Compress-then-analyze vs. analyze-then-compress: Two paradigms for image analysis in visual sensor networks

We compare two paradigms for image analysis in vi- sual sensor networks (VSN). In the compress-then-analyze (CTA) paradigm, images acquired from camera nodes are compressed and sent to a central controller for further analysis. Conversely, in the analyze-then-compress (ATC) approach, camera nodes perform visual feature extraction and transmit a compressed version of these features to a central controller. We focus on state-of-the-art binary features which are particularly suitable for resource-constrained VSNs, and we show that the ”winning” paradigm depends primarily on the network conditions. Indeed, while the ATC approach might be the only possible way to perform analysis at low available bitrates, the CTA approach reaches the best results when the available bandwidth enables the transmission of high-quality images

Bamboo: A fast descriptor based on AsymMetric pairwise BOOsting

A robust hash, or content-based fingerprint, is a succinct representation of the perceptually most relevant parts of a multimedia object. A key requirement of fingerprinting is that elements with perceptually similar content should map to the same fingerprint, even if their bit-level representations are different. In this work we propose BAMBOO (Binary descriptor based on AsymMetric pairwise BOOsting), a binary local descriptor that exploits a combination of content-based fingerprinting techniques and computationally efficient filters (box filters, Haar-like features, etc.) applied to image patches. In particular, we define a possibly large set of filters and iteratively select the most discriminative ones resorting to an asymmetric pair-wise boosting technique. The output values of the filtering process are quantized to one bit, leading to a very compact binary descriptor. Results show that such descriptor leads to compelling results, significantly outperforming binary descriptors having comparable complexity (e.g., BRISK), and approaching the discriminative power of state-of-the-art descriptors which are significantly more complex (e.g., SIFT and BinBoost)

A visual sensor network for object recognition: Testbed realization

This work describes the implementation of an object recognition service on top of energy and resource-constrained hardware. A complete pipeline for object recognition based on the BRISK visual features is implemented on Intel Imote2 sensor devices. The reference implementation is used to assess the performance of the object recognition pipeline in terms of processing time and recognition accuracy

Coding binary local features extracted from video sequences

Local features represent a powerful tool which is exploited in several applications such as visual search, object recognition and tracking, etc. In this context, binary descriptors provide an efficient alternative to real-valued descriptors, due to low computational complexity, limited memory footprint and fast matching algorithms. The descriptor consists of a binary vector, in which each bit is the result of a pairwise comparison between smoothed pixel intensities. In several cases, visual features need to be transmitted over a bandwidth-limited network. To this end, it is useful to compress the descriptor to reduce the required rate, while attaining a target accuracy for the task at hand. The past literature thoroughly addressed the problem of coding visual features extracted from still images and, only very recently, the problem of coding real-valued features (e.g., SIFT, SURF) extracted from video sequences. In this paper we propose a coding architecture specifically designed for binary local features extracted from video content. We exploit both spatial and temporal redundancy by means of intra-frame and inter-frame coding modes, showing that significant coding gains can be attained for a target level of accuracy of the visual analysis task

Rate-energy-accuracy optimization of convolutional architectures for face recognition

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Face recognition systems based on Convolutional Neural Networks (CNNs) or convolutional architectures currently represent the state of the art, achieving an accuracy comparable to that of humans. Nonetheless, there are two issues that might hinder their adoption on distributed battery-operated devices (e.g., visual sensor nodes, smartphones, and wearable devices). First, convolutional architectures are usually computationally demanding, especially when the depth of the network is increased to maximize accuracy. Second, transmitting the output features produced by a CNN might require a bitrate higher than the one needed for coding the input image. Therefore, in this paper we address the problem of optimizing the energy-rate-accuracy characteristics of a convolutional architecture for face recognition. We carefully profile a CNN implementation on a Raspberry Pi device and optimize the structure of the neural network, achieving a 17-fold speedup without significantly affecting recognition accuracy. Moreover, we propose a coding architecture custom-tailored to features extracted by such model. (C) 2015 Elsevier Inc. All rights reserved.Face recognition systems based on Convolutional Neural Networks (CNNs) or convolutional architectures currently represent the state of the art, achieving an accuracy comparable to that of humans. Nonetheless, there are two issues that might hinder their a36142148CNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)sem informação2013/11359-0sem informaçã

Trajectories of learning approaches during a full medical curriculum: impact on clinical learning outcomes.

Background No consensus exists on whether medical students develop towards more deep (DA) or surface learning approaches (SA) during medical training and how this impacts learning outcomes. We investigated whether subgroups with different trajectories of learning approaches in a medical students’ population show different long-term learning outcomes. Methods Person-oriented growth curve analyses on a prospective cohort of 269 medical students (Mage=21years, 59 % females) traced subgroups according to their longitudinal DA/SA profile across academic years 1, 2, 3 and 5. Post-hoc analyses tested differences in academic performance between subgroups throughout the 6-year curriculum until the national high-stakes licensing exam certifying the undergraduate medical training. Results Two longitudinal trajectories emerged: surface-oriented (n = 157; 58 %), with higher and increasing levels of SA and lower and decreasing levels of DA; and deep-oriented (n = 112; 42 %), with lower and stable levels of SA and higher but slightly decreasing levels of DA. Post hoc analyses showed that from the beginning of clinical training, deep-oriented students diverged towards better learning outcomes in comparison with surface-oriented students. Conclusions Medical students follow different trajectories of learning approaches during a 6-year medical curriculum. Deep-oriented students are likely to achieve better clinical learning outcomes than surface-oriented students

Integrated problem-based learning versus lectures: a path analysis modelling of the relationships between educational context and learning approaches.

Students' approaches to learning are central to the process of learning. Previous research has revealed that influencing students' approaches towards deep learning is a complex process and seems much more difficult than expected, even in student-activating learning environments. There is evidence that learning approaches are impacted not only by the learning environment, but also by how students perceive it. However the nature of the links between the environment itself, the way in which it is perceived by students and students' learning approaches is poorly understood. This study aimed at investigating the relationships between students' perception of their educational context and learning approaches in three learning environments differing by their teaching formats (lecture or problem-based-learning PBL) and integration level of the curriculum (traditional or integrated). We tested the hypothesis that a PBL format and an integrated curriculum are associated to deeper approaches to learning and that this is mediated by student perception. The study sample was constituted of 1394 medical students trained respectively in a traditional lecture-based (n = 295), in an integrated lecture-based (n = 612) and in an integrated PBL-based (n = 487) curricula. They completed a survey including the Dundee-Ready-Educational-Environment-Measure (students' perceptions of the educational environment) and the Revised-Study-Process-Questionnaire (learning approaches). Data were analysed by path analysis. The model showed that the learning environment was related to students' learning approaches by two paths, one direct and one mediated via students' perception of their educational context. In the lecture-based curricula students' used deeper approaches when it was integrated and both paths were cumulative. In the PBL-based curriculum students' did not use deeper approaches than with lectures, due to opposite effects of both paths. This study suggested that an integrated lecture-based curriculum was as effective as a PBL curriculum in promoting students' deep learning approaches, reinforcing the importance of integrating the curriculum before choosing the teaching format