Detector adaptation by maximising agreement between independent data sources

Traditional methods for creating classifiers have two main disadvantages. Firstly, it is time consuming to acquire, or manually annotate, the training collection. Secondly, the data on which the classifier is trained may be over-generalised or too specific. This paper presents our investigations into overcoming both of these drawbacks simultaneously, by providing example applications where two data sources train each other. This removes both the need for supervised annotation or feedback, and allows rapid adaptation of the classifier to different data. Two applications are presented: one using thermal infrared and visual imagery to robustly learn changing skin models, and another using changes in saturation and luminance to learn shadow appearance parameters

Shadow detection in video surveillance by maximizing agreement between independent detectors

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. J. C. SanMiguel, and J. M. Martínez, "Shadow detection in video surveillance by maximizing agreement between independent detectors", in 16th IEEE International Conference on Image Processing, ICIP 2009. p. 1141-1144This paper starts from the idea of automatically choosing the appropriate thresholds for a shadow detection algorithm. It is based on the maximization of the agreement between two independent shadow detectors without training data. Firstly, this shadow detection algorithm is described and then, it is adapted to analyze video surveillance sequences. Some modifications are introduced to increase its robustness in generic surveillance scenarios and to reduce its overall computational cost (critical in some video surveillance applications). Experimental results show that the proposed modifications increase the detection reliability as compared to some previous shadow detection algorithms and performs considerably well across a variety of multiple surveillance scenarios.Work supported by the Spanish Government (TEC2007- 65400 SemanticVideo), by Cátedra Infoglobal-UAM for “Nuevas Tecnologías de video aplicadas a la seguridad”, by the Spanish Administration agency CDTI (CENIT-VISION 2007-1007), by the Comunidad de Madrid (S-050/TIC-0223 - ProMultiDis), by the Consejería de Educación of the Comunidad de Madrid and by the European Social Fund

Coarse-to-Fine Adaptive People Detection for Video Sequences by Maximizing Mutual Information

Applying people detectors to unseen data is challenging since patterns distributions, such as viewpoints, motion, poses, backgrounds, occlusions and people sizes, may significantly differ from the ones of the training dataset. In this paper, we propose a coarse-to-fine framework to adapt frame by frame people detectors during runtime classification, without requiring any additional manually labeled ground truth apart from the offline training of the detection model. Such adaptation make use of multiple detectors mutual information, i.e., similarities and dissimilarities of detectors estimated and agreed by pair-wise correlating their outputs. Globally, the proposed adaptation discriminates between relevant instants in a video sequence, i.e., identifies the representative frames for an adaptation of the system. Locally, the proposed adaptation identifies the best configuration (i.e., detection threshold) of each detector under analysis, maximizing the mutual information to obtain the detection threshold of each detector. The proposed coarse-to-fine approach does not require training the detectors for each new scenario and uses standard people detector outputs, i.e., bounding boxes. The experimental results demonstrate that the proposed approach outperforms state-of-the-art detectors whose optimal threshold configurations are previously determined and fixed from offline training dataThis work has been partially supported by the Spanish government under the project TEC2014-53176-R (HAVideo

Bone cross-sectional geometry in male runners, gymnasts, swimmers and non-athletic controls: a hip-structural analysis study.

Loading of the skeleton is important for the development of a functionally and mechanically appropriate bone structure, and can be achieved through impact exercise. Proximal femur cross-sectional geometry was assessed in the male athletes (n = 55) representing gymnastics, endurance running and swimming, and non-athletic controls (n = 22). Dual energy X-ray absorptiometry (iDXA, GE Healthcare, UK) measurements of the total body (for body composition) and the left proximal femur were obtained. Advanced hip structural analysis (AHA) was utilised to determine the areal bone mineral density (aBMD), hip axis length (HAL), cross-sectional area (CSA), cross-sectional moment of inertia (CSMI) and the femoral strength index (FSI). Gymnasts and runners had greater age, height and weight adjusted aBMD than in swimmers and controls (p < 0.05). Gymnasts and runners had greater resistance to axial loads (CSA) and the runners had increased resistance against bending forces (CSMI) compared to swimmers and controls (p < 0.01). Controls had a lower FSI compared to gymnasts and runners (1.4 vs. 1.8 and 2.1, respectively, p < 0.005). Lean mass correlated with aBMD, CSA and FSI (r = 0.365-0.457, p < 0.01), particularly in controls (r = 0.657-0.759, p < 0.005). Skeletal loading through the gymnastics and running appears to confer a superior bone geometrical advantage in the young adult men. The importance of lean body mass appears to be of particular significance for non-athletes. Further characterisation of the bone structural advantages associated with different sports would be of value to inform the strategies directed at maximising bone strength and thus, preventing fracture

Observing the Galaxy's massive black hole with gravitational wave bursts

An extreme-mass-ratio burst (EMRB) is a gravitational wave signal emitted when a compact object passes through periapsis on a highly eccentric orbit about a much more massive object, in our case a stellar mass object about a 10^6 M_sol black hole. EMRBs are a relatively unexplored means of probing the spacetime of massive black holes (MBHs). We conduct an investigation of the properties of EMRBs and how they could allow us to constrain the parameters, such as spin, of the Galaxy's MBH. We find that if an EMRB event occurs in the Galaxy, it should be detectable for periapse distances r_p < 65 r_g for a \mu = 10 M_sol orbiting object, where r_g = GM/c^2 is the gravitational radius. The signal-to-noise ratio scales as \rho ~ -2.7 log(r_p/r_g) + log(\mu/M_sol) + 4.9. For periapses r_p < 10 r_g, EMRBs can be informative, and provide good constraints on both the MBH's mass and spin. Closer orbits provide better constraints, with the best giving accuracies of better than one part in 10^4 for both the mass and spin parameter.Comment: 25 pages, 17 figures, 1 appendix. One more typo fixe

Fair comparison of skin detection approaches on publicly available datasets

Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In this work, we investigate the most recent researches in this field and we propose a fair comparison among approaches using several different datasets. The major contributions of this work are an exhaustive literature review of skin color detection approaches, a framework to evaluate and combine different skin detector approaches, whose source code is made freely available for future research, and an extensive experimental comparison among several recent methods which have also been used to define an ensemble that works well in many different problems. Experiments are carried out in 10 different datasets including more than 10000 labelled images: experimental results confirm that the best method here proposed obtains a very good performance with respect to other stand-alone approaches, without requiring ad hoc parameter tuning. A MATLAB version of the framework for testing and of the methods proposed in this paper will be freely available from https://github.com/LorisNann

Masses of the components of SB2 binaries observed with Gaia. II. Masses derived from PIONIER interferometric observations for Gaia validation

In anticipation of the Gaia astrometric mission, a sample of spectroscopic binaries is being observed since 2010 with the Sophie spectrograph at the Haute--Provence Observatory. Our aim is to derive the orbital elements of double-lined spectroscopic binaries (SB2s) with an accuracy sufficient to finally obtain the masses of the components with relative errors as small as 1 % when combined with Gaia astrometric measurements. In order to validate the masses derived from Gaia, interferometric observations are obtained for three SB2s in our sample with F-K components: HIP 14157, HIP 20601 and HIP 117186. The masses of the six stellar components are derived. Due to its edge-on orientation, HIP 14157 is probably an eclipsing binary. We note that almost all the derived masses are a few percent larger than the expectations from the standard spectral-type-mass calibration and mass-luminosity relation. Our calculation also leads to accurate parallaxes for the three binaries, and the Hipparcos parallaxes are confirmed.Comment: 10 pages, 3 figures, accepted by MNRA

Adaptive detection and tracking using multimodal information

This thesis describes work on fusing data from multiple sources of information, and focuses on two main areas: adaptive detection and adaptive object tracking in automated vision scenarios. The work on adaptive object detection explores a new paradigm in dynamic parameter selection, by selecting thresholds for object detection to maximise agreement between pairs of sources. Object tracking, a complementary technique to object detection, is also explored in a multi-source context and an efficient framework for robust tracking, termed the Spatiogram Bank tracker, is proposed as a means to overcome the difficulties of traditional histogram tracking. As well as performing theoretical analysis of the proposed methods, specific example applications are given for both the detection and the tracking aspects, using thermal infrared and visible spectrum video data, as well as other multi-modal information sources

Quantum Absorbance Estimation and the Beer-Lambert Law

The utility of transmission measurement has made it a target for quantum enhanced measurement strategies. Here we find if the length of an absorbing object is a controllable variable, then via the Beer-Lambert law, classical strategies can be optimised to reach within 83% of the absolute quantum limit. Our analysis includes experimental losses, detector noise, and input states with arbitrary photon statistics. We derive optimal operating conditions for both classical and quantum sources, and observe experimental agreement with theory using Fock and thermal states.Comment: 12 pages, 8 figure