In a 3-D scanning task, a robot-sensor system controls a robotic arm to move a laser sensor. In order to align the coordinate system of the robotic arm and laser sensor, prior calibration is required to derive the transformation between both coordinate systems. This paper proposes a new calibration method in three steps: manual data collection, sensing data calculation, and transformation solution. First, at least four data are required to be collected by the user. The sensing data are then calculated from the collected data and adopted to provide the desired transformation. The proposed algorithm has two features: arbitrary placement of planar disk and noise tolerant. Using a planar disk, the algorithm will automatically derive the angular relationship between the disk and the sensor plane, enabling arbitrary orientation placement. Noise tolerant is guaranteed by fitting ellipses during the sensing data calculation and using a single set of sensing data in transformation solution. Experiments and comparisons are given to demonstrate the efficiency of the proposed calibration algorithm.Note to Practitioners—This paper was motivated by the problem of calibrating a laser sensor and a positioning device (robot arm, CMM, etc.) in a robust and fast manner. Specifically, the calibration is to derive the transformation by aligning the sensors coordinate system to the positioning devices coordinate system. The proposed calibration procedure consists of two parts: manual data collection and automatic transformation calculation. During manual data collection, users only need to select four different data; whereby each data contains of two positions with the same orientation. Then, the desired transformation will be derived automatically. The calibration is designed in an efficient and robust way whereby: 1) data collection is done using a simple planar disk placed in arbitrary orientations; 2) minimum human interaction required; 3) tolerant to noise in the sensor data; and 4) easy implementation by following a proven and standard protocol.

We propose a cascaded and a contrario hole crack detection (CHORD) method for defect inspection in digital images of turbine blade surfaces. This is the first time an automatic image processing-based method is proposed for such a task. It consists of two major steps: first, the appearance of holes is approximated by ellipses and cascaded pose regression is used to estimate the position and orientation of the holes; Second, we define hole cracks as geometrical structures and a contrario method is used to assess the meaningfulness of each crack. The model-based CHORD method fully considers the features of hole cracks including the characteristics of brightness, length, and orientation, and therefore can accurately detect cracks in the images. The threshold on the strength of cracks is determined automatically and the computational time is about five seconds for each image.

Laser stripe extraction is essential to 3D reconstructions of objects by triangulation laser scanning. Industrial applications require robustness against the spurious points due to multiple reflections among reflective and uneven surfaces. Our approach is a three-stage algorithm, which first detects the potential laser stripe regions, then ranks the regions on their corrected scene irradiance densities, and finally conducts the column-wise peak detection using the region ranking. The proposed method is evaluated in terms of speed, accuracy and robustness on both simulations and a real application for steel groove inspection.

Variational image segmentation based on the Mumford-Shah model requires to solve the heat diffusion equations in evolving irregular subdomains. It brings about significant difficulties in efficient and accurate segmentation, especially, in multi-phase scenarios. In this paper, we propose a new Mumford-Shah type model involving a smoothing operator, acting a similar role as the diffusion process and avoiding complicated computation. We introduce two simplified numerical models based on the Gaussian operator and the bilateral operator respectively, and provide efficient numerical algorithms to solve both segmentation problems. Numerical validation experiments on various images show better accuracy and robustness over existing methods.

Multi-people tracking has been studied for decades. It is already applied in many computer vision tasks. In this paper, the proposed framework performs frame to frame tracking and follows tracking-by-detection approach. Saliency detection is introduced to enhance multi-people tracking. It is performed on two layers in this method. Salient parts inside the human patch denote representative regions of the target, while parts around the target capture context information. Short term tracking of salient parts is applied along with data association. When data association with detections fails, the supporting models on-line learned are used to indicate the locations of targets based on the tracking results of salient parts. A Bayesian based method is used for mutual occlusion reasoning. Experiments are carried out on several public datasets to evaluate the proposed method. The experimental results show the promising performance of the proposed method compared with state-of-the-art works.

This paper presents a new approach for human hair length detection. In contrast to others, the proposed method is able to segment hairs from different views of human heads even with low resolution. Faces are not necessary to be visible in the images, as no face detection is needed in our method. Firstly, it conducts background subtraction to detect foreground objects and then detects human heads with a trained human head detector. Next, histogram analysis is carried out on the detected head region to segment hair region with K-mean clustering. Finally, hair length is determined by performing line scanning on the segmented hair region. The main advantages of the proposed method are able to handle the cases of (1) view variation and (2) low resolution or small human body appearance in the image. It is specially designed for surveillance system. The detected human hair length can be used as an invariant feature to be embedded into a human appearance model which can be employed for human detection, indexing and searching in multi cameras network system. This method aims at the fast processing speed and is able to achieve 7ms for hair length identification in a head patch detected. The experiments show the improvements in terms of speed and accuracy.

Occlusion is one of the major problems that object tracking faces in a clustered environment. In this paper, a tracking method which can deal with partial occlusion is proposed. There are two novelties in this paper: (1) using SURF keypoints to represent the object, key-points are evaluated and online learned by Random Ferns. (2) Bi-model is proposed to store key-points from object and surrounding background. In each frame, key-points inside or around the object bounding box will be assigned labels by matching with points stored in the Bi-model. These labeled points will be further used for improving the tracking accuracy and learning of Random Ferns. Long-term tracking is achieved by combining detection and tracking together. Experiments on videos with occlusion conditions show that the proposed method has good performance on tracking partial occluded objects, compared to some of the state-of-art methods.

Background subtraction is an essential technique for automatic video analysis. The main idea is to construct and update the model of the background scene. Foreground pixels are detected if they deviate from the background model to a certain extent. The model can consist of color, texture and gradient information [1]. In this paper, we focus on both color and texture information. The proposed texture feature is based on local binary pattern (LBP), while the color feature is represented by local color pattern (LCP). LBP is known to work well on texture rich regions and is invariant to subtle illumination variations, but it is inefficient on uniform regions. In view of this, color information can be incorporated to complement the texture feature. On the other hand, when the scene contrast or video quality is poor, color information may be unreliable and should be assigned lower priority than texture information. We propose a fuzzy rule-based system that adaptively adjusts the weights of the texture and color features based on the pixel's local properties. Experimental results on real scenes demonstrate the robustness of the proposed method.

Texture and color are two primitive forms of features that can be used to describe a scene. While conventional local binary pattern (LBP) texture based background subtraction performs well on texture rich regions, it fails to detect uniform foreground objects in large uniform background. As such, color information can be used to complement texture feature. In this study, we propose to incorporate local color feature based on Improved Hue, Luminance, and Saturation (IHLS) color space and introduce an adaptive scheme that automatically adjusts the weight between texture and color similarities based on the pixel's local properties: texture uniformity and color saturation. Experiments on eight challenging sequences demonstrate the effectiveness of the proposed method compared to the state-of-the-art algorithms.

This paper presents a new keypoint-based approach for Near-Duplicate (ND) keyframes identification using global and local features. In the proposed method, the matched keypoints are filtered for affine transform estimation based on an affine invariant feature which is using the lesser matching pairs and voting bins. To further confirm the matching, the Local Binary Pattern (LBP) and color features of areas formed by matched keypoints in two images are compared. This method combines the advantage of global and local matching, it is able to identify two images related with affine transform. The proposed algorithm has been tested in Columbia dataset and conducted the quantitative comparison with RANSAC algorithm and SR-PE algorithm. The experiment results show that the proposed method can achieve good results.

Most methods for multiple camera tracking rely on accurate calibration to associate data from multiple cameras. However, it often is not easy to have an accurate calibration in some real applications due to practical reasons. The inaccurate calibration can lead to wrong data association of objects between cameras. In this paper, we propose a method to handle the data association of objects in multiple cameras under inaccurate ground plane homography by using the RFS Bayes filter. Our method is based on modeling measurements from cameras to a random finite set. This random finite set includes the primary measurement from the object, extraneous measurements of the object, and clutter. Experimental results show the efficiency and robustness of our method through challenging cases such as occlusions, merged and split persons.

This paper presents a new keypoint-based approach to near-duplicate images detection. It consists of three steps. Firstly, the keypoints of images are extracted and then matched. Secondly, the matched keypoints are voted for estimation of affine transform based on an affine invariant ratio of normalized lengths. Finally, to further confirm the matching, the color histograms of areas formed by matched keypoints in two images are compared. This method has the advantage for handling the case when there are only a few matched keypoints. The proposed algorithm has been tested on Columbia dataset and conducted the quantitative comparison with RANdom SAmple Consensus (RANSAC) algorithm and Scale-Rotation Invariant Pattern Entropy (SR-PE) algorithm. The experiment result turns out that the proposed method compares favorably against the state-of-the-arts.

Abstract Fingerprint orientation plays important roles in fingerprint enhancement, fingerprint classification, and fingerprint recognition. This paper critically reviews the primary advances on fingerprint orientation estimation. Advantages and limitations of existing methods have been addressed. Issues on future development have been discussed. Copyright © 2010 John Wiley & Sons, Ltd.

A new deformable model called active b-snake model (ABM) is presented for object boundary extraction. First, an affine-invariant landmark point assignment strategy is proposed to avoid manually assign landmarks. Second, an adaptive control point insertion algorithm is used to enhance the flexibility of B-Snake to describe complex shape. Third, for modeling the shape distribution and appearance characteristics of landmark points in the training samples, a statistical framework is embedded into ABM. Finally, the minimum mean square error (MMSE) approach with control point insertion provides fine deformation to the desired object boundaries. Experimental results show that ABM can be used to achieve more accurate object extraction.

This paper presents a weighted support vector machine (WSVM) to improve the outlier sensitivity problem of standard support vector machine (SVM) for two-class data classification. The basic idea is to assign different weights to different data points such that the WSVM training algorithm learns the decision surface according to the relative importance of data points in the training data set. The weights used in WSVM are generated by a robust fuzzy clustering algorithm, kernel-based possibilistic c-means (KPCM) algorithm, whose partition generates relative high values for important data points but low values for outliers. Experimental results indicate that the proposed method reduces the effect of outliers and yields higher classification rate than standard SVM does when outliers exist in the training data set.

This paper presents a new affine-invariant matching algorithm based on B-Spline modeling, which solves the problem of the non-uniqueness of B-Spline in curve matching. This method first smoothes the B-Spline curve by increasing the degree of the curve. It is followed by a reduction of the curve degree using the Least Square Error (LSE) approach to construct the Curvature Scale Space (CSS) image. CSS matching is then carried out. Our method combines the advantages of B-Spline that are continuous curve representation and the robustness of CSS matching with respect to noise and affine transformation. It avoids the need for other matching algorithms that have to use the re-sampled points on the curve. Thus, the curve matching error is reduced. The proposed algorithm has been tested by matching similar shapes from a prototype database. The experimental results showed the robustness and accuracy of the proposed method in B-Spline curve matching.

This paper presents a novel algorithm for 2D planar curve recognition based on B-spline modeling. It combines the advantages of the B-spline which are continuous curve representation and affine invariant, and the robustness of the CSS (curvature scale space) matching with respect to noise and affine transformation. It solves the problem of the nonuniqueness of the B-spline in curve matching. A new algorithm, which contains degree increase and reduction, is proposed for smoothing the B-spline and constructing the CSS image. The proposed algorithm has been tested and good performance has been obtained.

A B-snake model using statistics information for segmenting 2D objects from medical images is presented in this paper. Based on our previous research work, a statistical model is proposed for our B-snake model, in order to use available priori knowledge about the object shape being studied. This method allows the deformation of B-snake to be influenced primarily by the most reliable matches. Experimental results show that our method is robust and accurate in object contour extraction in medical images.

In this paper, we presented a structure-adaptive B-snake model for segmenting the complex structures in medical images. A strategy of automatic control-point insertion, adaptive to the structure of the studied object, has been proposed. Furthermore, a method of minimum mean square energy (MMSE) is developed to iteratively estimate the position of those control points in the B-snake model. By applying the proposed structure-adaptive B-snake model to medical images, we show that our method is robust and accurate in object contour extraction.