Recognition of unsegmented targets invariant under transformations of intensity

Non-Contact Geomagnetic Detection Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Teager Energy Operator

During the non-contact geomagnetic detection of pipeline defects, measured signals generally contain noise, which reduces detection efficiency. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) has recently emerged as a signal filtering method, but its filtering performance is influenced by two parameters: the amplitude of added noise and the number of ensemble trials. To solve this issue and improve detection accuracy and distinguishability, a detection method based on improved CEEMDAN (ICEEDMAN) and the Teager energy operator (TEO) is proposed. The magnetic detection signal was first decomposed into a series of intrinsic mode functions (IMFs) by CEEMDAN with initial parameters. Signal IMFs were then distinguished using the Hurst exponent to reconstruct the preliminary filtered signal, and its maximum value (except the zero point) of the normalized autocorrelation function was defined as salp swarm algorithm (SSA) fitness. The optimal parameters that maximize fitness were found by SSA iterations, and their corresponding filtered signal was obtained. Finally, the gradient calculation and TEO were carried out to complete non-contact geomagnetic detection. The results of the simulated signal based on magnetic dipole under a noisy environment and field testing prove that ICEEMDAN denoising has better filtering performance than conventional CEEMDAN denoising methods, and ICEEMDAN-TEO has obvious advantages compared to other detection methods in the aspects of location error, peak side-lobe ratio, and integrated side-lobe ratio.

Intensity invariant nonlinear correlation filtering in spatially disjoint noise

We analyze the performance of a nonlinear correlation called the Locally Adaptive Contrast Invariant Filter in the presence of spatially disjoint noise under the peak-to-sidelobe ratio (PSR) metric. We show that the PSR using the nonlinear correlation improves as the disjoint noise intensity increases, whereas, for common linear filtering, it goes to zero. Experimental results as well as comparisons with a classical matched filter are given.

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.