Aviation display system designers and evaluators need to know how discriminable displayed symbols will be over a wide range of conditions to assess the adequacy and effectiveness of flight display systems. If flight display symbols are to be safely recognized by pilots, it is necessary that they can be easily
discriminated from each other. Sometimes psychophysical measurements can answer this question, but computational modeling may be required to assess the numerous conditions and even help design the
empirical experiments that may be needed. Here we present an image discrimination model that includes position compensation. The model takes as input the luminance values for the pixels of two symbol images, the effective viewing distance, and gives as output the discriminability in just-noticeable-differences (d')
and the x and y offset in pixels needed to minimize the discriminability. The model predictions are shown to be a useful upper bound for human symbol identification performance.
The detection and classification of objects in complicated backgrounds represents a difficult image analysis problem. Previous methods have employed additional information from dynamic scene processing to extract the object of interest from its environment and have produced efficient results. However, the study of object detection based on the information provided uniquely by still images has not been comprehensively studied. In this work, a different approach is proposed, when dynamic information is not available for detection. The presented scheme consists of two main stages. The first one includes a still image segmentation approach that makes use of multi-scale information and graph-based grouping to partition the image scene into meaningful regions. This is followed by a texture-based classification algorithm, in which correspondence analysis is used for feature selection and optimisation purposes. The outcomes of this methodology provide representative results at each stage of the study, to indicate the efficiency and potential of this approach for classification/detection in the difficult task of object detection in camouflaged environments.
Selecting the correct feature set is an essential basis for video sequence analysis that leads to applications such as tracking and recognition of vehicles. This paper selects diverse multiple features and tests their accuracy for tracking a static vehicle. The static vehicle images are captured with airborne infrared and color video cameras. The camera collects 30 frames per second of compressed video in Motion JPEG format. The diverse features are selected from representative histogram-based, invariant-based, spatial-temporal-based and the center-symmetric autocorrelation-based family of features. A small dataset of airborne video sequences that include static vehicles with variations in quality, orientation, resolution, foreground lighting and background lighting are used to test feature selection and static tracking. The track of the vehicles is hand selected frame-to-frame to create a truth track. The result of each feature to maintain track is tested and scored based on distance from the truth track. Once a significant break from track occurs, the truth data is used to reacquire track. One goodness score is based on how often a feature breaks track. This analysis shows promise for identifying appropriate features for improved tracking results. The suggested algorithm is demonstrated on only a few video sequences with limited variations in operating conditions but demonstrates improvement possibilities for near real-time application.
Positron Emission Tomography (PET) is a technology that uses short-lived radio nuclides altered by disease and precede changes that can be visualized by cross-sectional imaging. Over the last decade, this technique has become an important clinical tool for detection of tumors, follow-up treatment and drug research, providing an understanding of dynamic physiological processes. Since PET needs improved reconstruction algorithms to facilitate clinical diagnosis, we will investigate an improved iterative algorithm.
Amongst current algorithms applied for PET reconstruction, ART was first proposed as a method of reconstruction from CT projections. With appropriate tuning, the convergence of these algorithms could be very fast indeed. However, the quality of reconstruction using these methods has not been thoroughly investigated. We study a variant of these algorithms.
We present the state of the art, review well-known ART and investigate an optimum dynamically-changing block structure for the not yet fully explored variable-Block ART, which uses jointly the Inter-Update Metz filter for regularization and exploits the full symmetries in PET scanners. This reveals significant acceleration of initial convergence to an acceptable reconstruction of inconsistent cases. To assess the quality and analyze any discrepancy of the reconstructed images, two figures of merit (FOMs) are used to evaluate two 3D Data phantoms acquired on a GE-Advance scanner for high statistics.
A wide range of image processing studies are based on the extraction of texture features, the analysis of input data and the identification and design of appropriate classifiers given a particular application, for instance, in the fields of industrial inspection, remote sensing, medicine or biology amongst others. In this paper, we introduce a novel generalized classification framework for texture imagery based on a novel building blocks system architecture and present the advantages of such a system to tackle a variety of image analysis problems at the same time of obtaining good classification performances. Firstly, an overview of the system architecture is described from the texture feature extraction module to the data analysis and the classification building blocks. Thus, we obtain an optimized and generic classification framework which is highly flexible due to its scalable building blocks system approach and provides the facility to extend easily the study obtained for textural images to other kind of imagery. The results of this generalized classification framework are validated using imagery from two different application fields where texture plays a key role. The first one is in the field of remote sensing for agriculture crops classification and the second one, in the area of non-destructive industrial inspection.
Neural Networks and Fuzzy systems are considered two of the most important artificial intelligent algorithms which provide classification capabilities obtained through different learning schemas which capture knowledge and process it according to particular rule-based algorithms. These methods are especially suited to exploit the tolerance for uncertainty and vagueness in cognitive reasoning. By applying these methods with some relevant knowledge-based rules extracted using different data analysis tools, it is possible to obtain a robust classification performance for a wide range of applications. This paper will focus on non-destructive testing quality control systems, in particular, the study of metallic structures classification according to the corrosion time using a novel cellular neural network architecture, which will be explained in detail. Additionally, we will compare these results with the ones obtained using the Fuzzy C-means clustering algorithm and analyse both classifiers according to its classification capabilities.
An unavoidable problem of metal structures is their exposure to rust degradation during their operational life. Thus, the surfaces need to be assessed in order to avoid potential catastrophes. There is considerable interest in the use of patch repair strategies which minimize the project costs. However, to operate such strategies with confidence in the long useful life of the repair, it is essential that the condition of the existing coatings and the steel substrate can be accurately quantified and classified.
This paper describes the application of fuzzy set theory for steel surfaces classification according to the steel rust time. We propose a semi-automatic technique to obtain image clustering using the Fuzzy C-means (FCM) algorithm and we analyze two kinds of data to study the classification performance. Firstly, we investigate the use of raw images’ pixels without any pre-processing methods and neighborhood pixels. Secondly, we apply Gaussian noise to the images with different standard deviation to study the FCM method tolerance to Gaussian noise. The noisy images simulate the possible perturbations of the images due to the weather or rust deposits in the steel surfaces during typical on-site acquisition procedures
The exposure of metallic structures to rust degradation during their operational life is a known problem and it affects storage tanks, steel bridges, ships, etc. In order to prevent this degradation and the potential related catastrophes, the surfaces have to be assessed and the appropriate surface treatment and coating need to be applied according to the corrosion time of the steel. We previously investigated the potential of image processing techniques to tackle this problem. Several mathematical algorithms methods were analyzed and evaluated on a database of 500 images. In this paper, we extend our previous research and provide a further analysis of the textural mathematical methods for automatic rust time steel detection. Statistical descriptors are provided to evaluate the sensitivity of the results as well as the advantages and limitations of the different methods. Finally, a selector of the classifiers algorithms is introduced and the ratio between sensitivity of the results and time response (execution time) is analyzed to compromise good classification results (high sensitivity) and acceptable time response for the automation of the system.
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