images, using a 3D model of the eye and head and a physically correct rendering technique. By using rendering, we have full control over the parameters of the scene such as the gaze vector or camera position, which allows the calculation of ground truth data, while creating a realistic input for a video

Abstract — Measuring the quality of old document segmentation is not an easy task. We propose a strategy for the segmentation evaluation of Gutenberg’s Bible page contents using synthetic ground-truth images. Experiments performed with synthetic ground-truth pages have shown a new assessment

This paper presents a database containing ‘ground truth ’ segmentations produced by humans for images of a wide variety of natural scenes. We define an error measure which quantifies the consistency between segmentations of differing granularities and find that different human segmentations

We seek to build a large collection of images with ground truth labels to be used for object detection and recognition research. Such data is useful for supervised learning and quantitative evaluation. To achieve this, we developed a web-based tool that allows easy image annotation and instant

these features in an optimal way, we train a classifier using human labeled images as ground truth. The output of this classifier provides the posterior probability of a boundary at each image location and orientation. We present precision-recall curves showing that the resulting detector significantly

the influence of background correlations on recognition performance via extensive tests on the PASCAL database, for which ground-truth object localization information is available. Our experiments demonstrate that image representations based on distributions of local features are surprisingly effective

Abstract This paper presents a quantitative comparison of several multi-view stereo reconstruction algorithms. Until now, the lack of suitable calibrated multi-view image datasets with known ground truth (3D shape models) has prevented such direct comparisons. In this paper, we first survey multi

Figure 1: Left. Ground truth image. Center: backward irradiance estimation and the skeleton, red points are used for comparison. Right: several screenshot showing direction, a different point of view in the scene and an exemple with multiple lights

This paper proposes a data driven image segmentation algorithm, based on decomposing the target output (ground truth). Classical pixel labeling methods utilize machine learning algorithms that in-duce a mapping from pixel features to individual pixel labels. In contrast we propose to first extract

An algorithm is presented that automatically generates groundtruthed video from a symbolic description for an object and a specification for the movement of a handheld video camera around that object. This provides a method to generate large amounts of training and test data for the development