Asymmetric Totally-corrective Boosting for Real-time Object Detection

Real-time object detection is one of the core problems in computer vision. The cascade boosting framework proposed by Viola and Jones has become the standard for this problem. In this framework, the learning goal for each node is asymmetric, which is required to achieve a high detection rate and a moderate false positive rate. We develop new boosting algorithms to address this asymmetric learning problem. We show that our methods explicitly optimize asymmetric loss objectives in a totally corrective fashion. The methods are totally corrective in the sense that the coefficients of all selected weak classifiers are updated at each iteration. In contract, conventional boosting like AdaBoost is stage-wise in that only the current weak classifier's coefficient is updated. At the heart of the totally corrective boosting is the column generation technique. Experiments on face detection show that our methods outperform the state-of-the-art asymmetric boosting methods.Comment: 14 pages, published in Asian Conf. Computer Vision 201

Single image example-based super-resolution using cross-scale patch matching and Markov random field modelling

Example-based super-resolution has become increasingly popular over the last few years for its ability to overcome the limitations of classical multi-frame approach. In this paper we present a new example-based method that uses the input low-resolution image itself as a search space for high-resolution patches by exploiting self-similarity across different resolution scales. Found examples are combined in a high-resolution image by the means of Markov Random Field modelling that forces their global agreement. Additionally, we apply back-projection and steering kernel regression as post-processing techniques. In this way, we are able to produce sharp and artefact-free results that are comparable or better than standard interpolation and state-of-the-art super-resolution techniques

Using a novel petroselinic acid embedded cellulose acetate membrane to mimic plant partitioning and in vivo uptake of polycyclic aromatic hydrocarbons

A new type of composite membrane is introduced to mimic plant uptake of hydrophobic organic contaminants (HOCs). Petroselinic acid (cis-6-octadecenoic acid),the major component of plant lipids, was embedded in the matrix of cellulose acetate polymer to form the petroselinic acid embedded cellulose acetate membrane (PECAM). Accumulation of the polycyclic aromatic hydrocarbons (PAHs) naphthalene (Nap), phenanthrene (Phe), pyrene (Pyr), and benz(a)pyrene (Bap) by PECAM was compared with their uptake by plants. The accumulation of Nap, Phe, Pyr, and Bap by PECAM reached equilibrium in 24,48,144, and 192 h, respectively. The petroselinic acid-water partition coefficients (log K(pw), 3.37, 4.90, 5.24, and 6.28 for Nap, Phe, Pyr, and Bap, respectively) were positively correlated with the hydrophobicity of the compounds (R(2) = 0.995) and were almost the same as the lipid-normalized root partition coefficients (log K(lip)) for the corresponding compounds. Their relationship can be expressed as log K(pw) = 0.98 log K(lip). The normalized plant uptake coefficients (log K(u)) obtained by in vivo experiments with a range of plant species (2.92, 4.43, 5.06, and 6.13 on average for Nap, Phe, Pyr, and Bap, respectively) were slightly lower than those of the log K(pw) values for the corresponding compounds, presumably due to their acropetal translocation and biodegradation inside plants. This work suggests that PECAMs can well mimic plant partitioning and in vivo uptake of PAHs and may have good potential as a nonliving accumulator to mimic plant uptake of PAHs and perhaps other HOCs

Face Detection with Effective Feature Extraction

There is an abundant literature on face detection due to its important role in many vision applications. Since Viola and Jones proposed the first real-time AdaBoost based face detector, Haar-like features have been adopted as the method of choice for frontal face detection. In this work, we show that simple features other than Haar-like features can also be applied for training an effective face detector. Since, single feature is not discriminative enough to separate faces from difficult non-faces, we further improve the generalization performance of our simple features by introducing feature co-occurrences. We demonstrate that our proposed features yield a performance improvement compared to Haar-like features. In addition, our findings indicate that features play a crucial role in the ability of the system to generalize.Comment: 7 pages. Conference version published in Asian Conf. Comp. Vision 201

Fire detection in color images using Markov random fields

Automatic video-based fire detection can greatly reduce fire alert delay in large industrial and commercial sites, at a minimal cost, by using the existing CCTV camera network. Most traditional computer vision methods for fire detection model the temporal dynamics of the flames, in conjunction with simple color filtering. An important drawback of these methods is that their performance degrades at lower framerates, and they cannot be applied to still images, limiting their applicability. Also, real-time operation often requires significant computational resources, which may be unfeasible for large camera networks. This paper presents a novel method for fire detection in static images, based on a Markov Random Field but with a novel potential function. The method detects 99.6% of fires in a large collection of test images, while generating less false positives then a state-of-the-art reference method. Additionally, parameters are easily trained on a 12-image training set with minimal user input

Electrochemical nitrogen reduction: identification and elimination of contamination in electrolyte

Laiquan Li, Cheng Tang, Dazhi Yao, Yao Zheng, Shi-Zhang Qia

Contributions of dry and wet depositions of polychlorinated dibenzo-p-dioxins and dibenzofurans to a contaminated site resulting from a penetachlorophenol manufacturing process

The soils at a factory for manufacturing pentachlorophenol were heavily contaminated by polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs). In order to verify the contributions of dry and wet deposition of PCDD/Fs from the ambient air, the concentration of PCDD/Fs in ambient air and soil were measured, the partition of particle- and gas-phases of atmospheric PCDD/Fs was calculated, and the annual fluxes of total dry and wet PCDD/F depositions were modeled. Average atmospheric PCDD/F concentration was 1.24 ng Nm (-aEuro parts per thousand 3) (or 0.0397 ng I-TEQ Nm (-aEuro parts per thousand 3)). Moreover, over 92.8% of total PCDD/Fs were in the particle phase, and the dominant species were high chlorinated congeners. The total PCDD/F fluxes of dry and wet deposition were 119.5 ng m (-aEuro parts per thousand 2) year (-aEuro parts per thousand 1) (1.34 ng I-TEQ m (-aEuro parts per thousand 2) year (-aEuro parts per thousand 1)) and 82.0 ng m (-aEuro parts per thousand 2) year (-aEuro parts per thousand 1) (1.07 ng I-TEQ m (-aEuro parts per thousand 2) year (-aEuro parts per thousand 1)), respectively. By scenario simulation, the total fluxes of dry and wet PCDD/F depositions were 87.1 and 68.6 ng I-TEQ, respectively. However, the estimated PCDD/F contents in the contaminated soil were 839.9 mu g I-TEQ. Hence, the contributions of total depositions of atmospheric PCDD/F were only 0.02%. The results indicated that the major sources of PCDD/F for the contaminated soil could be attributed to the pentachlorophenol manufacturing process

Biometrics beyond the visible spectrum: Imaging technologies and applications

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-04391-8_20Proceedings of Joint COST 2101 and 2102 International Conference, BioID_MultiComm 2009, Madrid (Spain)Human body images acquired at visible spectrum have inherent restrictions that hinder the performance of person recognition systems built using that kind of information (e.g. scene artefacts under varying illumination conditions). One promising approach for dealing with those limitations is using images acquired beyond the visible spectrum. This paper reviews some of the existing human body imaging technologies working beyond the visible spectrum (X-ray, Infrared, Millimeter and Submillimeter Wave imaging technologies). The benefits and drawbacks of each technology and their biometric applications are presented.This work has been supported by Terasense (CSD2008-00068) Consolider project of the Spanish Ministry of Science and Technology

Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future