Local Difference Sign-Magnitude Transform of Edge/Corner Features for Robust Face Recognition

In this research, a new appearance based feature descriptor, named Local Difference Sign-Magnitude Transform (LDSMT) is developed for robust face recognition, which efficiently summarizes the local structure of face images. LDSMT is a nonparametric descriptor that utilizes a combined edge/corner detection strategy. We obtain the information about corners and edges of the face image using the Frei and Chen edge detector, then for each pixel position there are two local differences to describe the relationship of pixels to their local neighborhood. The first one is using the sign (positive or negative) of the difference between the values of the central pixel and the neighboring pixel. The second one is using the magnitude of the difference between the central pixel and the neighboring pixel. Then a histogram is built for each component from each edge and corner map respectively. Finally, we concatenate these histograms together to form the final LDSMT feature vector. The performance evaluation of the proposed LDSMT algorithm is conducted on several publicly available databases and observed promising recognition rates.https://ecommons.udayton.edu/stander_posters/1908/thumbnail.jp

A biologically inspired spiking model of visual processing for image feature detection

To enable fast reliable feature matching or tracking in scenes, features need to be discrete and meaningful, and hence edge or corner features, commonly called interest points are often used for this purpose. Experimental research has illustrated that biological vision systems use neuronal circuits to extract particular features such as edges or corners from visual scenes. Inspired by this biological behaviour, this paper proposes a biologically inspired spiking neural network for the purpose of image feature extraction. Standard digital images are processed and converted to spikes in a manner similar to the processing that transforms light into spikes in the retina. Using a hierarchical spiking network, various types of biologically inspired receptive fields are used to extract progressively complex image features. The performance of the network is assessed by examining the repeatability of extracted features with visual results presented using both synthetic and real images

Performance Assessment of Feature Detection Algorithms: A Methodology and Case Study on Corner Detectors

In this paper we describe a generic methodology for evaluating the labeling performance of feature detectors. We describe a method for generating a test set and apply the methodology to the performance assessment of three well-known corner detectors: the Kitchen-Rosenfeld, Paler et al. and Harris-Stephens corner detectors. The labeling deficiencies of each of these detectors is related to their discrimination ability between corners and various of the features which comprise the class of noncorners

Final Calibration of the Berkeley Extreme and Far-Ultraviolet Spectrometer on the ORFEUS-SPAS I and II Missions

The Berkeley Extreme and Far-Ultraviolet Spectrometer (BEFS) flew as part of the ORFEUS telescope on the ORFEUS-SPAS I and II space-shuttle missions in 1993 and 1996, respectively. The data obtained by this instrument have now entered the public domain. To facilitate their use by the astronomical community, we have re-extracted and re-calibrated both data sets, converted them into a standard (FITS) format, and placed them in the Multimission Archive at Space Telescope (MAST). Our final calibration yields improved wavelength scales and effective-area curves for both data sets.Comment: To appear in the January 2002 issue of the PASP. 17 pages with 9 embedded postscript figures; uses emulateapj5.st

Rapid Online Analysis of Local Feature Detectors and Their Complementarity

A vision system that can assess its own performance and take appropriate actions online to maximize its effectiveness would be a step towards achieving the long-cherished goal of imitating humans. This paper proposes a method for performing an online performance analysis of local feature detectors, the primary stage of many practical vision systems. It advocates the spatial distribution of local image features as a good performance indicator and presents a metric that can be calculated rapidly, concurs with human visual assessments and is complementary to existing offline measures such as repeatability. The metric is shown to provide a measure of complementarity for combinations of detectors, correctly reflecting the underlying principles of individual detectors. Qualitative results on well-established datasets for several state-of-the-art detectors are presented based on the proposed measure. Using a hypothesis testing approach and a newly-acquired, larger image database, statistically-significant performance differences are identified. Different detector pairs and triplets are examined quantitatively and the results provide a useful guideline for combining detectors in applications that require a reasonable spatial distribution of image features. A principled framework for combining feature detectors in these applications is also presented. Timing results reveal the potential of the metric for online applications. © 2013 by the authors; licensee MDPI, Basel, Switzerland