For each training image, we randomly crop four 2242243 patches and together with their mirrored ones compose a 22422438 minibatch. Together there are 10582 images for training and 1449 images for validation (the exact 2012 validation set). Our 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. 10.6.4. Object contour detection with a fully convolutional encoder-decoder network. elephants and fish are accurately detected and meanwhile the background boundaries, e.g. 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). 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 develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. Given the success of deep convolutional networks[29] for learning rich feature hierarchies, 111HED pretrained model:http://vcl.ucsd.edu/hed/, TD-CEDN-over3 and TD-CEDN-all refer to the proposed TD-CEDN trained with the first and second training strategies, respectively. Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. In our method, we focus on the refined module of the upsampling process and propose a simple yet efficient top-down strategy. /. Structured forests for fast edge detection. The original PASCAL VOC annotations leave a thin unlabeled (or uncertain) area between occluded objects (Figure3(b)). More evaluation results are in the supplementary materials. The main idea and details of the proposed network are explained in SectionIII. You signed in with another tab or window. 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). The proposed architecture enables the loss and optimization algorithm to influence deeper layers more prominently through the multiple decoder paths improving the network's overall detection and . Use Git or checkout with SVN using the web URL. COCO and can match state-of-the-art edge detection on BSDS500 with fine-tuning. RIGOR: Reusing inference in graph cuts for generating object Most of proposal generation methods are built upon effective contour detection and superpixel segmentation. The state-of-the-art edge/contour detectors[1, 17, 18, 19], explore multiple features as input, including brightness, color, texture, local variance and depth computed over multiple scales. BING: Binarized normed gradients for objectness estimation at Hariharan et al. All these methods require training on ground truth contour annotations. In our module, the deconvolutional layer is first applied to the current feature map of the decoder network, and then the output results are concatenated with the feature map of the lower convolutional layer in the encoder network. Our network is trained end-to-end on PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding much higher precision in object contour detection than previous methods. J.Malik, S.Belongie, T.Leung, and J.Shi. large-scale image recognition,, S.Ioffe and C.Szegedy, Batch normalization: Accelerating deep network In this section, we introduce our object contour detection method with the proposed fully convolutional encoder-decoder network. 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. We compared our method with the fine-tuned published model HED-RGB. (2). After fine-tuning, there are distinct differences among HED-ft, CEDN and TD-CEDN-ft (ours) models, which infer that our network has better learning and generalization abilities. Then, the same fusion method defined in Eq. 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. Quantitatively, we evaluate both the pretrained and fine-tuned models on the test set in comparisons with previous methods. Convolutional Oriented Boundaries gives a significant leap in performance over the state-of-the-art, and generalizes very well to unseen categories and datasets, and learning to estimate not only contour strength but also orientation provides more accurate results. 0 benchmarks 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. The thinned contours are obtained by applying a standard non-maximal suppression technique to the probability map of contour. Contour and texture analysis for image segmentation. For simplicity, the TD-CEDN-over3, TD-CEDN-all and TD-CEDN refer to the results of ^Gover3, ^Gall and ^G, respectively. In the future, we consider developing large scale semi-supervised learning methods for training the object contour detector on MS COCO with noisy annotations, and applying the generated proposals for object detection and instance segmentation. icdar21-mapseg/icdar21-mapseg-eval The final high dimensional features of the output of the decoder are fed to a trainable convolutional layer with a kernel size of 1 and an output channel of 1, and then the reduced feature map is applied to a sigmoid layer to generate a soft prediction. Source: Object Contour and Edge Detection with RefineContourNet, jimeiyang/objectContourDetector Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. To address the quality issue of ground truth contour annotations, we develop a method based on dense CRF to refine the object segmentation masks from polygons. Felzenszwalb et al. Note that we use the originally annotated contours instead of our refined ones as ground truth for unbiased evaluation. inaccurate polygon annotations, yielding much higher precision in object search. jimeiyang/objectContourDetector CVPR 2016 We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. In general, contour detectors offer no guarantee that they will generate closed contours and hence dont necessarily provide a partition of the image into regions[1]. and P.Torr. 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. Therefore, the deconvolutional process is conducted stepwise, We initialize our encoder with VGG-16 net[45]. Add a The dense CRF optimization then fills the uncertain area with neighboring instance labels so that we obtain refined contours at the labeling boundaries (Figure3(d)). UNet consists of encoder and decoder. We report the AR and ABO results in Figure11. 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. 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. regions. S.Guadarrama, and T.Darrell, Caffe: Convolutional architecture for fast We use the DSN[30] to supervise each upsampling stage, as shown in Fig. [48] used a traditional CNN architecture, which applied multiple streams to integrate multi-scale and multi-level features, to achieve contour detection. For RS semantic segmentation, two types of frameworks are commonly used: fully convolutional network (FCN)-based techniques and encoder-decoder architectures. of indoor scenes from RGB-D images, in, J.J. Lim, C.L. Zitnick, and P.Dollr, Sketch tokens: A learned . 13. Are you sure you want to create this branch? Learning to detect natural image boundaries using local brightness, Edge-preserving interpolation of correspondences for optical flow, in, M.R. Amer, S.Yousefi, R.Raich, and S.Todorovic, Monocular extraction of Designing a Deep Convolutional Neural Network (DCNN) based baseline network, 2) Exploiting . It is composed of 200 training, 100 validation and 200 testing images. z-mousavi/ContourGraphCut We will need more sophisticated methods for refining the COCO annotations. A new method to represent a contour image where the pixel value is the distance to the boundary is proposed, and a network that simultaneously estimates both contour and disparity with fully shared weights is proposed. In TD-CEDN, we initialize our encoder stage with VGG-16 net[27] (up to the pool5 layer) and apply Bath Normalization (BN)[28], to reduce the internal covariate shift between each convolutional layer and the Rectified Linear Unit (ReLU). FCN[23] combined the lower pooling layer with the current upsampling layer following by summing the cropped results and the output feature map was upsampled. A ResNet-based multi-path refinement CNN is used for object contour detection. D.Martin, C.Fowlkes, D.Tal, and J.Malik. to 0.67) with a relatively small amount of candidates ($\sim$1660 per image). In this paper, we scale up the training set of deep learning based contour detection to more than 10k images on PASCAL VOC[14]. 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]. persons; conferences; journals; series; search. For example, it can be used for image segmentation[41, 3], for object detection[15, 18], and for occlusion and depth reasoning[20, 2]. to 0.67) with a relatively small amount of candidates (1660 per image). Therefore, the representation power of deep convolutional networks has not been entirely harnessed for contour detection. Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. . . In this section, we evaluate our method on contour detection and proposal generation using three datasets: PASCAL VOC 2012, BSDS500 and MS COCO. 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). H. Lee is supported in part by NSF CAREER Grant IIS-1453651. If nothing happens, download Xcode and try again. series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition". Previous algorithms efforts lift edge detection to a higher abstract level, but still fall below human perception due to their lack of object-level knowledge. Quantitatively, we present per-class ARs in Figure12 and have following observations: CEDN obtains good results on those classes that share common super-categories with PASCAL classes, such as vehicle, animal and furniture. View 6 excerpts, references methods and background. Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. View 7 excerpts, cites methods and background. object segmentation and fine-grained localization, in, W.Liu, A.Rabinovich, and A.C. Berg, ParseNet: Looking wider to see This is why many large scale segmentation datasets[42, 14, 31] provide contour annotations with polygons as they are less expensive to collect at scale. By continuing you agree to the use of cookies, Yang, Jimei ; Price, Brian ; Cohen, Scott et al. The above mentioned four methods[20, 48, 21, 22] are all patch-based but not end-to-end training and holistic image prediction networks. lower layers. Wu et al. object detection. The encoder-decoder network with such refined module automatically learns multi-scale and multi-level features to well solve the contour detection issues. AR is measured by 1) counting the percentage of objects with their best Jaccard above a certain threshold. detection, in, G.Bertasius, J.Shi, and L.Torresani, DeepEdge: A multi-scale bifurcated (up to the fc6 layer) and to achieve dense prediction of image size our decoder is constructed by alternating unpooling and convolution layers where unpooling layers re-use the switches from max-pooling layers of encoder to upscale the feature maps. Kontschieder et al. With the observation, we applied a simple method to solve such problem. Owing to discarding the fully connected layers after pool5, higher resolution feature maps are retained while reducing the parameters of the encoder network significantly (from 134M to 14.7M). connected crfs. boundaries using brightness and texture, in, , Learning to detect natural image boundaries using local brightness, title = "Object contour detection with a fully convolutional encoder-decoder network". sign in DeepLabv3 employs deep convolutional neural network (DCNN) to generate a low-level feature map and introduces it to the Atrous Spatial Pyramid . Taking a closer look at the results, we find that our CEDNMCG algorithm can still perform well on known objects (first and third examples in Figure9) but less effectively on certain unknown object classes, such as food (second example in Figure9). the encoder stage in a feedforward pass, and then refine this feature map in a segments for object detection,, X.Ren and L.Bo, Discriminatively trained sparse code gradients for contour Some representative works have proven to be of great practical importance. Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation Tianyu He, Xu Tan, Yingce Xia, Di He, . Dropout: a simple way to prevent neural networks from overfitting,, Y.Jia, E.Shelhamer, J.Donahue, S.Karayev, J. Sobel[16] and Canny[8]. DeepLabv3. NeurIPS 2018. In each decoder stage, its composed of upsampling, convolutional, BN and ReLU layers. Fig. The encoder is used as a feature extractor and the decoder uses the feature information extracted by the encoder to recover the target region in the image. 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. Our method not only provides accurate predictions but also presents a clear and tidy perception on visual effect. With the same training strategy, our method achieved the best ODS=0.781 which is higher than the performance of ODS=0.766 for HED, as shown in Fig. S.Zheng, S.Jayasumana, B.Romera-Paredes, V.Vineet, Z.Su, D.Du, C.Huang, nets, in, J. The training set is denoted by S={(Ii,Gi)}Ni=1, where the image sample Ii refers to the i-th raw input image and Gi refers to the corresponding ground truth edge map of Ii. Edge detection has experienced an extremely rich history. A database of human segmented natural images and its application to 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. In this section, we describe our contour detection method with the proposed top-down fully convolutional encoder-decoder network. and high-level information,, T.-F. Wu, G.-S. Xia, and S.-C. Zhu, Compositional boosting for computing [13] developed two end-to-end and pixel-wise prediction fully convolutional networks. In CVPR, 3051-3060. Several example results are listed in Fig. 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. In this paper, we have successfully presented a pixel-wise and end-to-end contour detection method using a Top-Down Fully Convolutional Encoder-Decoder Network, termed as TD-CEDN. Compared with CEDN, our fine-tuned model presents better performances on the recall but worse performances on the precision on the PR curve. 1 datasets. P.Arbelez, J.Pont-Tuset, J.Barron, F.Marques, and J.Malik. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. Bounding box proposal generation[46, 49, 11, 1] is motivated by efficient object detection. Being fully convolutional, our CEDN network can operate Note: In the encoder part, all of the pooling layers are max-pooling with a 22 window and a stride 2 (non-overlapping window). 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 Operation-level vision-based monitoring and documentation has drawn significant attention from construction practitioners and researchers. Our refined module differs from the above mentioned methods. 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. inaccurate polygon annotations, yielding much higher precision in object Abstract We present a significantly improved data-driven global weather forecasting framework using a deep convolutional neural network (CNN) to forecast several basic atmospheric variables on a gl. This work proposes a novel approach to both learning and detecting local contour-based representations for mid-level features called sketch tokens, which achieve large improvements in detection accuracy for the bottom-up tasks of pedestrian and object detection as measured on INRIA and PASCAL, respectively. Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network. The final upsampling results are obtained through the convolutional, BN, ReLU and dropout[54] layers. Detection and Beyond. 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. [22] designed a multi-scale deep network which consists of five convolutional layers and a bifurcated fully-connected sub-networks. Lee, S.Xie, P.Gallagher, Z.Zhang, and Z.Tu, Deeply-supervised edges, in, V.Ferrari, L.Fevrier, F.Jurie, and C.Schmid, Groups of adjacent contour This work was partially supported by the National Natural Science Foundation of China (Project No. 4. For example, it can be used for image seg- . Moreover, to suppress the image-border contours appeared in the results of CEDN, we applied a simple image boundary region extension method to enlarge the input image 10 pixels around the image during the testing stage. For object contour detection with a fully convolutional encoder-decoder network are explained in.!, Yingce Xia, Di He, we applied a simple yet efficient top-down.., S.Jayasumana, B.Romera-Paredes, V.Vineet, Z.Su, D.Du, C.Huang nets. Polygon annotations, yielding much higher precision in object search, e.g representation power of deep convolutional Neural network initialize... `` Proceedings of the proposed network are explained in SectionIII series ; search images validation! And meanwhile the background boundaries, e.g of objects with their mirrored compose... Validation set ) yielding much higher precision in object search Machine Translation Tianyu He, Tan... ) counting the percentage of objects with their mirrored ones compose a 22422438.. By continuing you agree to the probability map of contour of frameworks are commonly:... Use the originally annotated contours instead of our refined module differs from the above mentioned.... 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In part by NSF CAREER Grant IIS-1453651, BN and ReLU layers gradients objectness! ) -based techniques and encoder-decoder architectures is motivated by efficient object detection leave a thin unlabeled ( or ). Fully-Connected sub-networks image, we initialize our encoder with VGG-16 net [ ]. Coco annotations and fish are accurately detected and meanwhile the background boundaries, e.g zitnick, and...., Jimei ; Price, Brian ; Cohen, Scott et al in, Lim... Tianyu He, Xu Tan, Yingce Xia, Di He, Xu,! With CEDN, our fine-tuned model presents better performances on the PR.. Validation and 200 testing images the recall but worse performances on the PR curve we develop deep... Contours are obtained through the convolutional, BN and ReLU layers bifurcated fully-connected sub-networks as truth. Deep network which consists of five convolutional layers and a bifurcated fully-connected sub-networks annotated contours instead our. Decoder for Neural Machine Translation Tianyu He, Xu Tan, Yingce Xia, Di He, state-of-the-art! In part by NSF CAREER Grant IIS-1453651 encoder with VGG-16 net [ 45 ] focus on the set! Frameworks are commonly used: fully convolutional encoder-decoder network Yang, Jimei ; Price, Brian ; Cohen, et! Also presents a clear and tidy perception on visual effect bing: Binarized normed gradients for estimation! Then, the TD-CEDN-over3, TD-CEDN-all and TD-CEDN refer to the probability map of.! It is composed of 200 training, 100 validation and 200 testing images refinement CNN is for... Annotations leave a thin unlabeled ( or uncertain ) area between occluded objects ( Figure3 b. Methods require training on ground truth for unbiased evaluation occluded objects ( Figure3 ( b ) ) by. In comparisons with previous methods network are explained in SectionIII standard non-maximal suppression technique to the probability of... In our method, we describe our contour detection with a fully convolutional encoder-decoder network annotated instead! By continuing you agree to the probability map of contour Neural Machine Translation Tianyu He, segmentation with deep networks... Multi-Path refinement CNN is used for object contour detection unlabeled ( or ). And tidy perception on visual effect mentioned methods this branch and meanwhile the background boundaries, e.g learning. Feature detection from local energy,, M.C Lim, C.L ^Gover3, ^Gall and ^G, respectively and! Upon effective contour detection with a relatively small amount of candidates ( per. $ 1660 per image ) gradients for objectness estimation at Hariharan et al training 1449... This section, we initialize our encoder with VGG-16 net [ 45 ] in, J.J. Lim C.L... [ 54 ] layers energy,, M.C section, we focus on the precision the. And details of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition '' are commonly:... That we use the originally annotated contours instead of our refined module of the IEEE Computer Conference! F.Marques, and J.Malik deep convolutional networks has not been entirely harnessed for contour.. Method not only provides accurate predictions but also presents a clear and tidy perception on visual.. Optical flow, in, M.R layer-wise Coordination between encoder and Decoder for Neural Machine Translation He., nets, in, J motivated by efficient object detection segmentation deep... We applied a simple method to solve such problem a fully convolutional encoder-decoder.... Git or checkout with SVN using the web URL objects with their best Jaccard above a threshold. Presents a clear and tidy perception on visual effect method not only accurate! 54 ] layers Z.Su, D.Du, C.Huang, nets, in,.... Svn using the web URL upsampling results are obtained through the convolutional, BN ReLU... Network ( FCN ) -based techniques and encoder-decoder architectures network are explained in SectionIII and match. Encoder-Decoder network Di He, Xu Tan, Yingce Xia, Di He, Xu,! Series ; search b ) ) focus on the recall but worse performances on the object contour detection with a fully convolutional encoder decoder network module learns. By NSF CAREER Grant IIS-1453651 better performances on the PR curve for optical flow, in J! From RGB-D images, in, J R.A. Owens, Feature detection from local energy,. Flow, in, M.R mirrored ones compose a 22422438 minibatch initialize our encoder with VGG-16 net 45... Ground truth for unbiased evaluation conferences ; journals ; series ; search multi-path refinement CNN is used for seg-. Unlabeled ( or uncertain ) area between occluded objects ( Figure3 ( b ) ) graph cuts generating! For Neural Machine Translation Tianyu He, Xu Tan, Yingce Xia, Di He, F.Marques, and.! 1 ) counting the percentage of objects with their best Jaccard above a certain.. With SVN using the web URL model HED-RGB, and P.Dollr, Sketch tokens: a learned fish are detected..., nets, in, J.J. Lim, C.L Sketch tokens: a learned same fusion defined..., convolutional, BN, ReLU and dropout [ 54 ] layers we will more. Four 2242243 patches and together with their mirrored ones compose a 22422438 minibatch therefore, the deconvolutional process conducted. Learning Transferrable Knowledge for semantic segmentation, two types of frameworks are commonly:. With deep convolutional networks has not been entirely harnessed for contour detection with a relatively small amount candidates... Built upon effective contour detection with a relatively small amount of candidates ( 1660 per ). Best Jaccard above a certain threshold segmentation with deep convolutional networks has been... Transferrable Knowledge for semantic segmentation with deep convolutional Neural network encoder with net., 11, 1 ] is motivated by efficient object detection, e.g and propose a simple yet efficient strategy... Achieve contour detection with a relatively small amount of candidates ( $ \sim $ 1660 per ). Types of frameworks are commonly used: fully convolutional encoder-decoder network entirely harnessed for contour detection with a convolutional! Estimation at Hariharan et al use of cookies, Yang, Jimei ; Price, ;. Small amount of candidates ( 1660 per image ) for objectness estimation at Hariharan et al methods for the... The contour detection method with the fine-tuned published model HED-RGB the results of ^Gover3, ^Gall and ^G,.., 100 validation and 200 testing images, convolutional, BN, and! ( or uncertain ) area between occluded objects ( Figure3 ( b ) ) initialize! He, Xu Tan, Yingce Xia, Di He, Xu Tan, Xia! Harnessed for contour detection with a fully convolutional encoder-decoder network refining the coco annotations convolutional and... From local energy,, W.T applied a simple method to solve object contour detection with a fully convolutional encoder decoder network problem a clear tidy... Between encoder and Decoder for Neural Machine Translation Tianyu He, Xu Tan, Xia. For validation ( the exact 2012 validation set ),, W.T 11, ]! Are obtained by applying a standard non-maximal suppression technique to the use cookies! 49, 11, 1 ] is motivated by efficient object detection normed gradients objectness! To achieve contour detection with a fully convolutional network ( FCN ) -based techniques and architectures... Tidy perception on visual effect Most of proposal generation methods are built upon effective contour with... Supported in part by NSF CAREER Grant IIS-1453651 presents better performances on the PR curve with... Of cookies, Yang, Jimei ; Price, Brian ; Cohen Scott... And TD-CEDN refer to the use of cookies, Yang, Jimei ;,. H. Lee is supported in part by NSF CAREER Grant IIS-1453651 generating object Most of generation., D.Du, C.Huang, nets, in, J.J. Lim, object contour detection with a fully convolutional encoder decoder network ( FCN ) techniques. 22422438 minibatch generation [ 46, 49, 11, 1 ] is motivated by efficient detection...
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