Object detection in images using morphlet descriptions
Y.V. Vizilter, V.S. Gorbatsevich, S.V. Sidyakin, B.V. Vishnyakov

 

State Research Institute of Aviation Systems (GosNIIAS), Moscow, Russia

Full text of article: Russian language.

 PDF

Abstract:
An original method for object detection based on morphlet trees is proposed in the paper. It allows the robust detection of heterogeneous objects in images to be done without pre-training. Besides, the detection process simultaneously includes a preliminary segmentation, which can be later used for recognition. Also, there is another important characteristic: the proposed approach does not require the use of sliding windows and feature pyramids to detect different-scale objects.

Keywords:
mathematical morphology, Pytiev morphology, object detection, morphlets.

Citation:
Vizilter YV, Gorbatsevich VS,Vishnyakov BV, Sidyakin SV. Object detection in images using morphlet descriptions, Computer Optics 2017; 41(3): 406-411. DOI: 10.18287/2412-6179-2017-41-3-406-411.

References:

  1. Joseph R, Divvala S, Girshick R, Farhadi A. You only look once: Unified, real-time object detection. Proc CVPR 2016: 779-788. DOI: 10.1109/CVPR.2016.91.
  2. Vizilter YuV, Gorbatsevich VS, Zheltov SYu, Rubis AYu, Vorotnikov AV. Morphlets: a new class of tree-structured morphological descriptors of image shape. Computer Optics 2015; 39(1): 101-108. DOI: 10.18287/0134-2452-2015-39-1-101-108.
  3. Pytiev Yu, Chulichkov A. Morphological methods for image analysis [In Russian]. Мoscow: "Fizmatlit" Publisher, 2010. ISBN: 978-5-9221-1225-3.
  4. The PASCAL Visual Object Classes Challenge 2007. Source: áhttp://host.robots.ox.ac.uk/pascal/VOC/voc2007/ñ.
  5. Otsu N. A threshold selection method from gray-level histograms // IEEE Trans Systems Man and Cybernetics 1979; 9(1): 66-69. DOI: 10.1109/TSMC.1979.4310076.
  6. Dalal N, Triggs B. Histogram of oriented gradients for human detection. CVPR 2005; 1(1): 886-893. DOI: 10.1109/CVPR.2005.177.
  7. Hou X, Zhang L. Saliency detection: A spectral residual approach. Proc CVPR 2007: 1-8. DOI: 10.1109/CVPR.2007.383267.
  8. Felzenszwalb P, Huttenlocher D. Efficient graph-based image segmentation. International Journal of Computer Vision 2004; 59(2): 167-181. DOI:10.1023/B:VI­SI.0000022288.19776.77.
  9. Alexe B, Deselaers T, Ferrari V. Measuring the objectness of image windows. IEEE Trans Pattern Anal Mach Intell 2012; 34(11): 2189-2202. DOI: 10.1109/TPAMI.2012.28.
  10. The KITTI Vision Benchmark Suite. Source: <http://www.cvlibs.net/datasets/ kitti/>.
© 2009, IPSI RAS
Institution of Russian Academy of Sciences, Image Processing Systems Institute of RAS, Russia, 443001, Samara, Molodogvardeyskaya Street 151; E-mail: journal@computeroptics.ru; Phones: +7 (846) 332-56-22, Fax: +7 (846) 332-56-20