Adaptive approach to small-object segmentation

2001 ◽  
Author(s):  
Tingzhi Shen ◽  
Lei Wang
Keyword(s):  
Object Segmentation ◽  
Small Object ◽  
Adaptive Approach

Small object segmentation with fully convolutional network based on overlapping domain decomposition

2019 ◽  
Vol 30 (4) ◽  
pp. 707-716 ◽  
Author(s):  
Jinhee Park ◽  
Dokyeong Kwon ◽  
Bo Won Choi ◽  
Ga Young Kim ◽  
Kwang Yong Kim ◽  
...  
Keyword(s):  
Domain Decomposition ◽  
Object Segmentation ◽  
Small Object ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Overlapping Domain Decomposition

scholarly journals Lesion-wise evaluation for effective performance monitoring of small object segmentation

2021 ◽  
Author(s):  
Irme Groothuis ◽  
Carole Sudre ◽  
Silvia Ingala ◽  
Josephine Barnes ◽  
Juan Domingo Gispert Lopez ◽  
...  
Keyword(s):  
Performance Monitoring ◽  
Object Segmentation ◽  
Small Object ◽  
Effective Performance

AVILNet: A New Pliable Network with a Novel Metric for Small-Object Segmentation and Detection in Infrared Images

2021 ◽  
Vol 13 (4) ◽  
pp. 555
Author(s):  
Ikhwan Song ◽  
Sungho Kim
Keyword(s):  
Object Segmentation ◽  
State Of The Art ◽  
Threshold Value ◽  
Small Object ◽  
Infrared Images ◽  
Average Precision ◽  
Trade Off ◽  
Good Evaluation ◽  
Single Dataset ◽  
Evaluation Metric

Infrared small-object segmentation (ISOS) has a persistent trade-off problem—that is, which came first, recall or precision? Constructing a fine balance between of them is, au fond, of vital importance to obtain the best performance in real applications, such as surveillance, tracking, and many fields related to infrared searching and tracking. F1-score may be a good evaluation metric for this problem. However, since the F1-score only depends upon a specific threshold value, it cannot reflect the user’s requirements according to the various application environment. Therefore, several metrics are commonly used together. Now we introduce F-area, a novel metric for a panoptic evaluation of average precision and F1-score. It can simultaneously consider the performance in terms of real application and the potential capability of a model. Furthermore, we propose a new network, called the Amorphous Variable Inter-located Network (AVILNet), which is of pliable structure based on GridNet, and it is also an ensemble network consisting of the main and its sub-network. Compared with the state-of-the-art ISOS methods, our model achieved an AP of 51.69%, F1-score of 63.03%, and F-area of 32.58% on the International Conference on Computer Vision 2019 ISOS Single dataset by using one generator. In addition, an AP of 53.6%, an F1-score of 60.99%, and F-area of 32.69% by using dual generators, with beating the existing best record (AP, 51.42%; F1-score, 57.04%; and F-area, 29.33%).

scholarly journals CentroidNetV2: A hybrid deep neural network for small-object segmentation and counting

2021 ◽  
Vol 423 ◽  
pp. 490-505
Author(s):  
Klaas Dijkstra ◽  
Jaap van de Loosdrecht ◽  
Waatze A. Atsma ◽  
Lambert R.B. Schomaker ◽  
Marco A. Wiering
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Object Segmentation ◽  
Small Object

Revealing gross three-dimensional structure in the SEM

1987 ◽  
Vol 45 ◽  
pp. 656-657
Author(s):  
Sterling P. Newberry
Keyword(s):  
Freeze Drying ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Small Object ◽  
Final Solution ◽  
Dimensional Representation ◽  
X Ray ◽  
Three Dimensional Representation ◽  
Freeze Dried ◽  
Critical Point Drying

The beautiful three dimensional representation of small object surfaces by the SEM leads one to search for ways to open up the sample and look inside. Could this be the answer to a better microscopy for gross biological 3-D structure? We know from X-Ray microscope images that Freeze Drying and Critical Point Drying give promise of adequately preserving gross structure. Can we slice such preparations open for SEM inspection? In general these preparations crush more readily than they slice. Russell and Dagihlian got around the problem by “deembedding” a section before imaging. This some what defeats the advantages of direct dry preparation, thus we are reluctant to accept it as the final solution to our problem. Alternatively, consider fig 1 wherein a freeze dried onion root has a window cut in its surface by a micromanipulator during observation in the SEM.

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