Many object recognition systems use a small number of pairings of data and model features to compute the 3D transformation from a model coordinate frame into the sensor coordinate system. With perfect image data, these systems work well. With uncertain image data, however, their performance is less clear. We examine the effects of 2D sensor uncertainty on the computation of 3D model transformations. We use this analysis to bound the uncertainty in the transformation parameters, and the uncertainty associated with transforming other model features into the image. We also examine the impact of the such transformation uncertainty on recognition methods.

Human performance in object recognition changes with practice, even in the absence of feedback to the subject. The nature of the change can reveal important properties of the process of recognition. We report an experiment designed to distinguish between non-specific task learning and object- specific practice effects. The results of the experiment support the notion that learning through modification of object representations can be separated from less interesting effects of practice, if appropriate response measures (specifically, the coefficient of variation of response time over views of an object) are used. Furthermore, the results, obtained with computer-generated amoeba-like objects, corroborate previous findings regarding the development of canonical views and related phenomena with practice.

A common method for finding an object's pose is the generalized Hough transform, which accumulates evidence for possible coordinate transformations in a parameter space and takes large clusters of similar transformations as evidence of a correct solution. We analyze this approach by deriving theoretical bounds on the set of transformations consistent with each data-model feature pairing, and by deriving bounds on the likelihood of false peaks in the parameter space, as a function of noise, occlusion, and tessellation effects. We argue that blithely applying such methods to complex recognition tasks is a risky proposition, as the probability of false positives can be very high.

Determining the identity and pose of occluded objects from noisy data is a critical step in interacting intelligently with an unstructured environment. Previous work has shown that local measurements of position and surface orientation may be used in a constrained search process to solve this problem, for the case of rigid objects, either two-dimensional or three-dimensional. This paper considers the more general problem of recognizing and locating objects that can vary in parameterized ways. We consider objects with rotational, translational, or scaling degrees of freedom, and objects that undergo stretching transformations. We show that the constrained search method can be extended to handle the recognition and localization of such generalized classes of object families.

Determining the identity and pose of occluded objects from noisy data is a critical part of a system's intelligent interaction with an unstructured environment. Previous work has shown that local measurements of the position and surface orientation of small patches of an object's surface may be used in a constrained search process to solve this problem for the case of rigid polygonal objects using two-dimensional sensory data, or rigid polyhedral objects using three-dimensional data. This note extends the recognition system to deal with the problem of recognizing and locating curved objects. The extension is done in two dimensions, and applies to the recognition of two-dimensional objects from two-dimensional data, or to the recognition of three-dimensional objects in stable positions from two- dimensional data.

Many 3D objects in the world around us are strongly constrained. For instance, not only cultural artifacts but also many natural objects are bilaterally symmetric. Thoretical arguments suggest and psychophysical experiments confirm that humans may be better in the recognition of symmetric objects. The hypothesis of symmetry-induced virtual views together with a network model that successfully accounts for human recognition of generic 3D objects leads to predictions that we have verified with psychophysical experiments.

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 173-186).

Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Includes bibliographical references (p. 149-166).

This thesis presents methods and results for optimizing subword detectors in continuous speech. Speech detectors are useful within areas like detection-based ASR, pronunciation training, phonetic analysis, word spotting, etc. Firstly, we propose a structure suitable for subword detection. This structure is based on the standard HMM framework, but in each detector the MFCC feature extractor and the models are trained for the specific detection problem. Our experiments in the TIMIT database validate the effectiveness of this structure for detection of phones and articulatory features. Secondly, two discriminative training techniques are proposed for detector training. The first one is a modification of Minimum Classification Error training. The second one, Minimum Detection Error training, is the adaptation of Minimum Phone Error to the detection problem. Both methods are used to train HMMs and filterbanks in the detectors, isolated or jointly. MDE has the advantage that any detection performance criterion can be optimized directly. F-score and class accuracy optimization experiments show that MDE training is superior to the MCE-based method. The optimized filterbanks reflect some acoustical properties of the detection classes. Moreover, some changes are consistent over classes with similar acoustical properties. In addition, MDE-training of filterbanks results in filters significatively different than in the standard filterbank. In fact, some filters extract information from different critical bands. Finally, we propose a detection-based automatic speech recognition system. Detectors are built with the proposed HMM-based detection structure and trained discriminatively. The linguistic merger is based on an MLP/Viterbi decoder.

In an interview with Jessica Benjamin and Margaret Simons in 1979, Simone de Beauvoir identified the problem that had preoccupied her across her lifetime, that is, “her” problem, as the problem of the “the consciousness of the other”. In making this claim, she echoed words she had written almost fifty years earlier, when in 1927 as an undergraduate student, she wrote in her journal that what interested her was “almost always this opposition of self and other that I have felt since beginning to live”. In bookending her career in this manner, Beauvoir points her readers to consider her work as a sustained engagement with Hegel’s Phenomenology of Spirit, for it is in this text that this problem takes shape. Hegel traces the journey of spirit from consciousness to Absolute Knowledge. In so doing he provides a description of how it is that the self comes to reside in the other and the other to reside in the self as the hostility that initially leads to the objectification of one by the other gives way to recognition. This study investigates the development of Beauvoir’s understanding of the problem of the other’s consciousness. Three times across her career Beauvoir would turn to Hegel’s text. Using these readings as guideposts, it traces her account of the relationship between self and other from her study of hostility in her early works, through to her discovery of the force of history and the interdependence of subjectivities in her moral period, to her exploration of the forms of reciprocity in her mature studies, finally through to her acknowledgement of mutual recognition via her reflections on writing in her late works.