Object Detection Network Robust to Local Illumination Variations

2021 ◽  
Author(s):  
Jinyeon Kim ◽  
Jonghee Park ◽  
Sang-Seol Lee ◽  
Sung-Joon Jang
Keyword(s):  
Object Detection ◽  
Illumination Variations ◽  
Local Illumination

scholarly journals A Fast Method for Localization of Local Illumination Variations and Photometric Normalization in Face Images

2006 ◽  
pp. 48-57
Author(s):  
Estela María Álvarez Morales ◽  
Francisco Silva Mata ◽  
Eduardo Garea Llano ◽  
Heydi Mendez Vazquez ◽  
Moisés Herrera
Keyword(s):  
Fast Method ◽  
Illumination Variations ◽  
Face Images ◽  
Local Illumination

Automatic accounting of Baikal diatomic algae: approaches and prospects

2019 ◽  
pp. 295-299
Author(s):  
Кonstantin А. Elshin ◽  
Еlena I. Molchanova ◽  
Мarina V. Usoltseva ◽  
Yelena V. Likhoshway
Keyword(s):  
Object Detection ◽  
Loss Function ◽  
Classification Accuracy ◽  
Diatom Species ◽  
Bounding Box ◽  
Synedra Acus ◽  
And Training

Using the TensorFlow Object Detection API, an approach to identifying and registering Baikal diatom species Synedra acus subsp. radians has been tested. As a result, a set of images was formed and training was conducted. It is shown that аfter 15000 training iterations, the total value of the loss function was obtained equal to 0,04. At the same time, the classification accuracy is equal to 95%, and the accuracy of construction of the bounding box is also equal to 95%.

Object Detection in Cluttered Range Images Using Edgel Geometry

2010 ◽  
Vol 130 (9) ◽  
pp. 1572-1580
Author(s):  
Dipankar Das ◽  
Yoshinori Kobayashi ◽  
Yoshinori Kuno
Keyword(s):  
Object Detection ◽  
Range Images
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