Bringing Vision-Based Measurements into our Daily Life: A Grand Challenge for Computer Vision Systems

Abstract

Bringing computer vision into our daily life has been challenging researchers in industry and in academia over the past decades. However, the continuous development of cameras and computing systems turned computer vision-based measurements into a viable option, allowing new solutions to known problems. In this context, computer vision is a generic tool that can be used to measure and monitor phenomena in wide range of fields. The idea of using vision-based measurements is appealing, since these measurements can be fast and easy to obtain and handle by humans. On the other hand, these vision-based measurements need to be at least as precise and accurate as those obtained by more traditional measuring systems. This article discusses the perspectives and challenges of introducing vision-based measurements into our daily life.

To image, or not to image: class-specific diffractive cameras with all-optical erasure of undesired objects

“…Different fromLoss + , the Loss − function is designed to reduce (1) the absolute correlation between the output O − and its corresponding input G − , (2) the absolute correlation between O − and an arbitrary object G + k from the target class, and (3) the correlation between O − and itself shifted by a few pixels O − sft , which can be formulated as:…”

“…The coefficients (α 1 , α 2 , β 1 , β 2 , β 3 ) in the two loss functions were empirically set to (1,3,6,3,2).…”

“…Digital cameras and computer vision techniques are ubiquitous in modern society. Over the past few decades, computer vision-assisted applications have been adapted massively in a wide range of fields [1][2][3], such as video surveillance [4,5], autonomous driving assistance [6,7], medical imaging [8], facial recognition, and body motion tracking [9,10]. With the comprehensive deployment of digital cameras in workspaces and public areas, a growing concern for privacy has emerged due to the tremendous amount of image data being collected continuously [11][12][13][14].…”

Face recognition based on geodesic distance approximations between multivariate normal distributions

“…Considering that the color channels are independent for each landmark, the geodesic distance approximation G f for multivariate normal distributions with diagonal covariance matrices provides a suitable geodesic distance metric. In this case, the dissimilarity S f a ,b a,b between the face image (i.e., head pose) b of face class a with the face image b of face class a can be scored by using G f as follows: F a,b,l , F a ,b ,l ), (14) where F a,b,l represents a multivariate normal distribution with null covariances for the landmark l in the face image b of face class a with the C-dimensional mean µ a,b,l and the (C × C)-dimensional covariance matrix Σ a,b,l . On the other hand, if the multivariate face data present relevant covariances between color channels, one of the proposed geodesic distance approximations for general multivariate normal distributions (G g or G h ) should be more adequate for the score calculation:…”

“…Face recognition is an instrumentation-related application which uses computer vision and pattern recognition techniques to identify individuals. Moreover, there are several emerging applications based in face recognition in augmented reality, gaming, security, and so on [3] [4] [13] [14]. Face recognition is also studied by neuroscientists and psychologists to provide useful insights in how the human brain works [15].…”