This paper describes a novel method for image corner detection based on the curvature scale-space (CSS) representation. The first step is to extract edges from the original image using a Canny detector (1986). The corner points of an image are defined as points where image edges have their maxima of absolute curvature. The corner points are detected at a high scale of the CSS and tracked through multiple lower scales to improve localization. This method is very robust to noise, and we believe that it performs better than the existing corner detectors An improvement to Canny edge detector's response to 45/spl deg/ and 135/spl deg/ edges is also proposed. Furthermore, the CSS detector can provide additional point features (curvature zero-crossings of image edge contours) in addition to the traditional corners.

/pdf/robust-image-corner-detection-through-curvature-scale-space-4y8gb64f1t.pdf

Robust image corner detection through curvature scale space

https://www.ece.lsu.edu/gunturk/EE7700/Mokhtarian2.pdf

Performance evaluation of corner detectors using consistency and accuracy measures

Systems and methods are disclosed herein to provide filtering techniques for image processing applications. For example, in accordance with an embodiment of the present invention, a Median-X filtering algorithm is disclosed that may be used for various infrared image processing applications.

/pdf/filtering-systems-and-methods-for-infrared-image-processing-5a3tgpr27h.pdf

Filtering systems and methods for infrared image processing

Corner detection has been shown to be extremely useful in many computer vision applications. In this paper, an improved SUSAN corner detector is proposed and its performance is compared with Harris and SUSAN corner detection. The method adopts an adaptive multi-threshold strategy based on local brightness rather than one threshold for the whole image, and divides the circular mask area of SUSAN into two or more parts. Next, the number of pixels in the part where the nucleus locates is calculated. If the number is less than half the total pixels of the circular mask, the nucleus becomes a corner candidate. The exact positions of image corners are those candidates with local minimum numbers. In order to improve the computational efficiency of the algorithm, we limit the search space for corner candidates to those pixels whose intensity gradient magnitudes by Sobel operator are higher than a threshold. Experiments have demonstrated that our corner detector is accurate and efficient.

An efficient and robust corner detection algorithm

Corners detection on finger vein images using the improved Harris algorithm

In this paper, a new computationally efficient method for the tracking and measurement of center location is presented. This algorithm is well suited to real-time focus-of-attention applications in which regions-of-interest, or windows, are used to reduce image data rates. These techniques can be used in robot guidance applications, where high speed image processing is required for real-time position control, such as robot assembly operations in flexible manufacturing. The method is applied in a system based on a 200 frames/s digital camera, programmable gate array technology, and a network of TMS320C40 digital signal processor modules. A real-time motion tracking experiment that uses this corner detection technique is described to demonstrate the advantages of this approach. Experimental results show that the center location of an object in a gray scale image can be determined at 114 Hz.

http://ieeexplore.ieee.org/iel3/4799/13268/00603925.pdf

D. Capson

Papers

Robust real-time corner location measurement