Homomorphic cameo filter for pattern recognition that is invariant with changes in illumination

Target recognition under nonuniform illumination conditions

A two-step algorithm for reliable recognition of a target imbedded into a two-dimensional nonuniformly illuminated and noisy scene is presented. The input scene is preprocessed with a space-domain pointwise procedure followed by an optimum correlation. The preprocessing is based on an estimate of the source illumination function, whereas the correlation filter is optimized with respect to the mean-squared-error criterion for detecting a target in the preprocessed scene. Computer simulations are provided and compared with those of various common techniques in terms of recognition performance and tolerance to nonuniform illumination as well as to additive noise. Experimental optodigital results are also provided and discussed.

Phase Fourier vector model for scale invariant three-dimensional image detection

A scale invariant 3D object detection method based on phase Fourier transform (PhFT) is addressed. Three-dimensionality is expressed in terms of range images. The PhFT of a range image gives information about the orientations of the surfaces in the 3D object. When the object is scaled, the PhFT becomes a distribution multiplied by a constant factor which is related to the scale factor. Then 3D scale invariant detection can be solved as illumination invariant detection process. Several correlation operations based on vector space representation are applied. Results show the tolerance of detection method to scale besides discrimination against false objects.

Three-dimensional object detection under arbitrary lighting conditions

A novel method of 3D object recognition independent of lighting conditions is presented. The recognition model is based on a vector space representation using an orthonormal basis generated by the Lambertian reflectance functions obtained with distant light sources. Changing the lighting conditions corresponds to multiplying the elementary images by a constant factor and because of that, all possible lighting views will be elements that belong to that vector space. The recognition method proposed is based on the calculation of the angle between the vector associated with a certain illuminated 3D object and that subspace. We define the angle in terms of linear correlations to get shift and illumination-invariant detection.

Intensity-invariant nonlinear filtering for detection in camouflage

We introduce a method based on an orthonormal vector space basis representation to detect camouflaged targets in natural environments. The method is intensity invariant so that camouflaged targets are detected independently of the illumination conditions. The detection technique does not require one to know the exact camouflage pattern, but only the class of patterns (e.g., foliage, netting, woods). We use nonlinear filtering and the calculation of several correlations. The nonlinearity of the filtering process also allows high discrimination against false targets. Several experiments confirm the target detectability where strong camouflage might delude even human viewers.

Nonlinear filter for pattern recognition invariant to illumination and to out-of-plane rotations

Automatic target recognition in uncontrolled conditions is a difficult task because many parametersare involved. This study deals with the recognition of targets under limited out-of-plane rotations while maintaining invariance to ambient light illumination. Contrast invariance is achieved by using the recently developed locally adaptive contrast-invariant filter, a method that yields correlation peaks whose values are invariant under any linear transformation of intensity. To reduce the sensitivity to the orientation of the object we replace the reference in the nonlinear filter by a synthetic discriminant filter. The range used for out-of-plane rotations was 40 degrees with a depression angle of 20 degrees. We present results for unsegmented targets on complex backgrounds with the presence of false targets.

Recognition of unsegmented targets invariant under transformations of intensity

Images taken in noncooperative environments do not always have targets under the same illumination conditions. There is a need for methods to detect targets independently of the illumination. We propose a technique that yields correlation peaks that are invariant under a linear intensity transformation of object intensity. The new locally adaptive contrast-invariant filter accomplishes this by combining three correlations in a nonlinear way. This method is not only intensity invariant but also has good discrimination and resistance to noise. We present simulation results for various intensity transformations with and without random and correlated noise. When the noise is high enough to threaten errors, the method trades off intensity invariance in order to achieve the optimum signal to noise ratio, and the peak to sidelobe ratio in the presence of clutter is always greater than one. In the presence of random disjoint noise, the signal to noise ratio is independent of the target contrast and of the level of the noise.

Reduction of the correlation sensitivity to the changes of the input illumination by a post processing based on the correlation statistics

Linear-correlation amplitude changes when the intensity level of the input image is modified. As recognition is often based on the correlation-peak level, a change of the input illumination may result in a false recognition. We propose an illumination-change compensation by a post processing of the correlation distribution that is based on statistical measures of the correlation histograms. The theoretical background and simulation results are provided in the frame of an actual application in biology.

Distortion tolerant image recognition receiver by use of a multiple-hypothesis method

A multiple-hypothesis method is used to detect a target or a reference signal in the presence of additive noise with unknown statistics. The receiver is designed to detect the target and to be tolerant of the variations in rotation and illumination of the target. A multiple-hypothesis test with unknown-noise parameters is used to locate the target position. The proposed method does not use any specific distortion-invariant-filtering technique, but it relies on a multiple-hypothesis approach. Maximum-likelihood estimates of the illumination constant and the unknown noise parameters are obtained. Computer simulations are presented to evaluate the performance of the receiver for various distorted noisy true-class targets with varying illumination and false-class objects.