Resolution Pyramid Research Articles

Quantitative Validation of the Correlation Between Optimized Pyramidal Tract Delineation After Brain Shift Compensation and Direct Electrical Subcortical Stimulation During Brain Tumor Surgery.

It remains unclear whether tractography of pyramidal tracts is correlated with the intraoperative direct electrical subcortical stimulation (DESS), and brain shift further complicates the issue. The objective of this research is to quantitatively verify the correlation between optimized tractography (OT) of pyramidal tracts after brain shift compensation and DESS during brain tumor surgery. OT was performed for 20 patients with lesions in proximity to the pyramidal tracts based on preoperative diffusion-weighted magnetic resonance imaging. During surgery, tumor resection was guided by DESS. A total of 168 positive stimulation points and their corresponding stimulation intensity thresholds were recorded. Using the brain shift compensation algorithm based on hierarchical B-spline grids combined with a Gaussian resolution pyramid, we warped the preoperative pyramidal tract models and used receiver operating characteristic (ROC) curves to investigate the reliability of our brain shift compensation method based on anatomic landmarks. Additionally, the minimum distance between the DESS points and warped OT (wOT) model was measured and correlated with DESS intensity threshold. Brain shift compensation was achieved in all cases, and the area under the ROC curve was 0.96 in the registration accuracy analysis. The minimum distance between the DESS points and the wOT model was found to have a significantly high correlation with the DESS stimulation intensity threshold (r = 0.87, P < 0.001), with a linear regression coefficient of 0.96. Our OT method can provide comprehensive and accurate visualization of the pyramidal tracts for neurosurgical navigation and was quantitatively verified by intraoperative DESS after brain shift compensation.

MTSCANet: Multi temporal resolution temporal semantic context aggregation network

AbstractTemporal action localisation is a challenging task, and video context is crucial to localisation actions. Most existing cases that incorporate temporal and semantic contexts into video features suffer from single contextual representation and blurred temporal boundaries. In this study, a multi‐temporal resolution pyramid structure model is proposed. Firstly, a temporal‐semantic context aggregation module (TSCF) is designed to assign different attention weights to temporal contexts and combine them with multi‐level semantics into video features. Secondly, for the problem of large differences in the time span between different actions in the video, a local‐global attention module is designed to combine local and global temporal dependencies for each temporal point to obtain a more flexible and robust representation of contextual relations. The redundant representation of the convolution kernel is reduced by modifying the convolution and the arithmetic power is redeployed at a microscopic granularity. To verify the effectiveness of the model, extensive experiments on three challenging datasets are performed. On THUMOS14, the best performance is obtained in IoU@0.3–0.6 with an average mAP of 47.02%. On ActivityNet‐1.3, an average mAP of 34.94% was obtained. On HACS, an average mAP of 28.46% was achieved.

Efficient semantic segmentation with pyramidal fusion

Emergence of large datasets and resilience of convolutional models have enabled successful training of very large semantic segmentation models. However, high capacity implies high computational complexity and therefore hinders real-time operation. We therefore study compact architectures which aim at high accuracy in spite of modest capacity. We propose a novel semantic segmentation approach based on shared pyramidal representation and fusion of heterogeneous features along the upsampling path. The proposed pyramidal fusion approach is especially effective for dense inference in images with large scale variance due to strong regularization effects induced by feature sharing across the resolution pyramid. Interpretation of the decision process suggests that our approach succeeds by acting as a large ensemble of relatively simple models, as well as due to large receptive range and strong gradient flow towards early layers. Our best model achieves 76.4% mIoU on Cityscapes test and runs in real time on low-power embedded devices.

Conflict Resolution on Discrimination: The Dichotomy of a Potential and Actual Action in Lithuania

The institutional protection of equal rights and opportunities is one of the most important features of the contemporary democratic society. In this context, there is someparadox in Lithuania. The modern mechanism for the protection of equal rights and opportunities is being developed, however, the population does not fully utilize its competences in the case of actual discrimination. The article enquires into the choice of potential and actual actions for a conflict resolution based on discrimination in the context of existing institutional possibilities in Lithuania. The concept of the conflict resolution pyramid is used. Both pyramids of potential and actual conflict resolution represent the trend towards a quantitative decline in the choices to be made for the conflict resolution. Some foreseen gaps between the potential and actual action in the case of discrimination are unexpectedly salient. Almost all Lithuanians express the determination to defend the violated rights that is not a case of represented actual actions by the victims of discrimination. Moreover, it can be assumed that at least half of all Lithuanian respondents are not eager for the conflict resolution even on the basic level of a close social environment. Also, the research data suggest that the public discourse on equal opportunities and discrimination is at the stage of formation.

Enhancement of Aerial and Medical Image using Multi resolution pyramid

Image enhancement has been an area of active research for decades. Most of the studies are aimed at improving the quality of image for better visualization. An approach for contrast enhancement utilizing multi-scale analysis is introduced. To show the effects of image enhancement, quantitative measures should be introduced. In this paper, we examine the effect of global and local enhancement using multi resolution pyramids. We identify a set of quality metric parameters for comparative performance analysis and use it to assess the enhanced output image for a number of image enhancement algorithms using pyramids.

A knowledge-based system for the non-destructive diagnostics of façade isolation using the information fusion of visual and IR images

This paper describes a knowledge-based system for the non-destructive diagnostics of façade isolation. The system uses the fusion of information extracted from images obtained from two electromagnetic wavebands: from low-resolution IR images (320 × 240 pixels; 7.5–13 μm wavelength), and from high-resolution visual RGB images (3264 × 2448 pixels; 0.39–0.75 μm wavelength). The fusion of the information is a perquisite for the design of an efficient knowledge-based system for non-destructive testing and façade diagnostics. In the paper, special attention is devoted to the following knowledge-based system components: system-core components (the database, the knowledge base, the inference engine, and the user interface) and application-specific components (image acquisition, the resolution pyramid, image pre-processing and feature extraction, image registration, object segmentation, fusion, and data analysis). Examples of the non-destructive testing and the façade diagnostics obtained with the system are presented.

Using resolution pyramids for watershed image segmentation

In this paper we build a shape preserving resolution pyramid and use it in the framework of image segmentation via watershed transformation. Our method is based on the assumption that the most significant image components perceived at high resolution will also be perceived at lower resolution. Thus, we detect the seeds for the watershed transformation at a low resolution, and use them to distinguish significant and non-significant seeds at any selected higher resolution. In this way, the watershed partition obtained at the selected pyramid level will include only the most significant components, and over-segmentation will be considerably reduced. Segmentations of the image at different scales will be available. Moreover, since the seeds can be detected at different pyramid levels, alternative segmentations of the image at a given resolution can be obtained, each characterized by a different level of detail.

Super-resolution imaging: use of zoom as a cue

In this paper, we propose a novel technique for super-resolution imaging of a scene from observations at different zoom levels. Given a sequence of images with different zoom factors of a static scene, the problem is to obtain a picture of the entire scene at a resolution corresponding to the most zoomed image in the scene. We not only obtain the super-resolved image for known integer zoom factors, but also for unknown arbitrary zoom factors. We model the super-resolution image as a Markov random field (MRF) and a maximum a posteriori (MAP) estimation method is used to derive a cost function which is then optimized to recover the high-resolution field. The entire observation conforms to the same MRF, but is viewed at the different resolution pyramid. Since there is no relative motion between the scene and the camera, as is the case with most of the super-resolution techniques, we do away with the correspondence problem. Results of the experimentation on real data are presented.

FAST SEARCH ALGORITHMS FOR INDUSTRIAL INSPECTION

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

Computing Stochastic Completion Fields in Linear-Time Using a Resolution Pyramid

We describe a linear-time algorithm for computing the likelihood that a completion joining two contour fragments passes through any given position and orientation in the image plane. Our algorithm is a resolution-pyramid-based method for solving a partial differential equation (PDE) characterizing a distribution of short, smooth completion shapes. The PDE consists of a set of independent advection equations in (x, y) coupled in the θ dimension by the diffusion equation. A previously described algorithm used a first-order, explicit finite difference scheme implemented on a rectangular grid. This algorithm required O(n3m) time for a grid of size n×n with m discrete orientations. Unfortunately, systematic error in solving the advection equations produced unwanted anisotropic smoothing in the (x, y) dimension. This resulted in visible artifacts in the completion fields. The amount of error and its dependence on θ have been previously characterized. We observe that by careful addition of extra spatial smoothing, the error can be made totally isotropic. The combined effect of this error and of intrinsic smoothness due to diffusion in the θ dimension is that the solution becomes smoother with increasing time, i.e., the high spatial frequencies drop out. By increasing Δx and Δt on a regular schedule, and using a second-order, implicit scheme for the diffusion term, it is possible to solve the modified PDE in O(n2m) time, i.e., time linear in the problem size. Using current hardware and for problems of typical size, this means that a solution which previously took 1 h to compute can now be computed in about 2 min.

A fast directional filter set for pyramidal image-based robotic systems

In seeking to develop a real-time, pyramidal imaging system for tracking and robotic visual servoing applications, inadequacies in the standard, commonly available kernels for directional filtering have led to the implementation of an alternative set of simple filters that combine to act as oriented edge-contrast operators fulfilling the familiar requirements for both speed and a highly localized response pattern. Although numerous edge operators, kernels and directional filters have been proposed, and low-level filtering is a well-established and mature area of computer vision, within the application described here, this operator set is proving superior to more established methods in efficiently preserving the response localization requirement while propagating the oriented gradient energies through multichannel resolution pyramids for further recursive processing. This paper provides a detailed discussion of the technique, the context of its development, and supporting empirical tests and statistical results. The evidence presented shows that the scheme provides a unified response to both step and line edge types, with acceptable performance in the presence of anticipated noise levels. The approach is pragmatic, of low complexity, and of general utility.

Scalable video transmission for the Internet

Information systems and distributed applications for the Internet show a growing demand for real-time audiovisual services. Support for those services within the Internet protocol layer will be widely available in the near future. Nevertheless, the heterogeneous structure of the Internet remains to be a great obstacle for establishing real-time video services. Scalable video codecs, generating bit-streams decodable at different rates, have been proposed to address the heterogeneity problem. In this article we present a scalable codec based on a spatio-temporal resolution pyramid combined with lattice vector quantization for efficient compression. With this approach additional lower bit-rate layers can be transmitted at the same overall bit-rate as needed by an H.263 single-layer transmission. The complexity of the codec is sufficiently low to allow software-only implementations of Internet video services. This is demonstrated on an Internet video server application.

Spatially scalable video compression employing resolution pyramids

A spatially scalable video coding scheme for low bit rates is proposed. The codec is especially well suited for communications applications because it is based on motion-compensated predictive coding which provides the necessary low-delay property. The frames to be coded are decomposed into a Gaussian pyramid. Motion estimation and compensation are performed between corresponding pyramid levels of successive frames. We show that, to fulfill specific needs of spatial scalability, the motion compensation on each level must result in compatible prediction errors (displaced frame differences, DFD). Compatibility of the prediction errors means that the pyramid formed by independently obtained DFD's (the DFD pyramid) is close to a Gaussian pyramid decomposition of the DFD of the highest resolution level. From the DFD pyramid, a least squares Laplacian pyramid is derived, which is quantized and coded. The DFD encoder outputs an embedded bit stream. Thus, the coder control may truncate the bit stream at any point, and can keep a fixed rate. The motion vector fields obtained at the different resolution levels are also encoded by employing a pyramid approach. Simulation results show that the proposed coder achieves a coding gain compared to simulcast coding.

Automated system for coarse-to-fine pyramidal area correlation stereo matching

An automatic, general purpose system for the production of dense digital disparity models (DDM) is described. The system applies an area-based correlation stereo matcher via an image resolution pyramid. The theory of this coarse-to-fine technique is described and possible improvements to it are outlined. The results of applying the system to different types of imagery including SPOT-1 stereo imagery of Western Cyprus, structured light stereo imagery of faces and knees and MRI scanner imagery of the human brain are presented.

Scalable video for multimedia workstations

This paper provides an overview about the problems of motion video representation for multimedia workstations and reviews the state-of-the-art. In addition to highly efficient data compression, coding algorithms for motion video have to provide extra flexibility to accommodate the varying computational, bandwidth and display parameters of open computer systems. This “scalability” should include image size scalability, partial bit-stream decodability, and computation-limited coding and decoding. Techniques standardized to date, such as CCITT H.261 or MPEG-1, are not ideally suited for motion video integration into computer systems. We show that resolution hierarchies are a straightforward approach to build a scalable video codec. Resolution pyramids offer varying degrees of asymmetry by increasing the coder complexity for a given decoder. Subband pyramids achieve spatial scalability without introducing redundant samples, however, there is no extension to a spatiotemporal system with motion compensation yet. Resolution pyramids with redundant samples can be extended to a 3D spatiotemporal resolution pyramid with motion compensation. Various examples of scalable image representations are given in the paper.

Generation of multi-resolution maps from run-length-encoded data

Abstract One motivation for the need of maps to be organized hierarchically in different resolutions is the fact that in most applications low-resolution maps require less heavy computations than maps represented at higher resolutions. Spatial data structures that permit generation of lower levels or resolution in a hierarchical fashion already exist, e.g., quad-trees and resolution pyramids. Many other spatial data structures that are non-hierarchical, and therefore do not permit the generation of resolution hierarchies, also exist. One such structure is the run-length-code (RLC), which has many powerful advantages that make the structure feasible in geographical information systems. In this article an approach to the problem of generating a resolution hierarchy from RLC is described and discussed.

Automated stereophotogrammetry

In this paper we outline a structural pattern recognition approach to the stereo matching problem of automated stereophotogrammetry. Oriented-edge graphs are obtained with the edge vectors and filtered to obtained feature points for matching purposes. A resolution pyramid is based to aid the accurate matching of the feature points. At each stage of the pyramid, pseudo-hexagonal gray scale arrays are used to bypass the four-eight connectivity paradox in the implementation of association of oblique vectors in Bowker's association filter. The Fisher- z transform is used to determine the correlation function threshold in matching. To fill the gaps between the matched nodes, a local interpolation method that linearly weights the disparity values in a window was developed. The interpolated points are not used for matching, but give the initial approximation at the next lower stage of the resolution pyramid. Finally, a modified normalized gray-scale correlation is used to refine the parallax found at the lowest level of the pseudo-hex resolution pyramid using the original 2D raster. The correlation scheme used here works in both directions: from left to right and from right to left, to obtain reliable matching. We have tried to use as simple operations as possible in each stage to allow the algorithm to run in real-time, possibly leading to an economical hardware implementation.

Automatic landmarking of cephalograms

This paper presents an algorithm for automatically locating certain characteristic anatomical points called landmarks on cephalograms (skull X-rays). These landmarks are used by orthodontists in diagnosis and treatment planning. The algorithm uses digital image processing and feature recognition techniques to locate the landmarks. A resolution pyramid of the digitized cephalogram is first created. The algorithm works on the smaller, lower resolution images to locate features of interest and moves to the bigger, higher resolution images if greater location accuracy is required. Prefiltering using the median filter, contrast enhancement using histogram equalization, and edge enhancement using different gradient operators are performed on the images. The algorithm uses anatomical knowledge of the human facial structure to search for features containing the landmarks. The accuracy of the algorithm in locating the landmarks is compared with values obtained from human experts. At present the algorithm attempts to locate 10 landmarks of 27 needed for a complete analysis. All 10 landmarks have been successfully located on five cephalograms of varying quality.

Hierarchical chamfer matching: a parametric edge matching algorithm

The algorithm matches edges by minimizing a generalized distance between them. The matching is performed in a series of images depicting the same scene with different resolutions, i.e. in a resolution pyramid. Using this hierarchical structure reduces the computational load significantly. The algorithm is reasonably simple to implement and is insensitive to noise and other disturbances. The algorithm has been tested in several applications. Two of them are briefly presented. In the first application the outlines of common tools are matched to gray-level images of the same tools, with overlapping. In the second application lake edges from aerial photographs are matched to lake edges from a map, with translation, rotation, scale, and perspective changes. The hierarchical chamfer matching algorithm gives correct results using a reasonable amount of computational resources in all tested applications.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The Converging Squares Algorithm: An Efficient Method for Locating Peaks in Multidimensions

The converging squares algorithm is a method for locating peaks in sampled data of two dimensions or higher. There are two primary advantages of this algorithm over conventional methods. First, it is robust with respect to noise and data type. There are no empirical parameters to permit adjustment of the process, so results are completely objective. Second, the method is computationally efficient. The inherent structure of the algorithm is that of a resolution pyramid. This enhances computational efficiency as well as contributing to the quality of noise immunity of the method. The algorithm is detailed for two-dimensional data, and is described for three-dimensional data. Quantitative comparisons of computation are made with two conventional peak picking methods. Applications to biomedical image analysis, and for industrial inspection tasks are discussed.