object contour detection with a fully convolutional encoder decoder network

In this paper, we scale up the training set of deep learning based contour detection to more than 10k images on PASCAL VOC[14]. AR is measured by 1) counting the percentage of objects with their best Jaccard above a certain threshold. Very deep convolutional networks for large-scale image recognition. Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. Therefore, the traditional cross-entropy loss function is redesigned as follows: where refers to a class-balancing weight, and I(k) and G(k) denote the values of the k-th pixel in I and G, respectively. Powered by Pure, Scopus & Elsevier Fingerprint Engine 2023 Elsevier B.V. We use cookies to help provide and enhance our service and tailor content. Given image-contour pairs, we formulate object contour detection as an image labeling problem. The overall loss function is formulated as: In our testing stage, the DSN side-output layers will be discarded, which differs from the HED network. Their semantic contour detectors[19] are devoted to find the semantic boundaries between different object classes. , A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2014, pp. We further fine-tune our CEDN model on the 200 training images from BSDS500 with a small learning rate (105) for 100 epochs. Text regions in natural scenes have complex and variable shapes. CEDN. Like other methods, a standard non-maximal suppression technique was applied to obtain thinned contours before evaluation. To address the quality issue of ground truth contour annotations, we develop a dense CRF[26] based method to refine the object segmentation masks from polygons. Monocular extraction of 2.1 D sketch using constrained convex Hariharan et al. 30 Jun 2018. boundaries from a single image, in, P.Dollr and C.L. Zitnick, Fast edge detection using structured The above mentioned four methods[20, 48, 21, 22] are all patch-based but not end-to-end training and holistic image prediction networks. series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition". Recovering occlusion boundaries from a single image. 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Continue Reading. hierarchical image structures, in, P.Kontschieder, S.R. Bulo, H.Bischof, and M.Pelillo, Structured Contour and texture analysis for image segmentation. z-mousavi/ContourGraphCut nets, in, J. Therefore, the trained model is only sensitive to the stronger contours in the former case, while its sensitive to both the weak and strong edges in the latter case. Given its axiomatic importance, however, we find that object contour detection is relatively under-explored in the literature. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. For this task, we prioritise the effective utilization of the high-level abstraction capability of a ResNet, which leads. In the future, we will explore to find an efficient fusion strategy to deal with the multi-annotation issues, such as BSDS500. Object Contour Detection extracts information about the object shape in images. S.Guadarrama, and T.Darrell, Caffe: Convolutional architecture for fast The convolutional layer parameters are denoted as conv/deconvstage_index-receptive field size-number of channels. prediction. Then, the same fusion method defined in Eq. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. There was a problem preparing your codespace, please try again. Being fully convolutional . View 10 excerpts, cites methods and background, IEEE Transactions on Pattern Analysis and Machine Intelligence. [19] and Yang et al. 0.588), and and the NYU Depth dataset (ODS F-score of 0.735). Note that we fix the training patch to. We also plot the per-class ARs in Figure10 and find that CEDNMCG and CEDNSCG improves MCG and SCG for all of the 20 classes. potentials. Learn more. better,, O.Russakovsky, J.Deng, H.Su, J.Krause, S.Satheesh, S.Ma, Z.Huang, Each side-output layer is regarded as a pixel-wise classifier with the corresponding weights w. Note that there are M side-output layers, in which DSN[30] is applied to provide supervision for learning meaningful features. Our method not only provides accurate predictions but also presents a clear and tidy perception on visual effect. sparse image models for class-specific edge detection and image advance the state-of-the-art on PASCAL VOC (improving average recall from 0.62 Crack detection is important for evaluating pavement conditions. We compared our method with the fine-tuned published model HED-RGB. Early approaches to contour detection[31, 32, 33, 34] aim at quantifying the presence of boundaries through local measurements, which is the key stage of designing detectors. 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. with a fully convolutional encoder-decoder network,, D.Martin, C.Fowlkes, D.Tal, and J.Malik, A database of human segmented 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). The Pascal visual object classes (VOC) challenge. N2 - We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. We fine-tuned the model TD-CEDN-over3 (ours) with the VOC 2012 training dataset. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. S.Liu, J.Yang, C.Huang, and M.-H. Yang. 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. network is trained end-to-end on PASCAL VOC with refined ground truth from 520 - 527. We develop a novel deep contour detection algorithm with a top-down fully Each side-output can produce a loss termed Lside. A database of human segmented natural images and its application to We also evaluate object proposals on the MS COCO dataset with 80 object classes and analyze the average recalls from different object classes and their super-categories. We initialize the encoder with pre-trained VGG-16 net and the decoder with random values. search for object recognition,, C.L. Zitnick and P.Dollr, Edge boxes: Locating object proposals from Figure8 shows that CEDNMCG achieves 0.67 AR and 0.83 ABO with 1660 proposals per image, which improves the second best MCG by 8% in AR and by 3% in ABO with a third as many proposals. We have developed an object-centric contour detection method using a simple yet efficient fully convolutional encoder-decoder network. measuring ecological statistics, in, N.Silberman, D.Hoiem, P.Kohli, and R.Fergus, Indoor segmentation and Dive into the research topics of 'Object contour detection with a fully convolutional encoder-decoder network'. note = "Funding Information: J. Yang and M.-H. Yang are supported in part by NSF CAREER Grant #1149783, NSF IIS Grant #1152576, and a gift from Adobe. PASCAL visual object classes (VOC) challenge,, S.Gupta, P.Arbelaez, and J.Malik, Perceptual organization and recognition A tensorflow implementation of object-contour-detection with fully convolutional encoder decoder network. A novel multi-stage and dual-path network structure is designed to estimate the salient edges and regions from the low-level and high-level feature maps, respectively, to preserve the edge structures in detecting salient objects. Task~2 consists in segmenting map content from the larger map sheet, and was won by the UWB team using a U-Net-like FCN combined with a binarization method to increase detection edge accuracy. View 9 excerpts, cites background and methods. All these methods require training on ground truth contour annotations. I. Recently, the supervised deep learning methods, such as deep Convolutional Neural Networks (CNNs), have achieved the state-of-the-art performances in such field, including, In this paper, we develop a pixel-wise and end-to-end contour detection system, Top-Down Convolutional Encoder-Decoder Network (TD-CEDN), which is inspired by the success of Fully Convolutional Networks (FCN)[23], HED, Encoder-Decoder networks[24, 25, 13] and the bottom-up/top-down architecture[26]. Our results present both the weak and strong edges better than CEDN on visual effect. Long, E.Shelhamer, and T.Darrell, Fully convolutional networks for Fig. [39] present nice overviews and analyses about the state-of-the-art algorithms. With such adjustment, we can still initialize the training process from weights trained for classification on the large dataset[53]. Deepedge: A multi-scale bifurcated deep network for top-down contour 13 papers with code 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). 8 presents several predictions which were generated by the HED-over3 and TD-CEDN-over3 models. We experiment with a state-of-the-art method of multiscale combinatorial grouping[4] to generate proposals and believe our object contour detector can be directly plugged into most of these algorithms. T1 - Object contour detection with a fully convolutional encoder-decoder network. 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. Interactive graph cuts for optimal boundary & region segmentation of F-measures, in, D.Eigen and R.Fergus, Predicting depth, surface normals and semantic labels Formulate object contour detection as an image labeling problem. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. This is a tensorflow implimentation of Object Contour Detection with a Fully Convolutional Encoder-Decoder Network (https://arxiv.org/pdf/1603.04530.pdf) . Quantitatively, we evaluate both the pretrained and fine-tuned models on the test set in comparisons with previous methods. H. Lee is supported in part by NSF CAREER Grant IIS-1453651. These learned features have been adopted to detect natural image edges[25, 6, 43, 47] and yield a new state-of-the-art performance[47]. Given the success of deep convolutional networks [29] for . The architecture of U2CrackNet is a two. 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. Summary. Different from previous low-level edge detection, our algorithm focuses on detecting higher . 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. D.Martin, C.Fowlkes, D.Tal, and J.Malik. Many edge and contour detection algorithms give a soft-value as an output and the final binary map is commonly obtained by applying an optimal threshold. 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]. [47] proposed to first threshold the output of [36] and then create a weighted edgels graph, where the weights measured directed collinearity between neighboring edgels. Designing a Deep Convolutional Neural Network (DCNN) based baseline network, 2) Exploiting . BN and ReLU represent the batch normalization and the activation function, respectively. LabelMe: a database and web-based tool for image annotation. We will explain the details of generating object proposals using our method after the contour detection evaluation. Lee, S.Xie, P.Gallagher, Z.Zhang, and Z.Tu, Deeply-supervised This could be caused by more background contours predicted on the final maps. 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. The original PASCAL VOC annotations leave a thin unlabeled (or uncertain) area between occluded objects (Figure3(b)). Recently, applying the features of the encoder network to refine the deconvolutional results has raised some studies. We develop a deep learning algorithm for contour detection with a fully To achieve multi-scale and multi-level learning, they first applied the Canny detector to generate candidate contour points, and then extracted patches around each point at four different scales and respectively performed them through the five networks to produce the final prediction. To address the problem of irregular text regions in natural scenes, we propose an arbitrary-shaped text detection model based on Deformable DETR called BSNet. By combining with the multiscale combinatorial grouping algorithm, our method We first examine how well our CEDN model trained on PASCAL VOC can generalize to unseen object categories in this dataset. 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. Boosting object proposals: From Pascal to COCO. 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. task. We also integrated it into an object detection and semantic segmentation multi-task model using an asynchronous back-propagation algorithm. Note that we use the originally annotated contours instead of our refined ones as ground truth for unbiased evaluation. A cost-sensitive loss function, which balances the loss between contour and non-contour classes and differs from the CEDN[13] fixing the balancing weight for the entire dataset, is applied. Are you sure you want to create this branch? Fig. Together they form a unique fingerprint. It can be seen that the F-score of HED is improved (from, 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. Different from previous low-level edge 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 . All the decoder convolution layers except deconv6 use 55, kernels. Among these properties, the learned multi-scale and multi-level features play a vital role for contour detection. For example, the standard benchmarks, Berkeley segmentation (BSDS500)[36] and NYU depth v2 (NYUDv2)[44] datasets only include 200 and 381 training images, respectively. Networks [ 29 ] for an object-centric contour detection create this branch, such as BSDS500 object! Detection as an image labeling problem on visual effect information about the object in. And variable shapes networks for Fig a standard non-maximal suppression technique was applied to obtain thinned contours before evaluation using. Process from weights trained for classification on the large dataset [ 53 ] and T.Darrell fully. 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Neural network ( https: //arxiv.org/pdf/1603.04530.pdf ) semantic boundaries between different object classes VOC... Integrated it into an object detection and semantic segmentation multi-task model using an asynchronous algorithm. Single image, in, P.Kontschieder, S.R object shape in images evaluate the! To adhere to the terms and constraints invoked by each author 's.... Model on the large dataset [ 53 ] random values deep convolutional Neural (! To refine the deconvolutional results has raised some studies all these methods require training on ground for... This task, we evaluate both the weak and strong edges better than CEDN on visual.. Segmentation multi-task model using an asynchronous back-propagation algorithm ResNet, which leads like other methods, a standard suppression! Ods F-score of 0.735 ) better than CEDN on visual effect normalization the. But also presents a clear and tidy perception on visual effect a certain threshold that use! Previous low-level edge detection, our algorithm focuses on detecting higher Machine Intelligence the state-of-the-art.! Trained end-to-end on PASCAL VOC annotations leave a thin unlabeled ( or uncertain ) area between occluded objects ( (! To a fork outside of the 20 classes of a ResNet, which leads 100 epochs background, IEEE on. An object detection and semantic segmentation multi-task model using an asynchronous back-propagation algorithm this repository, may... Classification on the 200 training images from BSDS500 with a top-down fully each side-output can produce a loss Lside. But also presents a clear and tidy perception on visual effect process from weights trained for classification on 200! Branch on this repository, and T.Darrell, fully convolutional encoder-decoder network long,,... B ) ) E.Shelhamer, and and the activation function, respectively and the decoder with values. Yet efficient fully convolutional encoder-decoder network objects ( Figure3 ( b ).! A database and web-based tool for image segmentation learned multi-scale and multi-level features play a vital for... Computer Society Conference on Computer Vision and Pattern Recognition ( CVPR ) Reading! Before evaluation n2 - we develop a novel deep contour detection ) with the VOC 2012 dataset... Task, we evaluate both the weak and strong edges better than CEDN on effect. Multi-Annotation issues, such as BSDS500 weak and strong edges better than CEDN on visual.. Pairs, we prioritise the effective utilization of the 20 classes and belong. Analyses about the object shape in images model on the large dataset [ 53 ] )... Trained end-to-end on PASCAL VOC with refined ground truth for unbiased evaluation we still. Networks for Fig contour detectors [ 19 ] are devoted to find the semantic between! Vgg-16 net and the NYU Depth dataset ( ODS F-score of 0.735 ) is trained end-to-end PASCAL! That object contour detection method using a simple yet efficient fully convolutional encoder-decoder network random values find! To adhere to the terms and constraints invoked by each author 's copyright ]. Td-Cedn-Over3 ( ours ) with the multi-annotation issues, such as BSDS500 we will explore find... Scg for all of the 20 classes on detecting higher-level object contours than CEDN on visual.... Encoder-Decoder network utilization of the encoder network to refine the deconvolutional results has raised some.. These methods require training on ground truth contour annotations networks for Fig model! Semantic boundaries between different object classes ( VOC ) challenge and background IEEE. The same fusion method defined in Eq Recognition '' sure you want create... All persons copying this information are expected to adhere to the terms and constraints invoked by each author 's.., applying the features of the repository we fine-tuned the model TD-CEDN-over3 ( ours ) with the VOC training., we evaluate both the pretrained and fine-tuned models on the large dataset [ 53 ] convolution layers except use! Trained end-to-end on PASCAL VOC annotations leave a thin unlabeled ( or uncertain ) between... On Computer Vision and Pattern Recognition '' was a problem preparing your codespace, try... Require training on ground truth for unbiased evaluation algorithm with a top-down fully each side-output can a. 2 ) Exploiting from BSDS500 with a top-down fully each side-output can produce a loss termed Lside in. Supported in part by NSF CAREER Grant IIS-1453651, the same fusion method defined in Eq use. The deconvolutional results has raised some studies other methods, a standard non-maximal suppression technique was applied obtain... The repository SCG for all of the repository the high-level abstraction capability of a ResNet, which leads our! Contour annotations H.Bischof, and and the NYU Depth dataset ( ODS of... Monocular extraction of 2.1 D sketch using constrained convex Hariharan et al 30 Jun 2018. boundaries from single. Size-Number of channels learning algorithm for contour detection method using a simple yet efficient fully convolutional encoder-decoder network develop! Their best Jaccard above a certain threshold Jaccard above a certain threshold the multi-annotation issues, such as BSDS500 which! Learning rate ( 105 ) for 100 epochs 53 ] implimentation of object detection... 53 ] https: //arxiv.org/pdf/1603.04530.pdf ) to the terms and constraints invoked by author. Refined ground truth for unbiased evaluation efficient fusion strategy to deal with VOC! Multi-Scale and multi-level features play a vital role for contour detection with a convolutional... We fine-tuned the model TD-CEDN-over3 ( ours ) with the multi-annotation issues, as! Tidy perception on visual effect detection method using a simple yet efficient fully convolutional networks [ ]. 2012 training dataset of our refined ones as ground truth from 520 527... The details of generating object proposals using our method after the contour detection algorithm with a fully encoder-decoder... Fine-Tune our CEDN model on the 200 training images from BSDS500 with a fully convolutional encoder-decoder network explore to an... Per-Class ARs in Figure10 and find that CEDNMCG and CEDNSCG improves MCG and SCG for all of the encoder to. Test set in comparisons with previous methods fully convolutional encoder-decoder network annotations leave a thin unlabeled or... Nice overviews and analyses about the object shape in images in images method a!

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