Each side-output can produce a loss termed Lside. 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 . 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. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Drawing detailed and accurate contours of objects is a challenging task for human beings. and the loss function is simply the pixel-wise logistic loss. The encoder-decoder network is composed of two parts: encoder/convolution and decoder/deconvolution networks. [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. search dblp; lookup by ID; about. It is likely because those novel classes, although seen in our training set (PASCAL VOC), are actually annotated as background. Given trained models, all the test images are fed-forward through our CEDN network in their original sizes to produce contour detection maps. sparse image models for class-specific edge detection and image Given the success of deep convolutional networks [29] for . The dataset is split into 381 training, 414 validation and 654 testing images. H. Lee is supported in part by NSF CAREER Grant IIS-1453651. machines, in, Proceedings of the 27th International Conference on In this paper, we scale up the training set of deep learning based contour detection to more than 10k images on PASCAL VOC[14]. 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. We train the network using Caffe[23]. By combining with the multiscale combinatorial grouping algorithm, our method to 0.67) with a relatively small amount of candidates (1660 per image). This could be caused by more background contours predicted on the final maps. As a result, the trained model yielded high precision on PASCAL VOC and BSDS500, and has achieved comparable performance with the state-of-the-art on BSDS500 after fine-tuning. 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). synthetically trained fully convolutional network, DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour This work proposes a novel yet very effective loss function for contour detection, capable of penalizing the distance of contour-structure similarity between each pair of prediction and ground-truth, and introduces a novel convolutional encoder-decoder network. View 9 excerpts, cites background and methods. The Pb work of Martin et al. 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]. The key contributions are summarized below: We develop a simple yet effective fully convolutional encoder-decoder network for object contour prediction and the trained model generalizes well to unseen object classes from the same super-categories, yielding significantly higher precision in object contour detection than previous methods. Skip connections between encoder and decoder are used to fuse low-level and high-level feature information. 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. 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. hierarchical image segmentation,, P.Arbelez, J.Pont-Tuset, J.T. Barron, F.Marques, and J.Malik, Proceedings of the IEEE 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. Object contour detection is fundamental for numerous vision tasks. Publisher Copyright: [13] developed two end-to-end and pixel-wise prediction fully convolutional networks. We formulate contour detection as a binary image labeling problem where 1 and 0 indicates contour and non-contour, respectively. Designing a Deep Convolutional Neural Network (DCNN) based baseline network, 2) Exploiting . 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 . natural images and its application to evaluating segmentation algorithms and The above mentioned four methods[20, 48, 21, 22] are all patch-based but not end-to-end training and holistic image prediction networks. 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. Are you sure you want to create this branch? 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).". You signed in with another tab or window. Therefore, the deconvolutional process is conducted stepwise, We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs). (2). 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 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. /. To guide the learning of more transparent features, the DSN strategy is also reserved in the training stage. 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. In the work of Xie et al. To perform the identification of focused regions and the objects within the image, this thesis proposes the method of aggregating information from the recognition of the edge on image. Therefore, the weights are denoted as w={(w(1),,w(M))}. / Yang, Jimei; Price, Brian; Cohen, Scott et al. Our fine-tuned model achieved the best ODS F-score of 0.588. 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. A tag already exists with the provided branch name. We use thelayersupto"fc6"fromVGG-16net[48]asourencoder. 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. 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. 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. (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. Rich feature hierarchies for accurate object detection and semantic Together they form a unique fingerprint. Fig. To find the high-fidelity contour ground truth for training, we need to align the annotated contours with the true image boundaries. 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). During training, we fix the encoder parameters and only optimize the decoder parameters. objectContourDetector. 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- Contour detection and hierarchical image segmentation. lower layers. 2.1D sketch using constrained convex optimization,, D.Hoiem, A.N. Stein, A. 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. detection, our algorithm focuses on detecting higher-level object contours. The final high dimensional features of the output of the decoder are fed to a trainable convolutional layer with a kernel size of 1 and an output channel of 1, and then the reduced feature map is applied to a sigmoid layer to generate a soft prediction. There are 1464 and 1449 images annotated with object instance contours for training and validation. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. We choose the MCG algorithm to generate segmented object proposals from our detected contours. 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. Ganin et al. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. invasive coronary angiograms, Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks, MSDPN: Monocular Depth Prediction with Partial Laser Observation using Both measures are based on the overlap (Jaccard index or Intersection-over-Union) between a proposal and a ground truth mask. We will need more sophisticated methods for refining the COCO annotations. Note that a standard non-maximum suppression is used to clean up the predicted contour maps (thinning the contours) before evaluation. 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. from RGB-D images for object detection and segmentation, in, Object Contour Detection with a Fully Convolutional Encoder-Decoder In this section, we introduce our object contour detection method with the proposed fully convolutional encoder-decoder network. functional architecture in the cats visual cortex,, D.Marr and E.Hildreth, Theory of edge detection,, J.Yang, B. 27 May 2021. S.Guadarrama, and T.Darrell. Traditional image-to-image models only consider the loss between prediction and ground truth, neglecting the similarity between the data distribution of the outcomes and ground truth. With such adjustment, we can still initialize the training process from weights trained for classification on the large dataset[53]. 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. measuring ecological statistics, in, N.Silberman, D.Hoiem, P.Kohli, and R.Fergus, Indoor segmentation and 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. [21] developed a method, called DeepContour, in which a contour patch was an input of a CNN model and the output was treated as a compact cluster which was assigned by a shape label. A novel semantic segmentation algorithm by learning a deep deconvolution network on top of the convolutional layers adopted from VGG 16-layer net, which demonstrates outstanding performance in PASCAL VOC 2012 dataset. V.Ferrari, L.Fevrier, F.Jurie, and C.Schmid. Edge detection has experienced an extremely rich history. feature embedding, in, L.Bottou, Large-scale machine learning with stochastic gradient descent, 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. Developments, libraries, methods, and datasets classes, although seen in our training set ( PASCAL VOC,... Contour detection maps guide the learning of more transparent features, the weights denoted... Network ( DCNN ) based baseline network, 2 ) Exploiting a standard non-maximum suppression used. 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