A normalized mirrored correlation measure for data symmetry detection

Symmetry detection algorithms are enjoying a renovated interest in the scientific community, fueled by recent advancements in computer vision and computer graphics applications. This paper is inspired by recent efforts in building a symmetric object detection system in natural images. In particular, it is first shown how correlation can be a core operator that allows finding local reflection symmetry points in 1-D sequences that are optimal in an energetic sense. Then, the importance of 2-D correlation in natural images to correctly align the symmetric object axis is demonstrated. Using the correlation as described is crucial in boosting the performance of the system, as proven by the results on a standard dataset

Facial Asymmetry Analysis Based on 3-D Dynamic Scans

Facial dysfunction is a fundamental symptom which often relates to many neurological illnesses, such as stroke, Bell’s palsy, Parkinson’s disease, etc. The current methods for detecting and assessing facial dysfunctions mainly rely on the trained practitioners which have significant limitations as they are often subjective. This paper presents a computer-based methodology of facial asymmetry analysis which aims for automatically detecting facial dysfunctions. The method is based on dynamic 3-D scans of human faces. The preliminary evaluation results testing on facial sequences from Hi4D-ADSIP database suggest that the proposed method is able to assist in the quantification and diagnosis of facial dysfunctions for neurological patients

Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images.

We compared the efficacy of three automated brain injury detection methods, namely symmetry-integrated region growing (SIRG), hierarchical region splitting (HRS) and modified watershed segmentation (MWS) in human and animal magnetic resonance imaging (MRI) datasets for the detection of hypoxic ischemic injuries (HIIs). Diffusion weighted imaging (DWI, 1.5T) data from neonatal arterial ischemic stroke (AIS) patients, as well as T2-weighted imaging (T2WI, 11.7T, 4.7T) at seven different time-points (1, 4, 7, 10, 17, 24 and 31 days post HII) in rat-pup model of hypoxic ischemic injury were used to assess the temporal efficacy of our computational approaches. Sensitivity, specificity, and similarity were used as performance metrics based on manual ('gold standard') injury detection to quantify comparisons. When compared to the manual gold standard, automated injury location results from SIRG performed the best in 62% of the data, while 29% for HRS and 9% for MWS. Injury severity detection revealed that SIRG performed the best in 67% cases while 33% for HRS. Prior information is required by HRS and MWS, but not by SIRG. However, SIRG is sensitive to parameter-tuning, while HRS and MWS are not. Among these methods, SIRG performs the best in detecting lesion volumes; HRS is the most robust, while MWS lags behind in both respects

A Multiple Component Matching Framework for Person Re-Identification

Person re-identification consists in recognizing an individual that has already been observed over a network of cameras. It is a novel and challenging research topic in computer vision, for which no reference framework exists yet. Despite this, previous works share similar representations of human body based on part decomposition and the implicit concept of multiple instances. Building on these similarities, we propose a Multiple Component Matching (MCM) framework for the person re-identification problem, which is inspired by Multiple Component Learning, a framework recently proposed for object detection. We show that previous techniques for person re-identification can be considered particular implementations of our MCM framework. We then present a novel person re-identification technique as a direct, simple implementation of our framework, focused in particular on robustness to varying lighting conditions, and show that it can attain state of the art performances.Comment: Accepted paper, 16th Int. Conf. on Image Analysis and Processing (ICIAP 2011), Ravenna, Italy, 14/09/201

Mitigating Direct Detection Bounds in Non-minimal Higgs Portal Scalar Dark Matter Models

Minimal scalar Higgs portal dark matter model is increasingly in tension with recent results form direct detection experiments like LUX and XENON. In this paper we make a systematic study of minimal extension of the $\mathbb{Z}_2$ stabilised singlet scalar Higgs portal scenario in terms of their prospects at direct detection experiments. We consider both enlarging the stabilising symmetry to $\mathbb{Z}_3$ and incorporating multipartite features in the dark sector. We demonstrate that in these non-minimal models the interplay of annihilation, co-annihilation and semi-annihilation processes considerably relax constraints from present and proposed direct detection experiments while simultaneously saturating observed dark matter relic density. We explore in particular the resonant semi-annihilation channel within the multipartite $\mathbb{Z}_3$ framework which results in new unexplored regions of parameter space that would be difficult to constrain by direct detection experiments in the near future. The role of dark matter exchange processes within multi-component $\mathbb{Z}_3 \times \mathbb{Z}_3'$ framework is illustrated. We make quantitative estimates to elucidate the role of the various annihilation processes in the different allowed regions of parameter space of these models.Comment: 31 pages, 15 figures, added brief discussion on vaccum stability and unitarity; minor changes in the text; updated references; typos fixed; matches published versio

The Dispirited Case of Gauged U(1)B−LU(1)_{B-L} Dark Matter

We explore the constraints and phenomenology of possibly the simplest scenario that could account at the same time for the active neutrino masses and the dark matter in the Universe within a gauged $U(1)_{B-L}$ symmetry, namely right-handed neutrino dark matter. We find that null searches from lepton and hadron colliders require dark matter with a mass below 900 GeV to annihilate through a resonance. Additionally, the very strong constraints from high-energy dilepton searches fully exclude the model for $150 \, \text{GeV} < m_{Z'} < 3 \, \text{TeV}$. We further explore the phenomenology in the high mass region (i.e. masses $\gtrsim \mathcal{O}(1) \, \text{TeV}$) and highlight theoretical arguments, related to the appearance of a Landau pole or an instability of the scalar potential, disfavoring large portions of this parameter space. Collectively, these considerations illustrate that a minimal extension of the Standard Model via a local $U(1)_{B-L}$ symmetry with a viable thermal dark matter candidate is difficult to achieve without fine-tuning. We conclude by discussing possible extensions of the model that relieve tension with collider constraints by reducing the gauge coupling required to produce the correct relic abundance.Comment: 21 pages, 8 figures. v2: References added. Matches the published versio

A Framework for Symmetric Part Detection in Cluttered Scenes

The role of symmetry in computer vision has waxed and waned in importance during the evolution of the field from its earliest days. At first figuring prominently in support of bottom-up indexing, it fell out of favor as shape gave way to appearance and recognition gave way to detection. With a strong prior in the form of a target object, the role of the weaker priors offered by perceptual grouping was greatly diminished. However, as the field returns to the problem of recognition from a large database, the bottom-up recovery of the parts that make up the objects in a cluttered scene is critical for their recognition. The medial axis community has long exploited the ubiquitous regularity of symmetry as a basis for the decomposition of a closed contour into medial parts. However, today's recognition systems are faced with cluttered scenes, and the assumption that a closed contour exists, i.e. that figure-ground segmentation has been solved, renders much of the medial axis community's work inapplicable. In this article, we review a computational framework, previously reported in Lee et al. (2013), Levinshtein et al. (2009, 2013), that bridges the representation power of the medial axis and the need to recover and group an object's parts in a cluttered scene. Our framework is rooted in the idea that a maximally inscribed disc, the building block of a medial axis, can be modeled as a compact superpixel in the image. We evaluate the method on images of cluttered scenes.Comment: 10 pages, 8 figure

Image processing for plastic surgery planning

This thesis presents some image processing tools for plastic surgery planning. In particular, it presents a novel method that combines local and global context in a probabilistic relaxation framework to identify cephalometric landmarks used in Maxillofacial plastic surgery. It also uses a method that utilises global and local symmetry to identify abnormalities in CT frontal images of the human body. The proposed methodologies are evaluated with the help of several clinical data supplied by collaborating plastic surgeons