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On matching latent fingerprints

On matching latent fingerprints

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Anil K. Jain Jianjiang Feng Abhishek Nagar Karthik Nandakumar Department of Computer Science and Engineering Michigan State University East Lansing MI USA Institute for Infocomm Research Singapore

Latent fingerprint identification is of critical importance to law enforcement agencies in forensics application. While tremendous progress has been made in the field of automatic fingerprint matching, latent fingerprint matching continues to be a difficult problem because the challenges involved in latent print matching are quite different from plain or rolled fingerprint matching. Poor quality of friction ridge impressions, small finger area and large non-linear distortion are some of the main difficulties in latent fingerprint matching. We propose a system for matching latent images to rolled fingerprints that takes into account the specific characteristics of the latent matching problem. In addition to minutiae, additional features like orientation field and quality map are also used in our system. Experimental results on the NIST SD27 latent database indicate that the introduction of orientation field and quality map to minutiae-based matching leads to good recognition performance despite the inherently difficult nature of the problem. We achieve the rank-20 accuracy of 93.4% in retrieving 258 latents from a background database of 2,258 rolled fingerprints.

关键词： Fingerprint recognition Law enforcement Forensics Friction Fingers Nonlinear distortion NIST Image databases Spatial databases Information retrieval

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Open boundary capable edge grouping with feature maps

Open boundary capable edge grouping with feature maps

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Joachim S. Stahl Kenton Oliver Song Wang Department of Computer Science and Engineering University of South Carolina Columbia SC USA

Edge grouping methods aim at detecting the complete boundaries of salient structures in noisy images. In this paper, we develop a new edge grouping method that exhibits several useful properties. First, it combines both boundary and region information by defining a unified grouping cost. The region information of the desirable structures is included as a binary feature map that is of the same size as the input image. Second, it finds the globally optimal solution of this grouping cost. We extend a prior graph-based edge grouping algorithm to achieve this goal. Third, it can detect both closed boundaries, where the structure of interest lies completely within the image perimeter, and open boundaries, where the structure of interest is cropped by the image perimeter. Given this capability for detecting both open and closed boundaries, the proposed method can be extended to segment an image into disjoint regions in a hierarchical way. Experimental results on real images are reported, with a comparison against a prior edge grouping method that can only detect closed boundaries.

关键词： Image edge detection Image segmentation Cost function Robustness computer science Degradation Humans Object recognition Image retrieval Content based retrieval

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Reliable detection of core and delta in fingerprints by using singular candidate method

Reliable detection of core and delta in fingerprints by usin...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Tomohiko Ohtsuka Daisuke Watanabe Daisuke Tomizawa Yuta Hasegawa Hiroyuki Aoki Department of Electronic Engineering Tokyo National College of Technology Hachioji Tokyo Japan

The singular points of fingerprints, namely, core and delta, are important referential points for the classification of fingerprints. Several conventional approaches such as the Poincare index method have been proposed; however, these approaches cannot achieve the reliable detection of poor-quality fingerprints. In this paper, we propose a new core and delta detection method by singular candidate analysis using an extended relational graph. In order to use both the local and global features of the ridge direction patterns and to realize a method with high tolerance to local image noise, singular candidate analysis is adopted in the detection process; this analysis involves the extraction of locations in which the probability of the existence of a singular point is high. The experimental results show that the success rate of this approach is higher than that of the Poincare index method by 10% for singularity detection using the fingerprint image databases FVC2000 and FVC2002. These databases contain several poor quality images, even though the average computation time is 15%-30% greater than the Poincare index method.

关键词： Fingerprint recognition Spatial databases Image databases Image analysis Indexes pattern analysis Image matching Educational institutions Personal communication networks Automatic control

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CUDA cuts: Fast graph cuts on the GPU

CUDA cuts: Fast graph cuts on the GPU

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Vibhav Vineet P. J. Narayanan Centre for Visual Information Technology International Institute of Information Technology Hyderabad India

Graph cuts has become a powerful and popular optimization tool for energies defined over an MRF and have found applications in image segmentation, stereo vision, image restoration, etc. The maxflow/mincut algorithm to compute graph-cuts is computationally heavy. The best-reported implementation of graph cuts takes over 100 milliseconds even on images of size 640times480 and cannot be used for real-time applications or when iterated applications are needed. The commodity Graphics Processor Unit (GPU) has emerged as an economical and fast computation co-processor recently. In this paper, we present an implementation of the push-relabel algorithm for graph cuts on the GPU. We can perform over 60 graph cuts per second on 1024times1024 images and over 150 graph cuts per second on 640times480 images on an Nvidia 8800 GTX. The time for each complete graph-cut is about 1 millisecond when only a few weights change from the previous graph, as on dynamic graphs resulting from videos. The CUDA code with a well-defined interface can be downloaded for anyonepsilas use.

关键词： computer vision Videos Information technology Image segmentation Stereo vision Coprocessors Application software computer graphics Image restoration Power generation economics

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Real-time human detection in urban scenes: Local descriptors and classifiers selection with AdaBoost-like algorithms

Real-time human detection in urban scenes: Local descriptors...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： J. Begard N. Allezard P. Sayd Embedded Vision Systems CEA/List Gif-sur-Yvette France

This paper deals with the study of various implementations of the AdaBoost algorithm in order to address the issue of real-time pedestrian detection in images. We use gradient-based local descriptors and we combine them to form strong classifiers organized in a cascaded detector. We compare the original AdaBoost algorithm with two other boosting algorithms we developed. One optimizes the use of each selected descriptor to minimize the operations done in the image (method 1), leading to an acceleration of the detection process without any loss in detection performances. The second algorithm (method 2) improves the selection of the descriptors by associating to each of them a more powerful weak-learner - a decision tree built from the components of the whole descriptor - and by evaluating them locally. We compare the results of these three learning algorithms on a reference database of color images and we then introduce our preliminary results on the adaptation of this detector on infrared vision. Our methods give better detection rates and faster processing than the original boosting algorithm and also provide interesting results for further studies.

关键词： Humans Layout Boosting Infrared detectors Optimization methods Acceleration Decision trees Image databases Color Infrared imaging

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3-D gesture-based scene navigation in medical imaging applications using Time-of-Flight cameras

3-D gesture-based scene navigation in medical imaging applic...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Stefan Soutschek Jochen Penne Joachim Hornegger Johannes Kornhuber Department of Computer Science Germany Department of Psychiatry and Psychotherapy University of Erlangen Nuremberg Erlangen Germany

For a lot of applications, and particularly for medical intra-operative applications, the exploration of and navigation through 3-D image data provided by sensors like ToF (time-of-flight) cameras, MUSTOF (multisensor-time-of-flight) endoscopes or CT (computed tomography) [8], requires a user-interface which avoids physical interaction with an input device. Thus, we process a touchless user-interface based on gestures classified by the data provided by a ToF camera. Reasonable and necessary user interactions are described. For those interactions a suitable set of gestures is introduced. A user-interface is then proposed, which interprets the current gesture and performs the assigned functionality. For evaluating the quality of the developed user-interface we considered the aspects of classification rate, real-time applicability, usability, intuitiveness and training time. The results of our evaluation show that our system, which provides a classification rate of 94.3% at a framerate of 11 frames per second, satisfactorily addresses all these quality requirements.

关键词： Layout Navigation Biomedical imaging Cameras Application software Surges Computed tomography Speech recognition Data gloves Biomedical optical imaging

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Improving the selection and detection of visual landmarks through object tracking

Improving the selection and detection of visual landmarks th...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： P. Espinace A. Soto Department of Computer Science Pontificia Universidad Catolica de Chile Santiago Chile

The unsupervised selection and posterior recognition of visual landmarks is a highly valuable perceptual capability for a mobile robot. Recently, we proposed a system that aims to achieve this capability by combining a bottom-up data driven approach with top-down feedback provided by high level semantic representations. The bottom-up approach is based on three main mechanisms: visual attention, area segmentation, and landmark characterization. The top-down feedback is based on two information sources: i) An estimation of the robot position that reduces the searching scope for potential matches with previously selected landmarks, ii) A set of weights that, according to the results of previous recognitions, controls the influence of different segmentation algorithms in the recognition of each landmark. In this paper we explore the benefits of extending our previous work by including a visual tracking step for each of the selected landmarks. Our intuition is that the inclusion of a tracking step can help to improve the model of each landmark by associating and selecting information from its most significant views. Furthermore, it can also help to avoid problems related to the selection of spurious landmarks. Our results confirm these intuitions by showing that the inclusion of the tracking step produces a significant increase in the recall rate for landmark recognition.

关键词： Object detection Mobile robots Navigation Feedback Robotics and automation Robustness computer science Indoor environments Global Positioning System Buildings

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Fast scale invariant feature detection and matching on programmable graphics hardware

Fast scale invariant feature detection and matching on progr...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Nico Cornelis Luc Van Gool Katholieke Universiteit Leuven Leuven Belgium K. U. Leuven and ETH Zurich Zurich Switzerland

Ever since the introduction of freely programmable hardware components into modern graphics hardware, graphics processing units (GPUs) have become increasingly popular for general purpose computations. Especially when applied to computer vision algorithms where a Single set of Instructions has to be executed on Multiple Data (SIMD), GPU-based algorithms can provide a major increase in processing speed compared to their CPU counterparts. This paper presents methods that take full advantage of modern graphics card hardware for real-time scale invariant feature detection and matching. The focus lies on the extraction of feature locations and the generation of feature descriptors from natural images. The generation of these feature-vectors is based on the Speeded Up Robust Features (SURF) method [1] due to its high stability against rotation, scale and changes in lighting condition of the processed images. With the presented methods feature detection and matching can be performed at framerates exceeding 100 frames per second for 640 times 480 images. The remaining time can then be spent on fast matching against large feature databases on the GPU while the CPU can be used for other tasks.

关键词： computer vision Hardware computer graphics Central Processing Unit Robust stability Data mining Feature extraction Image generation Image databases Spatial databases

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Controlled human pose estimation from depth image streams

Controlled human pose estimation from depth image streams

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Youding Zhu Behzad Dariush Kikuo Fujimura Dreese Laborartory Ohio State Uinversity Columbus OH USA Honda Research Institute USA Inc. Mountain View CA USA

This paper presents a model-based, Cartesian control theoretic approach for estimating human pose from features detected using depth images obtained from a time of flight imaging device. The features represent positions of anatomical landmarks, detected and tracked over time based on a probabilistic inferencing algorithm. The detected features are subsequently used as input to a constrained, closed loop tracking control algorithm which not only estimates the pose of the articulated human model, but also provides feedback to the feature detector in order to resolve ambiguities or to provide estimates of undetected features. Based on a simple kinematic model, constraints such as joint limit avoidance, and self penetration avoidance are enforced within the tracking control framework. We demonstrate the effectiveness of the algorithm with experimental results of upper body pose reconstruction from a small set of features. On average, the entire pipeline runs at approximately 10 frames per second on a standard 3 GHz PC using a 17 degree of freedom upper body human model.

关键词： Humans Streaming media computer vision Inference algorithms Estimation theory Tracking loops Feedback loop Detectors Kinematics Joints

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3D model search and pose estimation from single images using VIP features

3D model search and pose estimation from single images using...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Changchang Wu Friedrich Fraundorfer Jan-Michael Frahm Marc Pollefeys Department of Computer Science UNC Chapel Hill USA Department of Computer Science ETH Zurich Switzerland

This paper describes a method to efficiently search for 3D models in a city-scale database and to compute the camera poses from single query images. The proposed method matches SIFT features (from a single image) to viewpoint invariant patches (VIP) from a 3D model by warping the SIFT features approximately into the orthographic frame of the VIP features. This significantly increases the number of feature correspondences which results in a reliable and robust pose estimation. We also present a 3D model search tool that uses a visual word based search scheme to efficiently retrieve 3D models from large databases using individual query images. Together the 3D model search and the pose estimation represent a highly scalable and efficient city-scale localization system. The performance of the 3D model search and pose estimation is demonstrated on urban image data.

关键词： Image databases Spatial databases Visual databases Cameras computer science Robustness Information retrieval Feature extraction Image retrieval Image matching

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