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Research into computer vision techniques has far outpaced the development of interfaces (such as APIs) to support the techniques' accessibility, especially to developers who are not experts in the field. We present a new description-based interface designed to be mainstream-developer-friendly while retaining sufficient power and flexibility to solve a wide variety of computer vision problems. The...
A quality impairment assessment along with a quality score would enable Automatic Fingerprint Identification Systems (AFIS) to make appropriate decisions to a) reject the fingerprint and recapture another sample, b) use other fingers or biometric features for recognition, c) use image enhancement techniques. Our approach provides a quality score in addition to a quality impairment assessment into...
Reliably measuring the similarity of two shapes or images (instances) is an important problem for various computer vision applications such as classification, recognition, and retrieval. While pairwise measures take advantage of the geometric differences between two instances to quantify their similarity, recent advances use relationships among the population of instances when quantifying pairwise...
This paper addresses the problem of interactive image segmentation. We propose an extension of the GrowCut framework which follows Cellular Automaton theory and is comparable to a label propagation algorithm. Therefore, user labels are propagated according to Cellular Automaton until convergency. A common problem of GrowCut is the time consuming user initialization which requires distributed seeds...
This paper presents a method with which 3D images of tire track and footprint impressions at crime scenes can be captured with high fidelity, while capturing high resolution 2D color texture images simultaneously. The resulting device is portable, easy to use, is non-destructive of the evidence, and saves time at crime scenes. The same technique can also be used in the laboratory to create 3D depth...
We study the use of domain adaptation and transfer learning techniques as part of a framework for adaptive object detection. Unlike recent applications of domain adaptation work in computer vision, which generally focus on image classification, we explore the problem of extreme class imbalance present when performing domain adaptation for object detection. The main difficulty caused by this imbalance...
Object detection is an important and challenging problem in the field of computer vision. Classical object detection approaches such as background subtraction and saliency detection do not require manual collection of training samples, but can be easily affected by noise factors, such as luminance changes and cluttered background. On the other hand, supervised learning based approaches such as Boosting...
We propose to use action, scene and object concepts as semantic attributes for classification of video events in InTheWild content, such as YouTube videos. We model events using a variety of complementary semantic attribute features developed in a semantic concept space. Our contribution is to systematically demonstrate the advantages of this concept-based event representation (CBER) in applications...
Pose estimation of mobile devices is useful for a wide variety of applications, including augmented reality and geo-tagging. Even though most of today's cell phones are equipped with sensors such as GPS, accelerometers, and gyros, the pose estimated via these is often inaccurate, particularly in urban environments. In this paper, we describe an image based localization algorithm for estimating the...
We present OrcaM, a device for exploring new methods in the field of simultaneous acquisition of geometry, color and reflectance properties. OrcaM employs a full-spherical construction, a movable projector-camera unit, 633 individually controllable LEDs and a height-adjustable turntable with a glass carrier. In contrast to state of the art hardware layouts, this design allows data acquisition from...
Depth sensors have become increasingly popular in interactive computer vision applications. Currently, most of these applications are limited to indoor use. Popular IR-based depth sensors cannot provide depth data when exposed to sunlight. In these cases, one can still obtain depth information using a stereo camera set up or a special outdoor Time-of-Flight camera, at the cost of a reduced quality...
Wearable devices with gaze tracking can assist users in many daily-life tasks. When used for extended periods of time, it is desirable that such devices do not employ active illumination for safety reasons and to minimize interference from other light sources such as the sun. Most non active-illumination methods for gaze tracking attempt to locate the iris contour by fitting an ellipse. Although the...
Images for 3D mapping are always recorded in such a way that relevant scene parts are seen from multiple viewpoints, so as to facilitate camera orientation and 3D point triangulation. Beyond geometric reconstruction, automatic mapping also requires the semantic interpretation of the image content, and for that task the redundancy provided by overlapping images has been exploited much less. Here we...
We evaluate the performance of a widely used tracking-by-detection and data association multi-target tracking pipeline applied to an activity-rich video dataset. In contrast to traditional work on multi-target pedestrian tracking where people are largely assumed to be upright, we use an activity-rich dataset that includes a wide range of body poses derived from actions such as picking up an object,...
In this paper, we present an algorithm that is to estimate the position of a hand-held camera with respect to terrestrial LiDAR data. Our input is a set of 3D range scans with intensities and one or a set of 2D uncalibrated camera images of the scene. The algorithm that automatically registers range scans and 2D images is composed of following steps. In the first step, we project the terrestrial LiDAR...
Recovering the epipolar geometry of a stereo image pair is important for many computer and robotic vision systems, for performing motion recovering, 3D reconstruction and, more recently, image retrieval from large databases. Most state-of-the-art methods for estimating the fundamental matrix rely solely in putative image correspondences, and, therefore, heavily depend on the capability of the low-level...
Visual tracking is a critical task in surveillance and activity analysis. One of the major issues in visual target tracking is variations in illumination. In this paper, we propose a novel algorithm based on discrete cosine transform (DCT) to handle illumination variations, since illumination variations are mainly reflected in the low-frequency band. For instance, low illumination in a frame leads...
We present a novel method for matching ground-based query images to a georeferenced LIDAR 3D dataset acquired from an airborne platform in urban environments. We are addressing two main technical challenges: (i) different modalities between the query and the reference data (electro-optical vs. LIDAR) that impose unique challenges to the matching problem; (ii) very different viewing directions from...
The pollen grains of different plant taxa exhibit various shapes and sizes. This structural diversity has made the identification and classification of pollen grains an important tool in many fields. Despite the myriad of applications, the classification of pollen grains is still a tedious and time-consuming process that must be performed by highly skilled specialists. In this paper, we propose an...
We present a novel approach to automatically create efficient and accurate object detectors tailored to work well on specific video surveillance cameras (specific-domain detectors), using samples acquired with the help of a more expensive, general-domain detector (trained using images from multiple cameras). Our method requires no manual labels from the target domain. We automatically collect training...
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