PyMatting: A Python Library for Alpha Matting

Alpha matting aims to estimate the translucency of an object in a given image. The resulting alpha matte describes pixel-wise to what amount foreground and background colors contribute to the color of the composite image. While most methods in literature focus on estimating the alpha matte, the process of estimating the foreground colors given the input image and its alpha matte is often neglected, although foreground estimation is an essential part of many image editing workflows. In this work, we propose a novel method for foreground estimation given the alpha matte. We demonstrate that our fast multi-level approach yields results that are comparable with the state-of-theart while outperforming those methods in computational runtime and memory usage.

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“…Implementations of multi-level foreground estimation for both CPU and GPU are available in the open source PyMatting library [14].…”

Section: Discussionmentioning

confidence: 99%

“…12.92 l l ≤ 0.0031308 1.055 l 1/γ − 0.055 otherwise (14) to transform from F W lrgb to F W srgb , corresponding to the ground truth F gt . We compare the output of the various methods against F gt .…”

Section: B Datasetmentioning

confidence: 99%

Fast Multi-Level Foreground Estimation

Germer

Uelwer

Conrad

et al. 2021

2020 25th International Conference on Pattern Recognition (ICPR)

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“…We used large-kernel matting, a fast method for high quality matting [57]. We used a Python library PyMatting [58] for alpha matting. Trimap is used for alpha matting and is automatically generated by applying dilation processing to the image that the markers are removed.…”

Section: A Multi-viewpoint Object Image Acquisitionmentioning

confidence: 99%

Robotic Waste Sorter With Agile Manipulation and Quickly Trainable Detector

Kiyokawa¹,

Katayama

Tatsuta

et al. 2021

IEEE Access

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Owing to human labor shortages, the automation of labor-intensive manual waste-sorting is needed. The goal of automating waste-sorting is to replace the human role of robust detection and agile manipulation of waste items with robots. To achieve this, we propose three methods. First, we provide a combined manipulation method using graspless push-and-drop and pick-and-release manipulation. Second, we provide a robotic system that can automatically collect object images to quickly train a deep neuralnetwork model. Third, we provide a method to mitigate the differences in the appearance of target objects from two scenes: one for dataset collection and the other for waste sorting in a recycling factory. If differences exist, the performance of a trained waste detector may decrease. We address differences in illumination and background by applying object scaling, histogram matching with histogram equalization, and background synthesis to the source target-object images. Via experiments in an indoor experimental workplace for waste-sorting, we confirm that the proposed methods enable quick collection of the training image sets for three classes of waste items (i.e., aluminum can, glass bottle, and plastic bottle) and detection with higher performance than the methods that do not consider the differences. We also confirm that the proposed method enables the robot quickly manipulate the objects.

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“…We used large kernel matting, a fast method for high quality matting [57]. We used a Python library PyMatting [58] for alpha matting. Trimap, which is used for alpha matting, is automatically generated by applying dilation processing to the image, which provides the logical product of the object's approximate shape area after marker detection and that of the foreground area after chroma-key processing.…”

Section: A Multi-viewpoint Object Image Acquisitionmentioning

confidence: 99%

Robotic Waste Sorter with Agile Manipulation and Quickly Trainable Detector

Kiyokawa,

Katayama,

Tatsuta

et al. 2021

Preprint

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Owing to human labor shortages, the automation of labor-intensive manual waste-sorting is needed. The goal of automating the waste-sorting is to replace the human role of robust detection and agile manipulation of the waste items by robots. To achieve this, we propose three methods. First, we propose a combined manipulation method using graspless pushand-drop and pick-and-release manipulation. Second, we propose a robotic system that can automatically collect object images to quickly train a deep neural network model. Third, we propose the method to mitigate the differences in the appearance of target objects from two scenes: one for the dataset collection and the other for waste sorting in a recycling factory. If differences exist, the performance of a trained waste detector could be decreased. We address differences in illumination and background by applying object scaling, histogram matching with histogram equalization, and background synthesis to the source target-object images. Via experiments in an indoor experimental workplace for waste-sorting, we confirmed the proposed methods enable quickly collecting the training image sets for three classes of waste items, i.e., aluminum can, glass bottle, and plastic bottle and detecting them with higher performance than the methods that do not consider the differences. We also confirmed that the proposed method enables the robot quickly manipulate them.

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PyMatting: A Python Library for Alpha Matting

Cited by 12 publications

References 18 publications

Fast Multi-Level Foreground Estimation

Fast Multi-Level Foreground Estimation

Robotic Waste Sorter With Agile Manipulation and Quickly Trainable Detector

Robotic Waste Sorter with Agile Manipulation and Quickly Trainable Detector

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