173 research outputs found
Towards Explainability in Monocular Depth Estimation
The estimation of depth in two-dimensional images has long been a challenging
and extensively studied subject in computer vision. Recently, significant
progress has been made with the emergence of Deep Learning-based approaches,
which have proven highly successful. This paper focuses on the explainability
in monocular depth estimation methods, in terms of how humans perceive depth.
This preliminary study emphasizes on one of the most significant visual cues,
the relative size, which is prominent in almost all viewed images. We designed
a specific experiment to mimic the experiments in humans and have tested
state-of-the-art methods to indirectly assess the explainability in the context
defined. In addition, we observed that measuring the accuracy required further
attention and a particular approach is proposed to this end. The results show
that a mean accuracy of around 77% across methods is achieved, with some of the
methods performing markedly better, thus, indirectly revealing their
corresponding potential to uncover monocular depth cues, like relative size
VeSTIS: A Versatile Semi- Automatic Taxon Identification System from Digital Images
In this work we present a flexible Open Source software platform
for training classifiers capable of identifying the taxonomy of a specimen from
digital images. We demonstrate the performance of our system in a pilot
study, building a feed-forward artificial neural network to effectively classify
five different species of marine annelid worms of the class Polychaeta. We
also discuss on the extensibility of the system, and its potential uses either as
a research tool or in assisting routine taxon identification procedures
Fast Color Quantization Using Weighted Sort-Means Clustering
Color quantization is an important operation with numerous applications in
graphics and image processing. Most quantization methods are essentially based
on data clustering algorithms. However, despite its popularity as a general
purpose clustering algorithm, k-means has not received much respect in the
color quantization literature because of its high computational requirements
and sensitivity to initialization. In this paper, a fast color quantization
method based on k-means is presented. The method involves several modifications
to the conventional (batch) k-means algorithm including data reduction, sample
weighting, and the use of triangle inequality to speed up the nearest neighbor
search. Experiments on a diverse set of images demonstrate that, with the
proposed modifications, k-means becomes very competitive with state-of-the-art
color quantization methods in terms of both effectiveness and efficiency.Comment: 30 pages, 2 figures, 4 table
Improving the Performance of K-Means for Color Quantization
Color quantization is an important operation with many applications in
graphics and image processing. Most quantization methods are essentially based
on data clustering algorithms. However, despite its popularity as a general
purpose clustering algorithm, k-means has not received much respect in the
color quantization literature because of its high computational requirements
and sensitivity to initialization. In this paper, we investigate the
performance of k-means as a color quantizer. We implement fast and exact
variants of k-means with several initialization schemes and then compare the
resulting quantizers to some of the most popular quantizers in the literature.
Experiments on a diverse set of images demonstrate that an efficient
implementation of k-means with an appropriate initialization strategy can in
fact serve as a very effective color quantizer.Comment: 26 pages, 4 figures, 13 table
Artistic minimal rendering with lines and blocks
Many non-photorealistic rendering techniques exist to produce artistic effects from given images. Inspired by various artists, interesting effects can be produced by using a minimal rendering, where the minimum refers to the number of tones as well as the number and complexity of the primitives used for rendering. Our method is based on various computer vision techniques, and uses a combination of refined lines and blocks (potentially simplified), as well as a small number of tones, to produce abstracted artistic rendering with sufficient elements from the original image. We also considered a variety of methods to produce different artistic styles, such as colour and 2-tone drawings, and use semantic information to improve renderings for faces. By changing some intuitive parameters a wide range of visually pleasing results can be produced. Our method is fully automatic. We demonstrate the effectiveness of our method with extensive experiments and a user study
Event-condition-action rule languages over semistructured data
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