Corpus ID: 57373826

Interest Point Detection based on Adaptive Ternary Coding

  title={Interest Point Detection based on Adaptive Ternary Coding},
  author={Zhenwei Miao and Kim-Hui Yap and Xudong Jiang},
In this paper, an adaptive pixel ternary coding mechanism is proposed and a contrast invariant and noise resistant interest point detector is developed on the basis of this mechanism. Every pixel in a local region is adaptively encoded into one of the three statuses: bright, uncertain and dark. The blob significance of the local region is measured by the spatial distribution of the bright and dark pixels. Interest points are extracted from this blob significance measurement. By labeling the… Expand


Contrast Invariant Interest Point Detection by Zero-Norm LoG Filter.
This work derives a generalized LoG filter, and proposes a zero-norm LoG detector, which is proportional to the weighted number of bright/dark pixels in a local region, which makes this filter be invariant to the image contrast. Expand
Extracting Scale and Illuminant Invariant Regions through Color
This work proposes a method that combines information from the color channels to drive the detection of scale-invariant keypoints by factoring out the local effect of the illuminant using an expressive linear model, and demonstrates robustness to a change in the Illuminant without having to estimate its properties from the image. Expand
Interest point detection using rank order LoG filter
This paper proposes a novel nonlinear filter, named rank order Laplacian of Gaussian (ROLG) filter, based on which a new interest point detector is developed, which achieves superior performance comparing to four state-of-the-art detectors. Expand
Histogram-based interest point detectors
The experimental results show that the proposed histogram-based interest point detectors perform particularly well for the tasks of matching textured scenes under blur and illumination changes, in terms of repeatability and distinctiveness. Expand
Local Intensity Order Pattern for feature description
It is shown that the proposed descriptor is not only invariant to monotonic intensity changes and image rotation but also robust to many other geometric and photometric transformations such as viewpoint change, image blur and JEPG compression. Expand
A Comparison of Affine Region Detectors
A snapshot of the state of the art in affine covariant region detectors, and compares their performance on a set of test images under varying imaging conditions to establish a reference test set of images and performance software so that future detectors can be evaluated in the same framework. Expand
Principal Curvature-Based Region Detector for Object Recognition
Experiments show that PCBR is comparable or superior to state-of-art detectors for both feature matching and object recognition, and the application of PCBR to symmetry detection. Expand
Self-Similarity and Points of Interest
  • J. Maver
  • Mathematics, Medicine
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2010
The proposed approach gives a rich set of highly distinctive local regions that can be used for object recognition and image matching and compare favorably with the results obtained by the leading interest point detectors from the literature. Expand
A novel rank order LoG filter for interest point detection
  • Zhenwei Miao, Xudong Jiang
  • Mathematics, Computer Science
  • 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2012
This paper proposes a novel non-linear filter, named rank order LoG (ROLG) filter, and a new interest point detector, named ROLG detector, which is more robust to abrupt variations of images. Expand
SURF: Speeded Up Robust Features
In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features). It approximates or even outperforms previouslyExpand