Search

Presents an adaptive algorithm for image enhancement. Advantage of linear unsharp masking schemes; Discussion on the adaptive contrast enhancement algorithm; Objective of adaptation algorithm; Result of experiments on the application of schemes for image enhancement.

We address single image super-resolution using a statistical prediction model based on sparse representations of low- and high-resolution image patches. The suggested model allows us to avoid any invariance assumption, which is a common practice in sparsity-based approaches treating this task. Prediction of high resolution patches is obtained via MMSE estimation and the resulting scheme has the useful interpretation of a feedforward neural network. To further enhance performance, we suggest data clustering and cascading several levels of the basic algorithm. We suggest a training scheme for the resulting network and demonstrate the capabilities of our algorithm, showing its advantages over existing methods based on a low- and high-resolution dictionary pair, in terms of computational complexity, numerical criteria, and visual appearance. The suggested approach offers a desirable compromise between low computational complexity and reconstruction quality, when comparing it with state-of-the-art methods for single image super-resolution. [ABSTRACT FROM PUBLISHER]

We propose a new mathematical and algorithmic framework for unsupervised image segmentation, which is a critical step in a wide variety of image processing applications. We have found that most existing segmentation methods are not successful on histopathology images, which prompted us to investigate segmentation of a broader class of images, namely those without clear edges between the regions to be segmented. We model these images as occlusions of random images, which we call textures, and show that local histograms are a useful tool for segmenting them. Based on our theoretical results, we describe a flexible segmentation framework that draws on existing work on nonnegative matrix factorization and image deconvolution. Results on synthetic texture mosaics and real histology images show the promise of the method. [ABSTRACT FROM AUTHOR]

We present the first ratio-based image segmentation method that allows imposing curvature regularity of the region boundary. Our approach is a generalization of the ratio framework pioneered by Jermyn and Ishikawa so as to allow penalty functions that take into account the local curvature of the curve. The key idea is to cast the segmentation problem as one of finding cyclic paths of minimal ratio in a graph where each graph node represents a line segment. Among ratios whose discrete counterparts can be globally minimized with our approach, we focus in particular on the elastic ratio $ \displaystyle\int_0^\cal L(C)\nabla I(C(s))\cdot(C^\prime(s))^\perp\, ds\over \displaystyle \nu\, \cal L(C)+\int_0^\cal L(C)\vert \kappa_C(s)\vert ^q\,ds $that depends, given an image I, on the oriented boundary C of the segmented region candidate. Minimizing this ratio amounts to finding a curve, neither small nor too curvy, through which the brightness flux is maximal. We prove the existence of minimizers for this criterion among continuous curves with mild regularity assumptions. We also prove that the discrete minimizers provided by our graph-based algorithm converge, as the resolution increases, to continuous minimizers. In contrast to most existing segmentation methods with computable and meaningful, i.e., nondegenerate, global optima, the proposed approach is fully unsupervised in the sense that it does not require any kind of user input such as seed nodes. Numerical experiments demonstrate that curvature regularity allows substantial improvement of the quality of segmentations. Furthermore, our results allow drawing conclusions about global optima of a parameterization-independent version of the snakes functional: the proposed algorithm allows determining parameter values where the functional has a meaningful solution and simultaneously provides the corresponding global solution. [ABSTRACT FROM PUBLISHER]

Conventional linear subspace learning methods like principal component analysis (PCA), linear discriminant analysis (LDA) derive subspaces from the whole data set. These approaches have limitations in the sense that they are linear while the data distribution we are trying to model is typically nonlinear. Moreover, these algorithms fail to incorporate local variations of the intrinsic sample distribution manifold. Therefore, these algorithms are ineffective when applied on large scale datasets. Kernel versions of these approaches can alleviate the problem to certain degree but face a serious computational challenge when data set is large, where the computing involves Eigen/QP problems of size N\,\times\,N. When N is large, kernel versions are not computationally practical. To tackle the aforementioned problems and improve recognition/searching performance, especially on large scale image datasets, we propose a novel local subspace indexing model for image search termed Subspace Indexing Model on Grassmann Manifold (SIM-GM). SIM-GM partitions the global space into local patches with a hierarchical structure; the global model is, therefore, approximated by piece-wise linear local subspace models. By further applying the Grassmann manifold distance, SIM-GM is able to organize localized models into a hierarchy of indexed structure, and allow fast query selection of the optimal ones for classification. Our proposed SIM-GM enjoys a number of merits: 1) it is able to deal with a large number of training samples efficiently; 2) it is a query-driven approach, i.e., it is able to return an effective local space model, so the recognition performance could be significantly improved; 3) it is a common framework, which can incorporate many learning algorithms. Theoretical analysis and extensive experimental results confirm the validity of this model. [ABSTRACT FROM PUBLISHER]

In this work, we investigate how illuminant estimation techniques can be improved, taking into account automatically extracted information about the content of the images. We considered indoor/outdoor classification because the images of these classes present different content and are usually taken under different illumination conditions. We have designed different strategies for the selection and the tuning of the most appropriate algorithm (or combination of algorithms) for each class. We also considered the adoption of an uncertainty class which corresponds to the images where the indoor/outdoor classifier is not confident enough. The illuminant estimation algorithms considered here are derived from the framework recently proposed by Van de Weijer and Gevers. We present a procedure to automatically tune the algorithms' parameters. We have tested the proposed strategies on a suitable subset of the widely used Funt and Ciurea dataset. Experimental results clearly demonstrate that classification based strategies outperform general purpose algorithms. [ABSTRACT FROM AUTHOR]

Presents a study that developed a dual-metric direct binary search (DBS) algorithm that effectively provides a tone-dependent human visual system (HVS) model without a large increase in computational complexity. Classification of halftoning algorithms; Relationship between HVS/printer models and halftoning algorithms; Phases of DBS algorithm.

Presents an algorithm based on the winner-update strategy which utilizes an ascending lower bound list of the matching error to determine the temporary winner. Reasons for the ability of the algorithm to increase the computation speed of the block matching; Calculation of the lower bound from both partial accumulation and Minkowski's inequality; Combination with the three-step search algorithm.

Deals with a study which compared a set of algorithms for object localization and recognition of three-dimensional objects from single two-dimensional images. Discussion on the pose constraints; Determination of vehicle orientation; Determination of vehicle location.

Presents information on a study which proposed an adaptive algorithm for determining a stack filter that minimizes the mean absolute error criterion. Review of optimal stack filtering algorithms; Results; Comparison between adaptive algorithm and FASTAF algorithm; Conclusion.

Focuses on a study which addressed the problem of restoring an image from its noisy convolutions with two or more unknown finite impulse response (FIR) filters. Types of algorithms; Effect of the blind deconvolution problem; Reason nonblind convolution is a problem.

In image classification tasks, one of the most successful algorithms is the bag-of-features (BoFs) model. Although the BoF model has many advantages, such as simplicity, generality, and scalability, it still suffers from several drawbacks, including the limited semantic description of local descriptors, lack of robust structures upon single visual words, and missing of efficient spatial weighting. To overcome these shortcomings, various techniques have been proposed, such as extracting multiple descriptors, spatial context modeling, and interest region detection. Though they have been proven to improve the BoF model to some extent, there still lacks a coherent scheme to integrate each individual module together. To address the problems above, we propose a novel framework with spatial pooling of complementary features. Our model expands the traditional BoF model on three aspects. First, we propose a new scheme for combining texture and edge-based local features together at the descriptor extraction level. Next, we build geometric visual phrases to model spatial context upon complementary features for midlevel image representation. Finally, based on a smoothed edgemap, a simple and effective spatial weighting scheme is performed to capture the image saliency. We test the proposed framework on several benchmark data sets for image classification. The extensive results show the superior performance of our algorithm over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

When dealing with motion blur, there is an inevitable tradeoff between the amount of blur and the amount of noise in the acquired images. The effectiveness of any restoration algorithm typically depends on these amounts, and it is difficult to find their best balance in order to ease the restoration task. To face this problem, we provide a methodology for deriving a statistical model of the restoration performance of a given deblurring algorithm in case of arbitrary motion. Each restoration-error model allows us to investigate how the restoration performance of the corresponding algorithm varies as the blur due to motion develops. Our modeling treats the point-spread-function trajectories as random processes and, following a Monte Carlo approach, expresses the restoration performance as the expectation of the restoration error conditioned on some motion-randomness descriptors and on the exposure time. This allows us to coherently encompass various imaging scenarios, including camera shake and uniform (rectilinear) motion, and, for each of these, identify the specific exposure time that maximizes the image quality after deblurring. [ABSTRACT FROM AUTHOR]

We develop an efficient general-purpose blind/no-reference image quality assessment (IQA) algorithm using a natural scene statistics (NSS) model of discrete cosine transform (DCT) coefficients. The algorithm is computationally appealing, given the availability of platforms optimized for DCT computation. The approach relies on a simple Bayesian inference model to predict image quality scores given certain extracted features. The features are based on an NSS model of the image DCT coefficients. The estimated parameters of the model are utilized to form features that are indicative of perceptual quality. These features are used in a simple Bayesian inference approach to predict quality scores. The resulting algorithm, which we name BLIINDS-II, requires minimal training and adopts a simple probabilistic model for score prediction. Given the extracted features from a test image, the quality score that maximizes the probability of the empirically determined inference model is chosen as the predicted quality score of that image. When tested on the LIVE IQA database, BLIINDS-II is shown to correlate highly with human judgments of quality, at a level that is competitive with the popular SSIM index. [ABSTRACT FROM AUTHOR]

Active contours are very popular tools for video tracking and image segmentation. Parameterized contours are used due to their fast evolution and have become the method of choice in the Sobolev context. Unfortunately, these contours are not easily adaptable to topological changes, and they may sometimes develop undesirable loops, resulting in erroneous results. To solve such topological problems, one needs an algorithm for contour self-crossing detection. We propose a simple methodology via simple techniques from differential topology. The detection is accomplished by inspecting the total net change of a given contour's angle, without point sorting and plane sweeping. We discuss the efficient implementation of the algorithm. We also provide algorithms for locating crossings by angle considerations and by plotting the four-connected lines between the discrete contour points. The proposed algorithms can be added to any parametric active-contour model. We show examples of successful tracking in real-world video sequences by Sobolev active contours and the proposed algorithms and provide ideas for further research. [ABSTRACT FROM PUBLISHER]

In this correspondence, our goal is to develop a visual tracking algorithm that is able to track moving objects in the presence of illumination variations in the scene and that is robust to occlusions. We treat the illumination and motion (x\ -y translation and scale) parameters as the unknown “state” sequence. The observation is the entire image, and the observation model allows for occasional occlusions (modeled as outliers). The nonlinearity and multimodality of the observation model necessitate the use of a particle filter (PF). Due to the inclusion of illumination parameters, the state dimension increases, thus making regular PFs impractically expensive. We show that the recently proposed approach using a PF with a mode tracker can be used here since, even in most occlusion cases, the posterior of illumination conditioned on motion and the previous state is unimodal and quite narrow. The key idea is to importance sample on the motion states while approximating importance sampling by posterior mode tracking for estimating illumination. Experiments demonstrate the advantage of the proposed algorithm over existing PF-based approaches for various face and vehicle tracking. We are also able to detect illumination model changes, e.g., those due to transition from shadow to sunlight or vice versa by using the generalized expected log-likelihood statistics and successfully compensate for it without ever loosing track. [ABSTRACT FROM AUTHOR]

Multispectral image acquisition considerably improves color accuracy in comparison to RGB technology. A common multispectral camera design concept features a filter-wheel consisting of six or more optical bandpass filters. By shifting the filters sequentially into the optical path, the electromagnetic spectrum is acquired through the channels, thus making an approximate reconstruction of the spectrum feasible. However, since the optical filters exhibit different thicknesses, refraction indices and may not be aligned in a perfectly coplanar manner, geometric distortions occur in each spectral channel: The reconstructed RGB images thus show rainbow-like color fringes. To compensate for these, we analyze the optical path and derive a mathematical model of the distortions. Based on this model we present two different algorithms for compensation and show that the color fringes vanish completely after application of our algorithms. We also evaluate our compensation algorithms in terms of accuracy and execution time. [ABSTRACT FROM AUTHOR]

Terahertz imaging makes it possible to acquire images of objects concealed underneath clothing by measuring the radiometric temperatures of different objects on a human subject. The goal of this work is to automatically detect and segment concealed objects in broadband 0.1-1 THz images. Due to the inherent physical properties of passive terahertz imaging and associated hardware, images have poor contrast and low signal to noise ratio. Standard segmentation algorithms are unable to segment or detect concealed objects. Our approach relies on two stages. First, we remove the noise from the image using the anisotropic diffusion algorithm. We then detect the boundaries of the concealed objects. We use a mixture of Gaussian densities to model the distribution of the temperature inside the image. We then evolve curves along the isocontours of the image to identify the concealed objects. We have compared our approach with two state-of-the-art segmentation methods. Both methods fail to identify the concealed objects, while our method accurately detected the objects. In addition, our approach was more accurate than a state-of-the-art supervised image segmentation algorithm that required that the concealed objects be already identified. Our approach is completely unsupervised and could work in real-time on dedicated hardware. [ABSTRACT FROM AUTHOR]

In current level set image segmentation methods, the number of regions is assumed to known beforehand. As a result, it remains constant during the optimization of the objective functional. How to allow it to vary is an important question which has been generally avoided. This study investigates a region merging prior related to regions area to allow the number of regions to vary automatically during curve evolution, thereby optimizing the objective functional implicitly with respect to the number of regions. We give a statistical interpretation to the coefficient of this prior to balance its effect systematically against the other functional terms. We demonstrate the validity and efficiency of the method by testing on real images of intensity, color, and motion. [ABSTRACT FROM AUTHOR]

We extend the standard mean-shift tracking algorithm to an adaptive tracker by selecting reliable features from color and shape-texture cues according to their descriptive ability. The target model is updated according to the similarity between the initial and current models, and this makes the tracker more robust. The proposed algorithm has been compared with other trackers using challenging image sequences, and it provides better performance. [ABSTRACT FROM AUTHOR]

In order to reduce the computation load, many conventional fast block-matching algorithms have been developed to reduce the set of possible searching points in the search window. All of these algorithms produce some quality degradation of a predicted image. Alternatively, another kind of fast block-matching algorithms which do not introduce any prediction error as compared with the full-search algorithm is to reduce the number of necessary matching evaluations for every searching point in the search window. The partial distortion search (PDS) is a well-known technique of the second kind of algorithms. In the literature, many researches tried to improve both lossy and loss- less block-matching algorithms by making use of an assumption that pixels with larger gradient magnitudes have larger matching errors on average. Based on a simple analysis, it is found that, on average, pixel matching errors with similar magnitudes tend to appear in clusters for natural video sequences. By using this clustering characteristic, we propose an adaptive PDS algorithm which significantly improves the computation efficiency of the original PDS. This approach is much better than other algorithms which make use of the pixel gradients. Furthermore, the proposed algorithm is most suitable for motion estimation of both opaque and boundary macroblocks of an arbitrary-shaped object in MPEG-4 coding. [ABSTRACT FROM AUTHOR]

Presents a study which that focused on an algorithm for computing convolutions, a problem in image processing. Notion of a valid digitization of a geometric shape; Description of the convolution algorithm; Methods contained in the canonical convolution algorithm.

Deals with a study which presented an algorithm based on mathematical morphology and curvature evaluation for the detection of vessel-like patterns in a noisy environment. Properties of the vascular tree used in the experiment; Treatment of retinal fluorescein angiographies; Generalization to other retinal images; Robustness and the accuracy of the algorithm; Conclusions.

Presents a study which showed mathematical formulations for modeling object-based scalability and some functionalities that it brings with it. Evaluation of integer programming models that aid in semi-automating the authoring and subsequent selective addition/dropping of objects from a scene to provide content scalability; How additional constraints may be added to impose inter-dependencies among objects; How object aggregation can be exploited in reducing problem complexity.

Focuses on a study which presented a novel algorithm for fast computation of tomographic image projections. Problem statement; Multilevel reprojection; Numerical examples.

Presents information on a study which examined the generation of large deformation diffeomorphisms for landmark matching. Large deformation landmark matching problem; Implementation of the algorithm for the inexact landmark matching case; Results and conclusion.

Presents information on a study which examined a trellis-based algorithm that enables R-D optimum quantization decisions in the H.263+ video coding standard. Overview of the video coding framework; Explanations on trellis construction and processing; Experimental results.

Presents information on a study which examined a novel boundary detection technique based on edge flow for image segmentation. Techniques related to the proposed algorithm; Description of intensity and texture edges; Edge flow propagation and boundary protection.

Introduces a method for reducing the number of distance calculations in the generalized Lloyd algorithm (GLA) for constructing a codebook in vector quantization. Variants of the fast exact GLA; Implementation of the reduced comparison search.

Proposes an efficient algorithm to reduce the computation cost of block matching algorithms for motion estimation in video coding. Role of fast search algorithms in video coding applications; Review of the successive elimination algorithm and its multilevel extension; Calculation of the sum norms of all the blocks and subblocks; Simulation results of the multilevel successive algorithms combined with fast search algorithms.

Deals with the use of LBG algorithm and proposed block-averaging method to design the domain pools based on a proposed iteration-free fractal image codec. Discussion on the fractal cosing scheme technique used for image compression; Focus of studies on fractal coding techniques; Description on the domain pool designs in conventional fractal coding schemes; Details on the domain pool design and the computer simulation.

Presents a graph-theoretic interpretation of convergence of fractal encoding based on iterated function system. Discussion on the conventional encoding and decoding schemes; Algorithms for computation of spectral radius; Characterization of encoded image; Effect of encoding strategies on partition formation.

Presents the introduction of several fast encoding algorithms based on multiple triangle inequalities and wavelet transforms for the vector quantization (VQ) encoding of images. Reason for the use of VQ on signal compression; Definition of VQ images; Issues concerning the design of control points; Summary of the proposed algorithm.

Examines a study which presented a multiplicative algorithm for image reconstruction. Aims; Application to infrared astronomical satellite (IRAS); Difficulties in ringing artifact.

Provides information on the presentation of an unsupervised texture segmentation algorithm based on feature extraction using multichannel Gabor filtering. Information on segmentation with knowledge feedback; Detailed information on the experimental results; Conclusion reached.

Provides information on the presentation of subpixel registration algorithm that minimizes the mean square intensity difference between a reference and a test data set which can be images (two-dimensional) or volumes (three-dimensional). Information on the registration procedure; Detailed information on the interpolation model; Conclusion reached.

In the original paper named above (ibid., vol. 20, no. 9, pp. 2627-2635, Sep 2011), Table II incorrectly appeared as a duplicate of Table III. Instead, Table II should have correctly appeared as shown here. [ABSTRACT FROM AUTHOR]

Deals with a study on a fast full search algorithm for entropy-constrained vector quantization (ECVQ) and other Lagrangian distortion measures. Evaluation of the boundaries between ECVQ codewords; Implementation of the algorithm; Experimental results.