Person-Specific Gaze Estimation from Low-Quality Webcam Images.

Gaze estimation is an established research problem in computer vision. It has various applications in real life, from human-computer interactions to health care and virtual reality, making it more viable for the research community. Due to the significant success of deep learning techniques in other computer vision tasks-for example, image classification, object detection, object segmentation, and object tracking-deep learning-based gaze estimation has also received more attention in recent years.

Probabilistic Fingermark Quality Assessment with Quality Region Localisation.

The assessment of fingermark (latent fingerprint) quality is an intrinsic part of a forensic investigation. The fingermark quality indicates the value and utility of the trace evidence recovered from the crime scene in the course of a forensic investigation; it determines how the evidence will be processed, and it correlates with the probability of finding a corresponding fingerprint in the reference dataset. The deposition of fingermarks on random surfaces occurs spontaneously in an uncontrolled fashion, which introduces imperfections to the resulting impression of the friction ridge pattern.

Reconstructing Superquadrics from Intensity and Color Images.

The task of reconstructing 3D scenes based on visual data represents a longstanding problem in computer vision. Common reconstruction approaches rely on the use of multiple volumetric primitives to describe complex objects. Superquadrics (a class of volumetric primitives) have shown great promise due to their ability to describe various shapes with only a few parameters.

-Same-Net: -Anonymity with Generative Deep Neural Networks for Face Deidentification.

Image and video data are today being shared between government entities and other relevant stakeholders on a regular basis and require careful handling of the personal information contained therein. A popular approach to ensure privacy protection in such data is the use of deidentification techniques, which aim at concealing the identity of individuals in the imagery while still preserving certain aspects of the data after deidentification. In this work, we propose a novel approach towards face deidentification, called -Same-Net, which combines recent Generative Neural Networks (GNNs) with the well-known -Anonymitymechanism and provides formal guarantees regarding privacy protection on a closed set of identities.

Local pixel value collection algorithm for spot segmentation in two-dimensional gel electrophoresis research.

Two-dimensional gel-electrophoresis (2-DE) images show the expression levels of several hundreds of proteins where each protein is represented as a blob-shaped spot of grey level values. The spot detection, that is, the segmentation process has to be efficient as it is the first step in the gel processing. Such extraction of information is a very complex task.