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Introduction: The electrocardiogram (ECG) plays an important role in the diagnosis of heart diseases. However, most patterns of diseases are based on old datasets and stepwise algorithms that provide limited accuracy. Improving diagnostic accuracy of the ECG can be done by applying machine learning algorithms. This requires taking existing scanned or printed ECGs of old cohorts and transforming the ECG signal to the raw digital (time (milliseconds), voltage (millivolts)) form. Objectives: We present a MATLAB-based tool and algorithm that converts a printed or scanned format of the ECG into a digitized ECG signal. Methods: 30 ECG scanned curves are utilized in our study. An image processing method is first implemented for detecting the ECG regions of interest and extracting the ECG signals. It is followed by serial steps that digitize and validate the results. Results: The validation demonstrates very high correlation values of several standard ECG parameters: PR interval 0.984 +/-0.021 (p-value

Several miniaturized viscometers, or microviscometers, have been developed exploiting numerous rapid prototyping techniques. Among them, paper microstrips, famously known as microfluidic paper-based analytical devices ($\mu $ PADs), have become popular due to their cost-efficacy, simple fabrication, fast response, and easily disposable. Many fabrication methods are existing to develop paper microstrips. Herein, an alternative fabrication method is proposed where fused deposition modeling (FDM)-based 3-D printer (3DP) has been employed using polycaprolactone (PCL) filament. F, image processing has been utilized to measure viscosity in such microfluidic domain. Viscosity was calculated by measuring the time taken by the fluid to cover a fixed length between two spots in the microchannel based on the programed and color-coded regions-of-interest. The image processing program was developed considering the change in the gray scale in the virtual region of interests (ROIs) in the microchannel during the fluid flow in the paper microstrips. A 3-D printed handheld platform, containing raspberry pi with on-board camera and display, was developed to execute the image processing and automate the entire work flow. In the proposed device, the accuracy was measured to be >92%. [ABSTRACT FROM AUTHOR]

This paper presents a method for the extraction of contour lines and other geographic information from scanned color images of topographical maps. Although topographic maps are available from many suppliers, this work focuses on United States Geological Survey (USGS) maps. The extraction of contour lines, which are shown with brown color on USGS maps, is a difficult process due to aliasing and false colors induced by the scanning process and due to closely spaced and intersecting/overlapping features inherent to the map. These difficulties render simple approaches such as clustering ineffective. The proposed method overcomes these difficulties using a multistep process. First, a color key set, designed to comprehend color aliasing and false colors, is generated using an eigenvector line-fitting technique in RGB space. Next, area features, representing vegetation and bodies of water, are extracted using RGB color histogram analysis in order to simplify the next stage. Then, linear features corresponding to roads and rivers including contours, are extracted using a valley seeking algorithm operating on a transformed version of the original map. Finally, an A[sup *] search algorithm is used to link valleys together to form linear features and to close the gaps caused by intersecting features. The performance of the algorithm is tested on a number of USGS topographic map samples. [ABSTRACT FROM AUTHOR]

The goal of the research described in this paper is the design of a system to automatically extract information from paper-based maps and answer queries related to spatial features and structure of geographic data. The foundation to such a system is a set of image analysis algorithms to extract spatial features from images of paper-based maps. Efficient algorithms to detect symbols, identify and track various types of lines, follow closed contours, compute distances, find shortest paths, etc., from simplified map images have been developed. A query processor analyzes the queries presented by the user in a predefined syntax, controls the operation of the image processing algorithms, and interacts with the user. The query processor is written in Lisp and calls image analysis routines written in Fortran. [ABSTRACT FROM AUTHOR]

Multi-focus image fusion (MFIF) is an important area in image processing. Since 2017, deep learning has been introduced to the field of MFIF and various methods have been proposed. However, there is a lack of survey papers that discuss deep learning-based MFIF methods in detail. In this study, we fill this gap by giving a detailed survey on deep learning-based MFIF algorithms, including methods, datasets and evaluation metrics. To the best of our knowledge, this is the first survey paper that focuses on deep learning-based approaches in the field of MFIF. Besides, extensive experiments have been conducted to compare the performance of deep learning-based MFIF algorithms with conventional MFIF approaches. By analyzing qualitative and quantitative results, we give some observations on the current status of MFIF and discuss some future prospects of this field. [ABSTRACT FROM AUTHOR]

In this paper, we formulate a recently proposed qualitative imaging approach named near-field orthogonality sampling method (NOSM) for a general measurement configuration. In the very first version, the NOSM was basically a backpropagation method, which analytically transforms the scattered fields, which is obtained with some specific multiview–multistatic measurement configurations (circular or linear), to reduced scattered electric fields on the sampling domain, which encapsulates the targets. The contributions of this paper are: 1) physical meaning of the reduced scattered field of the NOSM is explained by showing how it approximates the contrast source values in the sampling domain for different generic and limited aperture configurations; 2) the application of the NOSM is generalized to a generic configuration and some comments are made how to reduce the computational complexity and source requirements; 3) the reciprocity of the data is accounted for to improve the performance for limited aperture measurement scenarios; and 4) finally, a useful modified indicator is obtained by relying on the physical meaning of the indicator. The accuracy and stability of the proposed formulations are corroborated with numerical examples for which the excitation-measurement configuration is of limited aperture. [ABSTRACT FROM AUTHOR]

Synthetic aperture sonar and synthetic aperture radar involve the creation of high-resolution images of a scene via scattered signals recorded at different locations. Each pixel of the reconstructed image includes information obtained from multiple aspects due to the changing position of the sources/receivers. In this paper, the aspect-dependent scattering at each pixel is exploited to provide additional information about the scene; this paper presents a framework for converting and utilizing multiaspect data, as well as several examples. For sonar data, as is presented here, the aspect dependence may be leveraged to separate objects of interest from the background, to understand the local bathymetry, or for visualizing acoustic shadowing in full circular synthetic aperture sonar images. Several examples of images of the seafloor containing objects of interest are presented for both circular and linear apertures. In addition, the aspect dependence of low-frequency elastic scattering from objects may be used to understand the underlying scattering physics, which is of potential use in fields such as target recognition and nondestructive testing; a laboratory example is presented. [ABSTRACT FROM AUTHOR]

This paper aims at proposing a novel 2D non-linear phase decomposition of images, which performs the image processing tasks better than the traditional Fourier transformation (linear phase decomposition), but further, it has additional mathematical properties allowing more effective image analysis, including adaptive decomposition components and positive instantaneous phase derivatives. 1D unwinding Blaschke decomposition has recently been proposed and studied. Through factorization it expresses arbitrary 1D signal into an infinite linear combination of Blaschke products. It offers fast converging positive frequency decomposition in the form of rational approximation. However, in the multi-dimensional cases, the usual factorization mechanism does not work. As a consequence, there is no genuine unwinding decomposition for multi-dimensions. In this paper, a 2D partial unwinding decomposition based on algebraic transforms reducing multi-dimensions to the 1D case is proposed and analyzed. The result shows that the fast convergence offers efficient image reconstruction. The tensor type decomposing terms are mutually orthogonal, giving rise to 2D positive frequency decomposition. The comparison results show that the proposed method outperforms the standard greedy algorithm and the most commonly used methods in the Fourier category. An application in watermarking is presented to demonstrate its potential in applications. [ABSTRACT FROM AUTHOR]

Reports on scientists at Philips Research in the Netherlands' development of a type of electronic paper that can show high-definition moving images. Researchers' use of a process called electrowetting which manipulates the motion of mixed fluids by applying electric current; Pixel components; Electric voltage requirement for the electronic-paper.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM PUBLISHER]

The papers in this special section focus on the technology and applications supported by hyperspectral imaging and signal processing. [ABSTRACT FROM PUBLISHER]

This paper presents a broadband poly-harmonic behavioral model for microwave mixers operating in the linear regime. It is extracted from large-signal vector measurements and predicts the response with respect to an arbitrary local oscillator (LO) phase. The model is capable of estimating the performance of the converted signals and the main mixing products for an intermediate frequency that spans within an a priori defined bandwidth. This paper gives a detailed model analytical treatment along with a discussion of its properties and the description of the characterization method. The model validation is achieved by the characterization of a subharmonic mixer implemented in the hybrid technology that operates with a 2.229-GHz LO, an intermediate frequency bandwidth of 732–754 MHz, and a corresponding radio frequency bandwidth of 5.191–5.211 GHz. Independent experimental validation is achieved by an image rejection mixer composed of two nominally identical mixers, individually characterized by the proposed technique. Validation is concluded with experimental data, demonstrating the model’s capability to deal with a phase-modulated LO signal. [ABSTRACT FROM PUBLISHER]

This paper aims to achieve computationally efficient and high-accuracy subpixel image registration with large displacements under the rotation–scale–translation model. This paper employs the classical phase correlation algorithm and the Lucas–Kanade (LK) algorithm in a two-stage coarse-to-fine framework, for which the motivation is from the observation that the two algorithms exhibit strong complementary property between convergence range and subpixel accuracy. In this framework, the LK algorithm will also become computationally efficient owing to the small residual displacement. On the other hand, this paper takes into account the residual model with respect to the compensation scheme explicitly, and deduces formulas for the final results combination, which is expected to be closer to the true displacement vector and thus further improve the estimation accuracy. Since the compensation can be applied to either the target image or the reference image, two algorithms are presented accordingly, and analysis as well as comparison are also performed. Finally, both simulations and real image experiments are performed to verify the motivation, and the results are consistent with the analysis. [ABSTRACT FROM PUBLISHER]

Small moving objects at far distance always occupy only one or a few pixels in image and exhibit extremely limited visual features, which bring great challenges to motion detection. Highly evolved visual systems endow flying insects with remarkable ability to pursue tiny mates and prey, providing a good template to develop image processing method for small target motion detection. The insects’ excellent sensitivity to small moving objects is believed to come from a class of specific neurons called small target motion detectors (STMDs). However, existing STMD-based methods often experience performance degradation when coping with complex natural scenes. In this paper, we propose a bio-inspired visual system with spatio-temporal feedback mechanism (called Spatio-Temporal Feedback STMD) to suppress false positive background movement while enhancing system responses to small targets. Specifically, the proposed visual system is composed of two complementary subnetworks and a feedback loop. The first subnetwork is designed to extract spatial and temporal movement patterns of cluttered background by neuronal ensemble coding. The second subnetwork is developed to capture small target motion information where its output and signal from the first subnetwork are integrated together via the feedback loop to filter out background false positives in a recurrent manner. Experimental results demonstrate that the proposed spatio-temporal feedback visual system is more competitive than existing methods in discriminating small moving targets from complex natural environments. [ABSTRACT FROM AUTHOR]

Ill-posed problems are very common in many image processing and computer vision tasks. To solve such problems, a regularization must be imposed. In this paper, we argue why mean curvature is a good regularization for these tasks. From a geometry point of view, we show that minimizing mean curvature is to assume that the ground truth is a piece-wise minimal surface. From a statistics point of view, we show that the mean curvature from natural images is sparse. From an optimization point of view, we show that the gradient of mean curvature regularization can be numerically approximated by very simple filters. This fact significantly simplifies the optimization procedure. Thus, the link between these filters and the gradient of mean curvature regularization is established in this paper. In summary, mean curvature is a proper regularization for various ill-posed problems in image processing and computer vision. [ABSTRACT FROM AUTHOR]

Image segmentation techniques are challenging to apply to large-size remote sensing imagery. Indeed, if the data to be processed are larger than the computer’s available memory, it must be split into smaller pieces. Without precaution, segmentation errors appear along the edges of these pieces. The goal of this paper is to present a tilewise processing method to overcome this issue for superpixel segmentation, applied in particular to the simple linear iterative clustering algorithm. Incidentally, tilewise methods allow for several pieces of the image to be processed simultaneously, which enables the deployment of these methods in a parallel processing environment. Estimations of the speed-up when using multiple processors are provided. Then, it is demonstrated that the result of the tilewise segmentation is equivalent to the segmentation of the complete image, with respect to a number of global unsupervised segmentation criteria. Finally, experimental results on a large-size Sentinel-2 time series validate the method’s feasibility. [ABSTRACT FROM AUTHOR]

The active contour model is a widely used technique for automatic object contour extraction. Existing methods based on this model can perform with high accuracy, even in the case of complex contours, but challenging issues remain, like the need for precise contour initialization for high curvature boundary segments or the handling of cluttered backgrounds. To deal with such issues, this paper presents a salient object extraction method, the first step of which is the introduction of an improved edge map that incorporates edge direction as a feature. The direction information in the small neighborhoods of image feature points is extracted, and the images’ prominent orientations are defined for direction-selective edge extraction. Using such improved edge information, we provide a highly accurate shape contour representation, which we also combine with texture features. The principle of the paper is to interpret an object as the fusion of its components: its extracted contour and its inner texture. Our goal in fusing textural and structural information is twofold: it is applied for automatic contour initialization, and it is also used to establish an improved external force field. This fusion then produces highly accurate salient object extractions. We performed extensive evaluations, which confirm that the presented object extraction method outperforms parametric active contour models and achieves higher efficiency than the majority of the evaluated automatic saliency methods. [ABSTRACT FROM AUTHOR]

We present an approach to extend the functionality and the use of kernel machines in image processing applications. We introduce a novel way to design spatial kernel machines with spatial properties and demonstrate how those newly introduced spatial properties enhance the possibilities of the use of kernel machines in image processing applications as a proof of concept. In this paper, we demonstrate four particular extensions: 1) how to model shapes efficiently with spatially computed kernel parameters in a geometrically scalable way; 2) how to visualize the kernel parameters precisely and intuitively on binary 2D shapes; 3) how to construct a one-class classifier from the binary classifier in a straightforward manner without re-training; and 4) how to use the computed kernel parameters for filtering. The existing literature on kernel machines mostly focuses on estimating the optimal kernel parameters via additional cost function(s). In this paper, instead of employing an additional cost function to estimate the kernel-related parameters, we investigate on an analytical solution to predict the actual kernel parameters locally and show how to build a spatial kernel machine with our analytical approach. Classical kernel machines do not perform well on precise shape modeling with a low number of support vectors as demonstrated in this paper. However, we demonstrate and visualize that our analytical approach provides a natural means to relate the kernel parameters to the 2D shapes for sparse shape modeling, where the shape boundary represents the decision boundary. For that, we incorporate the selected kernel function’s geometric properties as an additional constraint into the classifier’s optimization problem by defining an easy-to-explain and intuitive concept: similarity domains. In our experiments, we study and demonstrate how the resulting new kernel machine enhances the capabilities of the classical kernel machines with applications on shape modeling, (geometrically) scaling the non-linear decision boundary at various scales and precise visualization of the kernel parameters in 2D images. [ABSTRACT FROM AUTHOR]

Capon’s minimum-variance method (MVM) and amplitude and phase estimation (APES) spectral estimation algorithms can be applied to synthetic aperture radar (SAR) processing to improve the resolution and suppress sidelobe levels. In this paper, we use Capon-/APES-based SAR reprocessing algorithms to increase the persistent scatterer (PS) density in PS interferometry (PSI). We propose a PS candidate (PSC) selection algorithm applicable to the superresolution reprocessed images and the corresponding processing chain. The performance of the proposed algorithm is evaluated by a number of simulations and a stack of TerraSAR-X data. The results show that the Capon algorithm outperforms others in PSC selection. We present a full PSI time-series analysis on the PSCs extracted from the Capon-reprocessed stacks. The results show that the PS density is increased between 50% and 60%, while their interferometric quality is maintained. [ABSTRACT FROM AUTHOR]

Screen content coding plays an important role in many applications. Conventional reference picture management (RPM) strategies developed for natural content may not work well for screen content. This is because many regions in screen content remain static for a long time, causing a lot of repetitive contents to stay in the decoded picture buffer. The repetitive contents are not conducive to inter prediction, but still occupy valuable memory. This paper proposes a diversity-based RPM scheme for screen content coding. The concept of diversity is introduced for the reference picture set (RPS) to help formulate the RPM problem. By maximizing the diversity of RPS, more potentially better predictions are provided. Better compression performance can then be achieved. Meanwhile, the proposed scheme is nonnormative and compatible with existing video coding standards, such as High Efficiency Video Coding. The experimental results show that, for low delay screen content coding, the bit saving of the proposed scheme is 4.9% on average and up to 13.7%, without increasing encoding time. [ABSTRACT FROM AUTHOR]

This paper presents a subband-decomposed image-enhancement algorithm that can preserve the luminance and contrast levels in power-controllable thin-film-transistor liquid-crystal display devices. The proposed algorithm consists of three steps. First, an input image is decomposed to multiscale subbands via subband decomposition. Second, luminance compensation and contrast preservation are achieved simultaneously by adjusting the gain of each subband properly. Third, additional power consumption caused by excessive boosting, while coping with the local dimming through specific clipping, is prevented. The experimental results show that the proposed algorithm provides more natural details and contrast, while preserving more similarities with the original, than the existing methods. Even in terms of the peak signal-to-noise-ratio and the structural similarity index, the proposed algorithm has 27.8% and 3.8% higher values compared to a state-of-the-art method. The real-time operation of the proposed algorithm was verified through intensive optimization and the Compute Unified Device Architecture implementation. In addition, the power-reduction effect of the proposed algorithm on commercial monitors was investigated experimentally. [ABSTRACT FROM AUTHOR]

The Riemannian geometry of covariance matrices has been essential to several successful applications, in computer vision, biomedical signal and image processing, and radar data processing. For these applications, an important ongoing challenge is to develop Riemannian-geometric tools which are adapted to structured covariance matrices. This paper proposes to meet this challenge by introducing a new class of probability distributions, Gaussian distributions of structured covariance matrices. These are Riemannian analogs of Gaussian distributions, which only sample from covariance matrices having a preassigned structure, such as complex, Toeplitz, or block-Toeplitz. The usefulness of these distributions stems from three features: 1) they are completely tractable, analytically, or numerically, when dealing with large covariance matrices; 2) they provide a statistical foundation to the concept of structured Riemannian barycentre (i.e., Fréchet or geometric mean); and 3) they lead to efficient statistical learning algorithms, which realise, among others, density estimation and classification of structured covariance matrices. This paper starts from the observation that several spaces of structured covariance matrices, considered from a geometric point of view, are Riemannian symmetric spaces. Accordingly, it develops an original theory of Gaussian distributions on Riemannian symmetric spaces, of their statistical inference, and of their relationship to the concept of Riemannian barycentre. Then, it uses this original theory to give a detailed description of Gaussian distributions of three kinds of structured covariance matrices, complex, Toeplitz, and block-Toeplitz. Finally, it describes algorithms for density estimation and classification of structured covariance matrices, based on Gaussian distribution mixture models. [ABSTRACT FROM AUTHOR]

Forward-looking ground-penetrating radar (FLGPR) has recently been investigated as a remote-sensing modality for buried target detection (e.g., landmines). In this context, raw FLGPR data are beamformed into images, and then, computerized algorithms are applied to automatically detect subsurface buried targets. Most existing algorithms are supervised, meaning that they are trained to discriminate between labeled target and nontarget imagery, usually based on features extracted from the imagery. A large number of features have been proposed for this purpose; however, thus far it is unclear as to which are the most effective. The first goal of this paper is to provide a comprehensive comparison of detection performance using existing features on a large collection of FLGPR data. Fusion of the decisions resulting from processing each feature is also considered. The second goal of this paper is to investigate two modern feature learning approaches from the object recognition literature: the bag-of-visual words and the Fisher vector for FLGPR processing. The results indicate that the new feature learning approaches lead to the best performing FLGPR algorithm. The results also show that fusion between existing features and new features yields no additional performance improvements. [ABSTRACT FROM AUTHOR]

Deep neural networks have been tremendously successful at segmenting objects in images. However, it has been shown they still have limitations on challenging problems such as the segmentation of medical images. The main reason behind this lower success resides in the reduced size of the object in the image. In this paper we overcome this limitation through a cyclic collaborative framework, CyCoSeg. The proposed framework is based on a deep active shape model (D-ASM), which provides prior information about the shape of the object, and a semantic segmentation network (SSN). These two models collaborate to reach the desired segmentation by influencing each other: SSN helps D-ASM identify relevant keypoints in the image through an Expectation Maximization formulation, while D-ASM provides a segmentation proposal that guides the SSN. This cycle is repeated until both models converge. Extensive experimental evaluation shows CyCoSeg boosts the performance of the baseline models, including several popular SSNs, while avoiding major architectural modifications. The effectiveness of our method is demonstrated on the left ventricle segmentation on two benchmark datasets, where our approach achieves one of the most competitive results in segmentation accuracy. Furthermore, its generalization is demonstrated for lungs and kidneys segmentation in CT scans. [ABSTRACT FROM AUTHOR]

In this paper, we make the first benchmark effort to elaborate on the superiority of using RAW images in the low light enhancement and develop a novel alternative route to utilize RAW images in a more flexible and practical way. Inspired by a full consideration on the typical image processing pipeline, we are inspired to develop a new evaluation framework, Factorized Enhancement Model (FEM), which decomposes the properties of RAW images into measurable factors and provides a tool for exploring how properties of RAW images affect the enhancement performance empirically. The empirical benchmark results show that the Linearity of data and Exposure Time recorded in meta-data play the most critical role, which brings distinct performance gains in various measures over the approaches taking the sRGB images as input. With the insights obtained from the benchmark results in mind, a RAW-guiding Exposure Enhancement Network (REENet) is developed, which makes trade-offs between the advantages and inaccessibility of RAW images in real applications in a way of using RAW images only in the training phase. REENet projects sRGB images into linear RAW domains to apply constraints with corresponding RAW images to reduce the difficulty of modeling training. After that, in the testing phase, our REENet does not rely on RAW images. Experimental results demonstrate not only the superiority of REENet to state-of-the-art sRGB-based methods and but also the effectiveness of the RAW guidance and all components. [ABSTRACT FROM AUTHOR]

Convolutional dictionary learning has become increasingly popular in signal and image processing for its ability to overcome the limitations of traditional patch-based dictionary learning. Although most studies on convolutional dictionary learning mainly focus on the unimodal case, real-world image processing tasks usually involve images from multiple modalities, e.g., visible and near-infrared (NIR) images. Thus, it is necessary to explore convolutional dictionary learning across different modalities. In this paper, we propose a novel multi-modal convolutional dictionary learning algorithm, which efficiently correlates different image modalities and fully considers neighborhood information at the image level. In this model, each modality is represented by two convolutional dictionaries, in which one dictionary is for common feature representation and the other is for unique feature representation. The model is constrained by the requirement that the convolutional sparse representations (CSRs) for the common features should be the same across different modalities, considering that these images are captured from the same scene. We propose a new training method based on the alternating direction method of multipliers (ADMM) to alternatively learn the common and unique dictionaries in the discrete Fourier transform (DFT) domain. We show that our model converges in less than 20 iterations between the convolutional dictionary updating and the CSRs calculation. The effectiveness of the proposed dictionary learning algorithm is demonstrated on various multimodal image processing tasks, achieves better performance than both dictionary learning methods and deep learning based methods with limited training data. [ABSTRACT FROM AUTHOR]

In this paper, we present a comparative evaluation of various multi-person tracking-by-detection approaches on public data sets. This paper investigates five popular trackers coupled with six relevant visual people detectors evaluated on seven public data sets. The evaluation emphasizes on exhibited performance variation depending on tracker-detector choices. Our experimental results show that the overall performance depends on how challenging the data set is, the performance of the detector on the specific data set, and the tracker-detector combination. Some trackers are more sensitive to the choice of a detector and some detectors to the choice of a tracker than others. Based on our results, two of the trackers demonstrate the best performances consistently across different data sets, whereas the best performing detectors vary per data set. This underscores the need for careful application context specific evaluation when choosing a detector. [ABSTRACT FROM AUTHOR]

Seabed characterization has utility for numerous applications that seek to explore and interact with the seafloor, ranging from coastal habitat monitoring and subbottom profiling to man-made object detection. In this paper, we characterize seabeds based on the texture patterns within synthetic aperture SONAR (SAS) images constructed from high-frequency side-scan sonar. Features are measured from the SAS images (e.g., lacunarity, an established texture feature coding method, and a circularly shifted histogram of oriented gradients). Based on these SAS image features, we perform unsupervised clustering with a hierarchical Bayesian model, which creates categories of seabed textures. Our clustering algorithm is a new variant of the hierarchical Dirichlet process that is both adaptive to changes in seabeds and processes batches of SAS imagery in an online fashion to learn new seabed types as they are encountered. This allows observations to be clustered, as each batch is processed rather than only after all data have been collected. The model’s performance of seabed characterization by SAS image texture is demonstrated in the overall range and internal consistency of textures specific to each learned cluster with data across a variety of sites. [ABSTRACT FROM AUTHOR]

A common problem in wide-baseline matching is the sparse and non-uniform distribution of correspondences when using conventional detectors, such as SIFT, SURF, FAST, A-KAZE, and MSER. In this paper, we introduce a novel segmentation-based feature detector (SFD) that produces an increased number of accurate features for wide-baseline matching. A multi-scale SFD is proposed using bilateral image decomposition to produce a large number of scale-invariant features for wide-baseline reconstruction. All input images are over-segmented into regions using any existing segmentation technique, such as Watershed, Mean-shift, and simple linear iterative clustering. Feature points are then detected at the intersection of the boundaries of three or more regions. The detected feature points are local maxima of the image function. The key advantage of feature detection based on segmentation is that it does not require global threshold setting and can, therefore, detect features throughout the image. A comprehensive evaluation demonstrates that SFD gives an increased number of features that are accurately localized and matched between wide-baseline camera views; the number of features for a given matching error increases by a factor of 3–5 compared with SIFT; feature detection and matching performance are maintained with increasing baseline between views; multi-scale SFD improves matching performance at varying scales. Application of SFD to sparse multi-view wide-baseline reconstruction demonstrates a factor of 10 increases in the number of reconstructed points with improved scene coverage compared with SIFT/MSER/A-KAZE. Evaluation against ground-truth shows that SFD produces an increased number of wide-baseline matches with a reduced error. [ABSTRACT FROM AUTHOR]

A residue number system (RNS) has an inherent parallel structure that can be utilized for improving computer hardware systems. An RNS represents large integer numbers as a smaller integer set, or residues of a modulo set, without carry propagation between them. Hence mathematical operations, such as addition or subtraction, can be performed on the residues independently. This paper proposes an RNS implementation of motion estimation for the latest video coding standard known as high-efficiency video coding (HEVC) or H.265. Since motion estimation is the most computationally intensive task in video coding, several simplified algorithms are proposed for mitigating the problem, but the majority of them result in a worsening peak signal-to-noise ratio (PSNR) or bit-rate performance, or sometimes both. This paper also proposes a modified algorithm based on a test-zone (TZ) search algorithm, a widely used fast-search algorithm with good rate-distortion performance, suitable for hardware implementation for encoding ultra-high-definition videos in real time. The results show that worst-case PSNR degradation and bit-rate increases compared with the TZ search in the HEVC reference software implementation are negligible, and the hardware gate count is less than for many other designs in the literature. [ABSTRACT FROM AUTHOR]

Recently, multiple ($k, n$) region-incrementing VCSs (RIVCSs) have been proposed that can gradually reconstruct secrets in a single image. In ($k, n$)-RIVCS, the secret image is subdivided into multiple regions in such a way that any $t$ shadow images, where $k \le t \le n$ , can be used to reveal the secret in the ($t-k+1$)th region. This region-incrementing property provides progressive decoding. However, the regions in previous RIVCSs are disjointed: no two regions have overlapping areas. In this paper, we discuss a ($k, n$) region-in-region progressive VCS (RPVCS). Our ($k, n$)-RPVCS has ($n-k+1$) secrecy-level regions, and each region can be located within any previous region. This new type of region allocation not only provides more areas in which to hide the secret compared with the non-overlapping regions in RIVCS but also provides different presentation methods in progressive decoding. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the effect of frequency uncertainty in signals generated or measured by a microwave instrument on the resulting synthetic aperture radar (SAR) images, particularly for nondestructive testing (NDT) applications. Wideband SAR imaging systems measure reflections from a target by irradiating it with locally generated signals that can potentially have some level of frequency uncertainty. Quantifying this frequency uncertainty provides the user with a realistic and expected level of image distortion which may manifest itself as blurring, noise artifacts, etc. In this paper, we show that as uncertainty in the actual frequency value increases, the level of image distortions increases predominantly for distant targets. This is an important fact for NDT applications since the imaged object is commonly close to the imaging system. In addition, these imaging system usually have a limited “aperture” size, which makes target distance an important consideration. For complex targets, we show how frequency uncertainty-based image distortions can dominate features in an image depending on the reflected signal amplitude from the target. We also show that in real imaging systems, the statistical distribution of frequency uncertainty combined with practical, near-target ranges (distances) produce nondiscernible image distortions. [ABSTRACT FROM AUTHOR]

Image mosaicking applications require both geometrical and photometrical registrations between the images that compose the mosaic. This paper proposes a probabilistic color correction algorithm for correcting the photometrical disparities. First, the image to be color corrected is segmented into several regions using mean shift. Then, connected regions are extracted using a region fusion algorithm. Local joint image histograms of each region are modeled as collections of truncated Gaussians using a maximum likelihood estimation procedure. Then, local color palette mapping functions are computed using these sets of Gaussians. The color correction is performed by applying those functions to all the regions of the image. An extensive comparison with ten other state of the art color correction algorithms is presented, using two different image pair data sets. Results show that the proposed approach obtains the best average scores in both data sets and evaluation metrics and is also the most robust to failures. [ABSTRACT FROM AUTHOR]

The study of eye gaze fixations on photographic images is an active research area. In contrast, the image sub-category of freehand sketches has not received as much attention for such studies. In this paper, we analyze the results of a free-viewing gaze fixation study conducted on 3904 freehand sketches distributed across 160 object categories. Our analysis shows that fixation sequences exhibit marked consistency within a sketch, across sketches of a category and even across suitably grouped sets of categories. This multi-level consistency is remarkable given the variability in depiction and extreme image content sparsity that characterizes hand-drawn object sketches. In this paper, we show that the multi-level consistency in the fixation data can be exploited to 1) predict a test sketch’s category given only its fixation sequence and 2) build a computational model which predicts part-labels underlying fixations on objects. We hope that our findings motivate the community to deem sketch-like representations worthy of gaze-based studies vis-a-vis photographic images. [ABSTRACT FROM PUBLISHER]

While the detection of the interesting regions in images has been extensively studied, relatively few papers have addressed surfaces. This paper proposes an algorithm for detecting the regions of interest of surfaces. It looks for regions that are distinct both locally and globally and accounts for the distance to the foci of attention. It is also shown how this algorithm can be adopted to saliency detection in point clouds. Many applications can utilize these regions. In this paper we explore one such application—viewpoint selection. The most informative views are those that collectively provide the most descriptive presentation of the surface. We show that our results compete favorably with the state-of-the-art results. [ABSTRACT FROM AUTHOR]

Due to their good rotational invariance and stability, image continuous orthogonal moments are intensively applied in rotationally invariant recognition and image processing. However, most moments produce numerical instability, which impacts the image reconstruction and recognition performance. In this paper, a new set of invariant continuous orthogonal moments, polar harmonic Fourier moments (PHFMs), free of numerical instability is designed. The radial basis functions (RBFs) of the PHFMs are much simpler than those of the Chebyshev-Fourier moments (CHFMs), orthogonal Fourier-Mellin moments (OFMMs), Zernike moments (ZMs), and pseudo-Zernike moments (PZMs). For the same degree, the RBFs of the PHFMs have more zeros and are more evenly distributed than those of the ZMs and PZMs. Therefore, PHFMs do not suffer from information suppression problem; hence, the image description ability of the PHFMs is superior to that of the ZMs and PZMs. Moreover, the RBFs of the PHFMs are always less than or equal to 1.0 near the unit disk center, whereas those of the OFMMs, PZMs, CHFMs, and radial harmonic Fourier moments (RHFMs) are infinite (implying numerical instability). This indicates that PHFMs can outperform these moments in image reconstruction tasks. We theoretically and experimentally demonstrate that PHFMs outperform the above moments in reconstructing images and recognizing rotationally invariant objects considering noise and various attacks. This paper also details the significance of the PHFM phase in image reconstruction, angle estimation using PHFMs, and the accurate moment selection of the PHFMs. [ABSTRACT FROM AUTHOR]

Spatial failure patterns in wafer defect maps are very useful for root cause analysis, which is essential for yield optimization in the manufacturing process. Especially, the scratch defect type is the most challenging pattern to recognize because the position, shape, size and curvature vary widely from one scratch to another. Discontinuity points within scratches also contribute to the low recognition rate, and such points are often hidden defective dies that become reliability threat. Previous studies on defect pattern identification show that the recognition rate for scratches is among the lowest in all patterns even if the overall accuracy is high. In this paper, we propose a novel scratch pattern recognition method. In this paper, image processing techniques are applied to recognize scratch patterns in wafer maps. The Hough transform is first employed to identify line segments, from which scratches can be reconstructed. In contrast to previous machine-learning based methods, there is no need to train a complicated prediction model so that the computation time is small. The method is validated by using wafer maps from six real products and public data. Experimental results show that the proposed method achieves high recall, precision and accuracy in all cases. [ABSTRACT FROM AUTHOR]

In this paper, we propose a luminance and color correction scheme for multiview image compression for a 3-DTV system. According to characteristics of luminance and chrominance, we propose a 3-D discrete cosine transform (3-D DCT) for 3-D image compression. Then, a cubic memory-based 3-D DCT is proposed for 3-D image compression in this paper. 3-D display technology has become an important technology lately. TFT-based multiview 3-D display has the advantage of convenient setup, mass production, and a large amount of 3-D content support, making it a popular 3-D display product. However, multiview 3-D signals need huge storage space and wide channel bandwidth, especially when view number is larger than two views. To overcome the obstacles, we propose a 3-D DCT component based on cubic memory to perform 3-D image compression in this paper. The presented architecture reduces a great quantity of memory space and provides efficient 3-D signal storage. [ABSTRACT FROM PUBLISHER]

The vision-aided pedestrian dead reckoning (PDR) systems have become increasingly popular, thanks to the ubiquitous mobile phone embedded with several sensors. This is particularly important for indoor use, where other indoor positioning technologies require additional installation or body attachment of specific sensors. This paper proposes and develops a novel 3-D passive vision-aided PDR system that uses multiple surveillance cameras and smartphone-based PDR. The proposed system can continuously track users’ movement on different floors by integrating results of inertial navigation and Faster Region-Based Convolutional Neutral Network (Faster R-CNN)-based real-time pedestrian detection, while utilizing existing camera locations and embedded barometers to provide floor/height information to identify user positions in 3-D space. This novel system provides a relatively low-cost and user-friendly solution, which requires no modifications to currently available mobile devices and also the existing indoor infrastructures at many public buildings for the purpose of 3-D indoor positioning. This paper shows the case of testing the prototype in a four-floor building, where it can provide the horizontal accuracy of 0.16 m and the vertical accuracy of 0.5 m. This level of accuracy is even better than required accuracy targeted by several emergency services, including the Federal Communications Commission (FCC). This system is developed for both Android and iOS-running devices. [ABSTRACT FROM AUTHOR]

Recently, food recognition has received more and more attention in image processing and computer vision for its great potential applications in human health. Most of the existing methods directly extracted deep visual features via convolutional neural networks (CNNs) for food recognition. Such methods ignore the characteristics of food images and are, thus, hard to achieve optimal recognition performance. In contrast to general object recognition, food images typically do not exhibit distinctive spatial arrangement and common semantic patterns. In this paper, we propose a multi-scale multi-view feature aggregation (MSMVFA) scheme for food recognition. MSMVFA can aggregate high-level semantic features, mid-level attribute features, and deep visual features into a unified representation. These three types of features describe the food image from different granularity. Therefore, the aggregated features can capture the semantics of food images with the greatest probability. For that solution, we utilize additional ingredient knowledge to obtain mid-level attribute representation via ingredient-supervised CNNs. High-level semantic features and deep visual features are extracted from class-supervised CNNs. Considering food images do not exhibit distinctive spatial layout in many cases, MSMVFA fuses multi-scale CNN activations for each type of features to make aggregated features more discriminative and invariable to geometrical deformation. Finally, the aggregated features are more robust, comprehensive, and discriminative via two-level fusion, namely multi-scale fusion for each type of features and multi-view aggregation for different types of features. In addition, MSMVFA is general and different deep networks can be easily applied into this scheme. Extensive experiments and evaluations demonstrate that our method achieves state-of-the-art recognition performance on three popular large-scale food benchmark datasets in Top-1 recognition accuracy. Furthermore, we expect this paper will further the agenda of food recognition in the community of image processing and computer vision. [ABSTRACT FROM AUTHOR]

Remotely sensed images with very high spatial resolution provide a detailed representation of the surveyed scene with a geometrical resolution that, at the present, can be up to 30 cm (WorldView-3). A set of powerful image processing operators have been defined in the mathematical morphology framework. Among those, connected operators [e.g., attribute filters (AFs)] have proven their effectiveness in processing very high resolution images. AFs are based on attributes which can be efficiently implemented on tree-based image representations. In this paper, we considered the definition of \min, \max, \textdirect, and \textsubtractive filter rules for the computation of AFs over the tree-of-shapes representation. We study their performance on the classification of remotely sensed images. We compare the classification results over the tree of shapes with the results obtained when the same rules are applied on the component trees. The random forest is used as a baseline classifier, and the experiments are conducted using multispectral data sets acquired by QuickBird and IKONOS sensors over urban areas. [ABSTRACT FROM AUTHOR]

The Kinect depth sensor has become a versatile tool in different research fields due to its low cost and ease of use, yet its implementation still remains a black box to a large extent. Moreover, the accuracy and speed of Kinect are limited for certain applications. To boost the performance of Kinect while preserving its advantages, we have proposed a depth acquisition method that uses speckle patterns generated by commodity laser emitters as in Kinect, but the speckle density is modulated to allow higher accuracy depth measurement. In this paper, we present the field-programmable gate array (FPGA) design and implementation for the above Kinect-like depth sensing. To achieve a high-speed performance on a normal FPGA (Altera Stratix III EP3SL150), we elaborate the hardware architecture to realize a full-pipeline framework for the two main modules: 1) pixel-wise phase generation and 2) normalized cross-correlation-based phase unwrapping. Experiments show a reconstruction speed of 572 fps at a resolution of $640 \times 480$ with improved depth accuracy over Kinect and that an even higher resolution is supported. The previous pattern design and the FPGA design in this paper jointly demonstrate the feasibility of next-generation fast, accurate, and low-cost depth sensors. [ABSTRACT FROM PUBLISHER]

The vibration between the spacers of bundle conductors may cause the conductors to collide with each other, thereby damaging the conductors and the spacers, even resulting in adhesion. The main function of the spacers is to limit the relative movement between the bundle conductors, but it still exists. In order to find out the potential dangers caused by the movement and prevent adhesion among four or multibundle conductors, a measurement method of space distance among bundle conductors is presented in this paper. First, the images of bundle conductors are obtained by installing cameras on the optimum position of tower, which are segmented by the maximum interclass variance method after preprocess, and the axis of conductors can be obtained by the skeleton extraction. Second, the image coordinates of each point on the axis are transformed into the world coordinates by camera calibration, and these points are fit by least-squares method to get the conductor function. Then, the actual transmission conductor model can be reconstructed according to the fitting function, conductor diameter, and spacers. Finally, calculating the distance between two bundle conductors, the minimum distance can be obtained. We analyze the performance of the technology by a series of experiments; the results demonstrate that the proposed method can measure the space distance with an average relative error of 2%–4%. The running status of the bundle conductors can be monitored based on the presented method in this paper, by which the safe operation of transmission lines can be guaranteed. [ABSTRACT FROM AUTHOR]