2 research outputs found
Scene Image is Non-Mutually Exclusive - A Fuzzy Qualitative Scene Understanding
Ambiguity or uncertainty is a pervasive element of many real world decision
making processes. Variation in decisions is a norm in this situation when the
same problem is posed to different subjects. Psychological and metaphysical
research had proven that decision making by human is subjective. It is
influenced by many factors such as experience, age, background, etc. Scene
understanding is one of the computer vision problems that fall into this
category. Conventional methods relax this problem by assuming scene images are
mutually exclusive; and therefore, focus on developing different approaches to
perform the binary classification tasks. In this paper, we show that scene
images are non-mutually exclusive, and propose the Fuzzy Qualitative Rank
Classifier (FQRC) to tackle the aforementioned problems. The proposed FQRC
provides a ranking interpretation instead of binary decision. Evaluations in
term of qualitative and quantitative using large numbers and challenging public
scene datasets have shown the effectiveness of our proposed method in modeling
the non-mutually exclusive scene images.Comment: Accepted in IEEE Transactions on Fuzzy System
Fuzzy human motion analysis: A review
Human Motion Analysis (HMA) is currently one of the most popularly active
research domains as such significant research interests are motivated by a
number of real world applications such as video surveillance, sports analysis,
healthcare monitoring and so on. However, most of these real world applications
face high levels of uncertainties that can affect the operations of such
applications. Hence, the fuzzy set theory has been applied and showed great
success in the recent past. In this paper, we aim at reviewing the fuzzy set
oriented approaches for HMA, individuating how the fuzzy set may improve the
HMA, envisaging and delineating the future perspectives. To the best of our
knowledge, there is not found a single survey in the current literature that
has discussed and reviewed fuzzy approaches towards the HMA. For ease of
understanding, we conceptually classify the human motion into three broad
levels: Low-Level (LoL), Mid-Level (MiL), and High-Level (HiL) HMA.Comment: Accepted in Pattern Recognition, first survey paper that discusses
and reviews fuzzy approaches towards HM