Signal quality awareness has been found to increase recognition rates and to support decisions in multi-sensor environments significantly. Nevertheless, automatic quality assessment is still an open issue. Here we study the orientation tensor of fingerprint images to quantify signal impairments like noise, lack of structure, blur, with the help of symmetry descriptors. Especially favorable in Biometrics, strongly reduced reference, but not less information is sufficient for the approach. This is also supported by numerous experiments involving a simpler quality estimator, a trained method (NFIQ) as well as human perception of fingerprint quality on several public databases. Furthermore, quality measurements are extensively reused to adapt fusion parameters in a monomodal multi-algorithm fingerprint recognition environment. In this study, several trained and non-trained score level fusion schemes are investigated. A Bayes-based strategy for incorporating experts ’ past performances and current quality conditions, a novel cascaded scheme for computational efficiency, besides simple fusion rules, are presented. The quantitative results favor quality awareness under all aspects, boosting recognition rates and fusing differently skilled experts efficiently as well as effectively (by training).

Abstract — Intelligent tasks, such as visual perception, auditory perception, and language understanding require the construction of good internal representations of the world (or ”features”), which must be invariant to irrelevant variations of the input while, preserving relevant information. A major question for Machine Learning is how to learn such good features automatically. Convolutional Networks (ConvNets) are a biologicallyinspired trainable architecture that can learn invariant features. Each stage in a ConvNets is composed of a filter bank, some non-linearities, and feature pooling layers. With multiple stages, a ConvNet can learn multi-level hierarchies of features. While ConvNets have been successfully deployed in many commercial applications from OCR to video surveillance, they require large amounts of labeled training samples. We describe new unsupervised learning algorithms, and new non-linear stages that allow ConvNets to be trained with very few labeled samples. Applications to visual object recognition and vision navigation for off-road mobile robots are described.

We present a distributed system to extract text contained in natural scenes within consumer photographs. The objective is to automatically annotate pictures in order to make consumer photo sets searchable based on the image content. The system is designed to process a large volume of photos, by quickly isolating candidate text regions, and successively cascading them through a series of text recognition engines which jointly make a decision on whether or not the region contains text that is readable by OCR. In addition, a dedicated rejection engine is built on top of each text recognizer to adapt its confidence measure to the specifics of the task. The resulting system achieves very high text retrieval rate and data throughput with very small false detection rates.

Micro-robots, unmanned aerial vehicles (UAVs), imaging sensor networks, wireless phones, and other embedded vision systems all require low cost and high-speed implementations of synthetic vision systems capable of recognizing and categorizing objects in a scene. Many successful object recognition systems use dense features extracted on regularly-spaced patches over the input image. The majority of the feature extraction systems have a common structure composed of a filter bank (generally based on oriented edge detectors or 2D gabor functions), a non-linear operation (quantization, winner-take-all, sparsification, normalization, and/or point-wise saturation) and finally a pooling operation (max, average or histogramming). For example, the scale-invariant feature transform (SIFT (Lowe, 2004)) operator applies oriented edge filters to a small patch and determines the dominant orientation through a winner-take-all operation. Finally, the resulting sparse vectors are added (pooled) over a larger patch to form local orientation histogram. Some recognition systems use a single stage of feature extractors (Lazebnik