Video quality metrics (VQM) have often been evaluated and compared using simple measures of correlation with observers. This approach does not fully take into account the variability implicit in the observers. We present techniques for determining the statistical resolving power of a VQM, defined as the minimum change in the value of the metric for which subjective test scores show a significant change. These techniques have been applied to the VQEG dataset, and incorporated into the recent ATIS/T1A1 series of technical reports (TR), which provide a comprehensive framework for characterizing and validating full-reference video quality metrics (VQM). These approved TR, while not standards, will enable the USA telecommunications industry to incorporate video quality metrics into contracts and tariffs for compressed video distribution. New methods for assessing VQM accuracy and cross-calibrating VQM are an integral part of the framework. These methods have been applied to two VQM at this point: PSNR and the version of Sarnoff's JNDmetrix tested by VQEG. The framework is readily extensible to additional VQM.

This paper describes a pattern recognition architecture, which we term hierarchical pyramid/neural network (HPNN), that learns to exploit image structure at multiple resolutions for detecting clinically significant features in digital/digitized mammograms. The HPNN architecture consists of a hierarchy of neural networks, each network receiving feature inputs at a given scale as well as features constructed by networks lower in the hierarchy. Networks are trained using a novel error function for the supervised learning of image search/detection tasks when the position of the objects to be found is uncertain or ill defined. We have evaluated the HPNN's ability to eliminate false positive (FP) regions of interest generated by the University of Chicago's (UofC) Computer-aided diagnosis (CAD) systems for microcalcification and mass detection. Results show that the HPNN architecture, trained using the uncertain object position (UOP) error function, reduces the FP rate of a mammographic CAD system by approximately 50% without significant loss in sensitivity. Investigation into the types of FPs that the HPNN eliminates suggests that the pattern recognizer is automatically learning and exploiting contextual information. Clinical utility is demonstrated through the evaluation of an integrated system in a clinical reader study. We conclude that the HPNN architecture learns contextual relationships between features at multiple scales and integrates these features for detecting microcalcifications and breast masses.

Most contemporary speech recognizers are designed to operate with close-talking speech and they work best in a quiet laboratory condition. There is an apparent need to render environment robustness to these systems. The objective of the paper is to explore utility of existing speech recognition technology in adverse "real-world" environments for distant-talking applications. A synergistic system consisting of microphone array and neural network (MANN) is utilized to mitigate environmental interference introduced by reverberation, ambient noise, and channel mismatch between training and testing conditions. The MANN system is evaluated with experiments on continuous distant-talking speech recognition. The results show that the MANN system elevates the word recognition accuracy to a level which is competitive with a retrained speech recognizer and that the neural network compensation performs better than some previously researched techniques.

An important problem in image analysis is finding small objects in large images. The problem is challenging because: 1) searching a large image is computationally expensive; and 2) small targets (on the order of a few pixels in size) have relatively few distinctive features which enable them to be distinguished from non-targets. To overcome these challenges the authors have developed a hierarchical neural network architecture which combines multiresolution pyramid processing with neural networks. Here the authors discuss the application of their hierarchical neural network architecture to the problem of detecting microcalcifications in digital mammograms. Microcalcifications are cues for breast tumors. 30% to 50% of breast carcinomas have microcalcifications visible in mammograms while 60% to 80% of all breast tumors eventually show microcalcifications via histology. Similar to the building/ATR problem, microcalcifications are generally very small point-like objects (<10 pixels in mammograms) which are hard to detect. Radiologists must often exploit other information in the imagery (e.g. location of blood vessels, ducts, etc.) in order to detect these microcalcifications. Here the authors examine how well their hierarchical neural network architecture learns and exploits contextual information in mammograms.

The efficiency of image search can be greatly improved by using a coarse-to-fine search strategy with a multi-resolution image representa(cid:173) tion. However, if the resolution is so low that the objects have few dis(cid:173) tinguishing features, search becomes difficult. We show that the performance of search at such low resolutions can be improved by using context information, i.e., objects visible at low-resolution which are not the objects of interest but are associated with them. The networks can be given explicit context information as inputs, or they can learn to detect the context objects, in which case the user does not have to be aware of their existence. We also use Integrated Feature Pyramids, which repre(cid:173) sent high-frequency information at low resolutions. The use of multi(cid:173) resolution search techniques allows us to combine information about the appearance of the objects on many scales in an efficient way. A natural fOlm of exemplar selection also arises from these techniques. We illus(cid:173) trate these ideas by training hierarchical systems of neural networks to find clusters of buildings in aerial photographs of farmland.