10,184 research outputs found
Reflectance Hashing for Material Recognition
We introduce a novel method for using reflectance to identify materials.
Reflectance offers a unique signature of the material but is challenging to
measure and use for recognizing materials due to its high-dimensionality. In
this work, one-shot reflectance is captured using a unique optical camera
measuring {\it reflectance disks} where the pixel coordinates correspond to
surface viewing angles. The reflectance has class-specific stucture and angular
gradients computed in this reflectance space reveal the material class.
These reflectance disks encode discriminative information for efficient and
accurate material recognition. We introduce a framework called reflectance
hashing that models the reflectance disks with dictionary learning and binary
hashing. We demonstrate the effectiveness of reflectance hashing for material
recognition with a number of real-world materials
Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks
Online media outlets, in a bid to expand their reach and subsequently
increase revenue through ad monetisation, have begun adopting clickbait
techniques to lure readers to click on articles. The article fails to fulfill
the promise made by the headline. Traditional methods for clickbait detection
have relied heavily on feature engineering which, in turn, is dependent on the
dataset it is built for. The application of neural networks for this task has
only been explored partially. We propose a novel approach considering all
information found in a social media post. We train a bidirectional LSTM with an
attention mechanism to learn the extent to which a word contributes to the
post's clickbait score in a differential manner. We also employ a Siamese net
to capture the similarity between source and target information. Information
gleaned from images has not been considered in previous approaches. We learn
image embeddings from large amounts of data using Convolutional Neural Networks
to add another layer of complexity to our model. Finally, we concatenate the
outputs from the three separate components, serving it as input to a fully
connected layer. We conduct experiments over a test corpus of 19538 social
media posts, attaining an F1 score of 65.37% on the dataset bettering the
previous state-of-the-art, as well as other proposed approaches, feature
engineering or otherwise.Comment: Accepted at SIGIR 2018 as Short Pape
AirCode: Unobtrusive Physical Tags for Digital Fabrication
We present AirCode, a technique that allows the user to tag physically
fabricated objects with given information. An AirCode tag consists of a group
of carefully designed air pockets placed beneath the object surface. These air
pockets are easily produced during the fabrication process of the object,
without any additional material or postprocessing. Meanwhile, the air pockets
affect only the scattering light transport under the surface, and thus are hard
to notice to our naked eyes. But, by using a computational imaging method, the
tags become detectable. We present a tool that automates the design of air
pockets for the user to encode information. AirCode system also allows the user
to retrieve the information from captured images via a robust decoding
algorithm. We demonstrate our tagging technique with applications for metadata
embedding, robotic grasping, as well as conveying object affordances.Comment: ACM UIST 2017 Technical Paper
Quantum Turing automata
A denotational semantics of quantum Turing machines having a quantum control
is defined in the dagger compact closed category of finite dimensional Hilbert
spaces. Using the Moore-Penrose generalized inverse, a new additive trace is
introduced on the restriction of this category to isometries, which trace is
carried over to directed quantum Turing machines as monoidal automata. The
Joyal-Street-Verity Int construction is then used to extend this structure to a
reversible bidirectional one.Comment: In Proceedings DCM 2012, arXiv:1403.757
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