Sketch-to-image is an important task to reduce the burden of creating a color image from scratch. Unlike previous sketch-to-image models, where the image is synthesized in an end-to-end manner, leading to an unnaturalistic image, we propose a method by decomposing the problem into subproblems to generate a more naturalistic and reasonable image. It first generates an intermediate output which is a semantic mask map from the input sketch through instance and semantic segmentation in two levels, background segmentation and foreground segmentation. Background segmentation is formed based on the context of the foreground objects. Then, the foreground segmentations are sequentially added to the created background segmentation. Finally, the generated mask map is fed into an image-to-image translation model to generate an image. Our proposed method works with 92 distinct classes. Compared to state-of-the-art sketch-to-image models, our proposed method outperforms the previous methods and generates better images.
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Information-Theoretic Segmentation by Inpainting Error Maximization
We study image segmentation from an information-theoretic perspective, proposing a novel adversarial method that performs unsupervised segmentation by partitioning images into maximally independent sets. More specifically, we group image pixels into foreground and background, with the goal of minimizing predictability of one set from the other. An easily computed loss drives a greedy search process to maximize inpainting error over these partitions. Our method does not involve training deep networks, is computationally cheap, class-agnostic, and even applicable in isolation to a single unlabeled image. Experiments demonstrate that it achieves a new state-of-the-art in unsupervised segmentation quality, while being substantially faster and more general than competing approaches.
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- Award ID(s):
- 1925101
- NSF-PAR ID:
- 10291619
- Date Published:
- Journal Name:
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- ISSN:
- 2332-564X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Sketch-to-image synthesis method transforms a simple abstract black-and-white sketch into an image. Most sketch-to-image synthesis methods generate an image in an end-to-end manner, leading to generate a non-satisfactory result. The reason is that, in end-to-end models, the models generate images directly from the input sketches. Thus, with very abstract and complicated sketches, the models might struggle in generating naturalistic images due to the simultaneous focus on both factors: overall shape and fine-grained details. In this paper, we propose to divide the problem into subproblems. To this end, an intermediate output, which is a semantic mask map, is first generated from the input sketch via an instance and semantic segmentation. In the instance segmentation stage, the objects' sizes might be modified depending on the surrounding environment and their respective size prior to reflect reality and produce more realistic images. In the semantic seg-mentation stage, a background segmentation is first constructed based on the context of the detected objects. Various natural scenes are implemented for both indoor and outdoor scenes. Following this, a foreground segmentation process is commenced, where each detected object is semantically added into the constructed segmented background. Then, in the next stage, an image-to-image translation model is leveraged to convert the semantic mask map into a colored image. Finally, a post-processing stage is incorporated to further enhance the image result. Extensive experiments demonstrate the superiority of our proposed method over state-of-the-art methods.more » « less
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This paper presents a tool-pose-informed variable center morphological polar transform to enhance segmentation of endoscopic images. The representation, while not loss-less, transforms rigid tool shapes into morphologies consistently more rectangular that may be more amenable to image segmentation networks. The proposed method was evaluated using the U-Net convolutional neural network, and the input images from endoscopy were represented in one of the four different coordinate formats (1) the original rectangular image representation, (2) the morphological polar coordinate transform, (3) the proposed variable center transform about the tool-tip pixel and (4) the proposed variable center transform about the tool vanishing point pixel. Previous work relied on the observations that endoscopic images typically exhibit unused border regions with content in the shape of a circle (since the image sensor is designed to be larger than the image circle to maximize available visual information in the constrained environment) and that the region of interest (ROI) was most ideally near the endoscopic image center. That work sought an intelligent method for, given an input image, carefully selecting between methods (1) and (2) for best image segmentation prediction. In this extension, the image center reference constraint for polar transformation in method (2) is relaxed via the development of a variable center morphological transformation. Transform center selection leads to different spatial distributions of image loss, and the transform-center location can be informed by robot kinematic model and endoscopic image data. In particular, this work is examined using the tool-tip and tool vanishing point on the image plane as candidate centers. The experiments were conducted for each of the four image representations using a data set of 8360 endoscopic images from real sinus surgery. The segmentation performance was evaluated with standard metrics, and some insight about loss and tool location effects on performance are provided. Overall, the results are promising, showing that selecting a transform center based on tool shape features using the proposed method can improve segmentation performance.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
