Exploiting Walsh-based attributes to stereo vision

This paper presents a new stereo feature matching method that extracts the disparity measure for the recovery of depth information in 2-D stereo images. In this method, a stereo pair of images are transformed row for row into strings carrying spatially varying Walsh coefficients as attributes. The significance of the information carried by the Walsh coefficients is expressed mathematically and through experimental evaluations. The choice of the Walsh coefficients in contrast to other orthogonal transform coefficients is a direct result of their computational simplicity and their interpretative meaning in terms of the information contained in the spatial domain. The string-to-string matching technique used to bring the two strings into correspondence integrates, into a unified process, both the feature detection and the feature matching processes. The uniqueness and the ordering constraints are explicitly integrated into this string-to-string matching technique. Both the issues of Gaussian filtering and the importance of enforcing the epipolar line constraint are addressed in view of the application of the proposed method. Experimental results are given and assessed in terms of both the accuracy in stereo matching and the ensuing computational requirements     

Super-resolution of images based on local correlations

An adaptive two-step paradigm for the super-resolution of optical images is developed in this paper. The procedure locally projects image samples onto a family of kernels that are learned from image data. First, an unsupervised feature extraction is performed on local neighborhood information from a training image. These features are then used to cluster the neighborhoods into disjoint sets for which an optimal mapping relating homologous neighborhoods across scales can be learned in a supervised manner. A super-resolved image is obtained through the convolution of a low-resolution test image with the established family of kernels. Results demonstrate the effectiveness of the approach.     

A similarity measure for stereo feature matching

An approach to stereo feature matching is presented with the introduction of a similarity measure for evaluating and confirming a stereo match. The contributions of this study are reflected in (1) the development of a similarity measure which evaluates a stereo match based on feature locality and gray-level gradient associated with the feature; and (2) the use of a matching procedure that integrates local and global matching strategies based on matching first those features with the highest similarity measure among the set of all highest similarities found locally under confined search spaces, ensuring that each feature is matched with a high degree of certainty. A left-to-right and right-to-left consistency check is used for each feature to comply with the uniqueness constraint and to confirm if a potential match can be declared a correct match     

Analysis and enhancements to piecewise linear comparametric image registration.

This correspondence details enhancements to previous work on piecewise linear comparametric image registration. The enhancements come in the form of slope constraints and knots' location selection in the comparametric modeling. An analysis of the properties of comparametric functions leads to two general slope constraints that, along with a knot location selection based on the reference image's pixel value distribution, serves to reduce error and sensitivity to the number of segments considered as well as allow for the solution to cases in which there are gaps in the comparagram. Results on real images are presented.     

Jointly registering images in domain and range by piecewise linear comparametric analysis

This paper describes an approach whereby comparametric analysis is used in jointly registering image pairs in their domain and range, i.e., in their spatial coordinates and pixel values, respectively. This is accomplished by approximating a camera's nonlinear comparametric function with a constrained piecewise linear one. The optimal fitting of this approximation to comparagram data is then used in a re-parameterized version of the camera's comparametric function to estimate the exposure difference between images. Doing this allows the inherently nonlinear problem of joint domain and range registration to be performed using a computationally attractive least squares formalism. The paper first presents the range registration process and then describes the strategy for performing the joint registration. The models used allow for the pair-wise registration of images taken from a camera that can automatically adjust its exposure as well as tilt, pan, rotate and zoom about its optical center. Results concerning the joint registration as well as range-only registration are provided to demonstrate the method's effectiveness.     

A Novel Spectrum Encoding MIMO Communication System

This paper presents a novel spectrum/wavelet encoding MIMO system. To avoid spectrally crowded regions, the system synthesizes the fundamental modulation waveform in the spectral/wavelet domain rather than in the time domain at both the transmitter and the receiver. Based on the Vertical Bell Laboratories Layered Space-Time (V-BLAST) architecture, we propose two enhanced MIMO architectures, which utilize two different schemes: spectrum domain encoding (SDE) and wavelet domain encoding (WDE) respectively. Both approaches offer better bit error rate (BER) performance compared to other conventional interference mitigation techniques that process the signal at the receiver only. However, the SDE based MIMO system is not able to deal with non-stationary interference, and the WDE based MIMO system could overcome this shortcoming but slightly degrades the performance under stationary interference. The analysis shows that under interference the channel capacity of the proposed MIMO system is higher than that of conventional systems. Our simulation results also indicate that the system BER performance is significantly improved with little or no interference to the spectral crowed regions. By the virtue of its flexibility and adaptivity, the proposed system is one potential candidate for a reliable communication system or the cognitive radio technology.     

Simultaneous homographic and comparametric alignment of multiple exposure-adjusted pictures of the same scene

An approach is presented that can simultaneously align multiple exposure-adjusted pictures of the same scene both in their spatial coordinates as well as in their pixel values. The approach is featureless and produces an image mosaic at a common spatial and exposure reference and also addresses the misalignment problem common to methods that compose mosaics from only pair-wise registered image pairs. The objective function considered minimizes the sum of the collective variance over pixels of a global coordinate grid on which to create the final image. The models employed relate images spatially by homographic transformations and tonally by comparametric functions. The importance of performing joint spatial and tonal registration on exposure-adjusted images is emphasized by providing two examples in which spatial-only registration fails. The performance between pair-wise and simultaneous registration under both spatial-only and joint registration procedures is discussed.     

A semiparametric model for accurate camera response function modeling and exposure estimation from comparametric data.

A fundamentally new approach that accurately estimates the camera response function from comparametric data, i.e., pixel data from two differently exposed images over a common field of view, is presented. It does so by solving for the camera response function from its associated comparametric relation. The approach offers several advantageous features, including having a complexity that is independent of the number of pixel data considered, allowing for the modeling of saturated pixels, enabling an inherently constrained optimization problem to be solved in an unconstrained manner, and the easy incorporation into an existing framework for joint image registration. This is accomplished by approximating the camera response function with a constrained piecewise linear model so that its solution, within the comparametric camera relation, can be obtained. This results in a semiparametric comparametric model, optimally determined from pixel data, which is directly parameterized in terms of the exposure parameter. Subsequently, it is shown how this semiparametric model is used for exposure estimation from captured images. Finally, we incorporate the semiparametric model within an existing and previously published framework for simultaneous and joint spatial and tonal image registration in order to illustrate the developed model's performance.