18,255 research outputs found
Negative Results in Computer Vision: A Perspective
A negative result is when the outcome of an experiment or a model is not what
is expected or when a hypothesis does not hold. Despite being often overlooked
in the scientific community, negative results are results and they carry value.
While this topic has been extensively discussed in other fields such as social
sciences and biosciences, less attention has been paid to it in the computer
vision community. The unique characteristics of computer vision, particularly
its experimental aspect, call for a special treatment of this matter. In this
paper, I will address what makes negative results important, how they should be
disseminated and incentivized, and what lessons can be learned from cognitive
vision research in this regard. Further, I will discuss issues such as computer
vision and human vision interaction, experimental design and statistical
hypothesis testing, explanatory versus predictive modeling, performance
evaluation, model comparison, as well as computer vision research culture
What Can I Do Around Here? Deep Functional Scene Understanding for Cognitive Robots
For robots that have the capability to interact with the physical environment
through their end effectors, understanding the surrounding scenes is not merely
a task of image classification or object recognition. To perform actual tasks,
it is critical for the robot to have a functional understanding of the visual
scene. Here, we address the problem of localizing and recognition of functional
areas from an arbitrary indoor scene, formulated as a two-stage deep learning
based detection pipeline. A new scene functionality testing-bed, which is
complied from two publicly available indoor scene datasets, is used for
evaluation. Our method is evaluated quantitatively on the new dataset,
demonstrating the ability to perform efficient recognition of functional areas
from arbitrary indoor scenes. We also demonstrate that our detection model can
be generalized onto novel indoor scenes by cross validating it with the images
from two different datasets
A Review on Biological Inspired Computation in Cryptology
Cryptology is a field that concerned with cryptography and cryptanalysis. Cryptography, which is a key technology in providing a secure transmission of information, is a study of designing strong cryptographic algorithms, while cryptanalysis is a study of breaking the cipher. Recently biological approaches provide inspiration in solving problems from various fields. This paper reviews major works in the application of biological inspired computational (BIC) paradigm in cryptology. The paper focuses on three BIC approaches, namely, genetic algorithm (GA), artificial neural network (ANN) and artificial immune system (AIS). The findings show that the research on applications of biological approaches in cryptology is minimal as compared to other fields. To date only ANN and GA have been used in cryptanalysis and design of cryptographic primitives and protocols. Based on similarities that AIS has with ANN and GA, this paper provides insights for potential application of AIS in cryptology for further research
Are Face and Object Recognition Independent? A Neurocomputational Modeling Exploration
Are face and object recognition abilities independent? Although it is
commonly believed that they are, Gauthier et al.(2014) recently showed that
these abilities become more correlated as experience with nonface categories
increases. They argued that there is a single underlying visual ability, v,
that is expressed in performance with both face and nonface categories as
experience grows. Using the Cambridge Face Memory Test and the Vanderbilt
Expertise Test, they showed that the shared variance between Cambridge Face
Memory Test and Vanderbilt Expertise Test performance increases monotonically
as experience increases. Here, we address why a shared resource across
different visual domains does not lead to competition and to an inverse
correlation in abilities? We explain this conundrum using our
neurocomputational model of face and object processing (The Model, TM). Our
results show that, as in the behavioral data, the correlation between
subordinate level face and object recognition accuracy increases as experience
grows. We suggest that different domains do not compete for resources because
the relevant features are shared between faces and objects. The essential power
of experience is to generate a "spreading transform" for faces that generalizes
to objects that must be individuated. Interestingly, when the task of the
network is basic level categorization, no increase in the correlation between
domains is observed. Hence, our model predicts that it is the type of
experience that matters and that the source of the correlation is in the
fusiform face area, rather than in cortical areas that subserve basic level
categorization. This result is consistent with our previous modeling
elucidating why the FFA is recruited for novel domains of expertise (Tong et
al., 2008)
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