3 research outputs found
Unsupervised Classification of Intrusive Igneous Rock Thin Section Images using Edge Detection and Colour Analysis
Classification of rocks is one of the fundamental tasks in a geological
study. The process requires a human expert to examine sampled thin section
images under a microscope. In this study, we propose a method that uses
microscope automation, digital image acquisition, edge detection and colour
analysis (histogram). We collected 60 digital images from 20 standard thin
sections using a digital camera mounted on a conventional microscope. Each
image is partitioned into a finite number of cells that form a grid structure.
Edge and colour profile of pixels inside each cell determine its
classification. The individual cells then determine the thin section image
classification via a majority voting scheme. Our method yielded successful
results as high as 90% to 100% precision.Comment: To appear in 2017 IEEE International Conference On Signal and Image
Processing Application
Unsupervised Segmentation of Action Segments in Egocentric Videos using Gaze
Unsupervised segmentation of action segments in egocentric videos is a
desirable feature in tasks such as activity recognition and content-based video
retrieval. Reducing the search space into a finite set of action segments
facilitates a faster and less noisy matching. However, there exist a
substantial gap in machine understanding of natural temporal cuts during a
continuous human activity. This work reports on a novel gaze-based approach for
segmenting action segments in videos captured using an egocentric camera. Gaze
is used to locate the region-of-interest inside a frame. By tracking two simple
motion-based parameters inside successive regions-of-interest, we discover a
finite set of temporal cuts. We present several results using combinations (of
the two parameters) on a dataset, i.e., BRISGAZE-ACTIONS. The dataset contains
egocentric videos depicting several daily-living activities. The quality of the
temporal cuts is further improved by implementing two entropy measures.Comment: To appear in 2017 IEEE International Conference On Signal and Image
Processing Application