We address the problem of inferring the pose of an RGB-D camera relative to a known 3D scene, given only a single acquired image. Our approach employs a regression forest that is capable of inferring an estimate of each pixel's correspondence to 3D points in…
Conventional decision forest based methods for image labelling tasks like object segmentation make predictions for each variable (pixel) independently [3, 5, 8]. This prevents them from enforcing dependencies between variables and translates into locally inconsistent pixel labellings. Random field models, instead, encourage spatial consistency of…
In this paper, we propose an image-based detection method to identify web-based scareware attacks that is robust to evasion techniques. We evaluate the method on a large-scale data set that resulted in an equal error rate of 0.018%. Conceptually, false positives may occur when a…
Recommending phrases from web pages for advertisers to bid on against search engine queries is an important research problem with direct commercial impact. Most approaches have found it infeasible to determine the relevance of all possible queries to a given ad landing page and have…
Image segmentation is the process of partitioning an image into segments or subsets of pixels for purposes of further analysis, such as separating the interesting objects in the foreground from the un-interesting objects in the background. In many image processing applications, the process requires a…
This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision, MCV 2010, held in Beijing, China, in September 2010 as a satellite event of the 13th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2010. The…
Classification forests, as discussed in Chapter 2, have a series of advantageous properties which make them a very good choice for applications in medical image analysis. Classification forests are inherent multi-label classifiers (which allows for the simultaneous segmentation of different tissues), have good generalization properties…
In this paper we introduce Context-Sensitive Decision Forests - A new perspective to exploit contextual information in the popular decision forest framework for the object detection problem. They are tree-structured classifiers with the ability to access intermediate prediction (here: classification and regression) information during training…
Entity linking disambiguates a mention of an entity in text to a Knowledge Base (KB). Most previous studies disambiguate a mention of a name (e.g.“AZ”) based on the distribution knowledge learned from labeled instances, which are related to other names (e.g.“Hoffman”,“Chad Johnson”, etc.). The gaps…
Entity linking disambiguates a mention of an entity in text to a Knowledge Base (KB). Most previous studies disambiguate a mention of a name (e.g.“AZ”) based on the distribution knowledge learned from labeled instances, which are related to other names (e.g.“Hoffman”,“Chad Johnson”, etc.). The gaps…
Conformal prediction is a relatively recent approach to classification that offers a theoretical framework for generating predictions with precise levels of confidence. For each new object encountered, a conformal predictor outputs a set of class labels that contains the true label with probability at least…
In many segmentation scenarios, labeled images contain rich structural information about spatial arrangement and shapes of the objects. Integrating this rich information into supervised learning techniques is promising as it generates models which go beyond learning class association, only. This paper proposes a new supervised…
