Abstract
In this paper we present an evaluation of optical flow techniques applied to a case study in the perception of visual motion. This case study is being conducted in a project for the evaluation of human factors in road traffic, specifically, concerning the processing of visual information. We present the goals of the case study, discuss the need to apply optical flow techniques to synthesized image sequences and evaluate some limitations encountered in their use. © 1996 IEEE.

Abstract
Optical flow algorithms generally demand for high computational power and huge storage capacities. This paper is a contribution for real-time implementation of an optical flow algorithm on a pipeline machine. This overall optical flow computation methodology is presented and evaluated on a set of synthetic and real image sequences. Results are compared to other implementations using as measures the average angular error, the optical flow density and the root mean square error. The proposed implementation achieves very low computation delays, allowing operation at standard video frame-rate and resolution. It compares favorably to recent implementations in standard microprocessors and in parallel hardware.

Abstract

Abstract
This paper presents two initiatives run by groups of engineering students at the University of Porto: the Microelectronics Students' Group and BioStar. These groups are student-led initiatives that promote different scientific fields through self-guided projects. Both experiences have proven to be very successful in increasing the undergraduate student's interest in science and technology. This work reports the activities, organization and main methodologies employed by these groups, which can be seen as successful approaches to enhance the technical curriculum of students. © 2012 IEEE.

Abstract
Chest radiography is increasingly used worldwide to diagnose a series of illnesses targeting the lungs and heart. The high amount of examinations leads to a severe burden on radiologists, which benefit from the introduction of artificial intelligence tools in clinical practice, such as deep learning classification models. Nevertheless, these models are undergoing limited implementation due to the lack of trustworthy explanations that provide insights about their reasoning. In an attempt to increase the level of explainability, the deep learning approaches developed in this work incorporate in their decision process eye-tracking data collected from experts. More specifically, eye-tracking data is used in the form of heatmaps to change the input to the selected classifier, an EfficientNet-b0, and to guide its focus towards relevant parts of the images. Prior to the classification task, UNet-based models are used to perform heatmap reconstruction, making this framework independent of eye-tracking data during inference. The two proposed approaches are applied to all existing public eye-tracking datasets, to our knowledge, regarding chest X-ray screening, namely EGD, REFLACX and CXR-P. For these datasets, the reconstructed heatmaps highlight important anatomical/pathological regions and the area under the curve results are comparable to the state-of-the-art and to the considered baseline. Furthermore, the quality of the explanations derived from the classifier is superior for one of the approaches, which can be attributed to the use of eye-tracking data.

Abstract
Background and Objective: Convolutional neural networks are widely used to detect radiological findings in chest radiographs. Standard architectures are optimized for images of relatively small size (for exam-ple, 224 x 224 pixels), which suffices for most application domains. However, in medical imaging, larger inputs are often necessary to analyze disease patterns. A single scan can display multiple types of radi-ological findings varying greatly in size, and most models do not explicitly account for this. For a given network, whose layers have fixed-size receptive fields, smaller input images result in coarser features, which better characterize larger objects in an image. In contrast, larger inputs result in finer grained features, beneficial for the analysis of smaller objects. By compromising to a single resolution, existing frameworks fail to acknowledge that the ideal input size will not necessarily be the same for classifying every pathology of a scan. The goal of our work is to address this shortcoming by proposing a lightweight framework for multi-scale classification of chest radiographs, where finer and coarser features are com-bined in a parameter-efficient fashion. Methods: We experiment on CheXpert, a large chest X-ray database. A lightweight multi-resolution (224 x 224, 4 48 x 4 48 and 896 x 896 pixels) network is developed based on a Densenet-121 model where batch normalization layers are replaced with the proposed size-specific batch normalization. Each input size undergoes batch normalization with dedicated scale and shift parameters, while the remaining parameters are shared across sizes. Additional external validation of the proposed approach is performed on the VinDr-CXR data set. Results: The proposed approach (AUC 83 . 27 +/- 0 . 17 , 7.1M parameters) outperforms standard single-scale models (AUC 81 . 76 +/- 0 . 18 , 82 . 62 +/- 0 . 11 and 82 . 39 +/- 0 . 13 for input sizes 224 x 224, 4 48 x 4 48 and 896 x 896, respectively, 6.9M parameters). It also achieves a performance similar to an ensemble of one individual model per scale (AUC 83 . 27 +/- 0 . 11 , 20.9M parameters), while relying on significantly fewer parameters. The model leverages features of different granularities, resulting in a more accurate classifi-cation of all findings, regardless of their size, highlighting the advantages of this approach. Conclusions: Different chest X-ray findings are better classified at different scales. Our study shows that multi-scale features can be obtained with nearly no additional parameters, boosting performance. (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )