Efficient scale-space extraction and description of interest points

• US 20110194772A1
• Filed: 09/01/2010
• Published: 08/11/2011
• Est. Priority Date: 02/08/2010
• Status: Active Grant

• Current Assignee: Carear Incorporated (Xerox Holdings Corp.)
• Original Assignee: Telefonica SA
• Inventors: Llofriu, Arturo Bonnin, Adamek, Tomasz, Hernandez, Roger Gimeno, SanJuan, David Marimon
• Granted Patent: US 8,798,377 B2
• Time in Patent Office: Days
• US Class Current: 382/190
• CPC Class Codes: G06T 15/04 Texture mapping
• CPC Class Codes: G06V 10/462 Salient features, e.g. scale invariant feature transforms [SIFT]

Abstract

Method, system and computer program for efficiently extracting and describing scale-space interest points. It is designed towards low overall computational complexity. On one hand, the data acquired during extraction in the description phase is intensively re-used. On the other hand, an algorithmic optimization of the description that dramatically speeds up the process, is proposed. First, the image is filtered with triangle kernel at different scales. The triangle filtered images are reused for extraction of the keypoints dominant orientation and the computation of the DAISY-like descriptor

Claims

1. A method for keypoints scale-space extraction and description in an image, the method comprising the following steps:

a) Filtering the image with triangle kernel filters at different scales

b) Computing an approximation of a determinant of Hessian at each scale, where this approximation at each scale k, is calculated as |∂

^k_xx(i, j)·

∂

^k_yy(i, j)−

∂

^k_xy(i, j)²| where

where L(k, i, j) is the filtered image response obtained in step a) at scale k at point (i, j) and here d₁ and d₂ are design parameters

c) Searching for extremum values both within a single scale and along the scale space of the approximation of the determinant of Hessian obtained in step b) and calculating the keypoints from these extrema values

d) For each keypoint, localised at an extremum value, detecting the dominant orientations from gradient information calculated using the filtered image response obtained in step a)

e) Calculating for each dominant orientation a keypoint descriptor

2. The method of claim 1, where the step of detecting the dominant orientations comprises the following steps:

For each keypoint, a number of samples inside a neighborhood of the keypoint are selected.

For each sample (i,j), horizontal gradient is calculated as

∂

^k_xL(k, i−

d₃,j)−

L(k, i+d₃,j)

and vertical gradient is calculated as;

∂

^k_y=L(k, i, j−

d₃)−

L(k, i, j+d₃)

where L(k, i, j) is the filtered image response obtained in step a) at scale k at point (i, j) and d₃ is a design parameter.

Each gradient is accumulated into a histogram with a weight proportional to its magnitude and with a Gaussian kernel centered at the keypoint.

The dominant orientations are found by searching for peaks with values near the maximum

3. The method of claim 2, where the neighborhood is a circular neighborhood.

4. The method of claim 1 where the descriptor is composed of oriented gradients sampled with a specific layout, where the oriented gradients are calculated as:

where L(k, i, j) is the filtered image response obtained in step a) at scale k at point (i, j) and d₃ is a design parameter, and θ

is the angle of the dominant orientation of the keypoint.

5. The method of claim 4 wherein the layout consists of a number of segments and rings, each segment is a portion of the neighborhood of the keypoint and each ring is a group of segments that share the following property:

the centre of the segment is placed at the same Euclidean distance from the keypoint.

6. The method of claim 1, where the calculation of the keypoints is done with sub-pixel and sub-scale accuracy.

7. The method of claim 6 where the calculation of the keypoints is done fitting a quadratic function centred in each extremum value to determine the interpolated location and searching in the neighbouring pixels of the same scale, the upper and the lower ones.

8. The method of claim 1, where the filters are 2D triangle shaped filters.

9. The method of claim 1, where d₁ and d₂ are proportional to the a (sigma) of the approximated second derivative of Gaussian kernel.

10. A system comprising means adapted to perform the method according to claim 1.

11. A computer program comprising computer program code means adapted to perform the method according to claim 1 when said program is run on a computer, a digital signal processor, a field-programmable gate array, an application-specific integrated circuit, a micro-processor, a micro-controller, or any other form of programmable hardware.