Variação linguística na sala de aula : uma proposta de sequência didática

Orientador :Monografia (especialização) - Universidade Federal do Paraná, Setor de ..., Curso de Especialização em ...Inclui referência

On Calibration of a Low-Cost Time-of-Flight Camera

Abstract. Time-of-flight (ToF) cameras are becoming more and more popular in computer vision. In many applications 3D information de-livered by a ToF camera is used, and it is very important to know the camera’s extrinsic and intrinsic parameters, as well as precise depth in-formation. A straightforward algorithm to calibrate a ToF camera is to use a standard color camera calibration procedure [12], on the amplitude images. However, depth information delivered by ToF cameras is known to contain complex bias due to several error sources [6]. Additionally, it is desirable in many cases to determine the pose of the ToF camera relative to the other sensors used. In this work, we propose a method for joint color and ToF camera cali-bration, that determines extrinsic and intrinsic camera parameters and corrects depth bias. The calibration procedure requires a standard cali-bration board and around 20-30 images, as in case of a single color camera calibration. We evaluate the calibration quality in several experiments

Meta-analysis: the use of non-steroidal anti-inflammatory drugs and pancreatic cancer risk for different exposure categories

Background A better understanding of predictors of risk for pancreatic ductal adenocarcinoma (PDAC) could inform preventive efforts against this lethal cancer. While aspirin (ASA) and non-steroidal anti-inflammatory drugs (NSAIDS) might protect against several gastrointestinal cancers, their role in the development of PDAC remains unclear. Aim To conduct a systematic review and meta-analysis on the relation between ASA/NSAIDs exposure and the risk of PDAC. Methods We searched Pubmed, Embase, Scopus, Cochrane database of systematic reviews and reference lists of identified papers and included observational (cohort or case-control) studies and randomized controlled trials examining exposure to ASA and/or NSAIDs and the incidence or mortality of PDAC. We defined three categories (low, intermediate, high), based on exposure duration and dose. Results Eight studies fulfilled our inclusion criteria (four cohort, three case controls, and one randomized controlled trial studies) enrolling 6301 patients between 1971-2004; all but one study took place in the US. The pooled OR were 0.99 (0.83-1.19), 1.11 (0.84-1.47) and 1.09 (0.67-1.75) in the low, intermediate and high exposure groups respectively, with considerable heterogeneity (I-2 ranging 60-86%). Sensitivity analysis by ASA use only, study design or sex did not reveal additional important information. Conclusions This study did not show an association between ASA/NSAIDs and PDAC. The large baseline exposure in controls in North-America may have obscured an association. There is need for additional studies, especially in Europe, to clarify this issue

Computationally Efficient Data and Application Driven Color Transforms for the Compression and Enhancement of Images and Video

An important step in color image or video coding and enhancement is the linear transformation of input (typically red-green-blue (RGB)) data into a color space more suitable for compression, subsequent analysis, or visualization. The choice of this transform becomes even more critical when operating in distributed and low-computational power environments, such as visual sensor networks or remote sensing. Data-driven transforms are rarely used due to increased complexity. Most schemes adopt fixed transforms to decorrelate the color channels which are then processed independently. Here we propose two frameworks to find appropriate data-driven transforms in different settings. The first, named approximate Karhunen–Loève Transform (aKLT), performs comparable to the KLT at a fraction of the computational complexity, thus favoring adoption on sensors and resource-constrained devices. Furthermore, we consider an application-aware setting in which an expert system (e.g., a classifier) analyzes imaging data at the receiver’s end. In a compression context, distortion may jeopardize the accuracy of the analysis. Since the KLT is not optimal in this setting, we investigate formulations that maximize post-compression expert system performance. Relaxing decorrelation and energy compactness constraints, a second transform can be obtained offline with supervised learning methods. Finally, we propose transforms that accommodate both constraints, and are found using regularized optimization