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.

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FSIM: A Feature Similarity Index for Image Quality Assessment

We describe a method for removing noise from digital images, based on a statistical model of the coefficients of an overcomplete multiscale oriented basis. Neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables: a Gaussian vector and a hidden positive scalar multiplier. The latter modulates the local variance of the coefficients in the neighborhood, and is thus able to account for the empirically observed correlation between the coefficient amplitudes. Under this model, the Bayesian least squares estimate of each coefficient reduces to a weighted average of the local linear estimates over all possible values of the hidden multiplier variable. We demonstrate through simulations with images contaminated by additive white Gaussian noise that the performance of this method substantially surpasses that of previously published methods, both visually and in terms of mean squared error.

/pdf/image-denoising-using-scale-mixtures-of-gaussians-in-the-2qh5uqp80y.pdf

Image denoising using scale mixtures of Gaussians in the wavelet domain

Let's read! We will often find out this sentence everywhere. When still being a kid, mom used to order us to always read, so did the teacher. Some books are fully read in a week and we need the obligation to support reading. What about now? Do you still love reading? Is reading only for you who have obligation? Absolutely not! We here offer you a new book enPDFd the perception of the visual world to read.

The Perception of the Visual World. By James J. Gibson. U.S.A.: Houghton Mifflin Company, 1950 (George Allen & Unwin, Ltd., London). Price 35s.

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

Despite active research and significant progress in the last 30 years, eye detection and tracking remains challenging due to the individuality of eyes, occlusion, variability in scale, location, and light conditions. Data on eye location and details of eye movements have numerous applications and are essential in face detection, biometric identification, and particular human-computer interaction tasks. This paper reviews current progress and state of the art in video-based eye detection and tracking in order to identify promising techniques as well as issues to be further addressed. We present a detailed review of recent eye models and techniques for eye detection and tracking. We also survey methods for gaze estimation and compare them based on their geometric properties and reported accuracies. This review shows that, despite their apparent simplicity, the development of a general eye detection technique involves addressing many challenges, requires further theoretical developments, and is consequently of interest to many other domains problems in computer vision and beyond.

/pdf/in-the-eye-of-the-beholder-a-survey-of-models-for-eyes-and-oxe7gfw5jq.pdf

In the Eye of the Beholder: A Survey of Models for Eyes and Gaze

Videre: Journal of Computer Vision Research (ISSN 1089-2788) is a quarterly journal published electronically on the Internet by The MIT Press, Cambridge, Massachusetts, 02142. Subscriptions and address changes should be addressed to MIT Press Journals, Five Cambridge Center, Cambridge, MA 02142; phone: (617) 253-2889; fax: (617) 577-1545; e-mail: journals-orders@mit.edu. Subscription rates are: Individuals $30.00, Institutions $125.00. Canadians add additional 7% GST. Prices subject to change without notice.

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Image Features From Phase Congruency

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.

/pdf/phase-congruency-detects-corners-and-edges-5a2zha7nt3.pdf

Phase Congruency Detects Corners and Edges

Phase congruency is a low-level invariant property of image features. Interest in low-level image invariants has been limited. This is surprising, considering the fundamental importance of being able to obtain reliable results from low-level image operations in order to successfully perform any higher level operations. However, an impediment to the use of phase congruency to detect features has been its sensitivity to noise. This paper extends the theory behind the calculation of phase congruency in a number of ways. An effective method of noise compensation is presented that only assumes that the noise power spectrum is approximately constant. Problems with the localization of features are addressed by introducing a new, more sensitive measure of phase congruency. The existing theory that has been developed for 1D signals is extended to allow the calculation of phase congruency in 2D images. Finally, it is argued that high-pass filtering should be used to obtain image information at different scales. With this approach, the choice of scale only affects the relative significance of features without degrading their localization.

Phase congruency: a low-level image invariant.

The problem of automatically generating the possible camera locations for observing an object is defined, and an approach to its solution is presented. The approach, which uses models of the object and the camera, is based on meeting the requirements that: the spatial resolution be above a minimum value, all surface points be in focus, all surfaces lie within the sensor field of view and no surface points be occluded. The approach converts each sensing requirement into a geometric constraint on the sensor location, from which the three-dimensional region of viewpoints that satisfies that constraint is computed. The intersection of these regions is the space where a sensor may be located. The extension of this approach to laser-scanner range sensors is also described. Examples illustrate the resolution, focus, and field-of-view constraints for two vision tasks. >

Automatic sensor placement from vision task requirements

Symmetry is an important mechanism by which we identify the structure of objects. Man-made objects, plants and animals are usually highly recognizable from the symmetry, or partial symmetries that they often exhibit. Two difficulties found in most symmetry detection algorithms are firstly, that they usually require objects to be segmented prior to any symmetry analysis, and secondly, that they do not provide any absolute measure of the degree of symmetry at any point in an image. This paper presents a new measure of symmetry that is based on the analysis of local frequency information. It is shown that points of symmetry and asymmetry give rise to easily recognized patterns of local phase. This phase information can be used to construct a contrast invariant measure of symmetry that does not require any prior recognition or segmentation of objects.

Peter Kovesi

Papers

Image Features From Phase Congruency

Phase Congruency Detects Corners and Edges

Phase congruency: a low-level image invariant.

Automatic sensor placement from vision task requirements

Symmetry and Asymmetry from Local Phase