5 research outputs found
Causality-Based Feature Importance Quantifying Methods: PN-FI, PS-FI and PNS-FI
In the current ML field models are getting larger and more complex, and data
used for model training are also getting larger in quantity and higher in
dimensions. Therefore, in order to train better models, and save training time
and computational resources, a good Feature Selection (FS) method in the
preprocessing stage is necessary. Feature importance (FI) is of great
importance since it is the basis of feature selection. Therefore, this paper
creatively introduces the calculation of PN (the probability of Necessity), PN
(the probability of Sufficiency), and PNS (the probability of Necessity and
Sufficiency) of Causality into quantifying feature importance and creates 3 new
FI measuring methods, PN-FI, which means how much importance a feature has in
image recognition tasks, PS-FI that means how much importance a feature has in
image generating tasks, and PNS-FI which measures both. The main body of this
paper is three RCTs, with whose results we show how PS-FI, PN-FI, and PNS-FI of
3 features, dog nose, dog eyes, and dog mouth are calculated. The experiments
show that firstly, FI values are intervals with tight upper and lower bounds.
Secondly, the feature dog eyes has the most importance while the other two have
almost the same. Thirdly, the bounds of PNS and PN are tighter than the bounds
of PS.Comment: 7 page
Estudi comparatiu de la publicació cientÃfica de la UPC i l’ETSETB vs. altres universitats (2006-2016)
L'informe es centra en la publicació cientÃfica especialitzada en l'à mbit temà tic propi de l'ETSETB: l'enginyeria de telecomunicacions i l'electrònica. Es comparen indicadors bibliomètrics de la UPC i l'ETSETB amb els d'altres universitats nacionals, europees i internacionals amb activitat de recerca notable en l'à rea de les telecomunicacions i l'electrònica.Postprint (published version
Unsupervised video segmentation using temporal coherence of motion
Includes bibliographical references.2015 Fall.Spatio-temporal video segmentation groups pixels with the goal of representing moving objects in scenes. It is a difficult task for many reasons: parts of an object may look very different from each other, while parts of different objects may look similar and/or overlap. Of particular importance to this dissertation, parts of non-rigid objects such as animals may move in different directions at the same time. While appearance models are good for segmenting visually distinct objects and traditional motion models are good for segmenting rigid objects, there is a need for a new technique to segment objects that move non-rigidly. This dissertation presents a new unsupervised motion-based video segmentation approach. It segments non-rigid objects based on motion temporal coherence (i.e. the correlations of when points move), instead of motion magnitude and direction as in previous approaches. The hypothesis is that although non-rigid objects can move their parts in different directions, their parts tend to move at the same time. In the experiments, the proposed approach achieves better results than related state-of-the-art approaches on a video of zebras in the wild, and on 41 videos from the VSB100 dataset
Satellite Articulation Sensing using Computer Vision
Autonomous on-orbit satellite servicing benefits from an inspector satellite that can gain as much information as possible about the primary satellite. This includes performance of articulated objects such as solar arrays, antennas, and sensors. A method for building an articulated model from monocular imagery using tracked feature points and the known relative inspection route is developed. Two methods are also developed for tracking the articulation of a satellite in real-time given an articulated model using both tracked feature points and image silhouettes. Performance is evaluated for multiple inspection routes and the effect of inspection route noise is assessed. Additionally, a satellite model is built and used to collect stop-motion images simulating articulated motion over an inspection route under simulated space illumination. The images are used in the silhouette articulation tracking method and successful tracking is demonstrated qualitatively. Finally, a human pose tracking algorithm is modified for tracking the satellite articulation demonstrating the applicability of human tracking methods to satellite articulation tracking methods when an articulated model is available