Biologically-Inspired Motion Encoding for Robust Global Motion Estimation.

The growing use of cameras embedded in autonomous robotic platforms and worn by people is increasing the importance of accurate global motion estimation (GME). However, existing GME methods may degrade considerably under illumination variations. In this paper, we address this problem by proposing a biologically-inspired GME method that achieves high estimation accuracy in the presence of illumination variations. We mimic the early layers of the human visual cortex with the spatio-temporal Gabor motion energy by adopting the pioneering model of Adelson and Bergen and we provide the closed-form expressions that enable the study and adaptation of this model to different application needs. Moreover, we propose a normalisation scheme for motion energy to tackle temporal illumination variations. Finally, we provide an overall GME scheme which, to the best of our knowledge, achieves the highest accuracy on the Pose, Illumination, and Expression (PIE) database

Experimental demonstration of quantum teleportation of a squeezed state

We demonstrate an unconditional high-fidelity teleporter capable of preserving the broadband entanglement in an optical squeezed state. In particular, we teleport a squeezed state of light and observe -0.8 ± 0.2dB of squeezing in the teleported (output) state. We show that the squeezing criterion translates directly into a sufficient criterion for entanglement of the upper and lower sidebands of the optical field. Thus, this result demonstrates the first unconditional teleportation of broadband entanglement. Our teleporter achieves sufficiently high fidelity to allow the teleportation to be cascaded, enabling, in principle, the construction of deterministic non-Gaussian operations

Recent Progress in Image Deblurring

This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the critical role of image restoration in modern imaging systems to provide high-quality images under complex environments such as motion, undesirable lighting conditions, and imperfect system components, image deblurring has attracted growing attention in recent years. From the viewpoint of how to handle the ill-posedness which is a crucial issue in deblurring tasks, existing methods can be grouped into five categories: Bayesian inference framework, variational methods, sparse representation-based methods, homography-based modeling, and region-based methods. In spite of achieving a certain level of development, image deblurring, especially the blind case, is limited in its success by complex application conditions which make the blur kernel hard to obtain and be spatially variant. We provide a holistic understanding and deep insight into image deblurring in this review. An analysis of the empirical evidence for representative methods, practical issues, as well as a discussion of promising future directions are also presented.Comment: 53 pages, 17 figure

Full Frame Video Stabilization Using Motion Inpainting

The amount of video data has increased dramatically with the advent of digital imaging. Most of the video captured these days originates from a mobile phones and handheld video cameras. Such videos are shaky compared to videos that are shot with a tripod mounted camera. Stabilizing this video to remove the shaky effect using software is called Digital video stabilization which results in a stable and visually pleasant video. In order digitally stabilize the image, we need to (1) Estimate the motion of camera, (2) Regenerate the motion of camera without the undesirable artifacts and (3) Synthesize new video frames. This dissertation is targeted at improving the last two steps of stabilizing the video. Most of the previous techniques of video stabilization produce a lower resolution stabilized video output and clip portions of frames to remove the empty area formed by transformation of the video frames. We use a Gaussian averaging filter to smoother the global motion in the video. Then the frames are transformed using the new transformation matrices obtained by subtracting the original transformation chain from the modified transformation chain. For the last step of synthesizing new video frames, we introduce an improved completion technique which can produce full frame video by using the pixel information from nearby frames to estimate the intensity of the missing pixels. This technique uses motion inpainting to ensure that the video frames are filled in both the static image area and dynamic image area with the same consistency. Additionally, the quality of the video is improved by using a deblurring algorithm which further improves the smoothness of video by eliminating undesirable motion blur. We do not estimate the PSF, in its place, we transfer and interpolate the sharper pixels from nearby frames to improve the sharpness and deblur current frame. Completing the video with motion inpainting and deblurring technique allow us to construct a full frame video stabilization system with good image quality. This is verified by implementing the technique on different video sequences

Robust Registration of Dynamic Facial Sequences.

Accurate face registration is a key step for several image analysis applications. However, existing registration methods are prone to temporal drift errors or jitter among consecutive frames. In this paper, we propose an iterative rigid registration framework that estimates the misalignment with trained regressors. The input of the regressors is a robust motion representation that encodes the motion between a misaligned frame and the reference frame(s), and enables reliable performance under non-uniform illumination variations. Drift errors are reduced when the motion representation is computed from multiple reference frames. Furthermore, we use the L2 norm of the representation as a cue for performing coarse-to-fine registration efficiently. Importantly, the framework can identify registration failures and correct them. Experiments show that the proposed approach achieves significantly higher registration accuracy than the state-of-the-art techniques in challenging sequences.The research work of Evangelos Sariyanidi and Hatice Gunes has been partially supported by the EPSRC under its IDEAS Factory Sandpits call on Digital Personhood (Grant Ref.: EP/L00416X/1)

Perspective distortion modeling for image measurements

A perspective distortion modelling for monocular view that is based on the fundamentals of perspective projection is presented in this work. Perspective projection is considered to be the most ideal and realistic model among others, which depicts image formation in monocular vision. There are many approaches trying to model and estimate the perspective effects in images. Some approaches try to learn and model the distortion parameters from a set of training data that work only for a predefined structure. None of the existing methods provide deep understanding of the nature of perspective problems. Perspective distortions, in fact, can be described by three different perspective effects. These effects are pose, distance and foreshortening. They are the cause of the aberrant appearance of object shapes in images. Understanding these phenomena have long been an interesting topic for artists, designers and scientists. In many cases, this problem has to be necessarily taken into consideration when dealing with image diagnostics, high and accurate image measurement, as well as accurate pose estimation from images. In this work, a perspective distortion model for every effect is developed while elaborating the nature of perspective effects. A distortion factor for every effect is derived, then followed by proposed methods, which allows extracting the true target pose and distance, and correcting image measurements