Local energy-based shape histogram (LESH) is a proposed
image descriptor
In computer vision, visual descriptors or image descriptors are descriptions of the visual features of the contents in images, videos, or algorithms or applications that produce such descriptions. They describe elementary characteristics such as ...
in
computer vision
Computer vision is an Interdisciplinarity, interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate t ...
. It can be used to get a description of the underlying shape. The LESH feature descriptor is built on local energy model of feature perception, see e.g.
phase congruency
Phase congruency is a measure of feature significance in computer images, a method of edge detection that is particularly robust against changes in illumination and contrast.
Foundations
Phase congruency reflects the behaviour of the image in the ...
for more details. It encodes the underlying shape by accumulating local energy of the underlying signal along several filter orientations, several local
histograms
A histogram is an approximate representation of the distribution of numerical data. The term was first introduced by Karl Pearson. To construct a histogram, the first step is to " bin" (or " bucket") the range of values—that is, divide the ent ...
from different parts of the image/patch are generated and concatenated together into a 128-dimensional compact spatial histogram. It is designed to be
scale invariant
In physics, mathematics and statistics, scale invariance is a feature of objects or laws that do not change if scales of length, energy, or other variables, are multiplied by a common factor, and thus represent a universality.
The technical term ...
. The LESH features can be used in applications like shape-based image retrieval, medical image processing, object detection, and
pose estimation
3D pose estimation is a process of predicting the transformation of an object from a user-defined reference pose, given an image or a 3D scan. It arises in computer vision or robotics where the pose or transformation of an object can be used for ...
.
See also
*
Feature detection (computer vision)
In computer vision and image processing, a feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties. Features may be specific structures in the image such as poi ...
*
Scale-invariant feature transform
The scale-invariant feature transform (SIFT) is a computer vision algorithm to detect, describe, and match local '' features'' in images, invented by David Lowe in 1999.
Applications include object recognition, robotic mapping and navigation, ...
*
Speeded up robust features
In computer vision, speeded up robust features (SURF) is a patented local feature detector and descriptor. It can be used for tasks such as object recognition, image registration, classification, or 3D reconstruction. It is partly inspired ...
*
Gradient Location Orientation Histogram
References
* Code:
Sarfraz, S., Hellwich, O.:"Head Pose Estimation in Face Recognition across Pose Scenarios", Proceedings of VISAPP 2008, Int. conference on Computer Vision Theory and Applications, Madeira, Portugal, pp. 235-242, January 2008 (Best Student Paper Award).
Feature detection (computer vision)
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