599 research outputs found
A Kind of Affine Weighted Moment Invariants
A new kind of geometric invariants is proposed in this paper, which is called
affine weighted moment invariant (AWMI). By combination of local affine
differential invariants and a framework of global integral, they can more
effectively extract features of images and help to increase the number of
low-order invariants and to decrease the calculating cost. The experimental
results show that AWMIs have good stability and distinguishability and achieve
better results in image retrieval than traditional moment invariants. An
extension to 3D is straightforward
Structural Geodesic-Tchebychev Transform: An image similarity measure for face recognition
This work presents a new holistic measure for face recognition. Face recognition involves three steps: Face Detection, Feature Extraction and Matching. In the face detection process to identify the face area in face images, Viola-Jones algorithm has been used. Feature extraction is based on performing double-transformation, where discrete Tchebychev transform is performed on the geodesic distance transform of the grayscale image. Structural Similarity (SSIM) is applied to the resulting image double-transform to find matching factor with other image faces in the FEI (Brazilian) database. Performance is measured using a confidence criterion based on the similarity distance between the recognized person (best match) and the next possible ambiguity (second-best match). Simulation results showed that the proposed approach handles the face recognition efficiently as compared with SSIM
Construction of a complete set of orthogonal Fourier-Mellin moment invariants for pattern recognition applications
International audienceThe completeness property of a set of invariant descriptors is of fundamental importance from the theoretical as well as the practical points of view. In this paper, we propose a general approach to construct a complete set of orthogonal Fourier-Mellin moment (OFMM) invariants. By establishing a relationship between the OFMMs of the original image and those of the image having the same shape but distinct orientation and scale, a complete set of scale and rotation invariants is derived. The efficiency and the robustness to noise of the method for recognition tasks are shown by comparing it with some existing methods on several data sets
Revisiting Complex Moments For 2D Shape Representation and Image Normalization
When comparing 2D shapes, a key issue is their normalization. Translation and
scale are easily taken care of by removing the mean and normalizing the energy.
However, defining and computing the orientation of a 2D shape is not so simple.
In fact, although for elongated shapes the principal axis can be used to define
one of two possible orientations, there is no such tool for general shapes. As
we show in the paper, previous approaches fail to compute the orientation of
even noiseless observations of simple shapes. We address this problem. In the
paper, we show how to uniquely define the orientation of an arbitrary 2D shape,
in terms of what we call its Principal Moments. We show that a small subset of
these moments suffice to represent the underlying 2D shape and propose a new
method to efficiently compute the shape orientation: Principal Moment Analysis.
Finally, we discuss how this method can further be applied to normalize
grey-level images. Besides the theoretical proof of correctness, we describe
experiments demonstrating robustness to noise and illustrating the method with
real images.Comment: 69 pages, 20 figure
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