object contour detection with a fully convolutional encoder decoder network

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object contour detection with a fully convolutional encoder decoder network

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object contour detection with a fully convolutional encoder decoder network

object contour detection with a fully convolutional encoder decoder network

16/05/2023
This paper proposes a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%. Grabcut -interactive foreground extraction using iterated graph cuts. 27 May 2021. 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. H. Lee is supported in part by NSF CAREER Grant IIS-1453651. a fully convolutional encoder-decoder network (CEDN). Thus the improvements on contour detection will immediately boost the performance of object proposals. persons; conferences; journals; series; search. Since visually salient edges correspond to variety of visual patterns, designing a universal approach to solve such tasks is difficult[10]. Object Contour Detection extracts information about the object shape in images. CEDN fails to detect the objects labeled as background in the PASCAL VOC training set, such as food and applicance. 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. 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. Its precision-recall value is referred as GT-DenseCRF with a green spot in Figure4. W.Shen, X.Wang, Y.Wang, X.Bai, and Z.Zhang. Edit social preview. The dataset is mainly used for indoor scene segmentation, which is similar to PASCAL VOC 2012 but provides the depth map for each image. prediction. Hosang et al. LabelMe: a database and web-based tool for image annotation. 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. 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. 2. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. It takes 0.1 second to compute the CEDN contour map for a PASCAL image on a high-end GPU and 18 seconds to generate proposals with MCG on a standard CPU. PASCAL VOC 2012: The PASCAL VOC dataset[16] is a widely-used benchmark with high-quality annotations for object detection and segmentation. View 7 excerpts, references results, background and methods, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). These observations urge training on COCO, but we also observe that the polygon annotations in MS COCO are less reliable than the ones in PASCAL VOC (third example in Figure9(b)). These CVPR 2016 papers are the Open Access versions, provided by the. For each training image, we randomly crop four 2242243 patches and together with their mirrored ones compose a 22422438 minibatch. Deepedge: A multi-scale bifurcated deep network for top-down contour 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. We trained the HED model on PASCAL VOC using the same training data as our model with 30000 iterations. Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation Tianyu He, Xu Tan, Yingce Xia, Di He, . 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. Arbelaez et al. 9 presents our fused results and the CEDN published predictions. We use thelayersupto"fc6"fromVGG-16net[48]asourencoder. Yang et al. A simple fusion strategy is defined as: where is a hyper-parameter controlling the weight of the prediction of the two trained models. However, these techniques only focus on CNN-based disease detection and do not explain the characteristics of disease . 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. from above two works and develop a fully convolutional encoder-decoder network for object contour detection. Object contour detection is fundamental for numerous vision tasks. 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. As the contour and non-contour pixels are extremely imbalanced in each minibatch, the penalty for being contour is set to be 10 times the penalty for being non-contour. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. 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). This is a tensorflow implimentation of Object Contour Detection with a Fully Convolutional Encoder-Decoder Network (https://arxiv.org/pdf/1603.04530.pdf) . 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]. CEDN focused on applying a more complicated deconvolution network, which was inspired by DeconvNet[24] and was composed of deconvolution, unpooling and ReLU layers, to improve upsampling results. Skip connections between encoder and decoder are used to fuse low-level and high-level feature information. with a fully convolutional encoder-decoder network,, D.Martin, C.Fowlkes, D.Tal, and J.Malik, A database of human segmented H. Lee is supported in part by NSF CAREER Grant IIS-1453651. However, the technologies that assist the novice farmers are still limited. Xie et al. AndreKelm/RefineContourNet Lindeberg, The local approaches took into account more feature spaces, such as color and texture, and applied learning methods for cue combination[35, 36, 37, 38, 6, 1, 2]. Therefore, the representation power of deep convolutional networks has not been entirely harnessed for contour detection. author = "Jimei Yang and Brian Price and Scott Cohen and Honglak Lee and Yang, {Ming Hsuan}". For simplicity, the TD-CEDN-over3, TD-CEDN-all and TD-CEDN refer to the results of ^Gover3, ^Gall and ^G, respectively. [20] proposed a N4-Fields method to process an image in a patch-by-patch manner. machines, in, Proceedings of the 27th International Conference on With such adjustment, we can still initialize the training process from weights trained for classification on the large dataset[53]. We also compared the proposed model to two benchmark object detection networks; Faster R-CNN and YOLO v5. 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. As a result, our method significantly improves the quality of segmented object proposals on the PASCAL VOC 2012 validation set, achieving 0.67 average recall from overlap 0.5 to 1.0 with only about 1660 candidates per image, compared to the state-of-the-art average recall 0.62 by original gPb-based MCG algorithm with near 5140 candidates per image. /. mid-level representation for contour and object detection, in, S.Xie and Z.Tu, Holistically-nested edge detection, in, W.Shen, X.Wang, Y.Wang, X.Bai, and Z.Zhang, DeepContour: A deep Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. Since we convert the fc6 to be convolutional, so we name it conv6 in our decoder. Caffe: Convolutional architecture for fast feature embedding. Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. We develop a novel deep contour detection algorithm with a top-down fully The remainder of this paper is organized as follows. 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). CEDN. Our fine-tuned model achieved the best ODS F-score of 0.588. We will need more sophisticated methods for refining the COCO annotations. P.Arbelez, J.Pont-Tuset, J.Barron, F.Marques, and J.Malik. L.-C. Chen, G.Papandreou, I.Kokkinos, K.Murphy, and A.L. Yuille. Contour and texture analysis for image segmentation. boundaries using brightness and texture, in, , Learning to detect natural image boundaries using local brightness, Detection and Beyond. Like other methods, a standard non-maximal suppression technique was applied to obtain thinned contours before evaluation. Fig. 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. Drawing detailed and accurate contours of objects is a challenging task for human beings. We initialize the encoder with pre-trained VGG-16 net and the decoder with random values. For example, it can be used for image segmentation[41, 3], for object detection[15, 18], and for occlusion and depth reasoning[20, 2]. All these methods require training on ground truth contour annotations. 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). task. We show we can fine tune our network for edge detection and match the state-of-the-art in terms of precision and recall. 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. 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). objects in n-d images. . We believe our instance-level object contours will provide another strong cue for addressing this problem that is worth investigating in the future. . We have developed an object-centric contour detection method using a simple yet efficient fully convolutional encoder-decoder network. D.Hoiem, A.N. Stein, A.Efros, and M.Hebert. A computational approach to edge detection. to use Codespaces. 2013 IEEE International Conference on Computer Vision. Note that we did not train CEDN on MS COCO. Edge boxes: Locating object proposals from edge. Boosting object proposals: From Pascal to COCO. z-mousavi/ContourGraphCut We choose this dataset for training our object contour detector with the proposed fully convolutional encoder-decoder network. In this section, we introduce our object contour detection method with the proposed fully convolutional encoder-decoder network. More related to our work is generating segmented object proposals[4, 9, 13, 22, 24, 27, 40]. [46] generated a global interpretation of an image in term of a small set of salient smooth curves. loss for contour detection. selection,, D.R. Martin, C.C. Fowlkes, and J.Malik, Learning to detect natural image color, and texture cues. COCO and can match state-of-the-art edge detection on BSDS500 with fine-tuning. This is the code for arXiv paper Object Contour Detection with a Fully Convolutional Encoder-Decoder Network by Jimei Yang, Brian Price, Scott Cohen, Honglak Lee and Ming-Hsuan Yang, 2016.. In this paper, we address object-only contour detection that is expected to suppress background boundaries (Figure1(c)). Our method not only provides accurate predictions but also presents a clear and tidy perception on visual effect. with a common multi-scale convolutional architecture, in, B.Hariharan, P.Arbelez, R.Girshick, and J.Malik, Hypercolumns for sign in [19], a number of properties, which are key and likely to play a role in a successful system in such field, are summarized: (1) carefully designed detector and/or learned features[36, 37], (2) multi-scale response fusion[39, 2], (3) engagement of multiple levels of visual perception[11, 12, 49], (4) structural information[18, 10], etc. P.Rantalankila, J.Kannala, and E.Rahtu. quality dissection. Deepcontour: A deep convolutional feature learned by positive-sharing [19] study top-down contour detection problem. [13] developed two end-to-end and pixel-wise prediction fully convolutional networks. By combining with the multiscale combinatorial grouping algorithm, our method In SectionII, we review related work on the pixel-wise semantic prediction networks. Proceedings of the IEEE Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. class-labels in random forests for semantic image labelling, in, S.Nowozin and C.H. Lampert, Structured learning and prediction in computer 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). Wu et al. Their integrated learning of hierarchical features was in distinction to previous multi-scale approaches. 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]. booktitle = "Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016", Object contour detection with a fully convolutional encoder-decoder network, Chapter in Book/Report/Conference proceeding, 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016. Given trained models, all the test images are fed-forward through our CEDN network in their original sizes to produce contour detection maps. J.Hosang, R.Benenson, P.Dollr, and B.Schiele. Ganin et al. TableII shows the detailed statistics on the BSDS500 dataset, in which our method achieved the best performances in ODS=0.788 and OIS=0.809. vision,, X.Ren, C.C. Fowlkes, and J.Malik, Scale-invariant contour completion using Together they form a unique fingerprint. 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). Image labeling is a task that requires both high-level knowledge and low-level cues. hierarchical image structures, in, P.Kontschieder, S.R. Bulo, H.Bischof, and M.Pelillo, Structured regions. Expand. Inspired by the success of fully convolutional networks[34] and deconvolutional networks[38] on semantic segmentation, Bibliographic details on Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. Visual boundary prediction: A deep neural prediction network and 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]. Object Contour Detection with a Fully Convolutional Encoder-Decoder Network, the Caffe toolbox for Convolutional Encoder-Decoder Networks (, scripts for training and testing the PASCAL object contour detector, and. Learn more. 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. Download Free PDF. We present results in the MS COCO 2014 validation set, shortly COCO val2014 that includes 40504 images annotated by polygons from 80 object classes. View 9 excerpts, cites background and methods. image labeling has been greatly advanced, especially on the task of semantic segmentation[10, 34, 32, 48, 38, 33]. Several example results are listed in Fig. The combining process can be stack step-by-step. 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. Learning to detect natural image boundaries using local brightness, In addition to the structural at- prevented target discontinuity in medical images, such tribute (topological relationship), DNGs also have other as those of the pancreas, and achieved better results. By continuing you agree to the use of cookies, Yang, Jimei ; Price, Brian ; Cohen, Scott et al. segmentation, in, V.Badrinarayanan, A.Handa, and R.Cipolla, SegNet: A deep convolutional Detection, SRN: Side-output Residual Network for Object Reflection Symmetry [21] and Jordi et al. 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- 0.588), and and the NYU Depth dataset (ODS F-score of 0.735). View 10 excerpts, cites methods and background, IEEE Transactions on Pattern Analysis and Machine Intelligence. B.Hariharan, P.Arbelez, L.Bourdev, S.Maji, and J.Malik. P.Arbelez, M.Maire, C.Fowlkes, and J.Malik. (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. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. Different from the original network, we apply the BN[28] layer to reduce the internal covariate shift between each convolutional layer and the ReLU[29] layer. Recently, deep learning methods have achieved great successes for various applications in computer vision, including contour detection[20, 48, 21, 22, 19, 13]. boundaries, in, , Imagenet large scale We will explain the details of generating object proposals using our method after the contour detection evaluation. 6. S.Guadarrama, and T.Darrell. The above proposed technologies lead to a more precise and clearer which is guided by Deeply-Supervision Net providing the integrated direct There was a problem preparing your codespace, please try again. 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. 2015BAA027), the National Natural Science Foundation of China (Project No. The enlarged regions were cropped to get the final results. Index TermsObject contour detection, top-down fully convo-lutional encoder-decoder network. 11 Feb 2019. The number of channels of every decoder layer is properly designed to allow unpooling from its corresponding max-pooling layer. convolutional encoder-decoder 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. Unlike skip connections 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]. As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching -- and sometimes even surpassing -- human accuracy on a variety of visual recognition tasks. Operation-level vision-based monitoring and documentation has drawn significant attention from construction practitioners and researchers. Object proposals are important mid-level representations in computer vision. TD-CEDN performs the pixel-wise prediction by inaccurate polygon annotations, yielding much higher precision in object Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. We report the AR and ABO results in Figure11. 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. 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. multi-scale and multi-level features; and (2) applying an effective top-down Bounding box proposal generation[46, 49, 11, 1] is motivated by efficient object detection. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. N2 - We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. natural images and its application to evaluating segmentation algorithms and View 7 excerpts, cites methods and background. [3], further improved upon this by computing local cues from multiscale and spectral clustering, known as, analyzed the clustering structure of local contour maps and developed efficient supervised learning algorithms for fast edge detection. 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. . object detection. The proposed network makes the encoding part deeper to extract richer convolutional features. Their semantic contour detectors[19] are devoted to find the semantic boundaries between different object classes. 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. K.E.A. vande Sande, J.R.R. Uijlingsy, T.Gevers, and A.W.M. Smeulders. At the same time, many works have been devoted to edge detection that responds to both foreground objects and background boundaries (Figure1 (b)). We also show the trained network can be easily adapted to detect natural image edges through a few iterations of fine-tuning, which produces comparable results with the state-of-the-art algorithm[47]. The final contours were fitted with the various shapes by different model parameters by a divide-and-conquer strategy. Image labeling is a task that requires both high-level knowledge and low-level cues. Note that we fix the training patch to. Microsoft COCO: Common objects in context. Long, R.Girshick, better,, O.Russakovsky, J.Deng, H.Su, J.Krause, S.Satheesh, S.Ma, Z.Huang, RGB-D Salient Object Detection via 3D Convolutional Neural Networks Qian Chen1, Ze Liu1, . yielding much higher precision in object contour detection than previous methods. The Pb work of Martin et al. Accordingly we consider the refined contours as the upper bound since our network is learned from them. means of leveraging features at all layers of the net. 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. M.-M. Cheng, Z.Zhang, W.-Y. 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.

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object contour detection with a fully convolutional encoder decoder network