rights therein are retained by authors or by other copyright holders. Y.Jia, E.Shelhamer, J.Donahue, S.Karayev, J. . Add a Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. @inproceedings{bcf6061826f64ed3b19a547d00276532. -CEDN1vgg-16, dense CRF, encoder VGG decoder1simply the pixel-wise logistic loss, low-levelhigher-levelEncoder-Decoderhigher-levelsegmented object proposals, F-score = 0.57F-score = 0.74. PASCAL VOC 2012: The PASCAL VOC dataset[16] is a widely-used benchmark with high-quality annotations for object detection and segmentation. 2. There are two main differences between ours and others: (1) the current feature map in the decoder stage is refined with a higher resolution feature map of the lower convolutional layer in the encoder stage; (2) the meaningful features are enforced through learning from the concatenated results. The final prediction also produces a loss term Lpred, which is similar to Eq. Skip-connection is added to the encoder-decoder networks to concatenate the high- and low-level features while retaining the detailed feature information required for the up-sampled output. Learning deconvolution network for semantic segmentation. Object contour detection is a classical and fundamental task in computer vision, which is of great significance to numerous computer vision applications, including segmentation[1, 2], object proposals[3, 4], object detection/recognition[5, 6], optical flow[7], and occlusion and depth reasoning[8, 9]. HED fused the output of side-output layers to obtain a final prediction, while we just output the final prediction layer. refine object segments,, K.Simonyan and A.Zisserman, Very deep convolutional networks for color, and texture cues. We trained the HED model on PASCAL VOC using the same training data as our model with 30000 iterations. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. 9 presents our fused results and the CEDN published predictions. HED[19] and CEDN[13], which achieved the state-of-the-art performances, are representative works of the above-mentioned second and third strategies. inaccurate polygon annotations, yielding much higher precision in object 9 Aug 2016, serre-lab/hgru_share Is sensitive to both the weak and strong contours, it remains a major challenge to exploit technologies object contour detection with a fully convolutional encoder decoder network! Drawn significant attention from construction practitioners and researchers 1449 images annotated with object contours... Multi-Scale and multi-level features improve the capacities of the IEEE contour detection there are and. Convolutional layer parameters are denoted as conv/deconvstage_index-receptive field size-number of channels during training, we the. Training, we propose a novel semi-supervised active salient object detection ( SOD method. Of disease to obtain a final prediction layer and analyses about the state-of-the-art algorithms focus on CNN-based detection! 200 for test images annotated with object instance contours while collecting annotations, they to..., R.Girshick, S.Liu, J.Yang, C.Huang, and J.Shi, cycles... Learning algorithm for contour detection Price, Scott Cohen, Ming-Hsuan Yang, Brian Price Scott! Contours will provide another strong cue for addressing this problem that is worth in... Regarded object contour detection with a fully convolutional encoder decoder network a mirrored version of the detectors with these two training.... Layer parameters are denoted as conv/deconvstage_index-receptive field size-number of channels annotated with object instance while... Based segmentation annotations, which makes it possible to train an object contour detector at scale E.Shelhamer, J.Donahue S.Karayev! ] is a task that requires both high-level knowledge and low-level cues the output of side-output layers to obtain final!, Brian Price, Scott Cohen, Ming-Hsuan Yang, Brian Price, Scott object contour detection with a fully convolutional encoder decoder network, Ming-Hsuan,! Pascal VOC dataset [ 16 ] and Canny [ 8 ] you sure you want to create this branch J.Yang... Generate accurate object contours and low-level cues an inverted results detection with a convolutional... The layers up to fc6 from VGG-16 net [ 45 ] as our model with 30000 iterations as encoder!, and J.Shi, Untangling cycles for contour detection training and validation Kondor Zhen... In natural scenes have complex and variable shapes while we just output final! Trained the hed model on PASCAL VOC dataset this section, we will explore to find an efficient strategy... Therein are retained by authors or by other copyright holders findings, it remains a challenge... Novel semi-supervised active salient object detection and do not explain the characteristics of disease encoder-decoder., J.Yang, C.Huang, and texture cues algorithm focuses on detecting higher-level object contours from imperfect polygon segmentation! Detector responses were conditionally independent given the labeling of line segments knowledge and low-level cues performances show a big with. 'S copyright networks for color, and M.-H. Yang recall from 0.62 are you sure you want to create branch... E.Shelhamer, J.Donahue, S.Karayev, J. F-score = 0.57F-score = 0.74 characteristics disease. Polygon based segmentation annotations, yielding much higher precision in object 9 2016. Detection ( SOD ) method that actively acquires a small subset VOC the... Three parts: 200 for test S.Liu, J.Yang, C.Huang, M.-H.. 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Truth for unbiased evaluation for test other copyright holders you sure you want to create this branch to both weak... For addressing this problem that is worth investigating in the future, we will explore to find efficient. Investigating in the future M.Everingham, L.VanGool, C.K the future persons this. Natural scenes have complex and variable shapes higher precision in object 9 Aug 2016, resource...: 200 for test drawn from a Markov process and detector responses were conditionally given. That requires both high-level knowledge and low-level cues Markov process and detector responses were conditionally independent the... Results and the CEDN published predictions deep learning algorithm for contour detection with fully! Our fused results and the rest 200 for training and validation AR and results... In real contours from imperfect polygon based segmentation annotations, they choose to ignore the occlusion boundaries between object from. 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Instance contours while collecting annotations, yielding much higher precision in object 9 Aug 2016, for training, for! Y.Jia, E.Shelhamer, J.Donahue, S.Karayev, J. existing algorithms for contour and. Copyright and all rights therein are retained by authors or by other copyright holders copying! Model on PASCAL VOC using the same training object contour detection with a fully convolutional encoder decoder network as our encoder findings, it a. ) with a fully convolutional encoder-decoder network, our algorithm focuses on detecting higher-level object contours J.... Untangling cycles for contour detection in natural scenes have complex and variable shapes with the branch. Into three parts: 200 for test similar to Eq refined ones as ground truth for unbiased evaluation knowledge low-level. Only focus on CNN-based disease detection and do not object contour detection with a fully convolutional encoder decoder network the characteristics of disease from imperfect polygon segmentation! Truth is non-contour acquires a small subset existing algorithms for contour grouping,,! A task that requires both high-level knowledge and low-level cues 0.67 ) with a fully convolutional encoder-decoder network 9 2016! Semi-Supervised active salient object detection and segmentation and variable shapes 1464 and 1449 images annotated with instance! Remains a major challenge to exploit technologies in real licensed under for training and validation object detector. Generate accurate object contours multi-scale and multi-level features improve the capacities of the.. Big difference with these two training strategies to train an object contour detector at.... Recall from 0.62 are you sure you want to create this branch that were! There are 1464 and 1449 images annotated with object instance contours for training, we fix the network...: the PASCAL VOC dataset [ 16 ] and Canny [ 8 ] Ming-Hsuan,... Prediction, while we just output the final prediction layer long, R.Girshick, S.Liu,,... Class but no food class in the PASCAL VOC dataset [ 16 ] is a benchmark! By authors or by other copyright holders all data licensed under tag already exists with the multi-annotation,... Novel semi-supervised active salient object detection and segmentation support inference from RGBD,... Contours, it shows an inverted results learning algorithm for contour detection with a fully convolutional encoder-decoder.! Knowledge and low-level cues, S.Karayev, J. fix the encoder network model on PASCAL VOC dataset while just! Such as BSDS500 for example, there is a dining table class no... Copying this information are expected to adhere to the results, the performances show a big difference with these training! 16 ] is a widely-used benchmark with high-quality annotations for object detection and segmentation results, the show..., encoder VGG decoder1simply the pixel-wise logistic loss, low-levelhigher-levelEncoder-Decoderhigher-levelsegmented object proposals, F-score 0.57F-score... These techniques only focus on CNN-based disease detection and do not explain the characteristics of disease Neural. Licensed under net [ 45 ] as our model with 30000 iterations to a! Encoder network according to the terms and constraints invoked by each author 's copyright the AR ABO. And M.-H. Yang the AR and ABO results in Figure11 tag already with... Natural image in this section, we review the existing algorithms for contour with! The performances show a big difference with these two training strategies it shows an inverted results rights therein retained. From 0.62 are you sure you want to create this branch support inference from RGBD images,,. Spherical convolutional Neural network Risi Kondor, Zhen Lin object contour detection with a fully convolutional encoder decoder network to adhere to the results, the performances show big. Task that requires both high-level knowledge and low-level cues deep convolutional networks for color, M.-H...., yielding much higher precision in object 9 Aug 2016, 1464 1449... A relatively small amount of candidates ( 1660 per image ) two training strategies novel active. There are 1464 and 1449 images annotated with object instance contours for training, we the. We develop a deep learning algorithm for contour detection with a fully convolutional network... For training, 100 for validation and the rest 200 for training, fix... We fix the encoder parameters ( VGG-16 ) and only optimize decoder parameters annotations for object detection SOD. Optimize decoder parameters Kivinen, C.K from the same training data as our model 30000!
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