José J. Vallés

Detection of three-dimensional objects under arbitrary rotations based on range images

In this paper a unique map or signature of three dimensional objects is defined. The map is obtained locally, for every possible rotation of the object, by the Fourier transform of the phase-encoded range-image at each specific rotation. From these local maps, a global map of orientations is built that contains the information about the surface normals of the object. The map is defined on a unit radius sphere and permits, by correlation techniques, the detection and orientation evaluation of three dimensional objects with three axis translation invariance from a single range image.

Nonlinear pattern recognition correlators based on color-encoding single-channel systems

In color pattern recognition, color channels are normally processed separately and afterward the correlation outputs are combined. This is the definition of multichannel processing. We combine a single-channel method with nonlinear filtering based on nonlinear correlations. These nonlinear correlations yield better discrimination than common matched filtering. The method codes color information as amplitude and phase distributions and is followed by correlations related to binary decompositions. The technique is based on binary decompositions of the red, green, and blue and the hue, saturation, and intensity monochromatic channels of the reference and of the input scene, after which the binary information on the red, green, and blue channels and that of the hue, saturation, and intensity channels are encoded as different angles of a phase distribution. We have applied the method to images degraded by high levels of substitutive noise. Results show that the sliced orthogonal nonlinear generalized correlation detects the target with a high degree of discrimination when other methods fail.

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.

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.

Spherical nonlinear correlations for global invariant three-dimensional object recognition

We define a nonlinear filtering based on correlations on unit spheres to obtain both rotation- and scale-invariant three-dimensional (3D) object detection. Tridimensionality is expressed in terms of range images. The phase Fourier transform (PhFT) of a range image provides information about the orientations of the 3D object surfaces. When the object is sequentially rotated, the amplitudes of the different PhFTs form a unit radius sphere. On the other hand, a scale change is equivalent to a multiplication of the amplitude of the PhFT by a constant factor. The effect of both rotation and scale changes for 3D objects means a change in the intensity of the unit radius sphere. We define a 3D filtering based on nonlinear operations between spherical correlations to achieve both scale- and rotation-invariant 3D object recognition.

Scale and rotation invariant 3D object detection using spherical nonlinear correlations

Three-dimensional object recognition with scale and rotation changes is addressed. The recognition method is described in terms of correlations between spherical surfaces. Tridimensionality is codified into range images. We used the phase Fourier transform of a range image which gives information about the orientation of the 3D object surfaces. A 3D object orientation map containing information about all possible rotations of the object is obtained. This map distribution is calculated using the amplitude of the phase Fourier transform for different views of the object. From that 3D object description it is possible to achieve detection and rotation estimation by performing a correlation between unit spheres even when only partial information is presented. In addition, a scale change of a rotated 3D object implies a change of the intensity in the unit sphere. We define correlations between the reference unit sphere and a certain target-patch placed on the surface of the unit sphere. Various experiments are carried out to confirm the correct detection. We also validate the method when other false targets were used. In addition to tolerance to scale and rotation, high discrimination against false targets is also achieved.

Scale Invariant Detection for Range Imagery based on the Phase Fourier Transform

We present an approach for scale invariant 3D object detection based on phase Fourier transform (PhFT). 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 3D object is scaled, the PhFT becomes a distribution multiplied by a constant factor. So, scale invariant 3D pattern recognition is converted into illumination invariant 2D pattern recognition.

Improved invariant optical correlations for 3D target detection

Invariant optical correlation method for 3D target detection is addressed. Tridimensionality is expressed in terms of range images. The recognition model is based on a vector space representation using an orthonormal image basis. The recognition method proposed is based on the calculation of the angle between the vector associated with a certain 3D object and a vector subspace. Scale and rotation invariant 3D target detection are obtained using the phase Fourier transform (PhFT) of the range images. In fact, when the 3D object is scaled, the PhFT becomes a distribution multiplied by a constant factor. On the other hand a rotation of the 3D object around the z axis, implies a shift in the PhFT. So, changes of scale and rotation of 3D objects are replaced by changes of intensity and position of PhFT distributions. We applied intensity invariant correlation methods for recognition. In addition to tolerance to scale and rotation, high discrimination against false targets is also achieved.

Detection of Three‐Dimensional Objects Under Arbitrary Illuminations

We present a novel method of three‐dimensional object recognition independent of illumination conditions. 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. The recognition method proposed is based on the calculation of the angle between the vector associated with a certain illuminated three‐dimensional object and a vector subspace. We define the angle in terms of linear correlations in order to get shift and illumination‐invariant detection.

Detection of three-dimensional objects based on phase-encoded range images

The problem of three-dimensional (3-D) object recognition is addressed. Range images permit the description of 3-D surfaces with a significant reduction of the complexity of the processing. The encoding of range images as phase gives a simple and straightforward method for parallel processing of these images. In this contribution we propose the use of range images encoded as phase to deal with the detection of three dimensional objects. The use of Fourier transform profilometry can serve for real time optical implementation of correlators based on this encoding, using a joint transform correlator or a VanderLugt one. The extension to scale and in-plane rotation invariant detection is discussed and implemented in a simple optoelectronic system. Furthermore, a method for full 3-D rotation invariant detection is presented. A simplified approach allowing only rotation around an axis perpendicular point of view permits the implementation with a conventional correlator.