KEYWORDS: Analytics, Cameras, Video, Video surveillance, Machine vision, Computer vision technology, Data storage, Business intelligence, Statistical analysis, Imaging systems
Today retail video analytics has gone beyond the traditional domain of security and loss prevention by providing
retailers insightful business intelligence such as store traffic statistics and queue data. Such information allows for
enhanced customer experience, optimized store performance, reduced operational costs, and ultimately higher
profitability. This paper gives an overview of various camera-based applications in retail as well as the state-ofthe-
art computer vision techniques behind them. It also presents some of the promising technical directions for
exploration in retail video analytics.
Fingerprints are being extensively used for person identification in a number of commercial, civil, and forensic
applications. Most of the current fingerprint verification systems utilize features that are based on minutiae points and
ridge patterns. While minutiae based fingerprint verification systems have shown fairly high accuracies, further
improvements in their performance are needed for acceptable performance, especially in applications involving very
large scale databases. In an effort to extend the existing technology for fingerprint verification, we propose a new
representation and matching scheme for fingerprint using Scale Invariant Feature Transformation (SIFT). We extract
characteristic SIFT feature points in scale space and perform matching based on the texture information around the
feature points using the SIFT operator. A systematic strategy of applying SIFT to fingerprint images is proposed. Using
a public domain fingerprint database (FVC 2002), we demonstrate that the proposed approach complements the minutiae
based fingerprint representation. Further, the combination of SIFT and conventional minutiae based system achieves
significantly better performance than either of the individual schemes.
Current 'invisible' watermarking techniques aim at producing watermarked data that suffer no or little quality degradation and perceptually identical to the original versions. The most common utility of a watermarked image is (1) for image viewing and display, and (2) for extracting the embedded watermark in subsequent copy protection applications. The issue is often centered on the robustness of the watermark for detection and extraction. In addition to robustness studies, a fundamental question will center on the utilization value of the watermarked images beyond perceptual quality evaluation. Essentially we have to study how the watermarks inserted affect the subsequent processing and utility of images, and what watermarking schemes we can develop that will cater to these processing tasks. This work focuses on the study of watermarking on images used in automatic personal identification technology based fingerprints. We investigate the effects of watermarking fingerprint images on the recognition and retrieval accuracy using a proposed invisible fragile watermarking technique for image verification applications on a specific fingerprint recognition system. We shall also describe the watermarking scheme, fingerprint recognition and feature extraction techniques used. We believe that watermarking of images will provided value-added protection, as well as copyright notification capability, to the fingerprint data collection processes and subsequent usage.
Salient surface features play a central role in tasks related to 3-D object recognition and matching. There is a large body of psychophysical evidence demonstrating the perceptual significance of surface features such as local minima of principal curvatures in the decomposition of objects into a hierarchy of parts. Many recognition strategies employed in machine vision also directly use features derived from surface properties for matching. Hence, it is important to develop techniques that detect surface features reliably. Our proposed scheme consists of (1) a preprocessing stage, (2) a feature detection stage, and (3) a feature integration stage. The preprocessing step selectively smoothes out noise in the depth data without degrading salient surface details and permits reliable local estimation of the surface features. The feature detection stage detects both edge-based and region-based features, of which many are derived from curvature estimates. The third stage is responsible for integrating the information provided by the individual feature detectors. This stage also completes the partial boundaries provided by the individual feature detectors, using proximity and continuity principles of Gestalt. All our algorithms use local support and, therefore, are inherently parallelizable. We demonstrate the efficacy and robustness of our approach by applying it to two diverse domains of applications: (1) segmentation of objects into volumetric primitives and (2) detection of salient contours on free-form surfaces. We have tested our algorithms on a number of real range images with varying degrees of noise and missing data due to self-occlusion. The preliminary results are very encouraging.
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