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The graphical summary of multi-view scene can be readily utilized for tasks such as indoor localization. Existing methods for multi-view indoor localization consider the entire scene for localization purposes by assigning equal importance to all components/objects. In this paper, we propose a novel indoor localization method querying a graphical summary of the scene from the graphical summary of the multi-view RGB-D scenes. Salient objects have been utilized to construct the graphical summaries.

We propose an underwater image enhancement algorithm that leverages both model- and learning-based approaches by unfolding an iterative algorithm. We first formulate the underwater image enhancement task as a joint optimization problem, based on the image formation model with physical model and underwater-related priors. Then, we solve the optimization problem iteratively. Finally, we unfold the iterative algorithm so that, at each iteration, the optimization variables and regularizers for image priors are updated by closed-form solutions and learned deep networks, respectively.

In this paper, we present a method of feature integration via backprojection ordering multi-modal Gaussian process latent variable model (BPomGP) for rating prediction. In the proposed method, to extract features reflecting the users’ interest, we use the known ratings assigned to the viewed contents and users’ behavior information while viewing the contents which is related to the users’ interest. BPomGP has two important approaches. Unlike the training phase, where the above two types of heterogeneous information are available, behavior information is not given in the test phase.

This paper presents a new reinforcement-based method for video thumbnail selection (called RL-DiVTS), that relies on estimates of the aesthetic quality, representativeness and visual diversity of a small set of selected frames, made with

Thermal facial imagery offers valuable insight into physiological states such as inflammation and stress by detecting emitted radiation in the infrared spectrum, which is unseen in the visible spectra. Telemedicine applications could benefit from thermal imagery, but conventional computers are reliant on RGB cameras and lack thermal sensors. As a result, we propose the Visible-to-Thermal Facial GAN (VTF-GAN) that is specifically designed to generate high-resolution thermal faces by learning both the spatial and frequency domains of facial regions, across spectra.

Vehicles suddenly exiting road-side parking constitute a hazardous situation for vehicle drivers as well as for Connected and Autonomous Vehicles (CAV). In order to improve the awareness of road users, we propose an original cooperative information system based on image processing to monitor vehicles parked on the road-side and on communication for sending early warning to vehicles on the road about vehicles leaving their parking space.

CLIPStyler demonstrated image style transfer with realistic textures using only a style text description (instead of requir- ing a reference style image). However, the ground semantics of objects in the style transfer output is lost due to style spill- over on salient and background objects (content mismatch) or over-stylization. To solve this, we propose Semantic CLIP- Styler (Sem-CS), that performs semantic style transfer.

Following the significance of soft biometrics to facilitate seamless recognition or retrieval, the need for multi-modality annotated datasets is increasing - to evaluate any standalone soft biometrics system. Although, large-size datasets like PETA were annotated to evaluate soft biometrics systems, however, they were mainly annotated for global soft biometrics such as gender and age and for clothing modality.