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How to improve the high resolution and remove the noise of composite infrared thermal wave detection image have become important research topics in machine vision. Therefore, this paper proposes a composite image enhancement algorithm based on edge adaptive total variation of direction. Using the improved adaptive directional total variation model to construct the guidance image of the guidance filter can provide better structural information for the guidance filter and conduct the guidance filter. This process is iterated to eliminate noise and ladder effect. Experimental results show that this algorithm is superior to other advanced methods in objective evaluation index and subjective vision. The algorithm proposed in this paper can not only effectively eliminate noise and ladder effect, but also highlight image details and structure information, which proves the effectiveness of this algorithm. Discover the cutting-edge algorithm for high-resolution improvement and noise elimination in infrared thermal wave detection images. Our innovative composite image enhancement technique, based on edge adaptive total variation, outperforms traditional methods. The improved directional model provides better structural information, optimizing the guidance filter process. Experimental results reveal its superiority in objective evaluation and subjective vision. This algorithm effectively eliminates noise and ladder effect while highlighting image details, validating its remarkable effectiveness. [ABSTRACT FROM AUTHOR]

In computer vision, automatically identifying and locating text in images or videos is an important task. Traditional text detection methods are not effective in detecting complex scenes in irregular rectangular areas such as text bending, spacing, and special shapes, which are mainly reflected in the fragmentation of text detection areas. In this paper, a text detection method based on edge attention mechanism is proposed to better adapt to complex scenes. The proposed method takes Encoder–Decoder as the core idea. First of all, an edge attention module is designed, including global attention and local attention. The global attention module is used to perceive the features of text regions and nontext regions, while the local attention module is used to learn the information of text boundaries. Then a multi-scale feature fusion process is designed, which can strengthen the edge information and key information of text regions. Finally, the model outputs probability maps and threshold maps, and generates high-precision binary maps of text regions. After experimental verification, the proposed method on the public data set significantly reduces the fragmentation of the detection area, improves the detection accuracy of the text area, and has better robustness for text detection scenes with unconventional rectangular areas. [ABSTRACT FROM AUTHOR]

Computer Vision (CV) has become an essential field in Artificial Intelligence applications. Object detection and recognition (ODR) is one of the fundamental tasks of computer vision implementations. However, developing an efficient ODR model is still a significant problem. The model's execution time and speed are the most critical features during the inference or detection and recognition process, which need to be improved using the latest object detection architectures. In this paper, the handwritten detection and recognition (HDR) model is developed based on previously known algorithms with their efficiency, such as Faster R-CNN and YOLOv4 in the first hand. On the other hand, two new models capable of detecting and recognizing handwritten digits using the latest ODR algorithm are proposed, one based on the latest YOLO family architecture (YOLOv5-HDR) with high speed and accuracy and the other using the transformers architecture (DETR). To the best of our knowledge, this is the first study to achieve a details comparison between YOLOv5 and transformers-based models in handwritten digit detection. Finally, the detailed performance analysis achieved by the paper proves that the YOLOv4-based model achieved the testing inference 13% faster than Faster R-CNN. However, the proposed YOLOv5-based model outperformed the YOLOv4 and the transformers-based one as it increased the testing execution time 25% faster than the YOLOv4, three times faster than the DETR model. A further adversarial attack test has been conducted to ensure the robust performance of the proposed model. Furthermore, numerical experiment results and their analyses demonstrate the robustness and effectiveness of the proposed YOLOv5-based model being the most stable for handwritten digit detection and recognition tasks. [ABSTRACT FROM AUTHOR]

Intelligent agriculture has become the development trend of agriculture in the future, and it has a wide range of research and application scenarios. Using machine learning to complete basic tasks for people has become a reality, and this ability is also used in machine vision. In order to save the time in the fruit picking process and reduce the cost of labor, the robot is used to achieve the automatic picking in the orchard environment. Cherry target detection algorithms based on deep learning are proposed to identify and pick cherries. However, most of the existing methods are aimed at relatively sparse fruits and cannot solve the detection problem of small and dense fruits. In this paper, we propose a cherry detection model based on YOLOv5s. First, the shallow feature information is enhanced by convolving the feature maps sampled by two times down in BackBone layer of the original network model to the input end of the second and third CSP modules. In addition, the depth of CSP module is adjusted and RFB module is added in feature extraction stage to enhance feature extraction capability. Finally, Soft- Non-Maximum Suppression (Soft-NMS) is used to minimize the target loss caused by occlusion. We test the performance of the model, and the results show that the improved YOLOv5s-cherry model has the best detection performance for small and dense cherry detection, which is conducive to intelligent picking. [ABSTRACT FROM AUTHOR]

Occlusion area detection is a crucial step affecting the performance of the binocular stereo matching algorithm, but the traditional method of occlusion area detection has two major problems, including left–right consistency detection (LRC). First, these algorithms must obtain the left and right disparity maps with precision. Second, these algorithms cannot detect the occlusion region at the image's borders. We propose the single view occlusion area detective (SVOAD) algorithm to detect these occlusion areas and better deal with them. The SVOAD can detect the area of occlusion from a single image, thereby reducing the computational cost. Additionally, the algorithm can detect the occlusion region in all image regions. This paper also improves the guided filter so that it works better with the end-to-end neural network and makes the SVOAD algorithm work better. [ABSTRACT FROM AUTHOR]

Computer vision-based recognition of Bangle vehicle license plates (LPs) is an arduous task in dirty and muddy situations. This paper proposes an efficient method for real-time computer vision-based recognition of Bangla vehicle LPs using contour analysis and prediction algorithms. The method initially applies gray scaling the input RGB images, histogram equalization to improve the grayscale image quality, edge detection using Sobel edge detector, and adaptive thresholding to convert it to a binary image. The system localizes the vehicle LP based on the maximum rectangular contour area and converts it into a predefined size. Noise removal technique using morphological dilation and erosion operation is used, followed by Gaussian filtering on binary image to improve the image quality further. The system clusters the two-lined LP into seven clusters. The sub-clustering is applied on specific clusters and makes 68 individual sub-clusters. The system extracts vector contour (VC) from each 68 individual classes. After VC extraction, the system normalizes it into a q predefined length. The system applies inter co-relation function (ICF) to categorize each sub-cluster to its previously defined individual class. For that, it calculates the maximum similarity between test and previously trained VCs. The system applies the dependency prediction algorithm in parallel to predict the whole string (district name) in the cluster-1 based on previously categorized class or classes (starting character or characters of the district part). (Metro) or (null) from cluster-2, "-" (hyphen) from cluster-3 and 6 are predicted automatically as their positions are fixed. The system is trained using 68 classes in which each class contains 100 samples generated by the augmentation technique. The system is tested using another set of 68 classes with a total of 6 8 × 1 0 0 = 6 8 0 0 images acquiring the recognition accuracy of 96.62% with the mean computational cost of 8.363 ms/f. The system is also tested using 500 vehicle whole Bangla LPs achieving the mean whole LP recognition accuracy of 95.41% with a mean computational cost of 35.803 ms/f. [ABSTRACT FROM AUTHOR]

In order to detect the type of vehicle seat and the missing part of the spring hook, this paper proposes an improved RANSAC-SURF method. First, the image is filtered by a Gauss filter. Second, an improved RANSAC-SURF algorithm is used to detect the types of vehicle seats. Extract the feature points of vehicle seats. The feature points are matched according to the improved RANSAC-SURF algorithm. Third, the image distortion of the vehicle seat is corrected by the method of perspective transformation. Determine whether the seat's spring hook is missing or not according to the absolute value of the gray difference between the image collected by the camera and the image of the normal installation. The experimental results show that the MSE of the Gauss filter under a 5 * 5 template is 19.0753, and the PSNR is 35.3261, which is better than that of the mean filter and the median filter. The total matching logarithm of feature points and the number of intersection points are 188 and 18, respectively, in the improved RANSAC-SURF matching algorithm. [ABSTRACT FROM AUTHOR]

Innovative applications in rapidly evolving domains such as robotic navigation and autonomous (driverless) vehicles rely on binocular computer vision systems that meet stringent response time and accuracy requirements. A key problem in these vision systems is stereo matching, which involves matching pixels from two input images in order to construct the output, a 3D map. Building upon the existing local stereo matching algorithms, this paper proposes a novel stereo matching algorithm that is based on a weighted guided filtering foundation. The proposed algorithm consists of three main steps; each step is designed with the goal of improving accuracy. First, the matching costs are computed using a unique combination of complementary methods (absolute difference, Census, and gradient algorithms) to reduce errors. Second, the costs are aggregated using an adaptive weighted guided image filtering method. Here, the regularization parameters are adjusted adaptively using the Canny method, further reducing errors. Third, a disparity map is generated using the winner-take-all strategy; this map is subsequently refined using a densification method to reduce errors. Our experimental results indicate that the proposed algorithm provides a higher level of accuracy in comparison to a collection of the existing state-of-the-art local algorithms. [ABSTRACT FROM AUTHOR]

Background subtraction is a prerequisite and often the very first step employed in several high-level and real-time computer vision applications. Several parametric and non-parametric change detection algorithms employing multiple feature spaces have been proposed to date but none has proven to be robust against all challenges that can possibly be posed in a complex real-time environment. Amongst the varied challenges posed, illumination variations, shadows, dynamic backgrounds, camouflaged and bootstrapping artifacts are some of the well-known problems. This paper presents a light-weight hybrid change detection algorithm that integrates a novel combination of RGB color space and conditional YCbCr-based XCS-LBP texture descriptors (YXCS-LBP) into a modified pixel-based background model. The conditional employment of light-weight YXCS-LBP texture features with the modified Visual background extractor (ViBe) aiming at reduction in false positives, produces outperforming results without incurring much memory and computational cost. The random and time-subsampled update strategy employed with the proposed classification procedure ensures the efficient suppression of shadows and bootstrapping artifacts along with the complete retention of long-term static objects in the foreground masks. Comprehensive performance analysis of the proposed technique on publicly available Change Detection dataset (2014 CDnet dataset) demonstrates the superiority of the proposed technique over different state-of-the-art-methods against varied challenges. [ABSTRACT FROM AUTHOR]

Curved surface mesh generation is a key step for many areas. Here, a mesh generation algorithm for closed curved surface based on Delaunay refinement is proposed. We focus on improving the shape quality of the meshes generated and making them conform to 2-manifold. The Delaunay tetrahedralization of initial sample is generated first, the initial surface mesh which is a subset of the Delaunay tetrahedralization can be achieved. A triangle is refined by inserting a new point if it is large or of bad quality. For each sample, we also check the triangles that adjoin it whether from a topological disk. If not, the largest triangle will be refined. Finally, the surface mesh is updated after a new point is inserted into the sample. The definition of mesh size function for surface mesh generation is also put in this paper. Meshing experiments of some models demonstrate that the new algorithm is advantageous in generating high quality surface mesh, the count of mesh is suitable and can well approximate the curved surface. The presented method can be used for a wide range of problems including computer graphics, computer vision and finite element method. [ABSTRACT FROM AUTHOR]

A visual secret sharing (VSS) scheme is intended to share secret information in a group to avoid potential treat of interruption and modification. In this paper, we present a novel VSS scheme based on the improved local binary pattern (LBP) operator. It makes full use of local contrast features of LBP for concealing secret image data into different image shares, which can be used to recover the secret easily and exactly. By varying LBP extensions, we can design various kinds of VSS schemes for sharing secret information. Compared to the currently available VSS algorithms, the proposed scheme demonstrates better randomness in shares with less pixel expansion and exact determination in reconstruction with lower computational cost. [ABSTRACT FROM AUTHOR]

Fiducial marker systems are composed of a number of patterns that are mounted in the environment and automatically detected by computer vision algorithms using digital techniques. Thus, these systems are valuable in augmented reality (AR), robot navigation, and other applications. This paper proposes a new AR marker called CH-marker, which uses Hamming check codes to encode multiple kinds of colors and restore binary codes in the squares occluded in the markers. The marker solves the registration failure, which occurs when the markers are partially occluded in dynamic scenes. Experiments showed that the proposed marker is effective, reliable, and can meet the application demand of AR. [ABSTRACT FROM AUTHOR]

A significant component of driver assistance systems (DAS) is lane detection, and has been studied since the 1990s. However, improving and generalizing lane detection solutions proved to be a challenging task until recently. A (physical) lane is defined by road boundaries or various kinds of lane marks, and this is only partially applicable for modeling the space an ego-vehicle is able to drive in. This paper proposes a concept of (virtual) corridor for modeling this space. A corridor depends on information available about the motion of the ego-vehicle, as well as about the (physical) lane. This paper also suggests a modified version of Euclidean Distance Transform (EDT), named Row Orientation Distance Transform (RODT), to facilitate the detection of corridor boundary points. Then, boundary selection and road patch extension are applied as post-processing. Moreover, this paper also informs about the possible application of corridor for driver assistance. Finally, experiments using images from highways and urban roads with some challenging road situations are presented, illustrating the effectiveness of the proposed corridor detection algorithm. Comparison of lane and corridor on a public dataset is also provided. [ABSTRACT FROM AUTHOR]

The main goal of this present paper is to develop color detection, and counting algorithm for tangerine flowers under natural lighting conditions to estimate better yield of tangerine from orchards, and each tree picture was taken from four sides. As a result, total of 1340 subimages of tangerine flowers were detected by the newly introduced algorithm from a sample of 21 tangerine trees during blooming season. A Gaussian filter was used to reduce noise and illumination adjustment as much as possible for better clarity to identify exactly the tangerine flowers. The proposed algorithm gives accurate output of tangerine flower detection by including partially/semipartially occluded tangerine flowers and its clusters. Finally, the output of yield estimation reveals that about 10% of all total tangerine flowers turned out to be tangerine. [ABSTRACT FROM AUTHOR]

Classical computational geometry algorithms handle geometric constructs whose shapes and locations are exact. However, many real-world applications require modeling and computing with geometric uncertainties, which are often coupled and mutually dependent. In this paper we address the relative position of points, point set distance problems, and orthogonal range queries in the plane in the presence of geometric uncertainty. The uncertainty can be in the locations of the points, in the query range, or both, and is possibly coupled. Point coordinates and range uncertainties are modeled with the Linear Parametric Geometric Uncertainty Model (LPGUM), a general and computationally efficient worst-case, first-order linear approximation of geometric uncertainty that supports dependence among uncertainties. We present efficient algorithms for relative points orientation, minimum and maximum pairwise distance, closest pair, diameter, and efficient algorithms for uncertain range queries: uncertain range/nominal points, nominal range/uncertain points, uncertain range/uncertain points, with independent/dependent uncertainties. In most cases, the added complexity is sub-quadratic in the number of parameters and points, with higher complexities for dependent point uncertainties. [ABSTRACT FROM AUTHOR]

Robust tracking is a challenging problem, due to intrinsic appearance variability of objects caused by in-plane or out-plane rotation and extrinsic factors change such as illumination, occlusion, background clutter and local blur. In this paper, we present a novel tri-tracking framework combining different views (different models using independent features) for robust object tracking. This new tracking framework exploits a hybrid discriminative generative model based on online semi-supervised learning. We only need the first frame for parameters initialization, and then the tracking process is automatic in the remaining frames, with updating the model online to capture the changes of both object appearance and background. There are three main contributions in our tri-tracking approach. First, we propose a tracking framework for combining generative model and discriminative model, together with different cues that complement each other. Second, by introducing a third tracker, we provide a solution to the problem that it is difficult to combine two classification results in co-training framework when they are opposite. Third, we propose a principle way for combing different views, which based on their Discriminative power. We conduct experiments on some challenging videos, the results from which demonstrate that the proposed tri-tracking framework is robust. [ABSTRACT FROM AUTHOR]

Object tracking is one of the key techniques in computer vision. Present algorithms are mainly implemented in static platforms. In this paper, we propose a novel technique for real-time object tracking in videos captured by cameras on moving platforms. First, we rule out feature points that have optical flows inconsistent with those of background. Second, optical flows on the rest of the feature points are utilized to estimate the global motion of the camera. Finally, the kinematic function of particle filtering is modified by the global motion of the camera, together with color-space histogram as appearance model, to achieve robustness in unstable video sequences. The proposed algorithm is tested on several video sequences, compared to mean-shift algorithm and traditional particle filtering tracking, it shows promising real-time tracking performance. Experiments demonstrate that our algorithm can track moving object robustly in videos captured by moving cameras. [ABSTRACT FROM AUTHOR]

Occlusion handling is an old but important problem for the computer vision and pattern recognition community. Features from different objects may twist with each other, and any matched feature points may belong to different objects for many traditional object recognition algorithms. To recognize occlusions, we should not only match objects from different view points but also match features extracted from the same object. In this paper, we propose a method to consider these two perspectives simultaneously by encoding various types of features, such as geometry, color and texture relationships among feature points into a matrix and find the best quadratic feature correlation model to fit them. Experiments on our own built dataset and the publicly available PASCAL VOC dataset shows that, our method can robustly classify objects and handle occluded objects under large occlusions, and the performance is among the state-of-the-art. [ABSTRACT FROM AUTHOR]

Image segmentation is an important component in image processing, pattern recognition and computer vision. Many segmentation algorithms have been proposed. However, segmentation methods for both noisy and noise-free images have not been studied in much detail. Neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interaction with different ideational spectra. However, neutrosophic set needs to be specified and clarified from a technical point of view for a given application or field to demonstrate its usefulness. In this paper, we apply neutrosophic set and define some operations. Neutrosphic set is integrated with an improved fuzzy c-means method and employed for image segmentation. A new operation, α-mean operation, is proposed to reduce the set indeterminacy. An improved fuzzy c-means (IFCM) is proposed based on neutrosophic set. The computation of membership and the convergence criterion of clustering are redefined accordingly. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can segment images accurately and effectively. Especially, it can segment the clean images and the images having different gray levels and complex objects, which is the most difficult task for image segmentation. [ABSTRACT FROM AUTHOR]

We present in this paper an approach based on multiple strategies to tackle the problem of invariance in the framework of texture-based image retrieval. This approach, rather than to consider invariance at the representation level, considers it at the query level. We use two models to represent the visual content of textures, namely, the autoregressive model and a perceptual model based on a set of perceptual features. The perceptual model is used with two viewpoints: the original image's viewpoint and the autocovariance function viewpoint. After a brief presentation and discussion of these multiple representation models/viewpoints, we present the invariant texture retrieval algorithm. This algorithm considers results fusion (merging) at two different levels: (1) the first level consists in merging results returned by different models/viewpoints (representations) for the same query in one results list using a linear results fusion model; (2) the second level consists in merging each fused list of different queries into a unique fused list using a round robin fusion scheme. Experimental retrieval results and benchmarking based on the precision/recall measures applied on a large image database show interesting results. [ABSTRACT FROM AUTHOR]

Many algorithms for applications such as pattern recognition, computer vision, and computer graphics seek to compute actual optimal paths in weighted directed graphs. The standard approach for reporting an actual optimal path is based on building a single-source optimal path tree. A technique by Chen et al.2 was given for a class of problems such that a single actual optimal path can be reported without maintaining any single-source optimal path tree, thus significantly reducing the space bound of those problems with no or little increase in their running time. In this paper, we extend the technique by Chen et al.2 to the generalized problem of reporting many actual optimal paths with different starting and ending vertices in certain directed graphs, and show how this new technique yields improved results on several application problems, such as reconstructing a 3-D surface band bounded by two simple closed curves, finding various constrained segmentation of 2-D medical images, and circular string-to-string correction. We also correct an error in the time/space complexity for the well-cited circular string-to-string correction algorithm12 and give an improved result for this problem. Although the generalized many-path problem seems more difficult than the single-path cases, our algorithms have nearly the same space and time bounds as those of the single-path cases. Our technique is likely to help improve many other optimal paths or dynamic programming algorithms. [ABSTRACT FROM AUTHOR]

Manifold learning is an effective dimension reduction method to extract nonlinear structures from high dimensional data. Recently, manifold learning has received much attention within the research communities of image analysis, computer vision and document data analysis. In this paper, we propose a boosted manifold learning algorithm towards automatic 2D face recognition by using AdaBoost to select the best possible discriminating projection for manifold learning to exploit the strength of both techniques. Experimental results support that the proposed algorithm improves over existing benchmarks in terms of stability and recognition precision rates. [ABSTRACT FROM AUTHOR]

In this paper, a new machine vision algorithm for close-range position sensing and bin picking is presented where a Hopfield Neural Network (HNN) is used for the stereo matching process. Stereo Matching is formulated as an energy minimization task and this minimization is accomplished using the HNNs. Various other important aspects of this Vision System are discussed including camera calibration and objects localization using a clustering algorithm. [ABSTRACT FROM AUTHOR]

Surface matching is a common task in computer graphics and computer vision. In this paper, we introduce a novel algorithm that aligns scanned point-based surfaces to the related 3D model without assumptions about their relative position and orientation. Our method estimates the transformation that matches two surfaces from a pair of corresponding points using their surface positions, normals and an assistant direction on the tangent plane. This approximate alignment is used as the input of ICP algorithm to refine the registration of the surface to the model. The algorithm is easy to implement and proved to be reliable even with coarse point clouds. Furthermore, with the help of feature points, we can improve the reliability of our surface matching algorithm. [ABSTRACT FROM AUTHOR]

Our approach exploits a general-purpose decomposition algorithm, called GPDOF, and a dictionary of very efficient solving procedures, called r-methods, based on theorems of geometry. GPDOF decomposes an equation system into a sequence of small subsystems solved by r-methods, and produces a set of input parameters.1. Recursive assembly methods (decomposition-recombination), maximum matching based algorithms, and other famous propagation schema are not well-suited or cannot be easily extended to tackle geometric constraint systems that are under-constrained. In this paper, we show experimentally that, provided that redundant constraints have been removed from the system, GPDOF can quickly decompose large under-constrained systems of geometrical constraints. We have validated our approach by reconstructing, from images, 3D models of buildings using interactively introduced geometrical constraints. Models satisfying the set of linear, bilinear and quadratic geometric constraints are optimized to fit the image information. Our models contain several hundreds of equations. The constraint system is decomposed in a few seconds, and can then be solved in hundredths of seconds. [ABSTRACT FROM AUTHOR]

This paper presents a robust and direct algorithm for the least-square fitting of ellipses to scattered data. The proposed algorithm makes use of well-known techniques that improve the robustness of the direct least-square fitting with a modest increase of the computational burden. Furthermore, by trivial modifications of the constrained minimization problem the algorithm may be converted to perform the specific fitting of other types of conics such as hyperbola. The method is simple and accurate and can be implemented with fixed time of computation. These characteristics coupled to its robustness and specificity makes the algorithm well-suited for applications requiring real-time machine vision. [ABSTRACT FROM AUTHOR]

This paper presents an efficient method of reconstructing and recognizing broken handwritten digits. Constrained dilation algorithms are used to bridge small gaps and smooth some spurious points. The contours of broken handwritten digits are smoothed and linearized, and a set of structural points of digits are detected along the outer contours of digits. These structural points are used to describe the morphological structure of broken digits. The broken digits are skeletonized with an improved thinning algorithm. Spurious segments introduced during the extraction of digit fields are detected and deleted based on the structure analysis of digit fields, segment recognition, segment extension, skeleton structure and geometrical features. The broken points of the digits are preselected based on the minimum distance between the "end" points of skeletons of two neighboring regions. The correction rules of the preselected broken points are also based on the structure analysis and comparison of broken digits. Experimental results showing the effectiveness of the method are given. [ABSTRACT FROM AUTHOR]

In this paper we propose a fully automated offline handwriting recognition system that incorporates rule based segmentation, contour based feature extraction, neural network validation, a hybrid neural network classifier and a hamming neural network lexicon. The work is based on our earlier promising results in this area using heuristic segmentation and contour based feature extraction. The segmentation is done using many heuristic based set of rules in an iterative manner and finally followed by a neural network validation system. The extraction of feature is performed using both contour and structure based feature extraction algorithm. The classification is performed by a hybrid neural network that incorporates a hybrid combination of evolutionary algorithm and matrix based solution method. Finally a hamming neural network is used as a lexicon. A benchmark dataset from CEDAR has been used for training and testing. [ABSTRACT FROM AUTHOR]

Brownboost is an adaptive, continuous time boosting algorithm based on the Boost-by-Majority (BBM) algorithm. Though it has been little studied at the time of writing, it is believed that it should prove especially robust with respect to noisy data sets. This would make it a very useful boosting algorithm for real-world applications. More familiar algorithms such as Adaboost, or its successor Logitboost, are known to be especially susceptible to overfitting the training data examples. This can lead to a poor generalization error in the presence of class noise, since weak hypotheses induced at later iterations to fit the noisy examples will tend to be given undue influence in the final combined hypothesis. Brownboost allows us to specify an expected base-line error rate in advance, corresponding to our prior beliefs about the proportion of noise in the training data, and thus avoid overfitting. The original derivation of Brownboost is restricted to binary classification problems. In this paper we propose a natural multiclass extension to the basic algorithm, incorporating error-correcting output codes and a multiclass gain measure. We test two-class and multiclass versions of the algorithm on a number of real and simulated data sets with artificial class noise, and show that Brownboost consistently outperforms Adaboost in these situations. [ABSTRACT FROM AUTHOR]

This paper presents an efficient general purpose search algorithm for alignment and an applied procedure for IC print mark quality inspection. The search algorithm is based on normalized cross-correlation and enhances it with a hierarchical resolution pyramid, dynamic programming, and pixel over-sampling to achieve subpixel accuracy on one or more targets. The general purpose search procedure is robust with respect to linear change of image intensity and thus can be applied to general industrial visual inspection. Accuracy, speed, reliability, and repeatability are all critical for the industrial use. After proper optimization, the proposed procedure was tested on the IC inspection platforms in the Mechanical Industry Research Laboratories (MIRL), Industrial Technology Research Institute (ITRI), Taiwan. The proposed method meets all these criteria and has worked well in field tests on various IC products. [ABSTRACT FROM AUTHOR]

In this paper a new thinning algorithm is presented. It is simple yet robust, and can maintain the same advantages of other key skeletonization algorithms. In this new algorithm, only a very small set of rules of criteria for deleting pixels is used. It is faster and easier to implement. Its advantages and limitations are discussed and compared with others. Several examples are illustrated. [ABSTRACT FROM AUTHOR]