Results 1 - 10
of
277
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
- in Proc. 8th Int’l Conf. Computer Vision
, 2001
"... 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 of the s ..."
Abstract
-
Cited by 944 (15 self)
- Add to MetaCart
(Show Context)
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 of the same image are highly consistent. Use of this dataset is demonstrated in two applications: (1) evaluating the performance of segmentation algorithms and (2) measuring probability distributions associated with Gestalt grouping factors as well as statistics of image region properties. 1.
Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response
- IEEE Transactions on Medical Imaging
, 2000
"... Abstract—We describe an automated method to locate and outline blood vessels in images of the ocular fundus. Such a tool should prove useful to eye care specialists for purposes of patient screening, treatment evaluation, and clinical study. Our method differs from previously known methods in that i ..."
Abstract
-
Cited by 185 (2 self)
- Add to MetaCart
(Show Context)
Abstract—We describe an automated method to locate and outline blood vessels in images of the ocular fundus. Such a tool should prove useful to eye care specialists for purposes of patient screening, treatment evaluation, and clinical study. Our method differs from previously known methods in that it uses local and global vessel features cooperatively to segment the vessel network. We evaluate our method using hand-labeled ground truth segmentations of 20 images. A plot of the operating characteristic shows that our method reduces false positives by as much as 15 times over basic thresholding of a matched filter response (MFR), at up to a 75 % true positive rate. For a baseline, we also compared the ground truth against a second hand-labeling, yielding a 90% true positive and a 4 % false positive detection rate, on average. These numbers suggest there is still room for a 15 % true positive rate improvement, with the same false positive rate, over our method. We are making all our images and hand labelings publicly available for interested researchers to use in evaluating related methods. Index Terms—Adaptive thresholding, blood vessel segmentation, matched filter, retinal imaging. I.
Robust parameter estimation in computer vision
- SIAM Reviews
, 1999
"... Abstract. Estimation techniques in computer vision applications must estimate accurate model parameters despite small-scale noise in the data, occasional large-scale measurement errors (outliers), and measurements from multiple populations in the same data set. Increasingly, robust estimation techni ..."
Abstract
-
Cited by 162 (10 self)
- Add to MetaCart
(Show Context)
Abstract. Estimation techniques in computer vision applications must estimate accurate model parameters despite small-scale noise in the data, occasional large-scale measurement errors (outliers), and measurements from multiple populations in the same data set. Increasingly, robust estimation techniques, some borrowed from the statistics literature and others described in the computer vision literature, have been used in solving these parameter estimation problems. Ideally, these techniques should effectively ignore the outliers and measurements from other populations, treating them as outliers, when estimating the parameters of a single population. Two frequently used techniques are least-median of
A Robust Competitive Clustering Algorithm with Applications in Computer Vision
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1998
"... This paper addresses three major issues associated with conventional partitional clustering, namely, sensitivity to initialization, difficulty in determining the number of clusters, and sensitivity to noise and outliers. The proposed Robust Competitive Agglomeration (RCA) algorithm starts with a lar ..."
Abstract
-
Cited by 112 (4 self)
- Add to MetaCart
(Show Context)
This paper addresses three major issues associated with conventional partitional clustering, namely, sensitivity to initialization, difficulty in determining the number of clusters, and sensitivity to noise and outliers. The proposed Robust Competitive Agglomeration (RCA) algorithm starts with a large number of clusters to reduce the sensitivity to initialization, and determines the actual number of clusters by a process of competitive agglomeration. Noise immunity is achieved by incorporating concepts from robust statistics into the algorithm. RCA assigns two different sets of weights for each data point: the first set of constrained weights represents degrees of sharing, and is used to create a competitive environment and to generate a fuzzy partition of the data set. The second set corresponds to robust weights, and is used to obtain robust estimates of the cluster prototypes. By choosing an appropriate distance measure in the objective function, RCA can be used to find a...
3D building model reconstruction from point clouds and ground plans
- Int. Arch. of Photogrammetry and Remote Sensing
, 2001
"... Airborne laser altimetry has become a very popular technique for the acquisition of digital elevation models. The high point density that can be achieved with this technique enables applications of laser data for many other purposes. This paper deals with the construction of 3D models of the urban e ..."
Abstract
-
Cited by 99 (4 self)
- Add to MetaCart
(Show Context)
Airborne laser altimetry has become a very popular technique for the acquisition of digital elevation models. The high point density that can be achieved with this technique enables applications of laser data for many other purposes. This paper deals with the construction of 3D models of the urban environment. A three-dimensional version of the well-known Hough transform is used for the extraction of planar faces from the irregularly distributed point clouds. To support the 3D reconstruction usage is made of available ground plans of the buildings. Two different strategies are explored to reconstruct building models from the detected planar faces and segmented ground plans. Whereas the first strategy tries to detect intersection lines and height jump edges, the second one assumes that all detected planar faces should model some part of the building. Experiments show that the second strategy is able to reconstruct more buildings and more details of this buildings, but that it sometimes leads to additional parts of the model that do not exist. When restricted to buildings with rectangular segments of the ground plan, the second strategy was able to reconstruct 83 buildings out of a dataset with 94 buildings. 1
3-D Model Construction Using Range and Image Data
- In CVPR
, 2000
"... This paper deals with the automated creation of geometric and photometric correct 3-D models of the world. Those models can be used for virtual reality, tele-- presence, digital cinematography and urban planning applications. The combination of range (dense depth estimates) and image sensing (color ..."
Abstract
-
Cited by 78 (4 self)
- Add to MetaCart
(Show Context)
This paper deals with the automated creation of geometric and photometric correct 3-D models of the world. Those models can be used for virtual reality, tele-- presence, digital cinematography and urban planning applications. The combination of range (dense depth estimates) and image sensing (color information) provides data--sets which allow us to create geometrically correct, photorealistic models of high quality. The 3-D models are first built from range data using a volumetric set intersection method previously developed by us. Photometry can be mapped onto these models by registering features from both the 3--D and 2--D data sets. Range data segmentation algorithms have been developed to identify planar regions, determine linear features from planar intersections that can serve as features for registration with 2-D imagery lines, and reduce the overall complexity of the models. Results are shown for building models of large buildings on our campus using real data acquired from m...
Image Segmentation Evaluation: A Survey of Unsupervised Methods
, 2008
"... Image segmentation is an important processing step in many image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm produces more accurate seg ..."
Abstract
-
Cited by 78 (0 self)
- Add to MetaCart
Image segmentation is an important processing step in many image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm produces more accurate segmentations than another, whether it be for a particular image or set of images, or more generally, for a whole class of images. To date, the most common method for evaluating the effectiveness of a segmentation method is subjective evaluation, in which a human visually compares the image segmentation results for separate segmentation algorithms, which is a tedious process and inherently limits the depth of evaluation to a relatively small number of segmentation comparisons over a predetermined set of images. Another common evaluation alternative is supervised evaluation, in which a segmented image is compared against a manually-segmented or pre-processed reference image. Evaluation methods that require user assistance, such as subjective evaluation and supervised evaluation, are infeasible in many vision applications, so unsupervised methods are necessary. Unsupervised evaluation enables the objective comparison of both different segmentation methods and different parameterizations of a single method, without requiring human visual comparisons or comparison with a manually-segmented or pre-processed reference image. Additionally, unsupervised methods generate results for individual images and images whose characteristics
Activity recognition of assembly tasks using body-worn microphones and accelerometers
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2006
"... In order to provide relevant information to mobile users, such as workers engaging in the manual tasks of maintenance and assembly, a wearable computer requires information about the user’s specific activities. This work focuses on the recognition of activities that are characterized by a hand motio ..."
Abstract
-
Cited by 72 (13 self)
- Add to MetaCart
(Show Context)
In order to provide relevant information to mobile users, such as workers engaging in the manual tasks of maintenance and assembly, a wearable computer requires information about the user’s specific activities. This work focuses on the recognition of activities that are characterized by a hand motion and an accompanying sound. Suitable activities can be found in assembly and maintenance work. Here, we provide an initial exploration into the problem domain of continuous activity recognition using on-body sensing. We use a mock “wood workshop ” assembly task to ground our investigation. We describe a method for the continuous recognition of activities (sawing, ham-mering, filing, drilling, grinding, sanding, opening a drawer, tightening a vise, and turning a screwdriver) using microphones and 3-axis accelerometers mounted at two positions on the user’s arms. Potentially “interesting ” activities are segmented from continuous streams of data using an analysis of the sound intensity detected at the two different locations. Activity classification is then performed on these detected segments using linear discriminant analysis (LDA) on the sound channel and hidden Markov
