In this thesis, we explored two different applications of computer vision and pattern
recognition. The ideas put forth in this work can be expanded in the following
ways. First, for the physical therapy application, it would be interesting to leverage
recent research progress in gamification to keep the users motivated in performing
the daily exercises. Adding a “fun element” to a daily routine can result in a higher
acceptance of such systems among the users who need to do regular physical therapy.
Furthermore, from the experiments we conducted at patients’ home, we learned what
other information the users are interested to receive. For example, patients reported
that they wanted to understand the reasons why they did not receive the highest
possible score, or how they could have improved their performance. Such details
accuracy analysis is focused on the most important joint identified by each exercise.
The analysis process could be improved by leveraging the information in the rest of
the joints. To do this, we could define the movement of the user as a rigid body
where the distance between any two given points on this rigid body remains constant
in time, regardless of the movement of the user. Using the concept of a rigid body can
be helpful, first, to reduce any possible noise caused by the motion capture device,
and second, to provide the evaluation system with more descriptive information of
various joints during the exercise.
For the second application discussed in this thesis, the following ideas are inter-
esting as future directions in order to predict the visual complexity of images using
supervised approaches:
— Instead of training a regression model based on the extracted features of the
fourth layer, it is possible to train a deep regression model to finetune the weights
of all first four layers. Using this approach, the low-level features extracted from
the first layers can impact the visual complexity calculation more directly, which
may result in a more accurate prediction.
— It would also be interesting to use a limited number of buckets (e.g., 100) and
use a classification method instead of regression to predict the visual complexity
of images.
— Another approach to predict the visual complexity of images would be to train
a model based on the pairwise ground truth scores, where the model learns the
pairwise relationship between any two images. With this approach, it is possible
to first predict the pairwise visual complexity score between any pair of images,
and if needed, convert them into the absolute scores.
compelling extension to the current work. An important example of such applications
would be crowdsourcing a vision task. The image visual complexity score can be
beneficial in two different ways in crowdsourcing tasks:
1. Allocating the resources optimally: In general, images that are more vi-
sually complex take longer time to be processed by the crowd workers. Fur-
thermore, visual complexity can make a task more difficult and thus a higher
number of crowd workers may be needed. Analysis of visual complexity can be
added as a pre-processing step to allocate sufficient amounts of resources (e.g.,
budget, time) for a given task.
2. Determining the performance of the crowd workers: For the more vi-
sually complex images, wide disagreements among the crowd workers can be
expected and thus the results are more prone to noise. For example, for a
segmentation task, it is more challenging to distinguish and find the borders
of different items in an image. Visual complexity analysis can be added as a
post processing step that allows the researchers to identify the images that may
require further attention.
Our visual complexity analysis can also leverage research in other areas of com-
puter vision, such as segmentation, object recognition and detection, visual search,
image captioning, and visual question answering. Furthermore, our proposed dataset
facilitates research in the field of psychophysics and cognitive science to find the un-
derlying factors in the stimulus that affect the perception of visual complexity in
humans. Lastly, our proposed method enables artists, Web and graphic designers,
interior designers, and advertisers to estimate the level of visual complexity of their
work in order to maximize the quality of their design and the impact of their work
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