Texture-based Rotation-Invariant Histograms of Oriented Gradients

Cun Hang, Fei Hu, Aboul Ella Hassanieny, and K. Xiao, "Texture-based Rotation-Invariant Histograms of Oriented Gradients", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.

Date Presented:

30 Dec

Microorganism detection through computer vision
is of great essence in many scientific and industrial fields,
among which vorticella is always a challenging and focused topic,
especially for the sake of water quality monitoring and biological
diversity tracking. However, given the fact that vorticella is likely
to be obscured by alga and it float in different poses, the visual
detecting method must be tolerant of different rotations and slight
ambiguity. Previously, the Histograms of Oriented Gradients
(HOG) is widely used in pedestrian, hand gesture and many
other object detections. And the Local Binary Pattern (LBP)
has been proven to be a widely applicable image feature for
texture classification and face analysis. By combining the two
methods, this paper presents a method to build rotation-invariant
descriptors, in which the idea of dividing whole image into
spatial blocks to obtain local relative distribution and the concept
of utilizing the discrete Fourier transform (DFT) on cycle-shift
patterns in histogram to acquire rotation-invariant features are
efficiently bonded.