Hill climbing-based histogram equalization for camouflage object detection

2018 ◽  
Author(s):  
Long Bao ◽  
Karen Panetta ◽  
Sos S. Agaian
Keyword(s):  
Object Detection ◽  
Histogram Equalization ◽  
Hill Climbing

A review of the Multiple Object Detection, Tracking and Recognition in Video Surveillance Systems using different approaches

2019 ◽  
Vol 118 (7) ◽  
pp. 34-43
Author(s):  
C.M. Patil ◽  
Sunitha Y. N
Keyword(s):  
Object Detection ◽  
Video Processing ◽  
Moving Objects ◽  
Research Work ◽  
Histogram Equalization ◽  
Surveillance Systems ◽  
Exciting Field ◽  
Survey Paper ◽  
Multiple Object ◽  
Base Paper

Moving item location in any video scene and its detection are the 2 significant testing errands in the moving video investigation system which is dynamic in nature. It is trying because of impediment, nearness of shadows, jumbling, dynamic foundation, clamor etc. Detection of moving objects, following, object coordinating crosswise over multi-camera, and reidentification are the fundamental strides of multi camera video investigation system. Multiple object location and following in savvy city video investigation can be created by propriety of society, for example, shrewd observation, savvy stopping, traffic observing, vehicle route, savvy human services and so forth. The objective of this paper is to examine and audit different methodologies towards such different types of moving object detection, its segmentation, histogram equalization, filtering, classification etc…  Also, this survey paper gives a brief idea about the work done by various authors till date in the field of video image processing so that this review papers serves as a base paper for many of the researchers who want to do research in this exciting field of video processing. Some of the drawbacks or the disadvantages of the works that were carried out by the earlier researchers are considered in our research work, studied in brief & problem is being defined along with the objectives in order to overcome some of the deficiencies of the existing algos.

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

Privacy-Preserving Fingerprint Authentication Resistant to Hill-Climbing Attacks

2018 ◽  
Vol E101.A (1) ◽  
pp. 138-148 ◽  
Author(s):  
Haruna HIGO ◽  
Toshiyuki ISSHIKI ◽  
Kengo MORI ◽  
Satoshi OBANA
Keyword(s):  
Privacy Preserving ◽  
Hill Climbing
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