TRIC-track: tracking by regression with incrementally learned cascades

This paper proposes a novel approach to part-based track- ing by replacing local matching of an appearance model by direct prediction of the displacement between local image patches and part locations. We propose to use cascaded regression with incremental learning to track generic objects without any prior knowledge of an object’s structure or appearance. We exploit the spatial constraints between parts by implicitly learning the shape and deformation parameters of the object in an online fashion. We integrate a multiple temporal scale motion model to initialise our cascaded regression search close to the target and to allow it to cope with occlusions. Experimental results show that our tracker ranks first on the CVPR 2013 Benchmark

TRIC-track: tracking by regression with incrementally learned cascades

This paper proposes a novel approach to part-based track- ing by replacing local matching of an appearance model by direct prediction of the displacement between local image patches and part locations. We propose to use cascaded regression with incremental learning to track generic objects without any prior knowledge of an object’s structure or appearance. We exploit the spatial constraints between parts by implicitly learning the shape and deformation parameters of the object in an online fashion. We integrate a multiple temporal scale motion model to initialise our cascaded regression search close to the target and to allow it to cope with occlusions. Experimental results show that our tracker ranks first on the CVPR 2013 Benchmark

Busting a myth with the Bayes Factor: Effects of letter bigram frequency in visual lexical decision do not reflect reading processes

Psycholinguistic researchers identify linguistic variables and assess if they affect cognitive processes. One such variable is letter bigram frequency, or the frequency with which a given letter pair co-occurs in an orthography. While early studies reported that bigram frequency affects visual lexical decision, subsequent, well-controlled studies not shown this effect. Still, researchers continue to use it as a control variable in psycholinguistic experiments. We propose two reasons for the persistence of this variable: (1) Reporting no significant effect of bigram frequency cannot provide evidence for no effect. (2) Despite empirical work, theoretical implications of bigram frequency are largely neglected. We perform Bayes Factor analyses to address the first issue. In analyses of existing large-scale databases, we find no effect of bigram frequency in lexical decision in the British Lexicon Project, and some evidence for an inhibitory effect in the English Lexicon Project. We find strong evidence for an effect in reading aloud. This suggests that, for lexical decision, the effect is unstable, and may depend on item characteristics and task demands rather than reflecting cognitive processes underlying visual word recognition. We call for more consideration of theoretical implications of the presence or absence of a bigram frequency effect

Point-to-Pose Voting based Hand Pose Estimation using Residual Permutation Equivariant Layer

Recently, 3D input data based hand pose estimation methods have shown state-of-the-art performance, because 3D data capture more spatial information than the depth image. Whereas 3D voxel-based methods need a large amount of memory, PointNet based methods need tedious preprocessing steps such as K-nearest neighbour search for each point. In this paper, we present a novel deep learning hand pose estimation method for an unordered point cloud. Our method takes 1024 3D points as input and does not require additional information. We use Permutation Equivariant Layer (PEL) as the basic element, where a residual network version of PEL is proposed for the hand pose estimation task. Furthermore, we propose a voting based scheme to merge information from individual points to the final pose output. In addition to the pose estimation task, the voting-based scheme can also provide point cloud segmentation result without ground-truth for segmentation. We evaluate our method on both NYU dataset and the Hands2017Challenge dataset. Our method outperforms recent state-of-the-art methods, where our pose accuracy is currently the best for the Hands2017Challenge dataset

Neural correlates of emotion word processing: the complex relation between emotional valence and arousal

Poster Session 1: no. 2The Conference's website is located at http://events.unitn.it/en/psb2010Emotion is characterised by a two-dimensional structure: valence describes the extent to which an emotion is positive or negative, whereas arousal refers to the intensity of an emotion, how exciting or calming it is. Emotional content of verbal material influences cognitive processing during lexical decision, naming, emotional Stroop task and many others. Converging findings showed that emotionally valenced words (positive or negative) are processed faster than neutral words, as shown by reaction time and ERP measures, suggesting a prioritisation of emotional …published_or_final_versio