Recognition of stonefish from underwater video

There is thousands of organisms under the water and every group of organisms have many types or species. Some are dangerous and will attack when touched and some others will attack directly without any reason. In this research, a method that can recognize a stonefish, which is the most venomous fish in the world from a video to help divers or swimmers in open water to avoid danger, is presented. When a stonefish hides, it keeps its head visible until it can attack the small fish quickly. This property is used to formulate the algorithm. The video is enhanced using Gaussian filter, Median filter, and Wiener filter. The method uses a database of images that contain the images of the head of stonefish. The features of the stonefish head is detected from the video by comparing the features of head images in the database by using a part of the Speeded Up Robust Features (SURF) method. The features in the video are compared with the features of the images of the database by using the k-Nearest Neighbor algorithm and the Histogram. The results of the comparison will decide if there is a stonefish in the video or not. Finally, if there is a stonefish in the video, the system will generate a warning signal to help the divers or swimmers to move away from it immediately.

[1]  Elizabeth A. Taylor,et al.  Classification of marine organisms in underwater images using CQ-HMAX biologically inspired color approach , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[2]  Siva Ram Krishna Vadali,et al.  Shape recognition based on shape-signature identification and condensibility: Application to underwater imagery , 2013, 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG).

[3]  Simone Palazzo,et al.  Quantitative performance analysis of object detection algorithms on underwater video footage , 2012, MAED '12.

[4]  G.J. Dobeck,et al.  Active learning for detection of mine-like objects in side-scan sonar imagery , 2005, IEEE Journal of Oceanic Engineering.

[5]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[6]  Robert B. Fisher,et al.  Automatic fish classification for underwater species behavior understanding , 2010, ARTEMIS '10.

[7]  J. Andrade-Cetto.  Object Recognition , 2003 .

[8]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[9]  Charles E. Thorpe.  Sonar image processing: An application of template matching through relaxation , 1981 .

[10]  Phoenix X. Huang,et al.  Hierarchical Classification for Live Fish Recognition , 2012 .

[11]  Simon J. D. Prince,et al.  Computer Vision: Index , 2012 .

[12]  Zhengmao Ye,et al.  Processing of slow motion images out of underwater, ground and aerial vehicles , 2005, Canadian Conference on Electrical and Computer Engineering, 2005..

[13]  N. Altman.  An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[14]  Michael W. Berns,et al.  Digital Image Processing and Analysis , 1986 .

[15]  Jenq-Neng Hwang,et al.  Supervised and Unsupervised Feature Extraction Methods for Underwater Fish Species Recognition , 2014, 2014 ICPR Workshop on Computer Vision for Analysis of Underwater Imagery.

[16]  Sangseung Kang,et al.  Object recognition for cell manufacturing system , 2012, 2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI).

[17]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .