Phase congruency

About: Phase congruency is a research topic. Over the lifetime, 684 publications have been published within this topic receiving 15387 citations.

FSIM: A Feature Similarity Index for Image Quality Assessment

Abstract: Image quality assessment (IQA) aims to use computational models to measure the image quality consistently with subjective evaluations. The well-known structural similarity index brings IQA from pixel- to structure-based stage. In this paper, a novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimensionless measure of the significance of a local structure, is used as the primary feature in FSIM. Considering that PC is contrast invariant while the contrast information does affect HVS' perception of image quality, the image gradient magnitude (GM) is employed as the secondary feature in FSIM. PC and GM play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use PC again as a weighting function to derive a single quality score. Extensive experiments performed on six benchmark IQA databases demonstrate that FSIM can achieve much higher consistency with the subjective evaluations than state-of-the-art IQA metrics.

The importance of phase in signals

Abstract: In the Fourier representation of signals, spectral magnitude and phase tend to play different roles and in some situations many of the important features of a signal are preserved if only the phase is retained. Furthermore, under a variety of conditions, such as when a signal is of finite length, phase information alone is sufficient to completely reconstruct a signal to within a scale factor. In this paper, we review and discuss these observations and results in a number of different contexts and applications. Specifically, the intelligibility of phase-only reconstruction for images, speech, and crystallographic structures are illustrated. Several approaches to justifying the relative importance of phase through statistical arguments are presented, along with a number of informal arguments suggesting reasons for the importance of phase. Specific conditions under which a sequence can be exactly reconstructed from phase are reviewed, both for one-dimensional and multi-dimensional sequences, and algorithms for both approximate and exact reconstruction of signals from phase information are presented. A number of applications of the observations and results in this paper are suggested.

Feature Detection in Human Vision: A Phase-Dependent Energy Model

Abstract: This paper presents a simple and biologically plausible model of how mammalian visual systems could detect and identify features in an image. We suggest that the points in a waveform that have unique perceptual significance as 'lines' and 'edges' are the points where the Fourier components of the waveform come into phase with each other. At these points 'local energy' is maximal. Local energy is defined as the square root of the sum of the squared response of sets of matched filters, of identical amplitude spectrum but differing in phase spectrum by 90 degrees: one filter type has an even-symmetric line-spread function, the other an odd-symmetric line-spread function. For a line the main contribution to the local energy peak is in the output of the even-symmetric filters, whereas for edges it is in the output of the odd-symmetric filters. If both filter types respond at the peak of local energy, both edges and lines are seen, either simultaneously or alternating in time. The model was tested with a series of images, and shown to predict well the position of perceived features and the organization of the images.

Feature detection from local energy

Abstract: A more general definition of features such as edges, shadows and bars is developed, based on an analysis of the phase of the harmonic components. These features always occur at points of maximum phase congruency, the type of feature depending on the value of the phase. Using the image itself and its Hilbert transform, a local energy function is defined and it is shown that the local maxima of this energy function occur at points of maximum phase congruency.

Phase Congruency Detects Corners and Edges

Abstract: There are many applications such as stereo matching, mo- tion tracking and image registration that require so called 'corners' to be detected across image sequences in a reliable manner. The Harris cor- ner detector is widely used for this purpose. However, the response from the Harris operator, and other corner operators, varies considerably with image contrast. This makes the setting of thresholds that are appropri- ate for extended image sequences difficult, if not impossible. This paper describes a new corner and edge detector developed from the phase con- gruency model of feature detection. The new operator uses the principal moments of the phase congruency information to determine corner and edge information. The resulting corner and edge operator is highly local- ized and has responses that are invariant to image contrast. This results in reliable feature detection under varying illumination conditions with fixed thresholds. An additional feature of the operator is that the corner map is a strict subset of the edge map. This facilitates the cooperative use of corner and edge information.