object contour detection with a fully convolutional encoder decoder network

UR - http://www.scopus.com/inward/record.url?scp=84986265719&partnerID=8YFLogxK, UR - http://www.scopus.com/inward/citedby.url?scp=84986265719&partnerID=8YFLogxK, T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, BT - Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, T2 - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016. Their integrated learning of hierarchical features was in distinction to previous multi-scale approaches. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. Fig. Though the deconvolutional layers are fixed to the linear interpolation, our experiments show outstanding performances to solve such issues. Concerned with the imperfect contour annotations from polygons, we have developed a refinement method based on dense CRF so that the proposed network has been trained in an end-to-end manner. Hariharan et al. 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]. study the problem of recovering occlusion boundaries from a single image. At the core of segmented object proposal algorithms is contour detection and superpixel segmentation. Object Contour Detection With a Fully Convolutional Encoder-Decoder Network. object detection. By clicking accept or continuing to use the site, you agree to the terms outlined in our. We initialize our encoder with VGG-16 net[45]. Use this path for labels during training. INTRODUCTION O BJECT contour detection is a classical and fundamen-tal task in computer vision, which is of great signif-icance to numerous computer vision applications, including segmentation [1], [2], object proposals [3], [4], object de- Compared with CEDN, our fine-tuned model presents better performances on the recall but worse performances on the precision on the PR curve. J.Malik, S.Belongie, T.Leung, and J.Shi. Image labeling is a task that requires both high-level knowledge and low-level cues. Rich feature hierarchies for accurate object detection and semantic and P.Torr. We demonstrate the state-of-the-art evaluation results on three common contour detection datasets. Semantic image segmentation with deep convolutional nets and fully invasive coronary angiograms, Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks, MSDPN: Monocular Depth Prediction with Partial Laser Observation using We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. HED[19] and CEDN[13], which achieved the state-of-the-art performances, are representative works of the above-mentioned second and third strategies. Expand. This study proposes an end-to-end encoder-decoder multi-tasking CNN for joint blood accumulation detection and tool segmentation in laparoscopic surgery to maintain the operating room as clean as possible and, consequently, improve the . Edge detection has experienced an extremely rich history. This code includes; the Caffe toolbox for Convolutional Encoder-Decoder Networks (caffe-cedn)scripts for training and testing the PASCAL object contour detector, and B.Hariharan, P.Arbelez, L.Bourdev, S.Maji, and J.Malik. 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. The original PASCAL VOC annotations leave a thin unlabeled (or uncertain) area between occluded objects (Figure3(b)). For each training image, we randomly crop four 2242243 patches and together with their mirrored ones compose a 22422438 minibatch. Object contour detection is fundamental for numerous vision tasks. 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). Compared to PASCAL VOC, there are 60 unseen object classes for our CEDN contour detector. Sobel[16] and Canny[8]. Encoder-decoder architectures can handle inputs and outputs that both consist of variable-length sequences and thus are suitable for seq2seq problems such as machine translation. View 10 excerpts, cites methods and background, IEEE Transactions on Pattern Analysis and Machine Intelligence. Proceedings of the IEEE AB - We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. HED-over3 and TD-CEDN-over3 (ours) seem to have a similar performance when they were applied directly on the validation dataset. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. Measuring the objectness of image windows. tentials in both the encoder and decoder are not fully lever-aged. Each side-output can produce a loss termed Lside. Contour detection accuracy was evaluated by three standard quantities: (1) the best F-measure on the dataset for a fixed scale (ODS); (2) the aggregate F-measure on the dataset for the best scale in each image (OIS); (3) the average precision (AP) on the full recall range. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. Summary. 10.6.4. In this paper, we scale up the training set of deep learning based contour detection to more than 10k images on PASCAL VOC . 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. As a result, the boundaries suppressed by pretrained CEDN model (CEDN-pretrain) re-surface from the scenes. 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). In our method, we focus on the refined module of the upsampling process and propose a simple yet efficient top-down strategy. Our refined module differs from the above mentioned methods. elephants and fish are accurately detected and meanwhile the background boundaries, e.g. lixin666/C2SNet It is composed of 200 training, 100 validation and 200 testing images. regions. 2013 IEEE International Conference on Computer Vision. 2013 IEEE Conference on Computer Vision and Pattern Recognition. The above mentioned four methods[20, 48, 21, 22] are all patch-based but not end-to-end training and holistic image prediction networks. sparse image models for class-specific edge detection and image nets, in, J. It is apparently a very challenging ill-posed problem due to the partial observability while projecting 3D scenes onto 2D image planes. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. With the advance of texture descriptors[35], Martin et al. We set the learning rate to, and train the network with 30 epochs with all the training images being processed each epoch. J. Yang and M.-H. Yang are supported in part by NSF CAREER Grant #1149783, NSF IIS Grant #1152576, and a gift from Adobe. However, the technologies that assist the novice farmers are still limited. The enlarged regions were cropped to get the final results. RCF encapsulates all convolutional features into more discriminative representation, which makes good usage of rich feature hierarchies, and is amenable to training via backpropagation, and achieves state-of-the-art performance on several available datasets. 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. We evaluate the trained network on unseen object categories from BSDS500 and MS COCO datasets[31], They assumed that curves were drawn from a Markov process and detector responses were conditionally independent given the labeling of line segments. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. refers to the image-level loss function for the side-output. 6. Therefore, we apply the DSN to provide the integrated direct supervision from coarse to fine prediction layers. By combining with the multiscale combinatorial grouping algorithm, our method Download Free PDF. Learning to Refine Object Contours with a Top-Down Fully Convolutional We trained our network using the publicly available Caffe[55] library and built it on the top of the implementations of FCN[23], HED[19], SegNet[25] and CEDN[13]. support inference from RGBD images, in, M.Everingham, L.VanGool, C.K. Williams, J.Winn, and A.Zisserman, The optimization. A contour-to-saliency transferring method to automatically generate salient object masks which can be used to train the saliency branch from outputs of the contour branch, and introduces a novel alternating training pipeline to gradually update the network parameters. 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) [], HED, Encoder-Decoder networks [24, 25, 13] and the bottom-up/top-down architecture [].Being fully convolutional, the developed TD-CEDN can operate on an arbitrary image size and the . kmaninis/COB Fig. edges, in, V.Ferrari, L.Fevrier, F.Jurie, and C.Schmid, Groups of adjacent contour The Pascal visual object classes (VOC) challenge. We use the DSN[30] to supervise each upsampling stage, as shown in Fig. To automate the operation-level monitoring of construction and built environments, there have been much effort to develop computer vision technologies. 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. The same measurements applied on the BSDS500 dataset were evaluated. The most of the notations and formulations of the proposed method follow those of HED[19]. 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. 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. vision,, X.Ren, C.C. Fowlkes, and J.Malik, Scale-invariant contour completion using network is trained end-to-end on PASCAL VOC with refined ground truth from a fully convolutional encoder-decoder network (CEDN). color, and texture cues,, J.Mairal, M.Leordeanu, F.Bach, M.Hebert, and J.Ponce, Discriminative segmentation, in, V.Badrinarayanan, A.Handa, and R.Cipolla, SegNet: A deep convolutional 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. 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]. Learning deconvolution network for semantic segmentation. The proposed multi-tasking convolutional neural network did not employ any pre- or postprocessing step. generalizes well to unseen object classes from the same super-categories on MS 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 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. 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. 27 Oct 2020. We focus on the BSDS500 dataset were evaluated segmented object proposal algorithms is contour detection fundamental. 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