Learned boundary maps are known to outperform hand- crafted ones as a basis for the watershed algorithm. We show, for the first time, how to train watershed computation jointly with boundary map prediction. The estimator for the merging priorities is cast as a neural network that is con- volutional (over space) and recurrent (over iterations). The latter allows learning of complex shape priors. The method gives the best known seeded segmentation results on the CREMI segmentation challenge.
When ground truth is provided in terms of region labels rather than boundary maps, a suitable boundary map must be created first. Simple morphological operations were found sufficient in @cite_4 , while @cite_21 preferred smooth probabilities derived from a distance transform starting at the true boundaries. Outside connectomics, @cite_22 achieved superior results by defining the ground truth altitude map in terms of the vector distance transform, which allows optimizing the prediction's gradient direction and height separately.
@cite_21 @cite_4 @cite_22
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The goal of this work is to accurately detect and localize boundaries in natural scenes using local image measurements. We formulate features that respond to characteristic changes in brightness, color, and texture associated with natural boundaries. In order to combine the information from these features in an optimal way, we train a classifier using human labeled images as ground truth. The output of this classifier provides the posterior probability of a boundary at each image location and orientation. We present precision-recall curves showing that the resulting detector significantly outperforms existing approaches. Our two main results are 1) that cue combination can be performed adequately with a simple linear model and 2) that a proper, explicit treatment of texture is required to detect boundaries in natural images. Traditionally, land-cover mapping from remote sensing images is performed by classifying each atomic region in the image in isolation and by enforcing simple smoothing priors via random fields models as two independent steps. In this paper, we propose to model the segmentation problem by a discriminatively trained Conditional Random Field (CRF). To this end, we employ Structured Support Vector Machines (SSVM) to learn the weights of an informative set of appearance descriptors jointly with local class interactions. We propose a principled strategy to learn pairwise potentials encoding local class preferences from sparsely annotated ground truth. We show that this approach outperform standard baselines and more expressive CRF models, improving by 4–6 points the average class accuracy on a challenging dataset involving urban high resolution satellite imagery. In this paper, we focus on improving the proposal classification stage in the object detection task and present implicit negative sub-categorization and sink diversion to lift the performance by strengthening loss function in this stage. First, based on the observation that the “background” class is generally very diverse and thus challenging to be handled as a single indiscriminative class in existing state-of-the-art methods, we propose to divide the background category into multiple implicit sub-categories to explicitly differentiate diverse patterns within it. Second, since the ground truth class inevitably has low-value probability scores for certain images, we propose to add a “sink” class and divert the probabilities of wrong classes to this class when necessary, such that the ground truth label will still have a higher probability than other wrong classes even though it has low probability output. Additionally, we propose to use dilated convolution, which is widely used in the semantic segmentation task, for efficient and valuable context information extraction. Extensive experiments on PASCAL VOC 2007 and 2012 data sets show that our proposed methods based on faster R-CNN implementation can achieve state-of-the-art mAPs, i.e., 84.1 , 82.6 , respectively, and obtain 2.5 improvement on ILSVRC DET compared with that of ResNet. Figure ground assignment is a key step in perceptual organization which assigns contours to one of the two abutting regions, providing information about occlusion and allowing high-level processing to focus on non-accidental shapes of figural regions. In this paper, we develop a computational model for figure ground assignment in complex natural scenes. We utilize a large dataset of images annotated with human-marked segmentations and figure ground labels for training and quantitative evaluation. We operationalize the concept of familiar configuration by constructing prototypical local shapes, i.e. shapemes, from image data. Shapemes automatically encode mid-level visual cues to figure ground assignment such as convexity and parallelism. Based on the shapeme representation, we train a logistic classifier to locally predict figure ground labels. We also consider a global model using a conditional random field (CR.F) to enforce global figure ground consistency at T-junctions. We use loopy belief propagation to perform approximate inference on this model and learn maximum likelihood parameters from ground-truth labels. We find that the local shapeme model achieves an accuracy of 64 in predicting the correct figural assignment. This compares favorably to previous studies using classical figure ground cues [1]. We evaluate the global model using either a set of contours extracted from a low-level edge detector or the set of contours given by human segmentations. The global CRF model significantly improves the performance over the local model, most notably when using human-marked boundaries (78 ). These promising experimental results show that this is a feasible approach to bottom-up figure ground assignment in natural images.