129 research outputs found

    Similarity score of two images using different measures.

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    In the field of computer vision and image processing, image similarity has been a central concern for decades. If you compare two pictures, Image Similarity returns a value that tells you how physically they are close. A quantitative measure of the degree of correspondence between the images concerned is given by this test. The score of the similarity between images varies from 0 to 1. In this paper, ORB (Oriented Fast Rotated Brief) algorithm is used to measure the similarity and other types of similarity measures like Structural Similarity Index (SSIM), pixel similarity, Earth mover's Distance are used to obtain the score. When two images are compared, it shows how much identical (common) objects are there in the two images. So, the accuracy or similarity score is about 87 percent when the two images are compared

    Hybrid image representation methods for automatic image annotation: a survey

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    In most automatic image annotation systems, images are represented with low level features using either global methods or local methods. In global methods, the entire image is used as a unit. Local methods divide images into blocks where fixed-size sub-image blocks are adopted as sub-units; or into regions by using segmented regions as sub-units in images. In contrast to typical automatic image annotation methods that use either global or local features exclusively, several recent methods have considered incorporating the two kinds of information, and believe that the combination of the two levels of features is beneficial in annotating images. In this paper, we provide a survey on automatic image annotation techniques according to one aspect: feature extraction, and, in order to complement existing surveys in literature, we focus on the emerging image annotation methods: hybrid methods that combine both global and local features for image representation

    Shared Habitats: the MoverWitness Paradigm

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    Merged with duplicate record 10026.1/642 on 14.02.2017 by CS (TIS)This practice-led research thesis analyses and visualises central components of Authentic Movement, with particular reference to the work of Dr Janet Adler. By contextualising and comparing this improvisation method with modern, post-modern and contemporary movement practices the author describes the emergence of Authentic Movement and distinguishes it from other practices. A new and original viewpoint is adopted and the practice's aesthetic, visual and empathetic characteristics are explored in relationship to and through visual art. The author, a learned Authentic Movement practitioner, critiques, deconstructs and reframes the practice from a visual arts- and performance-based, phenomenological perspective renaming it 'the MoverWitness exchange'. Embedded aspects and skills of the MoverWitness exchange, usually only accessible to firsthand practitioners of the method, are made explicit through research processes of analysis, application and visualisation. Hereby the practice's unique capacity to contain and express binary embodied experiences and concepts is exposed. Resulting insights are crystallised in a distinctive understanding of the MoverWitness exchange that emphasises its suitability as a new learning and/or research methodology for inter- and cross-disciplinary application.Dartington College of Art

    Linear color correction for multiple illumination changes and non-overlapping cameras

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    Many image processing methods, such as techniques for people re-identification, assume photometric constancy between different images. This study addresses the correction of photometric variations based upon changes in background areas to correct foreground areas. The authors assume a multiple light source model where all light sources can have different colours and will change over time. In training mode, the authors learn per-location relations between foreground and background colour intensities. In correction mode, the authors apply a double linear correction model based on learned relations. This double linear correction includes a dynamic local illumination correction mapping as well as an inter-camera mapping. The authors evaluate their illumination correction by computing the similarity between two images based on the earth mover's distance. The authors compare the results to a representative auto-exposure algorithm found in the recent literature plus a colour correction one based on the inverse-intensity chromaticity. Especially in complex scenarios the authors’ method outperforms these state-of-the-art algorithms

    Human Computer Interaction Based HEMD Using Hand Gesture

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    Hand gesture based Human-Computer-Interaction (HCI) is one of the most normal and spontaneous ways to communicate between people and apparatus to present a hand gesture recognition system with Webcam, Operates robustly in unrestrained environment and is insensible to hand variations and distortions. This classification consists of two major modules, that is, hand detection and gesture recognition. Diverse from conventional vision-based hand gesture recognition methods that use color-markers for hand detection, this system uses both the depth and color information from Webcam to detect the hand shape, which ensures the sturdiness in disorderly environments. Assurance its heftiness to input variations or the distortions caused by the low resolution of webcam, to apply a novel shape distance metric called Handle Earth Mover\u27s Distance (HEMD) for hand gesture recognition. Consequently, in this paper concept operates accurately and efficiently. The intend of this paper is to expand robust and resourceful hand segmentation algorithm where three algorithms for hand segmentation using different color spaces with required thresholds have were utilized. Hand tracking and segmentation algorithm is found to be most resourceful to handle the challenge of apparition based organization such as skin dye detection. Noise may hold, for a moment, in the segmented image due to lively background. Tracking algorithm was developed and applied on the segmented hand contour for elimination of unnecessary background nois

    Post-training discriminative pruning for RBMs

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    One of the major challenges in the area of artificial neural networks is the identification of a suitable architecture for a specific problem. Choosing an unsuitable topology can exponentially increase the training cost, and even hinder network convergence. On the other hand, recent research indicates that larger or deeper nets can map the problem features into a more appropriate space, and thereby improve the classification process, thus leading to an apparent dichotomy. In this regard, it is interesting to inquire whether independent measures, such as mutual information, could provide a clue to finding the most discriminative neurons in a network. In the present work we explore this question in the context of Restricted Boltzmann Machines, by employing different measures to realize post-training pruning. The neurons which are determined by each measure to be the most discriminative, are combined and a classifier is applied to the ensuing network to determine its usefulness. We find that two measures in particular seem to be good indicators of the most discriminative neurons, producing savings of generally more than 50% of the neurons, while maintaining an acceptable error rate. Further, it is borne out that starting with a larger network architecture and then pruning is more advantageous than using a smaller network to begin with. Finally, a quantitative index is introduced which can provide information on choosing a suitable pruned network.Fil: Sånchez Gutiérrez, Måximo. Universidad Autónoma Metropolitana; MéxicoFil: Albornoz, Enrique Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Rufiner, Hugo Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina. Universidad Nacional de Entre Ríos; ArgentinaFil: Close, John Goddard. Universidad Autónoma Metropolitana; Méxic
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