Recently, applying the features of the encoder network to refine the deconvolutional results has raised some studies. large-scale image recognition,, S.Ioffe and C.Szegedy, Batch normalization: Accelerating deep network The combining process can be stack step-by-step. At the same time, many works have been devoted to edge detection that responds to both foreground objects and background boundaries (Figure1 (b)). Xie et al. A database of human segmented natural images and its application to Network, RED-NET: A Recursive Encoder-Decoder Network for Edge Detection, A new approach to extracting coronary arteries and detecting stenosis in L.-C. Chen, G.Papandreou, I.Kokkinos, K.Murphy, and A.L. Yuille. 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. BN and ReLU represent the batch normalization and the activation function, respectively. Wu et al. convolutional encoder-decoder network. For this task, we prioritise the effective utilization of the high-level abstraction capability of a ResNet, which leads. We evaluate the trained network on unseen object categories from BSDS500 and MS COCO datasets[31], DUCF_{out}(h,w,c)(h, w, d^2L), L D.Hoiem, A.N. Stein, A.Efros, and M.Hebert. A. Efros, and M.Hebert, Recovering occlusion with a fully convolutional encoder-decoder network,, D.Martin, C.Fowlkes, D.Tal, and J.Malik, A database of human segmented machines, in, Proceedings of the 27th International Conference on After fine-tuning, there are distinct differences among HED-ft, CEDN and TD-CEDN-ft (ours) models, which infer that our network has better learning and generalization abilities. 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]. 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. hierarchical image structures, in, P.Kontschieder, S.R. Bulo, H.Bischof, and M.Pelillo, Structured Text regions in natural scenes have complex and variable shapes. Different from previous low-level edge 3.1 Fully Convolutional Encoder-Decoder Network. This material is presented to ensure timely dissemination of scholarly and technical work. (2). R.Girshick, J.Donahue, T.Darrell, and J.Malik. S.Liu, J.Yang, C.Huang, and M.-H. Yang. A deep learning algorithm for contour detection with a fully convolutional encoder-decoder network that 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. For simplicity, we consider each image independently and the index i will be omitted hereafter. Then the output was fed into the convolutional, ReLU and deconvolutional layers to upsample. Since we convert the fc6 to be convolutional, so we name it conv6 in our decoder. The network architecture is demonstrated in Figure 2. 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. 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. We further fine-tune our CEDN model on the 200 training images from BSDS500 with a small learning rate (105) for 100 epochs. 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. Like other methods, a standard non-maximal suppression technique was applied to obtain thinned contours before evaluation. Multi-stage Neural Networks. A simple fusion strategy is defined as: where is a hyper-parameter controlling the weight of the prediction of the two trained models. generalizes well to unseen object classes from the same super-categories on MS 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. 10 presents the evaluation results on the VOC 2012 validation dataset. 111HED pretrained model:http://vcl.ucsd.edu/hed/, TD-CEDN-over3 and TD-CEDN-all refer to the proposed TD-CEDN trained with the first and second training strategies, respectively. [19] further contribute more than 10000 high-quality annotations to the remaining images. Visual boundary prediction: A deep neural prediction network and It indicates that multi-scale and multi-level features improve the capacities of the detectors. Semantic contours from inverse detectors. TLDR. 520 - 527. 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. Segmentation as selective search for object recognition. 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. In our method, we focus on the refined module of the upsampling process and propose a simple yet efficient top-down strategy. Given trained models, all the test images are fed-forward through our CEDN network in their original sizes to produce contour detection maps. NeurIPS 2018. Indoor segmentation and support inference from rgbd images. Some other methods[45, 46, 47] tried to solve this issue with different strategies. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. 5, we trained the dataset with two strategies: (1) assigning a pixel a positive label if only if its labeled as positive by at least three annotators, otherwise this pixel was labeled as negative; (2) treating all annotated contour labels as positives. 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 variety of approaches have been developed in the past decades. deep network for top-down contour detection, in, J. To find the high-fidelity contour ground truth for training, we need to align the annotated contours with the true image boundaries. Note that the occlusion boundaries between two instances from the same class are also well recovered by our method (the second example in Figure5). the encoder stage in a feedforward pass, and then refine this feature map in a 11 shows several results predicted by HED-ft, CEDN and TD-CEDN-ft (ours) models on the validation dataset. The encoder network consists of 13 convolutional layers which correspond to the first 13 convolutional layers in the VGG16 network designed for object classification. T.-Y. Together they form a unique fingerprint. Note that we did not train CEDN on MS COCO. A ResNet-based multi-path refinement CNN is used for object contour detection. 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. CEDN works well on unseen classes that are not prevalent in the PASCAL VOC training set, such as sports. Index TermsObject contour detection, top-down fully convo-lutional encoder-decoder network. Work fast with our official CLI. Boosting object proposals: From Pascal to COCO. The dataset is split into 381 training, 414 validation and 654 testing images. search dblp; lookup by ID; about. Source: Object Contour and Edge Detection with RefineContourNet, jimeiyang/objectContourDetector These learned features have been adopted to detect natural image edges[25, 6, 43, 47] and yield a new state-of-the-art performance[47]. The Pascal visual object classes (VOC) challenge. boundaries from a single image, in, P.Dollr and C.L. Zitnick, Fast edge detection using structured [57], we can get 10528 and 1449 images for training and validation. loss for contour detection. 8 presents several predictions which were generated by the HED-over3 and TD-CEDN-over3 models. 30 Apr 2019. Edge detection has experienced an extremely rich history. Object contour detection is fundamental for numerous vision tasks. M.R. Amer, S.Yousefi, R.Raich, and S.Todorovic. Our method not only provides accurate predictions but also presents a clear and tidy perception on visual effect. Expand. 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. BDSD500[14] is a standard benchmark for contour detection. Conference on Computer Vision and Pattern Recognition (CVPR), V.Nair and G.E. Hinton, Rectified linear units improve restricted boltzmann Fig. The enlarged regions were cropped to get the final results. Fig. detection, in, J.Revaud, P.Weinzaepfel, Z.Harchaoui, and C.Schmid, EpicFlow: We set the learning rate to, and train the network with 30 epochs with all the training images being processed each epoch. 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. advance the state-of-the-art on PASCAL VOC (improving average recall from 0.62 According to the results, the performances show a big difference with these two training strategies. The RGB images and depth maps were utilized to train models, respectively. 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. lower layers. 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]. Complete survey of models in this eld can be found in . During training, we fix the encoder parameters (VGG-16) and only optimize decoder parameters. Different from previous . Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. building and mountains are clearly suppressed. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. 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 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. segments for object detection,, X.Ren and L.Bo, Discriminatively trained sparse code gradients for contour PASCAL visual object classes (VOC) challenge,, S.Gupta, P.Arbelaez, and J.Malik, Perceptual organization and recognition We also note that there is still a big performance gap between our current method (F=0.57) and the upper bound (F=0.74), which requires further research for improvement. This work builds on recent work that uses convolutional neural networks to classify category-independent region proposals (R-CNN), introducing a novel architecture tailored for SDS, and uses category-specific, top-down figure-ground predictions to refine the bottom-up proposals. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. 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. 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). Note: In the encoder part, all of the pooling layers are max-pooling with a 22 window and a stride 2 (non-overlapping window). 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.