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We describe two advanced video analysis techniques, including video-indexed by voice annotations (VIVA) and multi-media indexing and explorer (MINER). VIVA utilizes analyst call-outs (ACOs) in the form of chat messages (voice-to-text) to associate labels with video target tracks, to designate spatial-temporal activity boundaries and to augment video tracking in challenging scenarios. Challenging scenarios include low-resolution sensors, moving targets and target trajectories obscured by natural and man-made clutter. MINER includes: (1) a fusion of graphical track and text data using probabilistic methods; (2) an activity pattern learning framework to support querying an index of activities of interest (AOIs) and targets of interest (TOIs) by movement type and geolocation; and (3) a user interface to support streaming multi-intelligence data processing. We also present an activity pattern learning framework that uses the multi-source associated data as training to index a large archive of full-motion videos (FMV). VIVA and MINER examples are demonstrated for wide aerial/overhead imagery over common data sets affording an improvement in tracking from video data alone, leading to 84% detection with modest misdetection/false alarm results due to the complexity of the scenario. The novel use of ACOs and chat Sensors 2014, 14 19844 messages in video tracking paves the way for user interaction, correction and preparation of situation awareness reports.

Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm). This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different scenarios and using different similarity metrics. Both of them improve the state of the art approaches.

This paper presents a transfer domain strategy to tackle the limitations of low-resolution thermal sensors and generate higher-resolution images of reasonable quality. The proposed technique employs a CycleGAN architecture and uses a ResNet as an encoder in the generator along with an attention module and a novel loss function. The network is trained on a multi-resolution thermal image dataset acquired with three different thermal sensors. Results report better performance benchmarking results on the 2nd CVPR-PBVS-2021 thermal image super-resolution challenge than state-of-the-art methods. The code of this work is available online.

Issue Title: Special Issue: Large Scale Visual Media Geo-Localization

This paper addresses the issue of driving while drowsy and proposes a passive eye monitoring-based driver eye closure recognition system. It reviews the core algorithmic building blocks of this system along with in-depth analysis of operational test field characteristics. The system operates equally in both day and night-time. It automatically finds the drivers eyes in the images, tracks the eye location in a wide range of head and eye motion, and estimates in real-time the eye state as either open or closed eye, and further infers to driver drowsiness state. This paper reports as well the experimental results on a diverse and challenging set of subjects and environmental driving conditions.

Modern satellites tag their images with geolocation information using GPS and star tracking systems. Depending on the quality of the geopositioning equipment, errors may range from a few meters to tens of meters on the ground. At the current state of art, there is no established method to automatically correct these errors limiting the large-scale joint utilization of cross-platform satellite images. In this paper, an automatic geolocation correction framework that corrects images from multiple satellites simultaneously is presented. As a result of the proposed correction process, all the images are effectively registered to the same absolute geodetic coordinate frame. The usability and the quality of the correction framework are demonstrated through a 3-D surface reconstruction application. The 3-D surface models given by original satellite geopositioning metadata, and the corrected metadata, are compared. The quality difference is measured through an entropy-based metric applied to the orthographic height maps given by the 3-D surface models. Measuring the absolute accuracy of the framework is harder due to lack of publicly available high-precision ground surveys. However, the geolocation of images of exemplar satellites from different parts of the globe are corrected, and the road networks given by OpenStreetMap are projected onto the images using original and corrected metadata to demonstrate the improved quality of alignment.

Issue Title: Special Issue: Object Tracking and Classification Beyond the Visible Spectrum

The paper is concerned with examining the sports and commercial advertisements in the Iraqi context. The research is situated in both linguistics and translation in terms of examining the way and procedure in which those ads can be translated from Arabic into English from a functional perspective. The data which have been selected for analysis are quoted from social media platforms and sites. We have selected 16 ads from both the commercial and sports domains. It has been found that the translations of ads face a little difficulty as compared to other genres. The translations of both discourses can be done by using a formal language aiming to achieve different goals such establishing social and national solidarity.

The real‐life retrospective observational study CARMYN aimed at investigating the long‐term efficacy and safety of carfilzomib in combination with dexamethasone and lenalidomide (KRd, 159 patients). These patients (62% in first and 38% in second relapse, median age 62 yo) were treated between 02/2014 and 02/2017. Most had been pre‐exposed to bortezomib (98.2%) and to an IMID (75.4%). At the time of collection, 90% had permanently discontinued carfilzomib. Data collection was conducted from January to July 2021 in 27 participating sites, after a median of 39 months follow‐up. For patients treated with KRd, an overall response rate of 78.4% translated in a median progression free survival (PFS) of 24.0 months (95% CI 18.8–27.6) and a median overall survival (OS) of 51.1 months (95% CI 41.3–not reached). Results were poorer but difficult to interpret in the small cohort of Kd recipients. The study is one of the longest real‐life studies of carfilzomib treatment in patients in first or second relapse. CARMYN confirmed the real‐life long‐term efficacy of carfilzomib in combination with lenalidomide and dexamethasone with results similar to those of clinical trials. The KRd regimen is thus an option to consider for late relapses in the current context of MM management.