Cross-Scale Cost Aggregation for Stereo Matching

Human beings process stereoscopic correspondence across multiple scales. However, this bio-inspiration is ignored by state-of-the-art cost aggregation methods for dense stereo correspondence. In this paper, a generic cross-scale cost aggregation framework is proposed to allow multi-scale interaction in cost aggregation. We firstly reformulate cost aggregation from a unified optimization perspective and show that different cost aggregation methods essentially differ in the choices of similarity kernels. Then, an inter-scale regularizer is introduced into optimization and solving this new optimization problem leads to the proposed framework. Since the regularization term is independent of the similarity kernel, various cost aggregation methods can be integrated into the proposed general framework. We show that the cross-scale framework is important as it effectively and efficiently expands state-of-the-art cost aggregation methods and leads to significant improvements, when evaluated on Middlebury, KITTI and New Tsukuba datasets.Comment: To Appear in 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2014 (poster, 29.88%

Towards training-free refinement for semantic indexing of visual media

Indexing of visual media based on content analysis has now moved beyond using individual concept detectors and there is now a fo- cus on combining concepts or post-processing the outputs of individual concept detection. Due to the limitations and availability of training cor- pora which are usually sparsely and imprecisely labeled, training-based refinement methods for semantic indexing of visual media suffer in cor- rectly capturing relationships between concepts, including co-occurrence and ontological relationships. In contrast to training-dependent methods which dominate this field, this paper presents a training-free refinement (TFR) algorithm for enhancing semantic indexing of visual media based purely on concept detection results, making the refinement of initial con- cept detections based on semantic enhancement, practical and flexible. This is achieved using global and temporal neighbourhood information inferred from the original concept detections in terms of weighted non- negative matrix factorization and neighbourhood-based graph propaga- tion, respectively. Any available ontological concept relationships can also be integrated into this model as an additional source of external a priori knowledge. Experiments on two datasets demonstrate the efficacy of the proposed TFR solution

The Mechanism of Secretion and Metabolism of Gut-Derived 5-Hydroxytryptamine

Serotonin, also known as 5-hydroxytryptamine (5-HT), is a metabolite of tryptophan and is reported to modulate the development and neurogenesis of the enteric nervous system, gut motility, secretion, inflammation, sensation, and epithelial development. Approximately 95% of 5-HT in the body is synthesized and secreted by enterochromaffin (EC) cells, the most common type of neuroendocrine cells in the gastrointestinal (GI) tract, through sensing signals from the intestinal lumen and the circulatory system. Gut microbiota, nutrients, and hormones are the main factors that play a vital role in regulating 5-HT secretion by EC cells. Apart from being an important neurotransmitter and a paracrine signaling molecule in the gut, gut-derived 5-HT was also shown to exert other biological functions (in autism and depression) far beyond the gut. Moreover, studies conducted on the regulation of 5-HT in the immune system demonstrated that 5-HT exerts anti-inflammatory and proinflammatory effects on the gut by binding to different receptors under intestinal inflammatory conditions. Understanding the regulatory mechanisms through which 5-HT participates in cell metabolism and physiology can provide potential therapeutic strategies for treating intestinal diseases. Herein, we review recent evidence to recapitulate the mechanisms of synthesis, secretion, regulation, and biofunction of 5-HT to improve the nutrition and health of humans

Improving the classification of quantified self activities and behaviour using a Fisher kernel

Visual recording of everyday human activities and behaviour over the long term is now feasible and with the widespread use of wearable devices embedded with cameras this offers the potential to gain real insights into wearers’ activities and behaviour. To date we have concentrated on automatically detecting semantic concepts from within visual lifelogs yet identifying human activities from such lifelogged images or videos is still a major challenge if we are to use lifelogs to maximum benefit. In this paper, we propose an activity classification method from visual lifelogs based on Fisher kernels, which extract discriminative embeddings from Hidden Markov Models (HMMs) of occurrences of semantic concepts. By using the gradients as features, the resulting classifiers can better distinguish different activities and from that we can make inferences about human behaviour. Experiments show the effectiveness of this method in improving classification accuracy, especially when the semantic concepts are initially detected with low degrees of accuracy