A PERTURBATION­BASED APPROACH FOR MULTI­CLASSIFIER SYSTEM DESIGN

Microsoft, Motorola, Siemens, Hitachi, IAPR, NICI, IUF This paper presents a perturbation­based approach useful to select the best combination method for a multi­classifier system. The basic idea is to simulate small variations in the performance of the set of classifiers and to evaluate to what extent they influence the performance of the combined classifier. In the experimental phase, the Behavioural Knowledge Space and the Dempster­Shafer combination methods have been considered. The experimental results, carried out in the field of hand­written numeral recognition, demonstrate the effectiveness of the new approach

Design of coupled mace filters for optical pattern recognition using practical spatial light modulators

Spatial light modulators (SLMs) are being used in correlation-based optical pattern recognition systems to implement the Fourier domain filters. Currently available SLMs have certain limitations with respect to the realizability of these filters. Therefore, it is necessary to incorporate the SLM constraints in the design of the filters. The design of a SLM-constrained minimum average correlation energy (SLM-MACE) filter using the simulated annealing-based optimization technique was investigated. The SLM-MACE filter was synthesized for three different types of constraints. The performance of the filter was evaluated in terms of its recognition (discrimination) capabilities using computer simulations. The correlation plane characteristics of the SLM-MACE filter were found to be reasonably good. The SLM-MACE filter yielded far better results than the analytical MACE filter implemented on practical SLMs using the constrained magnitude technique. Further, the filter performance was evaluated in the presence of noise in the input test images. This work demonstrated the need to include the SLM constraints in the filter design. Finally, a method is suggested to reduce the computation time required for the synthesis of the SLM-MACE filter

A TDDFT study of the excited states of DNA bases and their assemblies

We present a detailed study of the optical absorption spectra of DNA bases and base pairs, carried out by means of time dependent density functional theory. The spectra for the isolated bases are compared to available theoretical and experimental data and used to assess the accuracy of the method and the quality of the exchange-correlation functional: Our approach turns out to be a reliable tool to describe the response of the nucleobases. Furthermore, we analyze in detail the impact of hydrogen bonding and $\pi$-stacking in the calculated spectra for both Watson-Crick base pairs and Watson-Crick stacked assemblies. We show that the reduction of the UV absorption intensity (hypochromicity) for light polarized along the base-pair plane depends strongly on the type of interaction. For light polarized perpendicular to the basal plane, the hypochromicity effect is reduced, but another characteristic is found, namely a blue shift of the optical spectrum of the base-assembly compared to that of the isolated bases. The use of optical tools as fingerprints for the characterization of the structure (and type of interaction) is extensively discussed.Comment: 31 pages, 8 figure

Recent advances in coherent optics. Filtering of spatial frequencies, holography

Applications of coherent light in areas of spatial filtering and holograph

Invariant template matching in systems with spatiotemporal coding: a vote for instability

We consider the design of a pattern recognition that matches templates to images, both of which are spatially sampled and encoded as temporal sequences. The image is subject to a combination of various perturbations. These include ones that can be modeled as parameterized uncertainties such as image blur, luminance, translation, and rotation as well as unmodeled ones. Biological and neural systems require that these perturbations be processed through a minimal number of channels by simple adaptation mechanisms. We found that the most suitable mathematical framework to meet this requirement is that of weakly attracting sets. This framework provides us with a normative and unifying solution to the pattern recognition problem. We analyze the consequences of its explicit implementation in neural systems. Several properties inherent to the systems designed in accordance with our normative mathematical argument coincide with known empirical facts. This is illustrated in mental rotation, visual search and blur/intensity adaptation. We demonstrate how our results can be applied to a range of practical problems in template matching and pattern recognition.Comment: 52 pages, 12 figure

Sensitivity analysis of AI-based algorithms for autonomous driving on optical wavefront aberrations induced by the windshield

Autonomous driving perception techniques are typically based on supervised machine learning models that are trained on real-world street data. A typical training process involves capturing images with a single car model and windshield configuration. However, deploying these trained models on different car types can lead to a domain shift, which can potentially hurt the neural networks performance and violate working ADAS requirements. To address this issue, this paper investigates the domain shift problem further by evaluating the sensitivity of two perception models to different windshield configurations. This is done by evaluating the dependencies between neural network benchmark metrics and optical merit functions by applying a Fourier optics based threat model. Our results show that there is a performance gap introduced by windshields and existing optical metrics used for posing requirements might not be sufficient

Learning Human Motion Models for Long-term Predictions

We propose a new architecture for the learning of predictive spatio-temporal motion models from data alone. Our approach, dubbed the Dropout Autoencoder LSTM, is capable of synthesizing natural looking motion sequences over long time horizons without catastrophic drift or motion degradation. The model consists of two components, a 3-layer recurrent neural network to model temporal aspects and a novel auto-encoder that is trained to implicitly recover the spatial structure of the human skeleton via randomly removing information about joints during training time. This Dropout Autoencoder (D-AE) is then used to filter each predicted pose of the LSTM, reducing accumulation of error and hence drift over time. Furthermore, we propose new evaluation protocols to assess the quality of synthetic motion sequences even for which no ground truth data exists. The proposed protocols can be used to assess generated sequences of arbitrary length. Finally, we evaluate our proposed method on two of the largest motion-capture datasets available to date and show that our model outperforms the state-of-the-art on a variety of actions, including cyclic and acyclic motion, and that it can produce natural looking sequences over longer time horizons than previous methods

Fundamental remote sensing science research program. Part 1: Scene radiation and atmospheric effects characterization project

Brief articles summarizing the status of research in the scene radiation and atmospheric effect characterization (SRAEC) project are presented. Research conducted within the SRAEC program is focused on the development of empirical characterizations and mathematical process models which relate the electromagnetic energy reflected or emitted from a scene to the biophysical parameters of interest