Counting trees with point-wise supervised segmentation network

Deep convolutional neural networks (DCNNs) trained on the pixel-level annotated images have achieved improvements in semantic segmentation. Due to the high cost of labeling training data, their applications may have great limitation. However, weakly supervised segmentation approaches can significantly reduce human labeling efforts. In this paper, we introduce a new framework to generate high-quality initial pixel-level annotations. By using a hierarchical image segmentation algorithm to predict the boundary map, we select the optimal scale of high-quality hierarchies. In the initialization step, scribble annotations and the saliency map are combined to construct a graphic model over the optimal scale segmentation. By solving the minimal cut problem, it can spread information from scribbles to unmarked regions. In the training process, the segmentation network is trained by using the initial pixel-level annotations. To iteratively optimize the segmentation, we use a graphical model to refine segmentation masks and retrain the segmentation network to get more precise pixel-level annotations. The experimental results on Pascal VOC 2012 dataset demonstrate that the proposed framework outperforms most of weakly supervised semantic segmentation methods and achieves the state-of-the-art performance, which is [Formula: see text] mIoU.

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Simultaneous Semi-supervised Segmentation of Category-Independent Objects from a Collection of Images

Representations, Analysis and Recognition of Shape and Motion from Imaging Data - Communications in Computer and Information Science ◽

10.1007/978-3-030-19816-9_15 ◽

2019 ◽

pp. 192-203

Author(s):

Hager Merdassi ◽

Walid Barhoumi ◽

Ezzeddine Zagrouba

Keyword(s):

Supervised Segmentation

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A Novel Framework for Interactive Visualization and Analysis of Hyperspectral Image Data

Journal of Electrical and Computer Engineering ◽

10.1155/2016/2635124 ◽

2016 ◽

Vol 2016 ◽

pp. 1-17 ◽

Cited By ~ 3

Author(s):

Johannes Jordan ◽

Elli Angelopoulou ◽

Andreas Maier

Keyword(s):

Hyperspectral Image ◽

Imaging Modality ◽

High Capacity ◽

Interactive Visualization ◽

Image Data ◽

Hyperspectral Image Analysis ◽

Supervised Segmentation ◽

Hyperspectral Image Data ◽

Visualization Techniques ◽

Application Specific

Multispectral and hyperspectral images are well established in various fields of application like remote sensing, astronomy, and microscopic spectroscopy. In recent years, the availability of new sensor designs, more powerful processors, and high-capacity storage further opened this imaging modality to a wider array of applications like medical diagnosis, agriculture, and cultural heritage. This necessitates new tools that allow general analysis of the image data and are intuitive to users who are new to hyperspectral imaging. We introduce a novel framework that bundles new interactive visualization techniques with powerful algorithms and is accessible through an efficient and intuitive graphical user interface. We visualize the spectral distribution of an image via parallel coordinates with a strong link to traditional visualization techniques, enabling new paradigms in hyperspectral image analysis that focus on interactive raw data exploration. We combine novel methods for supervised segmentation, global clustering, and nonlinear false-color coding to assist in the visual inspection. Our framework coined Gerbil is open source and highly modular, building on established methods and being easily extensible for application-specific needs. It satisfies the need for a general, consistent software framework that tightly integrates analysis algorithms with an intuitive, modern interface to the raw image data and algorithmic results. Gerbil finds its worldwide use in academia and industry alike with several thousand downloads originating from 45 countries.

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