M. C. Morrone, D. C. Burr, Feature detection in human vision: A phase-dependent energy model. Proceedings of the Royal Society, London Series B 235 (1988) 221--245.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....the Local Energy Model postulates that features are perceived at points in an image where the Fourier components are maximally in phase. This model was developed by Morrone et al. 15] and Morrone and Owens [14] Other work on this model of feature perception can be found in Morrone and Burr [13], Owens et al. 16] Venkatesh and Owens [19] and Kovesi [6, 7, 8, 10, 11] The work of Morrone and Burr [13] has shown that this model successfully explains a number of psychophysical effects in human feature perception. The measurement of phase congruency at a point in a signal can be seen ....

....are maximally in phase. This model was developed by Morrone et al. 15] and Morrone and Owens [14] Other work on this model of feature perception can be found in Morrone and Burr [13] Owens et al. 16] Venkatesh and Owens [19] and Kovesi [6, 7, 8, 10, 11] The work of Morrone and Burr [13] has shown that this model successfully explains a number of psychophysical effects in human feature perception. The measurement of phase congruency at a point in a signal can be seen geometrically in Figure 4. The local Fourier components at a location . in the signal will each have an ....

M. C. Morrone and D. C. Burr. Feature detection in human vision: A phase-dependent energy model. Proc. R. Soc. Lond. B, 235:221--245, 1988.

....however, relatively invariant to the spatial phase of the stimulus. An example of this is that reversing the contrast polarity of the stimulus does not markedly alter the response of a typical complex cell. The responses of complex cells have often been modeled by the classical energy model [1, 98, 121], in which # # , 3.3) where w j1 (x, y) and w j 2 (x, y) are quadrature phase Gabor functions, see figure 3.4. The idea that V1 complex cells pool the responses of simple cells (as opposed to constructing their response properties directly from the LGN a#erents) is here attractive; ....

M. C. Morrone and D. C. Burr, "Feature detection in human vision: a phasedependent energy model," Proc. Royal Soc. London Ser. B, vol. 235, no. 1280, pp. 221--245, 1988.

....however, relatively invariant to the spatial phase of the stimulus. An example of this is that reversing the contrast polarity of the stimulus does not markedly alter the response of a typical complex cell. The responses of complex cells have often been modeled by the classical energy model [1, 98, 121], in which # # , 3.3) where w j 1 (x, y) and w j 2 (x, y) are quadrature phase Gabor functions, see figure 3.4. The idea that V1 complex cells pool the responses of simple cells (as opposed to constructing their response properties directly from the LGN a#erents) is here attractive; ....

M. C. Morrone and D. C. Burr, "Feature detection in human vision: a phasedependent energy model," Proc. Royal Soc. London Ser. B, vol. 235, no. 1280, pp. 221--245, 1988.

....correctly edges and lines simultaneously. This problem becomes relevant in real images as many natural edges are neither a pure edge or a line, but rather have complex intensity profiles. Second order non linearities in the form of local energy may provide a solution to the problem [6] 1] 13] [12] [15] 16] Yet, energy models and their linear precursors are intrinsically one dimensional. They cannot account for another important class of image features: corners, vertices, terminations, junctions etc. These two dimensional intensity variations indicate, for example, strong variations ....

....us to limit the application of the edge model to points that qualify as general edge points. The local maxima of oriented energy The research described in this paper has been supported by the Swiss National Science Foundation, Grant no. 32 8968.86. can then be used to localize the edges [16] [12]. b) The use of differential geometry applied to oriented energy maps, yielding a representation of strong 2D intensity variations (keypoints) The work presented here was partially motivated by our interest in biological mechanisms of contour processing [20] 19] 17] 7] 2 General Edge, ....

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Morrone, M. C. & Burr, D. C.: Feature detection in human vision: A phase-dependent energy model. Proceedings of the Royal Society, London Series B 235 (1988) 221-245

....be a thin line. Canny [1, 2] proposed an optimal ridge detector and introduced scale as an essential parameter, but he could not solve the problem of integrating the filter responses from different scales. The simultaneous detection of edges and lines was approached by proposing energy filters [11, 12]. Discrimination between the two types of features can be done by calculating the phase information, but this is much more sensitive to noise than the filter itself. Multiscale properties of quadratic edge detectors were studied by Kube et al. 9, 10] Another class of papers studied the ....

M. Morrone and D. Burr. Feature detection in human vision: A phase-dependent energy model. In Proceedings of the Royal Society of London, number 235 in B, pages 221--245, 1988.

....requiring more constraints to satisfy than stronger ones. 2 Filtering and Key point Detection 2. 1 Filters We convolve the image with filters of even and odd symmetry (6 orientations, channels) and combine their output to response modulus (square root of oriented energy) an approach similar to [5, 16, 15, 1, 17]. The filters have the following properties (see [8, 18] for a detailed description) They form a quadrature pair, are polar separable in the Fourier domain, and the response modulus yields a unified response to edges and lines. In this paper we use both the modulus representations and their 2nd ....

M.C. Morrone and D.C. Burr. Feature detection in human vision: a phasedependent energy model. Proceedings of the Royal Society of London, B 235:221-- 245, 1988.

....luminance profile for a feature and is therefore very general. Its drawback is its computational complexity. Fortunately, phase congruency is related to local energy, which can be calculated more simply. Many researchers have studied the local energy model, particularly Morrone, Burr and Owens [53, 54, 55] and Owens and Ross [60] and Venkatesh [84] The mathematics of phase congruency has been studied by Kovesi [41] and Ronse [78] Ronse s study of phase congruency contains a thorough review and exposition of the mathematical theory. Subsequent work has concentrated on extension of the energy ....

....phase offset of 2 . CHAPTER 2. LOCAL ENERGY FEATURE DETECTION 19 all other points, the phase values of individual frequency components assume differing values between 0 and 2. Similar congruency of phase values occurs at the apex if the image signal has a triangular profile. Morrone and Burr [54] point out that in general, the points in a signal where there is local maximal congruency, or order in the phase values, coincide precisely with points where human vision perceives features. Measuring phase congruency The minimum of phase deviation is measured as the maximum in a measure of ....

M. C. Morrone and D. C. Burr. Feature detection in human vision: A phase dependent energy model. Proc. R. Soc. London. B, 235:221--245, Nov. 1988.

....thereby not depending directly on the intensity function of the image. 2 Overview of the Model The proposed model consists of three successive processing stages which model some of the mechanisms found in visual cortex. The rst stage has been adapted from the phase dependent energy model [7] using quadrature phase Gabor Filters for the unique detection of lines and edges in the image. The responses of both squared even and odd symmetric orientation channels are summed pairwise, thresholded, and further processed by an oddsymmetric orientation selective Gabor Filter, leading to a ....

M. C. Morrone and D. C. Burr. Feature detection in human vision: a phase-dependent energy model. Proceedings of the Royal Society of London, B 235:221-245, 1988.

....as the even and odd symmetric Gabor functions, these filters perform well in detecting features of small spatial extent. This demonstrates the robustness of local energy with respect to the filters used, even if the filters distort the phase information, a point which is made by Morrone and Burr [44]. Figure 10 demonstrates CHAPTER 3. LOCAL ENERGY 30 the performance of local energy using 3 point and 5 point filters on a synthetic test image. It is clear from Figure 10 that local energy produces a locally maximal response at 1D image features for both step profiled and roof profiled features. ....

M. C. Morrone and D. C. Burr. Feature detection in human vision: A phasedependent energy model. Proc. R. Soc. Lond. B, 235:221--245, 1988.

....contain a mixture of lines, edges, and other contours, it is often desirable to find a contour detector that responds appropriately to the various contour types. A linear filter cannot serve this task, but a local energy measure derived from quadrature pairs can serveitquitewell. Morrone et al. [77, 76] have shown that local energy measures give peak response at points of constant phase as a function of spatial frequency, and that they correspond to the points where human 49 observers localize contours. Perona and Malik [89] haveshown that energy measures are optimal with respect to a variety ....

M. C. Morrone and D. C. Burr. Feature detection in human vision: a phasedependent energy model. Proc. R. Soc. Lond. B, 235:221--245, 1988.

....available only two image slices out of the full space time texture. Further, optical flow estimation is, in turn, nothing more than the analysis of local texture directions in space time patterns. So in retrospect, it is not too suprising that neural models proposed for neural texture analysis [39, 40], optical flow estimation [41] and stereo vision [42] are very similar in structure. 3.2 Input Layers of the Network Textures seem to be analyzed by humans mainly along the dimensions direction and granularity [43] Granularity refers here to textures with no prominent direction, but similar ....

M. Concetta Morrone, D. C. Burr, Feature Detection in Human Vision: a PhaseDependent Energy Model, Proc. R. Soc. Lond. B 235, 1988, 221--245.

.... visual system provided responses to several interesting questions: Firstly, 7] analyzed the comparative performance of selective measures and pixelby pixel error metrics (e.g. the root mean square error) A main conclusion of this work was that a phase congruency model of feature detection, [8], induces an error measure in the corresponding perceptual domain that improves the correlation between subjective rating by human observers and quantitative measures and consequently better captures the response of the human visual system. Secondly, the analysis of how conjunctions of features ....

....Of course, the rst problem to be solved is that of deriving a model of feature perception which is capable of successfully explaining a reasonable number of psychophysical e ects in human feature perception. We select a local energy model, based on the use of the maximum phase congruency [8,48], as the computational model for the perception of low level features. The local energy model of feature detection postulates that features are perceived at points where the Fourier components are maximally in phase. It is interesting to note that this model predicts the conditions under which ....

Morrone, M.C. and D. C. Burr. \Feature detection in human vision: A phase-dependent energy model." Proc. R. Soc. Lond. B, Vol. 235, pp. 221-245, (1988).

....First the spatial locations worth noting regarding the selection of local scales are defined as reasonable candidates for locations where the visual system perceives something of interest. To this aim, we used the locations of features contained in the local energy maps of the activated sensors [17,18]. The point is how, for the given image, they can be obtained, and we deal with it as follows. For each activated sensor Ch i , let J Ch i (x; y) be the image filtered by a complex 2 D Gabor filter, as given in equation (3) J Ch i (x; y) r(x; y) g(x; y; oe Ch i ; ae Ch i ; Ch i ) 6) with ....

Morrone, M.C. and D. C. Burr. "Feature detection in human vision: A phase-dependent energy model." Proc. R. Soc. Lond. B, Vol. 235, pp. 221-245, (1988).

....the localisation of (almost) continuously moving objects. There, successive flashes are within the range of effective spatio temporal interpolation. An avenue for future research is to calculate the zerocrossing trajectories (or another determinant of the position such as the maximum in the energy [Morrone and Burr, 1988]) while including spatial interactions. This may lead to an explanation of the lag and lead effects discussed in section 3.2. Moreover, it will allow us to include the possible role of parameters such as the distance between the stations in motion sequence without resorting to the ad hoc ....

Morrone, M. C. and Burr, D. C. (1988). Feature detection in human vision: a phase-dependent energy model. Proceedings of the Royal Society of London, B, 235:221--245.

....the second derivative of the intensity function using an even symmetric gradient filter [19] faces similar problems at line edges. A nonlinear combination using the summed squares of both even and odd symmetric filters has proven to be a good detector of edges composed of steps, peaks, and roofs [22, 24]. Using both the local energy and phase, it is possible to reconstruct the generating edge showing its applicability for image coding. However, to localise the edge exactly a search for the maximum response is necessary, contrary to the detection of zero crossings which are by definition ....

M. C. Morrone and D. C. Burr. Feature detection in human vision: a phase-dependent energy model. Proceedings of the Royal Society of London, B 235:221--245, 1988.

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M. C. Morrone, D. C. Burr, Feature detection in human vision: A phase-dependent energy model. Proceedings of the Royal Society, London Series B 235 (1988) 221--245.

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M. C. Morrone, D. C. Burr, (1988),"Feature detection in human vision: A phase-dependent energy model," Proceedings of the Royal Society, London Series, Vol. B 235, pp. 221-245.

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M. C. Morrone and D. C. Burr. Feature detection in human vision: a phase dependent energy model. Proceedings of the Royal Society, 235:221--245, 1988.

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M.C. Morrone and D.C. Burr. Feature detection in human vision: a phase dependent energy model. Proc. R. Soc. Lond. B, 235:221--45, 1988.

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M. C. Morrone and D. C. Burr, "Feature detection in human vision: A phasedependent energy model," Proc. R. Soc. Lond. B, vol. 235, pp. 221--245, 1988.

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M. Morrone and D. Burr, "Feature Detection in Human Vision: A Phase Dependent Energy Model," Proc. Royal Soc. of London B, vol. 235, pp. 221-245, 1988.

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M. C. Morrone and D. C. Burr, "Feature detection in human vision: A phasedependent energy model," Proc. R. Soc. Lond. B, vol. 235, pp. 221--245, 1988.

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M. C. Morrone and D. C. Burr, "Feature detection in human vision: a phase-dependent energy model," Proc. R. Soc. Lon- don Ser. B 235,221.245 (1988).

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Morrone, M.C. and Burr, D. C. (1988). Feature detection in human vision: A phasedependent energy model. Proc. R. Soc. Lond. B, 235, 221-245.

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Morrone, M.C. and Burr, D. C. "Feature detection in human vision: A phase-dependent energy model." Proc. R. Soc. Lond. B, Vol. 235, pp. 221-245, (1988).

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