Parametric Feature Detection

We propose an algorithm to automatically construct feature detectors for arbitrary parametric features. To obtain a high level of robustness we advocate the use of realistic multi-parameter feature models and incorporate optical and sensing effects. Each feature is represented as a densely sampled parametric manifold in a low dimensional subspace of a Hilbert space. During detection, the brightness distribution around each image pixel is projected into the subspace. If the projection lies sufficiently close to the feature manifold, the feature is detected and the location of the closest manifold point yields the feature parameters. The concepts of parameter reduction by normalization, dimension reduction, pattern rejection, and heuristic search are all employed to achieve the required efficiency. By applying the algorithm to appropriate parametric feature models, detectors have been constructed for five features, namely, step edge, roof edge, line, corner, and circular disc. Detailed experiments are reported on the robustness of detection and the accuracy of parameter estimation. In the case of the step edge, our results are compared with those obtained using popular detectors. We conclude with a brief discussion on the use of relaxation to rene outputs from multiple feature detectors, and sketch a hardware architecture for a general feature detection machine

Flexible shape extraction for micro/nano scale structured surfaces.

Surface feature is the one of the most important factors affecting the functionality and reliability of micro scale patterned surfaces. For micro scale patterned surface characterisation, it’s important to extract the surface feature effectively and accurately. The active contours, known as “snakes”, have been successfully used to segment, match and track the objects of interest. The active contours have been applied to facial boundary detection, medical image processing, motion correction, etc. In this paper, surface feature extraction techniques based on active contours have been investigated. Parametric active contour models and geometric active contour models have been presented. Also, a group of examples has been selected here to demonstrate the feasibility and applicability of the surface pattern extraction techniques based on active contours. At last, experimental results will be given and discussed

Beyond Individual Input for Deep Anomaly Detection on Tabular Data

Anomaly detection is crucial in various domains, such as finance, healthcare, and cybersecurity. In this paper, we propose a novel deep anomaly detection method for tabular data that leverages Non-Parametric Transformers (NPTs), a model initially proposed for supervised tasks, to capture both feature-feature and sample-sample dependencies. In a reconstruction-based framework, we train the NPT model to reconstruct masked features of normal samples. We use the model's ability to reconstruct the masked features during inference to generate an anomaly score. To the best of our knowledge, our proposed method is the first to combine both feature-feature and sample-sample dependencies for anomaly detection on tabular datasets. We evaluate our method on an extensive benchmark of tabular datasets and demonstrate that our approach outperforms existing state-of-the-art methods based on both the F1-Score and AUROC. Moreover, our work opens up new research directions for exploring the potential of NPTs for other tasks on tabular data

Detection of helicopters using neural nets

Artificial neural networks (ANNs), in combination with parametric spectral representation techniques, are applied for the detection of helicopter sound. Training of the ANN detectors was based on simulated helicopter sound from four helicopters and a variety of nonhelicopter sounds. Coding techniques based on linear prediction coefficients (LPCs) have been applied to obtain spectral estimates of the acoustic signals. Other forms of the LPC parameters such as reflection coefficients, cepstrum coefficients, and line spectral pairs (LSPs) have also been used as feature vectors for the training and testing of the ANN detectors. We have also investigated the use of wavelet transform for signal de-noising prior to feature extraction. The performance of various feature extraction techniques is evaluated in terms of their detection accurac

Synthetic aperture radar target detection, feature extraction, and image formation techniques

This report presents new algorithms for target detection, feature extraction, and image formation with the synthetic aperture radar (SAR) technology. For target detection, we consider target detection with SAR and coherent subtraction. We also study how the image false alarm rates are related to the target template false alarm rates when target templates are used for target detection. For feature extraction from SAR images, we present a computationally efficient eigenstructure-based 2D-MODE algorithm for two-dimensional frequency estimation. For SAR image formation, we present a robust parametric data model for estimating high resolution range signatures of radar targets and for forming high resolution SAR images