Jim Ivins

The ‘Ecological’ Probability Density Function for Linear Optic Flow: Implications for Neurophysiology

A theoretical analysis of the recovery of shape from optic flow highlights the importance of the deformation components; however, pure deforming stimuli elicit few responses from flow-sensitive neurons in the medial superior temporal (MST) area of the cerebral cortex. This finding has prompted the conclusion that MST cells are not involved in shape recovery. However, this conclusion may be unjustified in view of the emerging consensus that MST cells perform nonlinear pattern matching, rather than linear projection as implicitly assumed in many neurophysiological studies. Artificial neural models suggest that the input probability density function (PDF) is crucial in determining the distribution of responses shown by pattern-matching cells. This paper therefore describes a Monte-Carlo study of the joint PDF for linear optic-flow components produced by ego-motion in a simulated planar environment. The recent search for deformation-selective cells in MST is then used to illustrate the importance of the input PDF in determining cell characteristics. The results are consistent with the finding that MST cells exhibit a continuum of responses to translation, rotation, and divergence. In addition, there are negative correlations between the deformation and conformal components of optic flow. Consequently, if cells responsible for shape analysis are present in the MST area, they should respond best to combinations of deformation with other first-order flow components, rather than to the pure stimuli used in previous neurophysiological studies.

Software Engineers and Engineering: A Survey of Undergraduate Preconceptions

Past research has demonstrated that student misconceptions about degree programs can negatively affect enrolment and retention rates. Software engineering is a relatively new discipline that is distinct from computer science and other engineering specializations; however, it is still rapidly evolving and consequently there is potential for misconceptions about the new discipline to arise. A study was therefore undertaken to investigate the preconceptions of first-year students enrolled in various Bachelor of Engineering degrees. Students were asked to rank the importance of different skills and activities for software engineering, and to rate a variety of statements about software engineering using a Likert scale. First-year preconceptions were compared with the responses of fourth-year software engineering students who had completed a major industry-based project. The two groups of students had statistically significant differences of opinion with respect to many of the survey items. There were no statistically significant differences between the responses of first-year students from different engineering specializations. These findings are discussed in the context of recruiting and retaining software engineering students

The joint probability density function for linear optic flow components

Artificial neural models suggest that the probability density function (PDF) of available inputs is crucial in determining the distribution of responses shown by a group of pattern-matching cells. This paper therefore describes a Monte-Carlo study of the PDF for linear optic flow components produced by ego-motion in a simulated planar environment. The recent search for deformation-selective cells in the medial superior temporal (MST) area of the cerebral cortex is used to illustrate the biological significance of the optic flow PDF. The simulation results are consistent with the neurophysiological finding that MST cells exhibit a continuum of responses to translation, rotation and divergence. In addition, there are strong negative correlations between deformation and other first-order flow components. The deformation components contain information necessary for recovering shape from flow. Consequently, if cells responsible for shape analysis are present in the MST area they should respond best to combinations of deformation with other flow components, rather than to the pure stimuli used in previous neurophysiological studies.

A comparison of algorithms for calculating glaucoma change probability confidence intervals

PurposeTo evaluate the ability to detect change in standard automated perimetry data using 4 different methods for calculating the glaucoma change probability (GCP). MethodsA database of stable visual fields, collected within 1 week from 35 glaucoma patients and within 6 months from 15 normal patients, was used to determine confidence intervals for GCP using 4 different methods. The methods classified visual field locations on the basis of either defect or mean threshold, and used test-retest data or baseline-less-follow-up data to determine values for the confidence intervals. The specificity of the 4 methods was measured using 3700 locations artificially generated to simulate stable visual field data. The sensitivity of the methods was measured using 3330 artificially generated locations that decreased in either a linear, curvilinear, or bi-linear fashion by 2, 3, or 4 dB per year on average. ResultsUsing GCP with confidence intervals built using the methods described in the literature (on the basis of defect and test-retest differences) resulted in a higher specificity than techniques based on mean threshold. However, the mean-based methods were more sensitive at detecting a decrease in a location. Building confidence intervals using the difference between a baseline and the current measurement (baseline-less-follow-up), rather than test-retest differences, also improved the detection of visual field progression. ConclusionsStratifying baseline visual field measurements based on defect and eccentricity as described in the literature results in an unusually high specificity: 98% accuracy in classifying the same stable data that generated the 95% confidence intervals, rather than the expected 95% accuracy. By stratifying measurements based on mean threshold, and using baseline-less-follow-up rather than test-retest differences to build 95% confidence intervals, sensitivity is increased by 14.1%. This increase in sensitivity comes with a corresponding 2.2% decrease in specificity.

Monitoring glaucomatous progression: classification of visual field measurements using stable reference data

Glaucoma is a common disease of the eye that often results in partial blindness. The main symptom of glaucoma is the progressive deterioration of the visual field. Glaucoma management involves monitoring the progress of the disease using regular visual field tests but currently there is no standard method for classifying changes in visual field measurements. Sequence matching techniques typically rely on similarity measures. However, visual field measurements are very noisy, particularly in people with glaucoma. It is therefore difficult to establish a reference data set including both stable and progressive visual fields. We describe method that uses a baseline computed from a query sequence, to match stable sequences in a database collected from volunteers. The results suggest that the new method is more accurate than other techniques for identifying progressive sequences, though there is a small penalty for stable sequences.