414 research outputs found

    Blur Classification Using Segmentation Based Fractal Texture Analysis

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    The objective of vision based gesture recognition is to design a system, which can understand the human actions and convey the acquired information with the help of captured images. An image restoration approach is extremely required whenever image gets blur during acquisition process since blurred images can severely degrade the performance of such systems. Image restoration recovers a true image from a degraded version. It is referred as blind restoration if blur information is unidentified. Blur identification is essential before application of any blind restoration algorithm. This paper presents a blur identification approach which categories a hand gesture image into one of the sharp, motion, defocus and combined blurred categories. Segmentation based fractal texture analysis extraction algorithm is utilized for featuring the neural network based classification system. The simulation results demonstrate the preciseness of proposed method

    Ballistics Image Processing and Analysis for Firearm Identification

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    Firearm identification is an intensive and time-consuming process that requires physical interpretation of forensic ballistics evidence. Especially as the level of violent crime involving firearms escalates, the number of firearms to be identified accumulates dramatically. The demand for an automatic firearm identification system arises. This chapter proposes a new, analytic system for automatic firearm identification based on the cartridge and projectile specimens. Not only do we present an approach for capturing and storing the surface image of the spent projectiles at high resolution using line-scan imaging technique for the projectiles database, but we also present a novel and effective FFT-based analysis technique for analyzing and identifying the projectiles

    Temporally Biased Search Result Snippets

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    The search engine result snippets are an important source of information for the user to obtain quick insights into the corresponding result documents. When the search terms are too general, like a person\u27s name or a company\u27s name, creating an appropriate snippet that effectively summarizes the document\u27s content can be challenging owing to multiple occurrences of the search term in the top ranked documents, without a simple means to select a subset of sentences containing them to form result snippet. In web pages classified as narratives and news articles, multiple references to explicit, implicit and relative temporal expressions can be found. Based on these expressions, the sentences can be ordered on a timeline. In this thesis, we propose the idea of generation of an alternate search results snippet, by exploiting these temporal expressions embedded within the pages, using a timeline map. Our method of snippets generation is mainly targeted at general search terms. At present, when the search terms are too general, the existing systems generate static snippets for resultant pages like displaying the first line. In our approach, we introduce an alternate method of extracting and selecting temporal data from these pages to adapt a snippet to be a more effective summary. Specifically, it selects and blends temporally interesting sentences. Using weighted kappa measure, we evaluate our approach by comparing snippets generated for multiple search terms based on existing systems and snippets generated by using our approach

    NASA patent abstracts bibliography: A continuing bibliography. Section 1: Abstracts (supplement 34)

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    Abstracts are provided for 124 patents and patent applications entered into the NASA scientific and technical information systems during the period July 1988 through December 1988. Each entry consists of a citation, an abstract, and in most cases, a key illustration selected from the patent or patent application

    Active Tactile Sensing for Texture Perception in Robotic Systems

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    This thesis presents a comprehensive study of tactile sensing, particularly on the prob- lem of active texture perception. It includes a brief introduction to tactile sensing technology and the neural basis for tactile perception. It follows the literature review of textural percep- tion with tactile sensing. I propose a decoding and perception pipeline to tackle fine-texture classification/identification problems via active touching. Experiments are conducted using a 7DOF robotic arm with a finger-shaped tactile sensor mounted on the end-effector to per- form sliding/rubbing movements on multiple fabrics. Low-dimensional frequency features are extracted from the raw signals to form a perceptive feature space, where tactile signals are mapped and segregated into fabric classes. Fabric classes can be parameterized and sim- plified in the feature space using elliptical equations. Results from experiments of varied control parameters are compared and visualized to show that different exploratory move- ments have an apparent impact on the perceived tactile information. It implies the possibil- ity of optimising the robotic movements to improve the textural classification/identification performance

    On Designing Tattoo Registration and Matching Approaches in the Visible and SWIR Bands

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    Face, iris and fingerprint based biometric systems are well explored areas of research. However, there are law enforcement and military applications where neither of the aforementioned modalities may be available to be exploited for human identification. In such applications, soft biometrics may be the only clue available that can be used for identification or verification purposes. Tattoo is an example of such a soft biometric trait. Unlike face-based biometric systems that used in both same-spectral and cross-spectral matching scenarios, tattoo-based human identification is still a not fully explored area of research. At this point in time there are no pre-processing, feature extraction and matching algorithms using tattoo images captured at multiple bands. This thesis is focused on exploring solutions on two main challenging problems. The first one is cross-spectral tattoo matching. The proposed algorithmic approach is using as an input raw Short-Wave Infrared (SWIR) band tattoo images and matches them successfully against their visible band counterparts. The SWIR tattoo images are captured at 1100 nm, 1200 nm, 1300 nm, 1400 nm and 1500 nm. After an empirical study where multiple photometric normalization techniques were used to pre-process the original multi-band tattoo images, only one was determined to significantly improve cross spectral tattoo matching performance. The second challenging problem was to develop a fully automatic visible-based tattoo image registration system based on SIFT descriptors and the RANSAC algorithm with a homography model. The proposed automated registration approach significantly improves the operational cost of a tattoo image identification system (using large scale tattoo image datasets), where the alignment of a pair of tattoo images by system operators needs to be performed manually. At the same time, tattoo matching accuracy is also improved (before vs. after automated alignment) by 45.87% for the NIST-Tatt-C database and 12.65% for the WVU-Tatt database

    NASA patent abstracts bibliography: A continuing bibliography. Section 1: Abstracts (supplement 31)

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    Abstracts are provided for 85 patents and patent applications entered into the NASA scientific and technical information system during the period January 1987 through June 1987. Each entry consists of a citation, an abstract, and in most cases, a key illustration selected from the patent or patent application

    FEATUR.UX: An approach to leveraging multitrack information for artistic music visualization

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    FEATUR.UX (Feature - ous) is an audio visualisation tool, currently in the process of development, which proposes to introduce a new approach to sound visualisation using pre-mixed, independent multitracks and audio feature ex- traction. Sound visualisation is usually performed using a mixed mono or stereo track of audio. Audio feature ex- traction is commonly used in the field of music information retrieval to create search and recommendation systems for large music databases rather than generating live visual- isations. Visualizing multitrack audio circumvents prob- lems related to the source separation of mixed audio sig- nals and presents an opportunity to examine interdepen- dent relationships within and between separate streams of music. This novel approach to sound visualisation aims to provide an enhanced listening experience in a use case that employs non-tonal, non-notated forms of electronic music. Findings from prior research studies focused on live per- formance and preliminary quantitative results from a user survey have provided the basis from which to develop a prototype for an iterative design study that examines the impact of using multitrack audio and audio feature extrac- tion within sound visualisation practice

    FEATUR.UX: exploiting multitrack information for artistic visualization

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    FEATUR.UX (Feature - ous) is an audio visualization tool, currently in the process of development, which proposes to introduce a new approach to sound visualization using pre-mixed, independent multitracks and audio feature extraction. Sound visualization is usually performed using a final mix, mono or stereo track of audio. Audio feature extraction is commonly used in the field of music information retrieval to create search and recommendation systems for large music databases rather than generating live visualizations. Visualizing multitrack audio circumvents problems related to the source separation of mixed audio signals and presents an opportunity to examine interdependent relationships within and between separate streams of music. This novel approach to sound visualization aims to provide an enhanced accession to the listening experience corresponding to this use case that employs non-tonal, non-notated forms of electronic music. Findings from prior research studies focused on live performance and preliminary quantitative results from a user survey have provided the basis from which to develop a prototype that will be used throughout an iterative design study to examine the impact of using multitrack audio and audio feature extraction on sound visualization practice
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