Matching shapes using the current distance

posterCurrent Distance: It was introduced by Vaillant and Glaunès as a way of comparing shapes (point sets, curves, surfaces). This distance measure is defined by viewing a shape as a linear operator on a k-form field, and constructing a (dual) norm on the space of shapes. Shape Matching: Given two shapes P;Q, a distance measure d on shapes, and a transformation group T , the problem of shape matching is to determine a transformation T that minimizes d(P; T Q). Current Norm: For a point set P, current norm is kPk2 = X i X j K(pi; pj)) (p) (q) Current Distance: Distance between two point sets P and Q is D2(P;Q) = kP + (??1)Qk2 = kPk2 + kQk2 ?? 2 X i X j K(pi; qj)) (p) (q) It takes O(n2) time to compute the current distance between two shapes of size n. Also current distance between 2 surfaces or curves can be reduced to set of distance computations on appropriately weighted point sets

Is Face Recognition Biased by Unintentional Recognition of Distracting Information?

Research highlights that we are not as skilful in controlling our memory as we may believe. Instead, our everyday intentional recognition judgments are often biased by what we unintentionally recognise in the same context. So far, it has been demonstrated that the unintentional recognition of image distractors can bias the intentional recognition of word targets, in the form of a familiarity (old/new) congruency bias. This bias reflects improved recognition performance for targets when the distractor/context upon which it is present at test is of the same memory status (also old or new). However, this effect has not yet been explored using face stimuli, despite faces varying in pre-existing familiarity and often being encountered in different familiar or unfamiliar contexts in everyday life. Furthermore, the distractor stimuli used in past literature have often been limited to simple drawings. Past designs have also typically relied on the use of working memory load or divided-attention tasks, or healthy aging to magnify distractibility, which is arguably not ecological valid nor generalisable. Consequently, this research investigated whether distractor-induced congruency biases found for words also apply to faces, using a new database of up-to-date face stimuli and without secondary manipulations of distractibility. I also attempted to replicate these results in an alternative sample and compared effects between target types (words vs faces). Results show novel evidence for the idea that faces are also biased by distracting stimuli in the same manner that has been found in relation to words. In turn, providing evidence for specific cognitive theories (e.g. Perceptual load theory) while questioning others (face processing modularity). Lastly, the study also provides future direction for neurocognitive research to answer questions regarding the underlying mechanisms of distractor bias, based on past research findings of dissociating event-related potentials (ERPs) in relation to unintentional and intentional recognition