45,718 research outputs found
Segmentation of Sedimentary Grain in Electron Microscopy Image
This paper describes a novel method developed for the segmentation of sedimentary grains in electron microscopy images. The algorithm utilizes the approach of region splitting and merging. In the splitting stage, the marker-based watershed segmentation is used. In the merging phase, the typical characteristics of grains in electron microscopy images are exploited for proposing special metrics, which are then used during the merging stage to obtain a correct grain segmentation. The metrics are based on the typical intensity changes on the grain borders and the compact shape of grains. The experimental part describes the optimal setting of parameter in the splitting stage and the overall results of the proposed algorithm tested on available database of grains. The results show that the proposed technique fulfills the requirements of its intended application
Structure Preserving Large Imagery Reconstruction
With the explosive growth of web-based cameras and mobile devices, billions
of photographs are uploaded to the internet. We can trivially collect a huge
number of photo streams for various goals, such as image clustering, 3D scene
reconstruction, and other big data applications. However, such tasks are not
easy due to the fact the retrieved photos can have large variations in their
view perspectives, resolutions, lighting, noises, and distortions.
Fur-thermore, with the occlusion of unexpected objects like people, vehicles,
it is even more challenging to find feature correspondences and reconstruct
re-alistic scenes. In this paper, we propose a structure-based image completion
algorithm for object removal that produces visually plausible content with
consistent structure and scene texture. We use an edge matching technique to
infer the potential structure of the unknown region. Driven by the estimated
structure, texture synthesis is performed automatically along the estimated
curves. We evaluate the proposed method on different types of images: from
highly structured indoor environment to natural scenes. Our experimental
results demonstrate satisfactory performance that can be potentially used for
subsequent big data processing, such as image localization, object retrieval,
and scene reconstruction. Our experiments show that this approach achieves
favorable results that outperform existing state-of-the-art techniques
Automatic detection of welding defects using the convolutional neural network
Quality control of welded joints is an important step before commissioning of various types of metal structures. The main obstacles to the commissioning of such facilities are the areas where the welded joint deviates from acceptable defective standards. The defects of welded joints include non-welded, foreign inclusions, cracks, pores, etc. The article describes an approach to the detection of the main types of defects of welded joints using a combination of convolutional neural networks and support vector machine methods. Convolutional neural networks are used for primary classification. The support vector machine is used to accurately define defect boundaries. As a preprocessing in our work, we use the methods of morphological filtration. A series of experiments confirms the high efficiency of the proposed method in comparison with pure CNN method for detecting defects
Machine learning methods for histopathological image analysis
Abundant accumulation of digital histopathological images has led to the
increased demand for their analysis, such as computer-aided diagnosis using
machine learning techniques. However, digital pathological images and related
tasks have some issues to be considered. In this mini-review, we introduce the
application of digital pathological image analysis using machine learning
algorithms, address some problems specific to such analysis, and propose
possible solutions.Comment: 23 pages, 4 figure
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