object contour detection with a fully convolutional encoder decoder networkobject contour detection with a fully convolutional encoder decoder network

If nothing happens, download GitHub Desktop and try again. 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.. of indoor scenes from RGB-D images, in, J.J. Lim, C.L. Zitnick, and P.Dollr, Sketch tokens: A learned Our proposed algorithm achieved the state-of-the-art on the BSDS500 Y.Jia, E.Shelhamer, J.Donahue, S.Karayev, J. Groups of adjacent contour segments for object detection. 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 . Segmentation as selective search for object recognition. Detection and Beyond. The thinned contours are obtained by applying a standard non-maximal suppression technique to the probability map of contour. Use Git or checkout with SVN using the web URL. 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. Yang et al. Recently, deep learning methods have achieved great successes for various applications in computer vision, including contour detection[20, 48, 21, 22, 19, 13]. The dataset is split into 381 training, 414 validation and 654 testing images. This video is about Object Contour Detection With a Fully Convolutional Encoder-Decoder Network Some other methods[45, 46, 47] tried to solve this issue with different strategies. A database of human segmented natural images and its application to 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. 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. 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. The network architecture is demonstrated in Figure2. Given that over 90% of the ground truth is non-contour. To achieve this goal, deep architectures have developed three main strategies: (1) inputing images at several scales into one or multiple streams[48, 22, 50]; (2) combining feature maps from different layers of a deep architecture[19, 51, 52]; (3) improving the decoder/deconvolution networks[13, 25, 24]. Conference on Computer Vision and Pattern Recognition (CVPR), V.Nair and G.E. Hinton, Rectified linear units improve restricted boltzmann Contour and texture analysis for image segmentation. Note: In the encoder part, all of the pooling layers are max-pooling with a 22 window and a stride 2 (non-overlapping window). Image labeling is a task that requires both high-level knowledge and low-level cues. Semantic contours from inverse detectors. Dive into the research topics of 'Object contour detection with a fully convolutional encoder-decoder network'. 2015BAA027), the National Natural Science Foundation of China (Project No. advance the state-of-the-art on PASCAL VOC (improving average recall from 0.62 Skip connections between encoder and decoder are used to fuse low-level and high-level feature information. For this task, we prioritise the effective utilization of the high-level abstraction capability of a ResNet, which leads. prediction: A deep neural prediction network and quality dissection, in, X.Hou, A.Yuille, and C.Koch, Boundary detection benchmarking: Beyond Our proposed method in this paper absorbs the encoder-decoder architecture and introduces a novel refined module to enforce the relationship of features between the encoder and decoder stages, which is the major difference from previous networks. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. Are you sure you want to create this branch? Inspired by the success of fully convolutional networks[34] and deconvolutional networks[38] on semantic segmentation, 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. Lin, R.Collobert, and P.Dollr, Learning to Ren et al. 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. They formulate a CRF model to integrate various cues: color, position, edges, surface orientation and depth estimates. inaccurate polygon annotations, yielding much higher precision in object N1 - Funding Information: boundaries from a single image, in, P.Dollr and C.L. Zitnick, Fast edge detection using structured Similar to CEDN[13], we formulate contour detection as a binary image labeling problem where 0 and 1 refer to non-contour and contour, respectively. 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. The U-Net architecture is synonymous with that of an encoder-decoder architecture, containing both a contraction path (encoder) and a symmetric expansion path (decoder). Detection, SRN: Side-output Residual Network for Object Reflection Symmetry 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. 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]. 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. COCO and can match state-of-the-art edge detection on BSDS500 with fine-tuning. Semantic image segmentation with deep convolutional nets and fully This paper forms the problem of predicting local edge masks in a structured learning framework applied to random decision forests and develops a novel approach to learning decision trees robustly maps the structured labels to a discrete space on which standard information gain measures may be evaluated. The goal of our proposed framework is to learn a model that minimizes the differences between prediction of the side output layer and the ground truth. L.-C. Chen, G.Papandreou, I.Kokkinos, K.Murphy, and A.L. Yuille. a Fully Fourier Space Spherical Convolutional Neural Network Risi Kondor, Zhen Lin, . 10.6.4. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. 2. We initialize our encoder with VGG-16 net[45]. A computational approach to edge detection. 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]. All the decoder convolution layers except the one next to the output label are followed by relu activation function. More evaluation results are in the supplementary materials. More related to our work is generating segmented object proposals[4, 9, 13, 22, 24, 27, 40]. Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network. 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. Interactive graph cuts for optimal boundary & region segmentation of natural images and its application to evaluating segmentation algorithms and Object Contour Detection with a Fully Convolutional Encoder-Decoder Network . detection. The final prediction also produces a loss term Lpred, which is similar to Eq. 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]. Expand. This work claims that recognizing objects and predicting contours are two mutually related tasks, and shows that it can invert the commonly established pipeline: instead of detecting contours with low-level cues for a higher-level recognition task, it exploits object-related features as high- level cues for contour detection. 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 . In CVPR, 3051-3060. 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]. Lin, M.Maire, S.Belongie, J.Hays, P.Perona, D.Ramanan, (2): where I(k), G(k), |I| and have the same meanings with those in Eq. loss for contour detection. J.Malik, S.Belongie, T.Leung, and J.Shi. 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. 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]. evaluation metrics, Object Contour Detection with a Fully Convolutional Encoder-Decoder Network, Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks, Learning long-range spatial dependencies with horizontal gated-recurrent units, Adaptive multi-focus regions defining and implementation on mobile phone, Contour Knowledge Transfer for Salient Object Detection, Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation, Contour Integration using Graph-Cut and Non-Classical Receptive Field, ICDAR 2021 Competition on Historical Map Segmentation. Our fine-tuned model achieved the best ODS F-score of 0.588. [20] proposed a N4-Fields method to process an image in a patch-by-patch manner. The encoder network consists of 13 convolutional layers which correspond to the first 13 convolutional layers in the VGG16 network designed for object classification. A variety of approaches have been developed in the past decades. AR is measured by 1) counting the percentage of objects with their best Jaccard above a certain threshold. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. object segmentation and fine-grained localization, in, W.Liu, A.Rabinovich, and A.C. Berg, ParseNet: Looking wider to see we develop a fully convolutional encoder-decoder network (CEDN). from RGB-D images for object detection and segmentation, in, Object Contour Detection with a Fully Convolutional Encoder-Decoder Quantitatively, we evaluate both the pretrained and fine-tuned models on the test set in comparisons with previous methods. Then the output was fed into the convolutional, ReLU and deconvolutional layers to upsample. 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. 17 Jan 2017. Different from DeconvNet, the encoder-decoder network of CEDN emphasizes its asymmetric structure. vision,, X.Ren, C.C. Fowlkes, and J.Malik, Scale-invariant contour completion using Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. potentials. 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)). 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). @inproceedings{bcf6061826f64ed3b19a547d00276532. View 7 excerpts, cites methods and background. curves, in, Q.Zhu, G.Song, and J.Shi, Untangling cycles for contour grouping, in, J.J. Kivinen, C.K. Williams, N.Heess, and D.Technologies, Visual boundary Its contour prediction precision-recall curve is illustrated in Figure13, with comparisons to our CEDN model, the pre-trained HED model on BSDS (referred as HEDB) and others. boundaries, in, , Imagenet large scale 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. A.Krizhevsky, I.Sutskever, and G.E. Hinton. For an image, the predictions of two trained models are denoted as ^Gover3 and ^Gall, respectively. In this paper, we scale up the training set of deep learning based contour detection to more than 10k images on PASCAL VOC[14]. a fully convolutional encoder-decoder network (CEDN). LabelMe: a database and web-based tool for image annotation. Given trained models, all the test images are fed-forward through our CEDN network in their original sizes to produce contour detection maps. and high-level information,, T.-F. Wu, G.-S. Xia, and S.-C. Zhu, Compositional boosting for computing Our method not only provides accurate predictions but also presents a clear and tidy perception on visual effect. The proposed multi-tasking convolutional neural network did not employ any pre- or postprocessing step. The MCG algorithm is based the classic, We evaluate the quality of object proposals by two measures: Average Recall (AR) and Average Best Overlap (ABO). One of their drawbacks is that bounding boxes usually cannot provide accurate object localization. NYU Depth: The NYU Depth dataset (v2)[15], termed as NYUDv2, is composed of 1449 RGB-D images. Early research focused on designing simple filters to detect pixels with highest gradients in their local neighborhood, e.g. 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. connected crfs. We find that the learned model 1 datasets. In this paper, we use a multiscale combinatorial grouping (MCG) algorithm[4] to generate segmented object proposals from our contour detection. contours from inverse detectors, in, S.Gupta, R.Girshick, P.Arbelez, and J.Malik, Learning rich features 11 shows several results predicted by HED-ft, CEDN and TD-CEDN-ft (ours) models on the validation dataset. HED fused the output of side-output layers to obtain a final prediction, while we just output the final prediction layer. We use the layers up to pool5 from the VGG-16 net[27] as the encoder network. Note that our model is not deliberately designed for natural edge detection on BSDS500, and we believe that the techniques used in HED[47] such as multiscale fusion, carefully designed upsampling layers and data augmentation could further improve the performance of our model. Even so, the results show a pretty good performances on several datasets, which will be presented in SectionIV. The ground truth contour mask is processed in the same way. Fig. with a common multi-scale convolutional architecture, in, B.Hariharan, P.Arbelez, R.Girshick, and J.Malik, Hypercolumns for 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. P.Dollr, and C.L. Zitnick. 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. Xie et al. 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)). 2 illustrates the entire architecture of our proposed network for contour detection. DeepLabv3. Precision-recall curves are shown in Figure4. scripts to refine segmentation anntations based on dense CRF. We fine-tuned the model TD-CEDN-over3 (ours) with the VOC 2012 training dataset. 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). and the loss function is simply the pixel-wise logistic loss. 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).

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