Robust Speaker segmentation and clustering for Meetings (PhD Thesis Proposal) Thesis Advisor: Prof. Javier Hernando Peric´as TALP Research Center Department of Signal Theory and Communications Universitat Polit`ecnica de Catalunya e-mail: javier@gps
Discovering and segmenting objects in videos is a challenging task due to large variations of objects in appearances, deformed shapes and cluttered backgrounds. In this paper, we propose to segment objects and understand their visual semantics from
Cell image segmentation is a necessary first step of many automated biomedical imageprocessing procedures. There certainly has been much research in the area. To this, a new method has been added, which automatically extracts cells from microscopic
In this paper, an unsupervised image segmentation technique is presented, which combines pyramidal image segmentation with the fuzzy c-means clustering algorithm. Each layer of the pyramid is split into a number of regions by a root labeling techniq
In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features.We pro- pose a new geocentric embedding for depth images that encodes height above ground and angle with gravity for each pixel
Convolutional networks are the de-facto standard for analyzing spatio-temporal data such as images, videos, and 3D shapes. Whilst some of this data is naturally dense (e.g., photos), many other data sources are inherently sparse. Examples include 3D
This paper addresses the problem of video object segmentation, where the initial object mask is given in the first frame of an input video. We propose a novel spatiotemporal Markov Random Field (MRF) model defined over pixels to handle this problem.
