Object skeleton is a useful cue for object detection, complementary to the object contour, as it provides a structural representation to describe the relationship among object parts. While object skeleton extraction in natural images is a very challenging problem, as it requires the extractor to be able to capture both local and global image context to determine the intrinsic scale of each skeleton pixel. Existing methods rely on per-pixel based multi-scale feature computation, which results in difficult modeling and high time consumption. In this paper, we present a fully convolutional network with multiple scale-associated side outputs to address this problem. By observing the relationship between the receptive field sizes of the sequential stages in the network and the skeleton scales they can capture, we introduce a scale-associated side output to each stage. We impose supervision to different stages by guiding the scale-associated side outputs toward groundtruth skeletons of different scales. The responses of the multiple scale-associated side outputs are then fused in a scale-specific way to localize skeleton pixels with multiple scales effectively. Our method achieves promising results on two skeleton extraction datasets, and significantly outperforms other competitors.
Some pioneers try to extract skeletons from the gradient intensity maps computed on natural images. The gradient intensity map is generally obtained by applying directional derivative operators to a gray-scale image smoothed by a Gaussian kernel. For instance, in @cite_23 , the author provides an automatic mechanism to determine the best size of the Gaussian kernel used for gradient computation, and he propose to detect skeletons as the pixels for which the gradient intensity assumes a local maximum (minimum) in the direction of the main principal curvature. Jang and Hong @cite_30 extract the skeleton from the pseudo-distance map which is obtained by iteratively minimizing an object function defined on the gradient intensity map. Yu and Bajaj @cite_15 propose to trace the ridges of the skeleton intensity map calculated from the diffused vector field of the gradient intensity map, which can remove the undesirable biased skeletons. Due to the lack of object prior, these methods are only able to handle the images with simple scenes.
@cite_30 @cite_15 @cite_23
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In this paper we introduce a new tool, called a pseudo-distance map (PDM), for extracting skeletons from grayscale images without region segmentation or edge detection. Given an edge-strength function (ESF) of a gray-scale image, the PDM is computed from the ESF using the partial differential equations we propose. The PDM can be thought of as a relaxed version of a Euclidean distance map. Therefore, its ridges correspond to the skeleton of the original gray-scale image and it provides information on the approximate width of skeletonized structures. Since the PDM is directly computed from the ESF without thresholding it, the skeletonization result is generally robust and less noisy. We tested our method using a variety of synthetic and real images. The experimental results show that our method works well on such images. In this paper we describe a method for skeletonization of gray-scale images without segmentation. Our method is based on anisotropic vector diffusion. The skeleton strength map, calculated from the diffused vector field, provides us a measure of how possible each pixel could be on the skeletons. The final skeletons are traced from the skeleton strength map, which mimics the behavior of edge detection from the edge strength map of the original image. A couple of real or synthesized images will be shown to demonstrate the performance of our algorithm. Extracting skeletons from natural images is a challenging problem, due to complex backgrounds in the scene and various scales of objects. To address this problem, we propose a two-stream fully convolutional neural network which uses the original image and its corresponding semantic segmentation probability map as inputs and predicts the skeleton map using merged multi-scale features. We find that the semantic segmentation probability map is complementary to the corresponding color image and can boost the performance of our baseline model which trained only on color images. We conduct experiments on SK-LARGE dataset and the F-measure of our method on validation set is 0.738 which outperforms current state-of-the-art significantly and demonstrates the effectiveness of our proposed approach. Object skeletons are useful for object representation and object detection. They are complementary to the object contour, and provide extra information, such as how object scale (thickness) varies among object parts. But object skeleton extraction from natural images is very challenging, because it requires the extractor to be able to capture both local and non-local image context in order to determine the scale of each skeleton pixel. In this paper, we present a novel fully convolutional network with multiple scale-associated side outputs to address this problem. By observing the relationship between the receptive field sizes of the different layers in the network and the skeleton scales they can capture, we introduce two scale-associated side outputs to each stage of the network. The network is trained by multi-task learning, where one task is skeleton localization to classify whether a pixel is a skeleton pixel or not, and the other is skeleton scale prediction to regress the scale of each skeleton pixel. Supervision is imposed at different stages by guiding the scale-associated side outputs toward the ground-truth skeletons at the appropriate scales. The responses of the multiple scale-associated side outputs are then fused in a scale-specific way to detect skeleton pixels using multiple scales effectively. Our method achieves promising results on two skeleton extraction datasets, and significantly outperforms other competitors. In addition, the usefulness of the obtained skeletons and scales (thickness) are verified on two object detection applications: foreground object segmentation and object proposal detection.