Nagaraj Nandhakumar

On the computation of motion from sequences of images-A review

Recent developments are reviewed in the computation of motion and structure of objects in a scene from a sequence of images. Two distinct paradigms are highlighted: (i) the feature-based approach and (ii) the optical-flow-based approach. The comparative merits/demerits of these approaches are discussed. The current status of research in these areas is reviewed and future research directions are indicated. >

Empirical performance analysis of linear discriminant classifiers

In face recognition literature, holistic template matching systems and geometrical local feature based systems have been pursued. In the holistic approach, PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) are popular ones. More recently, the combination of PCA and LDA has been proposed as a superior alternative over pure PCA and LDA. In this paper, we illustrate the rationales behind these methods and the pros and cons of applying them to pattern classification task. A theoretical performance analysis of LDA suggests applying LDA over the principal components from the original signal space or the subspace. The improved performance of this combined approach is demonstrated through experiments conducted on both simulated data and real data.

Integrated analysis of thermal and visual images for scene interpretation

An approach for computer perception of outdoor scenes is presented. The approach is based on integrating information extracted from thermal images and visual images, which provides information not available by processing either type of image alone. The thermal image is analyzed to provide estimates of surface temperature. The visual image provides surface absorptivity and relative orientation. These parameters are used together to provide estimates of heat fluxes at the surfaces of viewed objects. The thermal behavior of scene objects is described in terms of surface heat fluxes. Features based on estimated values of surface heat fluxes are shown to be more meaningful and specific in distinguishing scene components. >

The artificial intelligence approach to pattern recognition—a perspective and an overview

Abstract The present paper reviews the techniques for automated extraction of information from signals. The techniques may be classified broadly into two categories—the conventional pattern recognition approach and the artificial intelligence (AI) based approach. The conventional approach comprises two methodologies—statistical and structural. The paper reviews salient issues in the application of conventional techniques for extraction of information. The systems that use the artificial intelligence approach are characterized with respect to three key properties. The basic differences between the approaches and the computational aspects are reviewed. Current trends in the use of the AI approach are indicated. Some key ideas in current literature are reviewed.

Effects of camera alignment errors on stereoscopic depth estimates

We present in this paper a new analysis of relative sensitivity/importance of camera calibration/alignment parameters on the performance of stereoscopic depth reconstruction. This quantitative analysis provides formulae which relate different parameter errors to the 3-D reconstruction measurements. The results of this analysis provide specifications of acceptable tolerances in individual calibration parameters for given 3-D measurement error tolerances. This information is useful in designing practical stereoscopic vision systems.

An improved power cepstrum based stereo correspondence method for textured scenes

This paper analyses the performance of cepstral approaches for solving the stereo correspondence problem. A quantitative analysis of the effects of noise, foreshortening differences, and photometric variations on existing cepstral correspondence techniques is presented. A modified approach that is less sensitive to these effects is developed for textured scenes, and analytical arguments for its robustness are developed. The results of a comparative study of the new cepstral technique, the original cepstral algorithm and the cross-correlation approach are shown and discussed. The performance of the new method is experimentally verified on textured surfaces.

Estimating the viewing parameters of random, noisy projections of asymmetric objects for tomographic reconstruction

The ability to determine the viewing parameters of objects from their projections has enabled well established tomographic techniques to be employed in the 3D reconstruction of objects from images obtained via modalities where the orientation of the objects cannot be controlled. A method is described for the determination of the viewing parameters of randomly acquired projections of asymmetric objects. It extends upon the common lines algorithm by determining the relative orientation of projections from the location of lines of intersection among the Fourier transforms of the projections in 3D Fourier space. A new technique for finding the lines of intersection in the presence of translational displacement, and for subsequently finding the translational displacement, is presented. The complete algorithm is described and its efficacy is demonstrated using real data. A new technique for dealing with noise is also discussed.

Object motion and structure recovery for robotic vision using scanning laser range sensors

Although many algorithms have been developed for motion estimation from range images, none are suited for use with scanning laser sensors. In this paper, the feature-based motion transformation model is restated to incorporate the nonzero pixel sampling rate of laser range cameras and a novel iterative, linear, feature-based technique for determining the 3D motion transformation of moving objects is developed using this new model. A technique is then presented which employs the motion recovered using the iterative algorithm to remove the structural distortion of the object in the range map. The performance of the motion recovery method is verified using simulated and experimental data.

Physics-based integration of multiple sensing modalities for scene interpretation

The fusion of multiple imaging modalities offers many advantages over the analysis, separately, of the individual sensory modalities. In this paper we present a unique approach to the integrated analysis of disparate sources of imagery for object recognition. The approach is based on physics-based modeling of the image generation mechanisms. Such models make possible features that are physically meaningful and have an improved capacity to differentiate between multiple classes of objects. We illustrate the use of physics-based approach to develop multisensory vision systems for different object recognition application domains. The paper discusses the integration of different suites of sensors, the integration of image-derived information with model-derived information and the physics-based simulation of multisensory imagery.

Recent progress in object recognition from range data

Abstract Within the last half decade active devices have been able to provide three-dimensional data directly to vision systems. This paper examines some of the progress that has been made with this data in the field of 3D computer vision. Aspects of this field from acquisition to recognition are discussed and some major research results are reviewed.