Performance Characterization in Computer Vision (Computational Imaging and Vision)


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Recently, selfies have emerged as a new, pervasive genre of identity performance.

Computational Imaging: Get the Image You Want | | Quality Magazine

In many ways unique, selfies bring us full circle to Goffman—blending the online and offline selves together. In this paper, we take an empirical, Goffman-inspired look at the phenomenon of selfies. We report a large-scale, mixed-method analysis of the categories in which selfies appear on Instagram—an online community comprising over M people. Applying computer vision and network analysis techniques to 2. To the best of our knowledge, this is the first large-scale, empirical research on selfies.

We conclude, contrary to common portrayals in the press, that selfies are really quite ordinary: they project identity signals such as wealth, health and physical attractiveness common to many online media, and to offline life. Widespread and pervasive adoption of smartphones has led to instant sharing of photographs that capture events ranging from mundane to life-altering happenings. We propose to capture sentiment information of such social event images leveraging their visual content.

Ryszard Kozera

Our method extracts an intermediate visual representation of social event images based on the visual attributes that occur in the images going beyond sentiment-specific attributes. We map the top predicted attributes to sentiments and extract the dominant emotion associated with a picture of a social event. Unlike recent approaches, our method generalizes to a variety of social events and even to unseen events, which are not available at training time. We demonstrate the effectiveness of our approach on a challenging social event image dataset and our method outperforms state-of-the-art approaches for classifying complex event images into sentiments.

Web images are obtained for each discovered event concept and we use pre-trained CNN features to train concept classifiers. The massive growth of sports videos has resulted in a need for automatic generation of sports highlights that are comparable in quality to the hand-edited highlights produced by broadcasters such as ESPN. Unlike previous works that mostly use audio-visual cues derived from the video, we propose an approach that additionally leverages contextual cues derived from the environment that the game is being played in.

The contextual cues provide information about the excitement levels in the game, which can be ranked and selected to automatically produce high-quality basketball highlights. We introduce a new dataset of 25 NCAA games along with their play-by-play stats and the ground-truth excitement data for each basket. We explore the informativeness of five different cues derived from the video and from the environment through user studies.

Our experiments show that for our study participants, the highlights produced by our system are comparable to the ones produced by ESPN for the same games. This work is in part aimed at combining efforts like Video Textures and Video Stabilization and a lot more. We use our video stabilization technology to freeze the background into a still photo or create sweeping cinematic pans. Published by Wiley? Blackwell About this Item: Wiley?

Blackwell, Condition: Used; Good. Simply Brit: We have dispatched from our UK warehouse books of good condition to over 1 million satisfied customers worldwide.

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We are committed to providing you with a reliable and efficient service at all times. A copy that has been read, but remains in clean condition. All pages are intact, and the cover is intact. The spine may show signs of wear. Pages can include limited notes and highlighting, and the copy can include previous owner inscriptions. Seller Inventory GI3N Condition: New. From: medimops Berlin, Germany. Seller Inventory MG. Soft Cover. LNCS Soft cover. Usual ex-library features.

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The interior is clean and tight. Binding is good. Cover shows slight wear and has library label on spine. Published by North-Holland About this Item: North-Holland, From: Books for Libraries, Inc. Santa Clarita, CA, U. Condition: Fine. Text is clean. Vol 5 of "Special Topics in Supercomputing". No former owner's name or marks. Text is clean, Binding is strong. Create an account Institutional Access:.

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Journal of Electronic Imaging. Call For Papers. Submit a Manuscript. How to Submit to a Special Section. Upcoming Special Sections. Quality Control by Artificial Vision. List of Previously Published Special Sections. Publication Date.

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Performance Characterization in Computer Vision (Computational Imaging and Vision) Performance Characterization in Computer Vision (Computational Imaging and Vision)
Performance Characterization in Computer Vision (Computational Imaging and Vision) Performance Characterization in Computer Vision (Computational Imaging and Vision)
Performance Characterization in Computer Vision (Computational Imaging and Vision) Performance Characterization in Computer Vision (Computational Imaging and Vision)
Performance Characterization in Computer Vision (Computational Imaging and Vision) Performance Characterization in Computer Vision (Computational Imaging and Vision)
Performance Characterization in Computer Vision (Computational Imaging and Vision) Performance Characterization in Computer Vision (Computational Imaging and Vision)
Performance Characterization in Computer Vision (Computational Imaging and Vision) Performance Characterization in Computer Vision (Computational Imaging and Vision)
Performance Characterization in Computer Vision (Computational Imaging and Vision) Performance Characterization in Computer Vision (Computational Imaging and Vision)
Performance Characterization in Computer Vision (Computational Imaging and Vision) Performance Characterization in Computer Vision (Computational Imaging and Vision)
Performance Characterization in Computer Vision (Computational Imaging and Vision)

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