Presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain local binary patterns, termed "uniform," are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. We derive a generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray-scale variations since the operator is, by definition, invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table. Experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns.

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Multiresolution gray-scale and rotation invariant texture classification with local binary patterns

A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.

Mean shift: a robust approach toward feature space analysis

A comparative study of texture measures with classification based on featured distributions

This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed

Face Description with Local Binary Patterns: Application to Face Recognition

The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, nonrigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. In this survey, we categorize the tracking methods on the basis of the object and motion representations used, provide detailed descriptions of representative methods in each category, and examine their pros and cons. Moreover, we discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.

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Object tracking: A survey

Interaction with tumbling objects will become more common as human activities in space expand Attempting to interact with a large complex object translating and rotating in space, a human operator using only his visual and mental capacities may not be able to estimate the object motion, plan actions or control those actions A robot system (RAMBO) equipped with a camera, which, given a sequence of simple tasks, can perform these tasks on a tumbling object, is being developed RAMBO is given a complete geometric model of the object A low level vision module extracts and groups characteristic features in images of the object The positions of the object are determined in a sequence of images, and a motion estimate of the object is obtained This motion estimate is used to plan trajectories of the robot tool to relative locations rearby the object sufficient for achieving the tasks More specifically, low level vision uses parallel algorithms for image enhancement by symmetric nearest neighbor filtering, edge detection by local gradient operators, and corner extraction by sector filtering The object pose estimation is a Hough transform method accumulating position hypotheses obtained by matching triples of image features (corners) to triples of model features To maximize computing speed, the estimate of the position in space of a triple of features is obtained by decomposing its perspective view into a product of rotations and a scaled orthographic projection This allows use of 2-D lookup tables at each stage of the decomposition The position hypotheses for each possible match of model feature triples and image feature triples are calculated in parallel Trajectory planning combines heuristic and dynamic programming techniques Then trajectories are created using dynamic interpolations between initial and goal trajectories All the parallel algorithms run on a Connection Machine CM-2 with 16K processors

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Robot Acting on Moving Bodies (RAMBO): Interaction with tumbling objects

Automated Visual Inspection of Textured Surfaces

A robot system called RAMBO is being developed. It is equipped with a camera, which, given a sequence of simple tasks, can perform these tasks on a moving object. RAMBO is given a complete geometric model of the object. A low level vision module extracts and groups characteristic features in images of the object. The positions of the object are determined in a sequence of images, and a motion estimate of the object is obtained. This motion estimate is used to plan trajectories of the robot tool to relative locations nearby the object sufficient for achieving the tasks. More specifically, low level vision uses parallel algorithms for image enchancement by symmetric nearest neighbor filtering, edge detection by local gradient operators, and corner extraction by sector filtering. The object pose estimation is a Hough transform method accumulating position hypotheses obtained by matching triples of image features (corners) to triples of model features. To maximize computing speed, the estimate of the position in space of a triple of features is obtained by decomposing its perspective view into a product of rotations and a scaled orthographic projection. This allows the use of 2-D lookup tables at each stage of the decomposition. The position hypotheses for each possible match of model feature triples and image feature triples are calculated in parallel. Trajectory planning combines heuristic and dynamic programming techniques. Then trajectories are created using parametric cubic splines between initial and goal trajectories. All the parallel algorithms run on a Connection Machine CM-2 with 16K processors.

Robot acting on moving bodies (RAMBO): Preliminary results

An approach for constructing a dynamic gallery of people observed in a video stream is described. We consider two scenarios that require determining the number and identity of participants: outdoor surveillance and meeting rooms. In these applications face identification is typically not feasible due to the low resolution across the face. The proposed approach automatically computes an appearance model based on the clothing of people and employs this model in constructing and matching the gallery of participants. The appearance model usescolor/path-lengthprofile and a robust distance measure based on Kernel Density Estimation (KDE) and Kullback-Leibler (KL) distance, to evaluate similarity between people and add models to the gallery. A one-to-one constraint is enforced to correctly match instances to models at each frame. In the meeting room scenario we exploit the fact that the relative locations of subjects are likely to remain unchanged for the whole sequence.

David Harwood

Papers

Robot Acting on Moving Bodies (RAMBO): Interaction with tumbling objects

Automated Visual Inspection of Textured Surfaces

Robot acting on moving bodies (RAMBO): Preliminary results

Appearance-based human gallery construction from video

Weighted Generalized Hough Transform by Using Partial Least Squares Regression Analysis