קולוקוויום וסמינרים

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Computer Science events calendar in HTTP ICS format for of Google calendars, and for Outlook.

Academic Calendar at Technion site.

קולוקוויום וסמינרים בקרוב

  • Pixel Club: Models of Stochastic Textures and their Applications in Image Processing

    דובר:
    עידו זצ'בסקי (הנדסת חשמל, טכניון)
    תאריך:
    יום שלישי, 25.7.2017, 11:30
    מקום:
    חדר 815, בניין מאייר, הפקולטה להנדסת חשמל

    Textures are what differentiates true, real-life images, from cartoon images. The latter emphasize mainly smooth content other than the edges and contours, whereas the former stresses the importance of the details within the contours. Textures are found in facial images, natural scenery, aerial, medical and other types of images, and affect the image perception and recognition. Images with smoothed-out textures appear artificial and cartoon-like.

    This study is devoted to a subset of textures known as Natural Stochastic Textures (NST). Stochastic textures are best modelled as random processes, as their repetitive structure is characterized in the statistical sense, and not in the spatial domain. After identifying the important statistical properties of NST, we propose several algorithms for texture super-resolution, denoising, deconvolution and other image processing tasks, using the fractional Brownian motion (fBm) as a suitable prior model.

    Some NST cannot be fully described via a Gaussian model such as the fBm. This is due to the fact that Gaussian models are defined via their first and second order statistics and cannot, therefore, represent edges. Some NST, contain, however, additional structural characteristics that can not be modelled by fBm models. The complementary structural characteristics such as edges and thin lines is properly represented by the Fourier phase. We propose a suitable prior model for the local phase present in textures by incorporating complex wavelet decomposition into the combined model.

    The analysis of textures that contain both stochastic and structural elements calls for texture embedding in low-dimensional spaces. We take a close look at the challenges involved in such embeddings. We conclude this study by proposing a method for texture analysis and show that the properties we propose in the first part arise naturally by learning the low-dimensional texture space.

  • Compositional Semantic Parsing of Instructions in Unseen Domains

    דובר:
    עופר גבעולי, הרצאה סמינריונית למגיסטר
    תאריך:
    יום רביעי, 26.7.2017, 10:00
    מקום:
    טאוב 601
    מנחה:
    Prof. R. Reichart

    Semantic parsing is the task of mapping natural language sentences into a formal representation of their meaning, often defined as logical forms. One of the prominent uses of semantic parsing is parsing natural language instructions in the context of natural language interfaces (NLIs) for various types of software applications. In this work, we present a novel task: parsing instructions in simple domains that are unseen during training, into logical forms with deep compositionality. Previous work on parsing natural language instructions either did not support unseen domains or did not support mapping instructions to logical forms with deep compositionality. We constructed a new dataset for this task, covering linguistic phenomena such as superlatives, comparatives and spatial and temporal language. The dataset includes annotated examples from 7 simple domains (e.g. a toy calendar application). To facilitate the collection and usage of the dataset, we developed a framework attempting to minimize the effort of adding an NLI to simple Java applications. Using our new dataset, we evaluate a log-linear model tailored to this task, implemented using a toolkit called SEMPRE. We present a novel training approach where the AdaGrad weight updates are conditioned on evidence indicating the update is beneficial for multiple domains. Also, we present a training method in which AdaGrad training is separated into two steps, using examples from different domains in each and learning different weight subsets.

  • Gathering of Agents on the Line

    דובר:
    דמיטרי רבינוביץ, הרצאה סמינריונית למגיסטר
    תאריך:
    יום רביעי, 26.7.2017, 14:30
    מקום:
    טאוב 601
    מנחה:
    Prof. A.M.Bruckstein

    We consider a group of mobile agents on a line, identical and indistinguishable, memoryless, having the capability to only sense the presence of neighboring agents to the left and to the right. The agents' rule of motion is as follows : at each moment, agents with neighbors on both sides stay put, while agents with neighbors on one side only jump with high probability a unit distance towards the neighbors (otherwise, they jump one unit away). We prove that all agents, except two, gather almost surely inside a unit size interval in finite expected time. Two agents, the current left-most and right-most ones perform random walks strongly biased towards the cluster of other agents. The cluster of gathered agents slowly moves on the line. Interesting interactions occur when the left and/or right Random Walkers reach the clustered agents and these interactions are completely analyzed herein.

  • On Visibility and Point Clouds

    דובר:
    נתי קליגלר, הרצאה סמינריונית למגיסטר
    תאריך:
    יום שלישי, 29.8.2017, 11:30
    מקום:
    טאוב 601
    מנחה:
    Prof. A. Tal

    We introduce the concept of visibility detection within a point set to new domains. Specifically, we show that a simple representation of an image as a 3D point cloud lets us use visibility detection in classical image processing tasks, improving state-of-the-art results. Given an image, each pixel is represented as a feature point, a viewpoint is set, and points that are visible to the viewpoint are detected. What does it mean for a point to be visible? Although this question has been widely studied within computer graphics, it has never been regarded when the point set consists of feature vectors (rather than a real scene). We show that the answer to this question reveals unique information about the image, enabling us to modify state-of-the-art algorithms and improve their own results. As proof of concept, we demonstrate this idea within three applications: text image binarization, document unshadowing and stippling-style illustration.

  • בית-ספר קיץ השישי בנושא אבטחת סייבר

    The 6th Summer School on Cyber and Computer Security

    תאריך:
    יום ראשון, 10.9.2017, 09:00
    מקום:
    אודיטוריום 1 בניין טאוב למדעי המחשב

    מרכז המחקר לאבטחת סייבר ע"ש הירושי פוג'יווארה יקיים את בית-ספר קיץ השישי על אבטחת סייבר ומחשבים: "Decentralized Cryptographic Currencies and Blockchains"

    הכנס יתקיים בימים א'-ה', 10-14 בספטמבר, 2017, באודיטוריום 1, בניין טאוב למדעי המחשב בטכניון, חיפה

    מארגני הכנס:
    פרופ' אלי ביהם
     – טכניון
    פרופ' אלי בן-ששון
     – טכניון

    ההשתתפות חופשית אך דורשת הרשמה.

    פרטים נוספים ומידע על מרכז המחקר לאבטחת סייבר ע"ש הירושי פוג'יווארה.