Automated Image Annotation System Based on an Open Source Object Database

Author(s):  
Gabriel Mihai ◽  
Liana Stanescu ◽  
Dumitru Dan Burdescu ◽  
Cosmin Stoica Spahiu
2000 ◽  
Vol 7 (3) ◽  
pp. 61-71 ◽  
Author(s):  
R.K. Srihari ◽  
Z. Zhang

2019 ◽  
Author(s):  
Yimin Wang ◽  
Qi Li ◽  
Lijuan Liu ◽  
Zhi Zhou ◽  
Yun Wang ◽  
...  

AbstractNeuron morphology is recognized as a key determinant of cell type, yet the quantitative profiling of a mammalian neuron’s complete three-dimensional (3-D) morphology remains arduous when the neuron has complex arborization and long projection. Whole-brain reconstruction of neuron morphology is even more challenging as it involves processing tens of teravoxels of imaging data. Validating such reconstructions is extremely laborious. We developed TeraVR, an open-source virtual reality annotation system, to address these challenges. TeraVR integrates immersive and collaborative 3-D visualization, interaction, and hierarchical streaming of teravoxel-scale images. Using TeraVR, we produced precise 3-D full morphology of long-projecting neurons in whole mouse brains and developed a collaborative workflow for highly accurate neuronal reconstruction.


Author(s):  
Yosuke Furukawa ◽  
◽  
Yusuke Kamoi ◽  
Tatsuya Sato ◽  
Tomohiro Takagi

This paper presents a new method of an automatic image annotation system that estimates keywords from an image. Typical automatic image annotation systems extract features from an image and recognize keywords. However this method has two problems. One is that it treats features statically. Features should change depending on what keywords are attached so keywords should not be treated equally. Another is that it does not consider the level of keywords. Visual keywords, such as color or texture, can be recognized easily from image features, while high-level semantics such as context are hard to recognize from the features. To solve these problems, our approach is to recognize context by using networked specialist knowledge and to recognize keywords by changing feature values dynamically depending on the context. To evaluate our system, we conducted two experiments of applying it to textile images. As a result, we obtained improved accuracy and confirmed the effectiveness of using networked knowledge.


2015 ◽  
Vol 42 (24) ◽  
pp. 9539-9553 ◽  
Author(s):  
Marina Ivasic-Kos ◽  
Ivo Ipsic ◽  
Slobodan Ribaric

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