The application of STEM and in situ controlled dehydration to bacterial systems using ESEM.

Transmission imaging with an environmental scanning electron microscope (ESEM) (Wet STEM) is a recent development in the field of electron microscopy, combining the simple preparation inherent to ESEM work with an alternate form of contrast available through a STEM detector. Because the technique is relatively new, there is little information available on how best to apply this technique and which samples it is best suited for. This work is a description of the sample preparation and microscopy employed by the authors for imaging bacteria with Wet STEM (scanning transmission electron microscopy). Three different bacterial samples will be presented in this study: first, used as a model system, is Escherichia coli for which the contrast mechanisms of STEM are demonstrated along with the visual effects of a dehydration-induced collapse. This collapse, although clearly in some sense artifactual, is thought to lead to structurally meaningful morphological information. Second, Wet STEM is applied to two distinct bacterial systems to demonstrate the novel types of information accessible by this approach: the plastic-producing Cupriavidus necator along with wild-type and ΔmreC knockout mutants of Salmonella enterica serovar Typhimurium. Cupriavidus necator is shown to exhibit clear internal differences between bacteria with and without plastic granules, while the ΔmreC mutant of S. Typhimurium has an internal morphology distinct from that of the wild type.

MCBoost: Multiple classifier boosting for perceptual co-clustering of images and visual features

We present a new co-clustering problem of images and visual features. The problem involves a set of non-object images in addition to a set of object images and features to be co-clustered. Co-clustering is performed in a way that maximises discrimination of object images from non-object images, thus emphasizing discriminative features. This provides a way of obtaining perceptual joint-clusters of object images and features. We tackle the problem by simultaneously boosting multiple strong classifiers which compete for images by their expertise. Each boosting classifier is an aggregation of weak-learners, i.e. simple visual features. The obtained classifiers are useful for object detection tasks which exhibit multimodalities, e.g. multi-category and multi-view object detection tasks. Experiments on a set of pedestrian images and a face data set demonstrate that the method yields intuitive image clusters with associated features and is much superior to conventional boosting classifiers in object detection tasks.

The application of STEM and in situ controlled dehydration to bacterial systems using ESEM

Transmission imaging with an environmental scanning electron microscope (ESEM) (Wet STEM) is a recent development in the field of electron microscopy, combining the simple preparation inherent to ESEM work with an alternate form of contrast available through a STEM detector. Because the technique is relatively new, there is little information available on how best to apply this technique and which samples it is best suited for. This work is a description of the sample preparation and microscopy employed by the authors for imaging bacteria with Wet STEM (scanning transmission electron microscopy). Three different bacterial samples will be presented in this study: first, used as a model system, is Escherichia coli for which the contrast mechanisms of STEM are demonstrated along with the visual effects of a dehydration-induced collapse. This collapse, although clearly in some sense artifactual, is thought to lead to structurally meaningful morphological information. Second, Wet STEM is applied to two distinct bacterial systems to demonstrate the novel types of information accessible by this approach: the plastic-producing Cupriavidus necator along with wild-type and δmreC knockout mutants of Salmonella enterica serovar Typhimurium. Cupriavidus necator is shown to exhibit clear internal differences between bacteria with and without plastic granules, while the δmreC mutant of S. Typhimurium has an internal morphology distinct from that of the wild type. © 2012 Wiley Periodicals, Inc.

Expressive visual text-to-speech using active appearance models

This paper presents a complete system for expressive visual text-to-speech (VTTS), which is capable of producing expressive output, in the form of a 'talking head', given an input text and a set of continuous expression weights. The face is modeled using an active appearance model (AAM), and several extensions are proposed which make it more applicable to the task of VTTS. The model allows for normalization with respect to both pose and blink state which significantly reduces artifacts in the resulting synthesized sequences. We demonstrate quantitative improvements in terms of reconstruction error over a million frames, as well as in large-scale user studies, comparing the output of different systems. © 2013 IEEE.

A dedicated binding mechanism for the visual control of movement.

The human motor system is remarkably proficient in the online control of visually guided movements, adjusting to changes in the visual scene within 100 ms [1-3]. This is achieved through a set of highly automatic processes [4] translating visual information into representations suitable for motor control [5, 6]. For this to be accomplished, visual information pertaining to target and hand need to be identified and linked to the appropriate internal representations during the movement. Meanwhile, other visual information must be filtered out, which is especially demanding in visually cluttered natural environments. If selection of relevant sensory information for online control was achieved by visual attention, its limited capacity [7] would substantially constrain the efficiency of visuomotor feedback control. Here we demonstrate that both exogenously and endogenously cued attention facilitate the processing of visual target information [8], but not of visual hand information. Moreover, distracting visual information is more efficiently filtered out during the extraction of hand compared to target information. Our results therefore suggest the existence of a dedicated visuomotor binding mechanism that links the hand representation in visual and motor systems.

Biologically inspired visual odometer for navigation of a flying robot

Experimental research in biology has uncovered a number of different ways in which flying insects use cues derived from optical flow for navigational purposes, such as safe landing, obstacle avoidance and dead reckoning. In this study, we use a synthetic methodology to gain additional insights into the navigation behavior of bees. Specifically, we focus on the mechanisms of course stabilization behavior and visually mediated odometer by using a biological model of motion detector for the purpose of long-range goal-directed navigation in 3D environment. The performance tests of the proposed navigation method are conducted by using a blimp-type flying robot platform in uncontrolled indoor environments. The result shows that the proposed mechanism can be used for goal-directed navigation. Further analysis is also conducted in order to enhance the navigation performance of autonomous aerial vehicles. © 2003 Elsevier B.V. All rights reserved.