Tied factor analysis for face recognition across large pose differences

Face recognition algorithms perform very unreliably when the pose of the probe face is different from the gallery face: typical feature vectors vary more with pose than with identity. We propose a generative model that creates a one-to-many mapping from an idealized “identity” space to the observed data space. In identity space, the representation for each individual does not vary with pose. We model the measured feature vector as being generated by a pose-contingent linear transformation of the identity variable in the presence of Gaussian noise. We term this model “tied” factor analysis. The choice of linear transformation (factors) depends on the pose, but the loadings are constant (tied) for a given individual. We use the EM algorithm to estimate the linear transformations and the noise parameters from training data. We propose a probabilistic distance metric that allows a full posterior over possible matches to be established. We introduce a novel feature extraction process and investigate recognition performance by using the FERET, XM2VTS, and PIE databases. Recognition performance compares favorably with contemporary approaches

Scaling depth from shadow offset

When an object casts a shadow on a background surface, both the offset of the shadow and the blur of itspenumbra are potential cues to the distance betweenthe object and the background. However, the shadowoffset and blur are also affected by the direction andangular extent of the light source and these are oftenunknown. This means that the observer must makesome assumptions about the illumination, the expecteddistribution of depth, or the relation between offset anddepth in order to use shadows to make distancejudgments. Here, we measure human judgments ofperceived depth over a range of shadow offsets, blurs,and lighting directions to gain insight into this internalmodel.We find that distance judgments are relativelyunaffected by blur or light direction, whereas theshadow offset has a strong and linear effect. The dataare consistent with two models, a genericshadow-to-depth model and a Bayesian mode

The effects of blur and size on monocular and stereoscopic localization

AbstractMonocular localization of non-abutting stimuli and stereoscopic localization of the same second-order targets are performed with the same precision (Wilcox, L.M. & Hess, R.F. (1996) Is the site of non-linear filtering in stereopsis before or after binocular combination? Vision Research, 36, 391–399). Further, both tasks show a similar dependence on the scale of the stimulus. Since prior studies used Gaussian-enveloped stimuli, modifications of stimulus scale produced concurrent changes in edge blur. The experiments reported here assess the relative contributions of size and blur to the observed dependence on envelope scale for both monocular localization and stereoacuity. Stereoacuity for first-order targets was found to be an order of magnitude better than stereoacuity for second-order targets and monocular acuity for both first- and second-order targets. Further, while first-order stereopsis was found to depend solely on blur, second-order stereoacuity and monocular acuity were affected by both size and blur. These results suggest that while stereoacuity for first-order stimuli may be determined by a correlative process limited by early additive noise, stereoacuity for second-order stimuli and monocular acuity for non-abutting targets are more likely limited by stimulus-dependent spatial subsampling

An Engineering, Geological and Hydrological Environmental Assessment of a 250 MMSCFD Dry Ash Lurgi Coal Gasification Facility

A preliminary engineering, geological, and hydrological environmental assessment of a proposed 250 MMSCFD dry ash Lurgi coal gasification facility is discussed. The facility\u27s emission spectrum is examined on the basis of the proposed design and empirical data. This system utilizes approximately 13 million tons of lignite and 17,000 acre feet of water per year and consumes 6500 tons of oxygen per day. The results of the study indicate that the major gaseous effluent is CO2, that the federal limits on SO2 effluent may be met, and that the atmospheric degradation criterion will be the most difficult one to meet. The fate of trace elements during the gasification process is discussed. Available preliminary data indicate that the majority of the trace elements will be concentrated in and leave the system with the ash. The probable hydrological and geological impacts pertinent to ash and sludge disposal and water table depression are discussed. The results of the study indicate that the water table will be depressed during mine operations and that some groundwater pollution will occur due to waste disposal

Design and perceptual validation of performance measures for salient object segmentation

Empirical evaluation of salient object segmentation methods requires i) a dataset of ground truth object segmen-tations and ii) a performance measure to compare the out-put of the algorithm with the ground truth. In this paper, we provide such a dataset, and evaluate 5 distinct performance measures that have been used in the literature practically and psychophysically. Our results suggest that a measure based upon minimal contour mappings is most sensitive to shape irregularities and most consistent with human judge-ments. In fact, the contour mapping measure is as predic-tive of human judgements as human subjects are of each other. Region-based methods, and contour methods such as Hausdorff distances that do not respect the ordering of points on shape boundaries are significantly less consistent with human judgements. We also show that minimal contour mappings can be used as the correspondence paradigm for Precision-Recall analysis. Our findings can provide guid-ance in evaluating the results of segmentation algorithms in the future. 1