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

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

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

Standard (8 weeks) vs long (12 weeks) timing to minimally-invasive surgery after NeoAdjuvant Chemoradiotherapy for rectal cancer: A multicenter randomized controlled parallel group trial (TiMiSNAR)

Background: The optimal timing of surgery in relation to chemoradiation is still controversial. Retrospective analysis has demonstrated in the recent decades that the regression of adenocarcinoma can be slow and not complete until after several months. More recently, increasing pathologic Complete Response rates have been demonstrated to be correlated with longer time interval. The purpose of the trial is to demonstrate if delayed timing of surgery after neoadjuvant chemoradiotherapy actually affects pathologic Complete Response and reflects on disease-free survival and overall survival rather than standard timing. Methods: The trial is a multicenter, prospective, randomized controlled, unblinded, parallel-group trial comparing standard and delayed surgery after neoadjuvant chemoradiotherapy for the curative treatment of rectal cancer. Three-hundred and forty patients will be randomized on an equal basis to either robotic-assisted/standard laparoscopic rectal cancer surgery after 8 weeks or robotic-assisted/standard laparoscopic rectal cancer surgery after 12 weeks. Discussion: To date, it is well-know that pathologic Complete Response is associated with excellent prognosis and an overall survival of 90%. In the Lyon trial the rate of pCR or near pathologic Complete Response increased from 10.3 to 26% and in retrospective studies the increase rate was about 23-30%. These results may be explained on the relationship between radiation therapy and tumor regression: DNA damage occurs during irradiation, but cellular lysis occurs within the next weeks. Study results, whether confirmed that performing surgery after 12 weeks from neoadjuvant treatment is advantageous from a technical and oncological point of view, may change the current pathway of the treatment in those patient suffering from rectal cancer. Trial registration: ClinicalTrials.gov NCT3465982

Standard (8 weeks) vs long (12 weeks) timing to minimally-invasive surgery after NeoAdjuvant Chemoradiotherapy for rectal cancer: a multicenter randomized controlled parallel group trial (TiMiSNAR)

BACKGROUND: The optimal timing of surgery in relation to chemoradiation is still controversial. Retrospective analysis has demonstrated in the recent decades that the regression of adenocarcinoma can be slow and not complete until after several months. More recently, increasing pathologic Complete Response rates have been demonstrated to be correlated with longer time interval. The purpose of the trial is to demonstrate if delayed timing of surgery after neoadjuvant chemoradiotherapy actually affects pathologic Complete Response and reflects on disease-free survival and overall survival rather than standard timing. METHODS: The trial is a multicenter, prospective, randomized controlled, unblinded, parallel-group trial comparing standard and delayed surgery after neoadjuvant chemoradiotherapy for the curative treatment of rectal cancer. Three-hundred and forty patients will be randomized on an equal basis to either robotic-assisted/standard laparoscopic rectal cancer surgery after 8\u2009weeks or robotic-assisted/standard laparoscopic rectal cancer surgery after 12\u2009weeks. DISCUSSION: To date, it is well-know that pathologic Complete Response is associated with excellent prognosis and an overall survival of 90%. In the Lyon trial the rate of pCR or near pathologic Complete Response increased from 10.3 to 26% and in retrospective studies the increase rate was about 23-30%. These results may be explained on the relationship between radiation therapy and tumor regression: DNA damage occurs during irradiation, but cellular lysis occurs within the next weeks. Study results, whether confirmed that performing surgery after 12\u2009weeks from neoadjuvant treatment is advantageous from a technical and oncological point of view, may change the current pathway of the treatment in those patient suffering from rectal cancer