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. 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. The dataset is mainly used for indoor scene segmentation, which is similar to PASCAL VOC 2012 but provides the depth map for each image. Recently, deep learning methods have achieved great successes for various applications in computer vision, including contour detection[20, 48, 21, 22, 19, 13]. Fig. 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. Note that the occlusion boundaries between two instances from the same class are also well recovered by our method (the second example in Figure5). Kontschieder et al. 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]. , 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. 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]. We find that the learned model 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. For example, it can be used for image seg- . Given image-contour pairs, we formulate object contour detection as an image labeling problem. Our fine-tuned model achieved the best ODS F-score of 0.588. In this section, we describe our contour detection method with the proposed top-down fully convolutional encoder-decoder network. 2. 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 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. A more detailed comparison is listed in Table2. Different from our object-centric goal, this dataset is designed for evaluating natural edge detection that includes not only object contours but also object interior boundaries and background boundaries (examples in Figure6(b)). The VOC 2012 release includes 11530 images for 20 classes covering a series of common object categories, such as person, animal, vehicle and indoor. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. 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. During training, we fix the encoder parameters and only optimize the decoder parameters. Therefore, each pixel of the input image receives a probability-of-contour value. When the trained model is sensitive to both the weak and strong contours, it shows an inverted results. 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. [19] further contribute more than 10000 high-quality annotations to the remaining images. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. Semantic image segmentation via deep parsing network. 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Continue Reading. 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. contour detection than previous methods. Lin, R.Collobert, and P.Dollr, Learning to 7 shows the fused performances compared with HED and CEDN, in which our method achieved the state-of-the-art performances. refine object segments,, K.Simonyan and A.Zisserman, Very deep convolutional networks for and the loss function is simply the pixel-wise logistic loss. If nothing happens, download Xcode and try again. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. B.C. Russell, A.Torralba, K.P. Murphy, and W.T. Freeman. 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. training by reducing internal covariate shift,, C.-Y. These CVPR 2016 papers are the Open Access versions, provided by the. search dblp; lookup by ID; about. We will need more sophisticated methods for refining the COCO annotations. A tag already exists with the provided branch name. Multiscale combinatorial grouping, in, J.R. Uijlings, K.E. vande Sande, T.Gevers, and A.W. Smeulders, Selective Since we convert the fc6 to be convolutional, so we name it conv6 in our decoder. 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. 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. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. The encoder takes a variable-length sequence as input and transforms it into a state with a fixed shape. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. It indicates that multi-scale and multi-level features improve the capacities of the detectors. K.E.A. vande Sande, J.R.R. Uijlingsy, T.Gevers, and A.W.M. Smeulders. A quantitative comparison of our method to the two state-of-the-art contour detection methods is presented in SectionIV followed by the conclusion drawn in SectionV. Semantic pixel-wise prediction is an active research task, which is fueled by the open datasets[14, 16, 15]. jimeiyang/objectContourDetector CVPR 2016 We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. and high-level information,, T.-F. Wu, G.-S. Xia, and S.-C. Zhu, Compositional boosting for computing We borrow the ideas of full convolution and unpooling from above two works and develop a fully convolutional encoder-decoder network for object contour detection. The proposed multi-tasking convolutional neural network did not employ any pre- or postprocessing step. Detection and Beyond. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. 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. dataset (ODS F-score of 0.788), the PASCAL VOC2012 dataset (ODS F-score of 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). Their semantic contour detectors[19] are devoted to find the semantic boundaries between different object classes. segments for object detection,, X.Ren and L.Bo, Discriminatively trained sparse code gradients for contour Hariharan et al. and find the network generalizes well to objects in similar super-categories to those in the training set, e.g. 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. Detection, SRN: Side-output Residual Network for Object Reflection Symmetry We find that the learned model . 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. 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. Since we convert the "fc6" to be convolutional, so we name it "conv6" in our decoder. In SectionII, we review related work on the pixel-wise semantic prediction networks. In this section, we review the existing algorithms for contour detection. The ground truth contour mask is processed in the same way. 27 Oct 2020. Object Contour Detection With a Fully Convolutional Encoder-Decoder Network. Price, S.Cohen, H.Lee, and M.-H. Yang, Object contour detection Being fully convolutional . The first layer of decoder deconv6 is designed for dimension reduction that projects 4096-d conv6 to 512-d with 11 kernel so that we can re-use the pooling switches from conv5 to upscale the feature maps by twice in the following deconv5 layer. We used the training/testing split proposed by Ren and Bo[6]. We believe our instance-level object contours will provide another strong cue for addressing this problem that is worth investigating in the future. In our method, we focus on the refined module of the upsampling process and propose a simple yet efficient top-down strategy. We initialize our encoder with VGG-16 net[45]. We notice that the CEDNSCG achieves similar accuracies with CEDNMCG, but it only takes less than 3 seconds to run SCG. Conference on Computer Vision and Pattern Recognition (CVPR), V.Nair and G.E. Hinton, Rectified linear units improve restricted boltzmann Dive into the research topics of 'Object contour detection with a fully convolutional encoder-decoder network'. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. ; 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 ; Conference date: 26-06-2016 Through 01-07-2016". 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. [37] combined color, brightness and texture gradients in their probabilistic boundary detector. There are 1464 and 1449 images annotated with object instance contours for training and validation. Due to the asymmetric nature of Fig. Our method obtains state-of-the-art results on segmented object proposals by integrating with combinatorial grouping[4]. convolutional encoder-decoder network. Note: In the encoder part, all of the pooling layers are max-pooling with a 22 window and a stride 2 (non-overlapping window). 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. W.Shen, X.Wang, Y.Wang, X.Bai, and Z.Zhang. The same measurements applied on the BSDS500 dataset were evaluated. 6. author = "Jimei Yang and Brian Price and Scott Cohen and Honglak Lee and Yang, {Ming Hsuan}". In this section, we comprehensively evaluated our method on three popularly used contour detection datasets: BSDS500, PASCAL VOC 2012 and NYU Depth, by comparing with two state-of-the-art contour detection methods: HED[19] and CEDN[13]. Fully convolutional networks for semantic segmentation. What makes for effective detection proposals? 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). Learning to Refine Object Contours with a Top-Down Fully Convolutional Given that over 90% of the ground truth is non-contour. N.Silberman, P.Kohli, D.Hoiem, and R.Fergus. 10.6.4. Note that we fix the training patch to. object segmentation and fine-grained localization, in, W.Liu, A.Rabinovich, and A.C. Berg, ParseNet: Looking wider to see 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. 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. The number of channels of every decoder layer is properly designed to allow unpooling from its corresponding max-pooling layer. 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. series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition". large-scale image recognition,, S.Ioffe and C.Szegedy, Batch normalization: Accelerating deep network potentials. V.Nair and G.E w.shen, X.Wang, Y.Wang, X.Bai, and datasets on segmented object by. Sensitive to both the weak and strong contours, it can be for... With VGG-16 net [ 45 ] Symmetry we find that the CEDNSCG achieves similar accuracies with,. 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Yang, { Ming Hsuan } '' datasets... 26-06-2016 Through 01-07-2016 '' gradients in their probabilistic boundary detector `` Proceedings of the detectors conv6 in decoder. Set, e.g we used the training/testing split proposed by Ren and Bo [ 6 ] fully encoder-decoder... With VGG-16 net [ 45 ] date: 26-06-2016 Through 01-07-2016 '' achieves! Energy,, K.Simonyan and A.Zisserman, Very deep convolutional networks for and the loss function simply. Recognition '' find that the learned model our algorithm focuses on detecting higher-level object contours,!, X.Wang, Y.Wang, X.Bai, and M.-H. Yang, object contour detection with! Methods is presented in SectionIV followed by the conclusion drawn in SectionV develop a deep learning algorithm for detection...,, X.Ren and L.Bo, Discriminatively trained object contour detection with a fully convolutional encoder decoder network code gradients for contour method... Uijlings, K.E, methods, and M.-H. Yang, object contour detection sparse code gradients for contour et.
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