The PASCAL Visual Object Classes Challenge

Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts. Many machine learning-based approaches have been developed for the prognosis and diagnosis of COVID-19 from medical images and this Analysis identifies over 2,200 relevant published papers and preprints in this area. After initial screening, 62 studies are analysed and the authors find they all have methodological flaws standing in the way of clinical utility. The authors have several recommendations to address these issues.

/pdf/common-pitfalls-and-recommendations-for-using-machine-5gdzsd2e98.pdf

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011-2022)

Gait is a biometric trait that can allow user authentication, though it is classified as a “soft” one due to a certain lack in permanence and to sensibility to specific conditions. The earliest research relies on computer vision, especially applied in video surveillance. More recently, the spread of wearable sensors, especially those embedded in mobile devices, has spurred a different research line. In fact, they are able to capture the dynamics of the walking pattern through simpler one-dimensional signals. This capture modality can avoid some problems related to computer vision-based techniques but suffers from specific limitations. Related research is still in a less advanced phase with respect to other biometric traits. However, many factors - the promising results achieved so far, the increasing accuracy of sensors, the ubiquitous presence of mobile devices, and the low cost of related techniques - contribute to making this biometrics attractive and suggest continuing investigating. This survey provides interested readers with a reasoned and systematic overview of problems, approaches, and available benchmarks.

https://dl.acm.org/doi/pdf/10.1145/3340293

A Survey on Gait Recognition via Wearable Sensors

Authenticating fingerphoto images captured using a smartphone camera, provide a good alternate solution in place of traditional pin or pattern based approaches. There are multiple challenges associated with fingerphoto authentication such as background variations, environmental illumination, estimating finger position, and camera resolution. In this research, we propose a novel ScatNet feature based fingerphoto matching approach. Effective fingerphoto segmentation and enhancement are performed to aid the matching process and to attenuate the effect of capture variations. Further, we propose and create a publicly available smartphone fingerphoto database having three different subsets addressing the challenges of environmental illumination and background, along with their corresponding live scan fingerprints. Experimental results show improved performance across multiple challenges present in the database.

On smartphone camera based fingerphoto authentication

In many applications such as law enforcement, attendance systems, and medical services, biometrics is utilized for identifying individuals. However, current systems, in general, do not enroll all possible age groups, particularly, toddlers and pre-school children. This research is the first of its kind attempt to prepare a multimodal biometric database for such potential users of biometric systems. In the proposed database, face, fingerprint, and iris modalities of over 100 children (age range of 18 months to 4 years) are captured in two different sessions, months apart. We also perform benchmarking evaluation of existing tools and algorithms to establish the baseline results for different unimodal and multimodal scenarios. Our experience and results suggest that while iris is highly accurate, it requires constant adult supervision to attain cooperation from children. On the other hand, face is the most easy-to-capture modality but yields very low verification performance. We assert that the availability of this database can instigate research in this important research problem.

Multimodal biometric recognition for toddlers and pre-school children

With increasing number of COVID-19 cases globally, all the countries are ramping up the testing numbers. While the RT-PCR kits are available in sufficient quantity in several countries, others are facing challenges with limited availability of testing kits and processing centers in remote areas. This has motivated researchers to find alternate methods of testing which are reliable, easily accessible and faster. Chest X-Ray is one of the modalities that is gaining acceptance as a screening modality. Towards this direction, the paper has two primary contributions. Firstly, we present the COVID-19 Multi-Task Network which is an automated end-to-end network for COVID-19 screening. The proposed network not only predicts whether the CXR has COVID-19 features present or not, it also performs semantic segmentation of the regions of interest to make the model explainable. Secondly, with the help of medical professionals, we manually annotate the lung regions of 9000 frontal chest radiographs taken from ChestXray-14, CheXpert and a consolidated COVID-19 dataset. Further, 200 chest radiographs pertaining to COVID-19 patients are also annotated for semantic segmentation. This database will be released to the research community.

Multi-Task Driven Explainable Diagnosis of COVID-19 using Chest X-ray Images

Multi-Task Driven Explainable Diagnosis of COVID-19 using Chest X-ray Images.

With the advancements in technology, smartphones’ capabilities have increased immensely. For instance, the smartphone cameras are being used for face and ocular biometric-based authentication. This research proposes finger-selfie based authentication mechanism, which uses a smartphone camera to acquire a selfie of a finger. In addition to personal device-level authentication, finger-selfies may also be matched with livescan fingerprints present in the legacy/national ID databases for remote or touchless authentication. We propose an algorithm which comprises segmentation, enhancement, Deep Scattering Network based feature extraction, and Random Decision Forest to authenticate finger-selfies. This paper also presents one of the largest finger-selfie database with over 19, 400 images. The images in the IIIT-D Smartphone Finger-selfie Database v2 are captured using multiple smartphones and include variations due to background, illumination, resolution, and sensors. Results and comparison with existing algorithms show the efficacy of the proposed algorithm which yields equal error rates in the range of 2.1 – 5.2% for different experimental protocols.

Aakarsh Malhotra

Papers

On smartphone camera based fingerphoto authentication

Multimodal biometric recognition for toddlers and pre-school children

Multi-Task Driven Explainable Diagnosis of COVID-19 using Chest X-ray Images

Multi-Task Driven Explainable Diagnosis of COVID-19 using Chest X-ray Images.

On Matching Finger-Selfies Using Deep Scattering Networks