Search

Public health initiatives must be made using evidence-based decision-making to have the greatest impact. Machine learning algorithms are created to gather, store, process, and analyze data to provide knowledge and guide decisions. A crucial part of any surveillance system is image analysis. The communities of computer vision and machine learning have become curious about it as of late. This study uses a variety of machine learning, and image processing approaches to detect and forecast malarial illness. In our research, we discovered the potential of deep learning techniques as innovative tools with a broader applicability for malaria detection, which benefits physicians by assisting in the diagnosis of the condition. We investigate the common confinements of deep learning for computer frameworks and organizing, including the requirement for data preparation, preparation overhead, real-time execution, and explaining ability, and uncover future inquiries about bearings focusing on these constraints. [ABSTRACT FROM AUTHOR]

The compressor is the most core component of refrigeration products, and its performance directly affects the overall performance of the product. The exhaust silencing chamber and coil are key components of a refrigeration compressor, and their working stability and lifespan determine the reliability and economy of the refrigeration compressor. This paper obtains reference data for the fluid domain of the exhaust silencing chamber through simulation analysis and image processing of the existing exhaust silencing chamber structure and inner diameter size of the coil. Then, the optimal model of the prioritized exhaust chamber is obtained through pattern recognition method. Based on the image processing method, the internal flow field of the exhaust chamber are obtained, providing a strong reference for the optimal structural design of the exhaust silencing chamber and coil of the refrigeration compression body. [ABSTRACT FROM AUTHOR]

This research undertakes an exhaustive exploration of the Auto Machine Learning-based approach (AutoML) to ascertain the source printer for microscopic printed image forensics. The primary focus is on enhancing precision and efficacy in a scenario with limited sample data, achieved by integrating traditional image processing methods with diverse AutoML representatives. The investigation involves a meticulous evaluation and comparative analysis of classical Machine Learning (ML) models and AutoML models in microscopic printed image forensics. Through extensive experiments, we highlight the inherent limitations of traditional models and underscore the compelling advantages offered by AutoML. Notably, we recognize AutoML's unique capability to discern varying degrees of uncertainty within printed patterns. The outcomes emphasize the prowess of Auto Machine Learning in improving the accuracy of printed image forensics, making it particularly promising for future research, especially given the tabular nature of the data and the imperative for a lightweight model suitable for embedded systems. [ABSTRACT FROM AUTHOR]

Financial risk warning is a crucial technical issue for enterprises. Traditionally, it relied on modeling analysis of a single data type, which does not fully capture the diverse characteristics of financial risk activities. To address this, this paper introduces a multimodal deep learning framework driven by semantic analysis and image processing for intelligent warning of enterprise financial risks. Initially, natural language processing algorithms analyze textual data such as financial statements, news reports, and social media comments within the industry. Concurrently, two convolutional neural network models, M-CNN and M-RNN, extract features from images and chart data. These textual and visual feature representations are then fused to create a multimodal deep neural network framework. Extensive experimental evaluation and comparative analysis of the proposed framework were conducted. The results indicate that the financial risk rate of consumer fraud graph analysis varies significantly, displaying a fluctuating state with values ranging from 2.1% to 16.8%. Compared to other methods, the proposed approach demonstrates superior performance in financial risk warning tasks, with the risk rate increasing from 1.2% to 26.5% as it iterates from 1 to 6. [ABSTRACT FROM AUTHOR]

Generation of random fields (GRF) for image segmentation represents partitioning an image into different regions that are homogeneous or have similar facets of the image. It is one of the most challenging tasks in image processing and a very important pre-processing step in the fields of computer vision, image analysis, medical image processing, pattern recognition, remote sensing, and geographical information system. Many researchers have presented numerous image segmentation approaches, but still, there are challenges like segmentation of low contrast images, removal of shadow in the images, reduction of high dimensional images, and computational complexity of segmentation techniques. In this review paper, the authors address these issues. The experiments are conducted and tested on the Berkely dataset (BSD500), Semantic dataset, and our own dataset, and the results are shown in the form of tables and graphs. [ABSTRACT FROM AUTHOR]

Single image super-resolution (SISR) aims to obtain a high-resolution output from one low-resolution image. Currently, deep learning-based SISR approaches have been widely discussed in medical image processing, because of their potential to achieve high-quality, high spatial resolution images without the cost of additional scans. However, most existing methods are designed for scale-specific SR tasks and are unable to generalize over magnification scales. In this paper, we propose an approach for medical image arbitrary-scale super-resolution (MIASSR), in which we couple meta-learning with generative adversarial networks (GANs) to super-resolve medical images at any scale of magnification in (1 , 4 ]. Compared to state-of-the-art SISR algorithms on single-modal magnetic resonance (MR) brain images (OASIS-brains) and multi-modal MR brain images (BraTS), MIASSR achieves comparable fidelity performance and the best perceptual quality with the smallest model size. We also employ transfer learning to enable MIASSR to tackle SR tasks of new medical modalities, such as cardiac MR images (ACDC) and chest computed tomography images (COVID-CT). The source code of our work is also public. Thus, MIASSR has the potential to become a new foundational pre-/post-processing step in clinical image analysis tasks such as reconstruction, image quality enhancement, and segmentation. [ABSTRACT FROM AUTHOR]

Edge is basically the symbol and reflection of partial image discreteness. It is one of the most commonly used operations in image processing and pattern recognition, it contains a wealth of internal information leading to strong interpretation of image. Resisting against noise, illumination and extracting appropriate features from an image is a great challenge in many computer vision applications. Indeed this topic participates to reduce the handled information and focuses on those related to existing objects. Efficient and accurate edge detection will lead to increase in the performance of many computer vision applications, including image segmentation, object-based image coding and image retrieval. Contour detection contributes to locate pixel sets which correspond to sudden intensities variation, these unstable properties of the given image commonly suggest to important events on going in the scene. In this paper, we present in the first time a novel and robust method for edge detection based on joint and conditional entropy when we highlight a Shannon theory, the second part of this paper is dedicated to decision making of edge pixels membership by intelligent method based on fuzzy logic tool. [ABSTRACT FROM AUTHOR]

Biomedical signal/image processing and analysis are always fascinating tasks in scientific researches, both theoretical and practical. One of the powerful tools in such topics is wavelet theory which has been proved to be challenging since its discovery. One of the best measures of the optimality of reconstruction of signals/images is the well-known Shannon's entropy. In wavelet theory, this is very well known and researchers are familiar with it. In the present work, a step forward is proposed based on more general wavelet tools. New approach is proposed for the reconstruction of signals/images provided with multiwavelets Shannon-type entropy to evaluate the order/disorder of the reconstructed signals/images. Efficiency and accuracy of the approach is confirmed by a simulation study on several models such as ECG, EEG and DNA/Proteins' signals. [ABSTRACT FROM AUTHOR]

A biologically inspired image fusion mechanism is analyzed in this paper. A pseudo-color image fusion method is proposed based on the improvement of a traditional method. The proposed model describes the fusion process using several abstract definitions which correspond to the detailed behaviors of neurons. Firstly, the infrared image and visible image are respectively ON against enhanced and OFF against enhanced. Secondly, we feed back the enhanced visible images given by the ON-antagonism system to the active cells in the center-surrounding antagonism receptive field. The fused VISIR signal are obtained by feeding back the OFF-enhanced infrared image to the corresponding surrounding-depressing neurons. Then we feed back the enhanced visible signal from OFF-antagonism system to the depressing cells in the center-surrounding antagonism receptive field. The ON-enhanced infrared image is taken as the input signal of the corresponding active cells in the neurons, then the cell response of infrared-enhance-visible is produced in the process, it is denoted as IRVIS. The three kinds of signal are considered as R, G and B components in the output composite image. Finally, some experiments are performed in order to evaluate the performance of the proposed method. The information entropy, average gradient and objective image fusion measure are used to assess the performance of the proposed method objectively. Some traditional digital signal processing-based fusion methods are also evaluated for comparison in the experiments. In this paper, the Quantitative assessment indices show that the proposed fusion model is superior to the classical Waxman's model, and some of its performance is better than the other image fusion methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel scheme to learn high-level representative features and conduct classification for hyperspectral image (HSI) data in an automatic fashion. The proposed method is a collaboration of a wavelet-based extended morphological profile (WTEMP) and a deep autoencoder (DAE) ('WTEMP-DAE'), with the aim of exploiting the discriminative capability of DAE when using WTEMP features as the input. Each part of WTEMP-DAE is ingenious and contributes to the final classification performance. Specifically, in WTEMP-DAE, the spatial information is extracted from the WTEMP, which is then joined with the wavelet denoised spectral information to form the spectral-spatial description of HSI data. The obtained features are fed into DAE as the original input, where the good weights and bias of the network are initialized through unsupervised pre-training. Once the pre-training is completed, the reconstruction layers are discarded and a logistic regression (LR) layer is added to the top of the network to perform supervised fine-tuning and classification. Experimental results on two real HSI data sets demonstrate that the proposed strategy improves classification performance in comparison with other state-of-the-art hand-crafted feature extractors and their combinations. [ABSTRACT FROM AUTHOR]

In this study, we propose a new framework for 3D porous media shape characterization using quantitative data driven shape descriptors. The main conceptual innovations are the introduction of shape descriptors based on geometrical parameters of the shape and empirical mode decomposition (EMD) of the shape skeleton. The EMD yields a decomposition with intrinsic mode functions based on local properties of the skeleton. Efficient methods for computing the defined descriptors from a network of balls representation of the pore space are provided. A multivariate analysis is carried out to describe the relationships between the shape descriptors and justify their use in the context of shape analysis. The proposed descriptors are also used to group together objects by their similarity and perform optimal approximation of the porous media by primitives set. Properties on the approximation are given and discussed. The proposed framework is illustrated with computed tomography images of real soil sample. The results have demonstrated the performance of our methodology for shape characterization, optimal shape decomposition and comparison. [ABSTRACT FROM AUTHOR]

Region of interest (ROI) is the most important part of an image that expresses the effective content of the image. Extracting regions of interest from images accurately and efficiently can reduce computational complexity and is essential for image analysis and understanding. In order to achieve the automatic extraction of regions of interest and obtain more accurate regions of interest, this paper proposes Optimized Automatic Seeded Region Growing (OASRG) algorithm. The algorithm uses the affinity propagation (AP) clustering algorithm to extract the seeds automatically, and optimizes the traditional region growing algorithm by regrowing strategy to obtain the regions of interest where target objects are contained. Experimental results show that our algorithm can automatically locate seeds and produce results as good as traditional region growing with seeds selected manually. Furthermore, the precision is improved and the extraction effect is better after the optimization with regrowing strategy. [ABSTRACT FROM AUTHOR]

The article proposes an adaptive, noise-exclusive and assessment-based fuzzy switching median filter for high intensity impulse noise. The filter adaptively changes the size of sliding window in accordance with noise intensity and noise-exclusive operation is performed only on noise-free pixels. These steps thus preserve the image details effectively. Judicious assessment of the previously restored pixels is made in order to use them in replacing the noisy pixels and further improve the efficiency of filter. With these possibilities, fuzzy switching median filter-based approach is also incorporated for improving the performance. Experimental results justified the efficacy of the proposed fuzzy-based filtering method qualitatively and quantitatively for twelve different highly corrupted noisy images, and showed significant improvement to existing algorithms. [ABSTRACT FROM AUTHOR]

Localization of texts in natural images could be an important stage in many applications such as content-based image retrieval, visual impairment assistance systems, automatic robot navigation in urban environments and tourist assistance systems. However due to the variations of font, script, scale, orientations, color, shadow and lighting conditions, robust scene text localization is still a challenging task. In this paper, we propose a novel method to localize not only Farsi/Arabic and Latin texts with different sizes, fonts and orientations but also low luminance contrast and poor quality ones in the natural images taken with uneven illumination conditions. Firstly, fast weighted median filtering as a nonlinear edge-preserving smoothing filter and then color contrast preserving decolorization are exploited to make the text localization system more robust for low luminance contrast and poor quality texts. In order to extract the Farsi/Arabic and Latin scene texts and also filter the nontext ones, a unified framework is proposed incorporating the maximally stable extremal regions and a novel proposed region detector called Stable Width Stroke Regions which is based on closed boundary regions. Phase congruency and Laplacian operators are exploited to extract the closed boundary regions. Finally, to extract the single text lines, the Meanshift clustering and radon transform were used. Experimental results show that the proposed method localize low luminance contrast and low quality scene texts for both Farsi/Arabic and Latin scripts encouragingly. [ABSTRACT FROM AUTHOR]

The present work focuses on the development of a novel computer-based approach for tear ferning (TF) featuring. The original TF images of the recently developed five-point grading scale have been used to assign a grade for any TF image automatically. A vector characteristic (VC) representing each grade was built using the reference images. A weighted combination between features selected from textures analysis using gray level co-occurrence matrix (GLCM), power spectrum (PS) analysis and linear specificity of the image were used to build the VC of each grade. A total of 14 features from texture analysis were used. PS at different frequency points and number of line segments in each image were also used. Five features from GLCM have shown significant differences between the recently developed grading scale images which are: angular second moment at 0° and 45°, contrast, and correlation at 0° and 45° these five features were all included in the characteristic vector. Three specific power frequencies were used in the VC because of the discrimination power. Number of line segments was also chosen because of dissimilarities between images. A VC for each grade of TF reference images was constructed and was found to be significantly different from each other's. This is a basic and fundamental step toward an automatic grading for computer-based diagnosis for dry eye. [ABSTRACT FROM AUTHOR]

Image completion is an active and interesting research area in image processing and computer graphics. Restoration and retouching of damaged areas in an undetectable form is the objective of image completion techniques. Most of the recently developed video completion methods are extensions of image completion techniques to restore the damaged frames. With respect to video completion challenges and image completion future work, we survey existing methods and introduce a new classification. The methods in each category are described in detail. In the second part of the paper, we provide a comparison and evaluation study between the most recent image completion methods qualitatively as well as quantitatively. For a fair comparison, we introduced a new dataset and evaluated four available image completion methods on the same hardware. Experimental results are conducted to highlight the strengths and drawbacks of each image completion method. [ABSTRACT FROM AUTHOR]

Optical Music Recognition (OMR) is a technique for converting printed musical documents into computer readable formats. In this paper, we present a simple OMR system that can perform well for ordinary musical documents such as ballad and pop music. This system is constructed based on fundamental image processing and pattern recognition techniques, thus it is easy to implement. Moreover, this system has a strong capability in skew restoration and inverted musical score detection. From a series of experiments, the error for our skew restoration is below 0.2° for any possible document rotation and the accuracy of inverted musical score detection is up to 98.89%. The overall recognition accuracy of our OMR can achieve to nearly 97%, a figure comparable with current commercial OMR software. However, if taking into image skew into consideration, our system is superior to commercial software in terms of recognition accuracy. [ABSTRACT FROM AUTHOR]

In this paper, an efficient preprocessing module has been described which focuses on building a trademark database that can be used for developing a trademark retrieval system. The preprocessing module focuses on noise removal from the trademark images using an adaptive filtering technique using Wiener filters, followed by Karhunen-Loève transform that makes the trademark search process rotation invariant by rotating the object along positive y direction. Since the registered trademarks are huge in number and will increase invariantly in the future it will be strenuous for the search system to search for similarity in such huge database. Intention was to reduce the search space hence fuzzy clustering has been applied. All these preprocessing steps make a retrieval system more efficient and reduce computation cost. [ABSTRACT FROM AUTHOR]

In many modern image processing applications determining quality of the image is one of the most challenging tasks. Researchers working in the field of image quality assessment design algorithms for measuring and quantifying image quality. The human eye can identify the difference between a good quality image and a noisy image by simply looking at the image, but designing a computer algorithm to automatically determine the quality of an image is a very challenging task. In this paper, we propose an image quality measure using the concept of object separability. We define object separability using variance. Two objects are very well separated if variance of individual object is less and mean pixel values of neighboring objects are very different. Degradation in images can be due to a number of reasons like additive noises, quantization defects, sampling defects, etc. The proposed no-reference image quality measure will determine quality of degraded images and differentiate between good and degraded images. [ABSTRACT FROM AUTHOR]

The region-based segmentation paradigm is a well known technique for image segmentation. In the first part of this work the robustness of region-based algorithms is studied. It is shown that within a small parameter range, which leads to good segmentation results in the majority of cases, bad segmentation results may occur. In fact, such local instability is a problem of region-based methods and reasons for its occurrence are discussed. In the second part of the work, an ensemble solution for this problem based on the median concept is proposed. Two variants, set median and generalized median, are presented and experimentally compared. Extensive experimental results demonstrate the potential of the proposed median approach for solving the instability problem. [ABSTRACT FROM AUTHOR]

Edge detection is often regarded as a basic step in range image processing by virtue of its crucial effect. The majority of existing edge detection methods cannot satisfy the requirement of efficiency in many industrial applications due to huge computational costs. In this paper, a novel instantaneous method, named RIDED-2D is proposed for denoising and edge detection for 2D scan line in range images. In the method, silhouettes of 2D scan line are classified into eight types by defining a few new coefficients. Several discriminant criteria on large noise filtering and edge detection are stipulated based on qualitative feature analysis on each type. Selecting some feature point candidates, a practical parameter learning method is provided to determine the threshold set, along with the implementation of an integrated algorithm by merging calculation steps. Because all the coefficients are established based on distances among the points or their ratio, RIDED-2D is inherently invariant to translation and rotation transformations. Furthermore, a forbidden region approach is proposed to eliminate interference of the mixed pixels. Key performances of RIDED-2D are evaluated in detail by including computational complexity, time expenditure, accuracy and stability. The results indicate that RIDED-2D can detect edge points accurately from several real range images, in which large noises and systematic noises are involved, and the total processing time is less than 0.1 millisecond on an ordinary PC platform using the integrated algorithm. Comparing with other state-of-the-art edge detection methods qualitatively, RIDED-2D exhibits a prominent advantage on computational efficiency. Thus, the proposed method qualifies for real-time processing in stringent industrial applications. Besides, another contribution of this paper is to introduce CPU clock counting technique to evaluate the performance of the proposed algorithm, and suggest a convenient and objective way to estimate the algorithm's time expenditure in other platforms. [ABSTRACT FROM AUTHOR]

Texture analysis plays a vital role in image processing. The prospect of texture based image analysis depends on the texture features and the texture model. This paper presents a new texture feature extraction method 'Fuzzy Local Texture Patterns (FLTP)' and 'Fuzzy Pattern Spectrum (FPS)', suitable for texture analysis. The local image texture is described by FLTP and the global image texture is described by FPS. The proposed method is tested with texture classification, texture segmentation and texture edge detection. The results show that the proposed method provides a very good and robust performance for texture analysis. [ABSTRACT FROM AUTHOR]

Recognition of engineering drawing entities is one of the most difficult stages in engineering drawing interpretation and many attempts to recognize various types of ED entities have been made. In this paper, we review algorithms for the recognition of ED entities, especially dimensions and crosshatching areas. For the recognition of dimensions, we analyze how dimension texts can be separated from graphics and how arrowheads of dimension lines are recognized. We also analyze the recent systems of ED interpretation. Finally, future tasks are discussed. [ABSTRACT FROM AUTHOR]

The Fisher–Rao metric on the parameter space for the set of lines in a two-dimensional convex image is approximated under the assumption that the errors in the measurements are small. The volume of the parameter space under the approximating metric is proportional to the area of the image under the Euclidean metric. In the case of a rectangular image, expressions for the approximating metric are obtained and an algorithm is given for sampling the parameter space. The sample points are used in an algorithm for detecting lines in a rectangular image. Experimental results are reported. In the case of a disc shaped image the parameter space for lines embeds isometrically, under the approximating metric, into three-dimensional Euclidean space. [ABSTRACT FROM AUTHOR]

With the rapid development of multi-detector computed tomography (MDCT) that results in improving the temporal and spatial resolution of patient data, clinical use of computed tomographic angiography (CTA) is increasing. Vessel segmentation can be challenging in CTA, but is needed for isolation of vascular structures. In this paper, a novel method for computation of vesselness in CTA images is presented, including a CTA transfer function prior to the vesselness computation for reducing the artifacts caused by the false-positive responses of a Hessian-based line filter, as well as a hierarchical structure, called MIP-volume pyramid, for accelerating the computation of vesselness. Using the computed vesselness, we present an interactive segmentation method for each individual vessel by applying a vesselness speed function in a fast marching level set method. Our new method was shown to provide an effective and efficient way that allows vesselness to be applied to large CTA images. This method has been implemented successfully in CTA vessel segmentation and evaluation. [ABSTRACT FROM AUTHOR]

Following the rapid advance in applications of Magnetic Resonance (MR) imaging, there is an upsurge in contemporary image analysis. Image analysis techniques are implemented in different ways in the various frameworks of image processing. This paper proposes a new statistical approach. It first analyzes statistical properties of MR data (bulk magnetizations, MR signals, and k-space samples); then based on these statistics and through MR image reconstruction, derives statistical properties of MR image (a single pixel, any two pixels, and a group of pixels — i.e. an image region); finally by using these image statistics establishes a new stochastic image model (the correlated Finite Normal Mixture (cFNM)) and develops a new statistical image analysis method (the extended Expectation-Maximization (eEM) algorithm). The results obtained by using this cFNM-eEM framework demonstrate promise. The novel feature of this approach is that it eliminates heuristic assumptions and ad-hoc conditions in currently employed image analysis techniques and therefore leads to more accurate image analysis results. Although this approach is developed for the conventional MRI, but, following the same strategy and with some modifications, it can be extended to more advanced MRI protocols and other medical imaging modalities. [ABSTRACT FROM AUTHOR]

Digital model of human metabolic system incorporated with real anatomical structure can be seen as an important tool in order to understand and study the processes of metabolism. This paper presented a whole body digital model framework of [18F]2-fluoro-2-deoxy-D-glucose (FDG) metabolism based on the segmented Visible Human Project (VHP) data set as the anatomical structure framework. First, the blood time-activity curve (BTAC) and the tissues time-activity curves (TTAC) were obtained from kinetic analysis. According to the obtained BTAC and TTACs, a set of corresponding values were assigned to the segmented VHP image frames. Thus a set of dynamic volume data were derived to show the 18F distribution in human body for the predetermined sampling schedule. Finally, the simulated FDG distribution was visualized by coronal, axial, and sagittal slices and volume rendering method. The model established here was especially for the metabolism of FDG; however, the modeling method is general for all tracers. The proposed model represented functional information of dynamical process of human metabolism with high visualization resolution. The results of modeling and visualization provide a useful tool for education and research on nuclear imaging, and may provide a novel technical platform for the study of pharmacokinetics. [ABSTRACT FROM AUTHOR]

In a research context in which multiple and well-behaved Surface Reconstruction algorithms already exist, the main goal is not to implement a visualization toolkit able render complex object, but the implementation of methods which can improve our knowledge on the observed world. This work presents a general Surface Reconstruction framework which encapsulates the uncertainty of the sampled data, making no assumption on the shape of the surface to be reconstructed. Starting from the input points (either points clouds or multiple range images), an Estimated Existence Function (EEF) is built which models the space in which the desired surface could exist and, by the extraction of EEF critical points, the surface is reconstructed. The final goal is the development of a generic framework that is able to adapt the result to different kinds of additional information coming that is from multiple sensors. [ABSTRACT FROM AUTHOR]

The paper developed an approach to extract the VOI (Volume of Interest) from a CT dataset based on volume rendering, which can get a rough VOI from the volumetric data by simply adjusting the Window Level and the Window Width, then enhances the contrast among the voxels according to the Linear General Fuzzy Operator (LGFO) and extracts a desired structure from the above-mentioned enhanced 3D data through the feature function in rapid sequence; Our method adjusts the parameters according to the condition until the satisfied VOI is extracted. Experimental results show that the method combined with multi-manner can extract the VOI which represents clearly three dimensional anatomical structure of the object, such as tumors or normal organs, and can find potential applications in diagnosis and education. [ABSTRACT FROM AUTHOR]

Multiscale image analysis has been used successfully in a number of applications to segment image features according to their relative scale. In this paper, we present a new framework for the hierarchical segmentation of gray level image. The proposed scheme comprises a nonlinear scale-space and morphological gradient watersheds. Our aim is to produce a meaningful hierarchy among the objects in the image. The scale-space is based on morphological anisotropic diffusion that uses reconstruction morphological operators. Furthermore, we introduce the method to reconstruct morphological operators and the principle of the dynamics of contours in scale-space that combines scale and contrast information. The performance of the proposed segmentation scheme is presented via experimental results obtained with a wide range of image including natural scenes. [ABSTRACT FROM AUTHOR]

A new feature based image mosaic method is presented in this paper. Our main contribution is an improved RANSAC algorithm based on the modified median flow filter to find the inliers and the homography between the images. This algorithm overcomes the limit of local feature detection and thus improves the stability of homography calculation. Except for the RANSAC algorithm, PCA-SIFT is used to detect features because it is more compact, more robust and faster than the standard SIFT. The priority search is also used to match the feature points in a faster way than BBF search. Experiments show that our method is robust for image mosaic. [ABSTRACT FROM AUTHOR]

Moments are widely used in pattern recognition, image processing, computer vision and multi-resolution analysis. It can also be use to detect the moving objects. In this paper, we address the problems about detecting moving objects in a video stream obtained by a fixed camera. To detect the moving objects, a method is proposed using the fuzzy integrating moment that combines the 1st, 3rd and 5th order temporal orthogonal Gaussian-Hermite moments and it takes into account the non-symmetric membership function π. To evaluate the performances of moving detection, the performance comparisons of different methods are carried out. The experiment results show good performance of our method for detecting moving objects. [ABSTRACT FROM AUTHOR]

The mean shift algorithm is an efficient way for object tracking. However, there is presently no clear mechanism for selecting kernel bandwidth while the object is changing in size. This paper presents a novel bandwidth selection method for mean shift based rigid object tracking. The kernel bandwidth is updated by discovering the scale parameters of the object's affine model that are estimated by using the correspondences between the corner object in two consecutive frames. The centroid of the object is registered by a special backward tracking method. Therefore, we can not only get translation parameter to simplify affine model but also improve the accuracy of finding corner correspondences. In addition, the M-estimate method is employed to reject mismatched pairs (outliers) so as to get better regression results. We have applied the proposed method to track vehicles changing in size with encouraging results. [ABSTRACT FROM AUTHOR]

This paper investigates adult images detection based on the shape features of skin regions. In order to accurately detect skin regions, we propose a skin detection method using multi-Bayes classifiers in the paper. Based on skin color detection results, shape features are extracted and fed into a boosted classifier to decide whether or not the skin regions represent a nude. We evaluate adult image detection performance using different boosted classifiers and different shape descriptors. Experimental results show that classification using boosted C4.5 classifier and combination of different shape descriptors outperforms other classification schemes. [ABSTRACT FROM AUTHOR]

In [Westhoff et al., 2005], we proposed a novel method to determine illumination-invariant features in images. The quantitative bilateral symmetry of a given scene is computed using dynamic programming before applying the resulting symmetry image and feature vector signatures to an omnidirectional motion tracking task. Compared to other approaches to symmetry detection in computer vision, the results of our novel algorithm describe symmetry in terms of an absolute region instead of a relative degree. In this paper, we describe further optimizations and details of the proposed Dynamic Programming Symmetry feature detection algorithm (DPS algorithm). [ABSTRACT FROM AUTHOR]

We study the problem of thresholding the residual of template matching as a preprocess for selecting the correct matches between feature points in two images. In order to determine the threshold dynamically, we introduce a statistical model of the residual and compute an optimal threshold according to that model. The model parameters are estimated from the histogram of the residuals of candidate matches. Using real images, we show that our method can substantially upgrade the quality of the initial matches by simply adjusting the threshold. [ABSTRACT FROM AUTHOR]

We present a new method for the automatic detection of circular objects in images: we detect an osculating circle to an elliptic arc using a Hough transform, iteratively deforming it into an ellipse, removing outlier pixels, and searching for a separate edge. The voting space for the Hough transform is restricted to one and two dimensions for efficiency, and special weighting schemes are introduced to enhance the accuracy. We demonstrate the effectiveness of our method using real images. Finally, we apply our method to the calibration of a turntable for 3D object shape reconstruction. [ABSTRACT FROM AUTHOR]

The focus of this paper is optimizing the recognition of vehicles in Synthetic Aperture Radar (SAR) imagery by exploiting the azimuthal variance of scatterers using multiple SAR recognizers at different look angles. The variance of SAR scattering center locations with target azimuth leads to recognition system results at different azimuths that are independent, even for small azimuth deltas. Extensive experimental recognition results are presented in terms of receiver operating characteristic (ROC) curves to show the effects of multiple look angles on recognition performance for MSTAR vehicle targets with configuration variants, articulation, and occlusion. [ABSTRACT FROM AUTHOR]

In this article, we present a fast and accurate stereo vision-based system that detects and tracks road obstacles, as well as computes their 3D position and their axial motion. To do so, axial motion maps are constructed and the inclination angles of 3D straight segments are computed. 3D straight segments are obtained after the construction of 3D sparse maps based on dynamic programming and multi-criteria analysis. Axial motion maps are computed from a sequence of dense 3D maps without region matching. [ABSTRACT FROM AUTHOR]

In this paper, we present major issues in video parsing, abstraction, retrieval and semantic analysis. We discuss the success, the difficulties and the expectations in these areas. In addition, we identify important opened problems that can lead to more sophisticated ways of video content analysis. For video parsing, we discuss topics in video partitioning, motion characterization and object segmentation. The success in video parsing, in general, will have a great impact on video representation and retrieval. We present three levels of abstracting video content by scene, keyframe and key object representations. These representation schemes in overall serve as a good start for video retrieval. We then describe visual features, in particular motion, and similarity measures adopted for retrieval. Next, we discuss the recent computational approaches in bridging the semantic gap for video content understanding. [ABSTRACT FROM AUTHOR]

Image processing and analysis in fuzzy set theoretic framework is addressed. Various uncertainties involved in these problems and the relevance of fuzzy set theory in handling them are explained. Different image ambiguity measures based on fuzzy entropy and fuzzy geometry of image subsets are mentioned. A discussion is made on the flexibility in choosing membership functions. Illustrations of commonly used fuzzy image processing operations such as enhancement, edge detection segmentation, skeleton extraction, feature extraction are then provided, along with their significance and characteristics. Their applications to some real life problems, e.g., motion frame analysis, remotely sensed image analysis, modeling face images are finally described. An extensive bibliography is also provided. [ABSTRACT FROM AUTHOR]

No abstract received. [ABSTRACT FROM AUTHOR]

In this paper, we present a binary image registration strategy for the registration of segmented CT scan data of the human head. For the characterization of the 3D skull binary images we adopt a powerful representation of the binary images: the geometric moment invariants (GMIs). They provide pertinent and discriminant information related to the geometry of the binary objects, thereby leading to match points which have similar geometric properties, and also to enhance the quality of the matching process. For the registration algorithm we propose to use the topology-preserving B-spline-based registration method proposed by Noblet et al. for the registration of MRI head images. The algorithm has proven its efficiency and high registration precision. Since the information carried out by the different GMIs is complementary, we study and discuss the importance of combining various GMIs to better guide the matching process. Results obtained using synthetic deformation fields highlight the promising performance of the strategy. [ABSTRACT FROM AUTHOR]

Distributed video coding (DVC) is new video coding paradigm for emerging applications such as wireless video cameras, wireless low-power surveillance networks, disposable video cameras, sensor networks, networked camcorders, etc. In traditional video coding standards (MPEG/H.264/DivX/VC1), typically the encoder is five to 10 times more complex than the decoder, which is well suited for broadcast and streaming video-on-demand systems, where video is compressed once and decoded many times. However, the emerging applications require dual system, i.e. low complex encoders, possibly at the expense of high complex decoders. Here, low complexity encoders are must because memory, computational power and energy are scarce at the encoder. Distributed coding exploits source statistics in decoder and hence encoder can be very simple, at the expense of the more complex decoder. In literature, various DVC Architectures proposed depend on availability of feedback channel from decoder to encoder, to achieve minimum rate for target quality. In practical systems usually bidirectional communication channels are not available. Other implications are in terms of decoding delay and decoder complexity, due to usage of feedback channel. Hence it is highly desirable to design DVC without need for feedback channel. In this paper, feedback-free DVC Architecture is proposed and C model implementation results are presented. [ABSTRACT FROM AUTHOR]

In this paper, we propose a global and local Chan-Vese model which utilizes both global image information and local image information for image segmentation. We define an energy functional with a global term, which incorporates global image information to improve the robustness of the proposed method, and a local term which is dominant near the object boundaries. The regularization term is added to the energy functional to avoid the time-consuming re-initialization. The comparisons with the C-V model, LBF model and LGIF model show that our model can segment images with intensity inhomogeneity in less iteration steps and take less time. [ABSTRACT FROM AUTHOR]

Although many image restoration methods have been proposed recently, they do not result in a good reconstructed image when the missing region is large or contains lines and edges, because the direction of lines is not easy to predict. This paper proposes a line prediction method to predict the lines and edges in the missing regions. Furthermore, gray levels of pixels are usually smooth in images. Thus, smooth gray-level detection is proposed to restore the missing regions using the surrounded existing regions according to the smoothness of pixels in the image. Therefore, this paper proposes a novel image restoration method based on smooth gray-level detection, and a line prediction method. Experimental results reveal that the proposed method outperforms other methods. [ABSTRACT FROM AUTHOR]