Background Suppression Factor Research Articles

METRICS FOR PERFORMANCE EVALUATION OF PREPROCESSING ALGORITHMS IN INFRARED SMALL TARGET IMAGES

Image preprocessing is commonly used in infrared (IR) small target detection to suppress background clutter and enhance target signature. To evaluate the performance of preprocessing algorithms, two performance metrics, namely PFTN (potential false targets number) decline ratio and BRI (background relative intensity) decline ratio are developed in this paper. The proposed metrics evaluate the performance of given preprocessing algorithm by comparing the qualities of input and output images. The new performance metrics are based on the theories of PFTN and BRI, which describe the quality of IR small target image, by representing the di-culty degree of target detection. Theoretical analysis and experimental results show that the proposed performance metrics can accurately re∞ect the efiect of the image preprocessing stage on reducing false alarms and target shielding. Compared to the traditional metrics, such as signal-to-noise ratio gain and background suppression factor, the new ones are more intuitive and valid.

Double Layered-Background Removal Filter for Detecting Small Infrared Targets in Heterogenous Backgrounds

Detecting small targets is essential for mitigating the sea-based Infrared search and track (IRST) problem. It is easy to detect small targets in homogeneous backgrounds such as the sky. When targets are on the border line of heterogeneous backgrounds such as the horizon in the sky and sea surface, solving the problem of detection becomes difficult. This paper presents a novel spatial filtering method, called Double Layered-Background Removal Filter (DL-BRF), for achieving high detection rates and low false alarm rates. DL-BRF consists of a Modified-Mean Subtraction Filter (M-MSF) and a consecutive Local-Directional Background Removal Filter (L-DBRF). M-MSF enhances the target signal and reduces background noise. L-DBRF removes horizontal structures, which upgrade the signal-to-clutter ratio and background suppression factor. L-DBRF used after M-MSF enhances the synergistic performance of horizontal target detection. Additionally, the adaptive Hysteresis threshold-based scheme is a suitable detection method. We validate the superior performance of the proposed method via three types of evaluation tests, including a real test scenario.

Point target detection of infrared images with eigentargets

A novel detection method for infrared (IR) point targets based on eigentargets is presented. The conception of eigentargets is proposed by making reference to eigenfaces in the field of face recognition. After creating the target training images using the Gaussian intensity function, we refine our method and obtain eigentargets that are a group of eigenvectors of the training set. These eigenvectors are then used to calculate the target map, defined by a target map function, at every location in the image to be detected. The large values in the target map image indicate the presence of targets. In comparison with other commonly used detection methods, our method has better performance with better receiver operating characteristics, and larger signal-to-noise ratio (SNR) and background suppression factor.

A SECOND LEVEL NEURAL NETWORK TRIGGER IN THE H1 EXPERIMENT AT HERA

At the HERA e-p collider the expected machine background rates are typically 105 times higher than the rates from physics. The greatest challenge in the trigger is finding methods which suppress the machine background without using lengthy pattern recognition algorithms with prohibitive computing times. This task is optimally suited to a neural network solution. Our present investigations show that feed-forward networks, trained on the topological energy sums from the H1 calorimeter and on first level tracking trigger information, provide an additional background suppression factor compared to the traditional method which operates with fixed thresholds. The neural network algorithm — implemented by special purpose fast matrix-vector multiplier chips at the second level of the trigger — allows the lowering of the first level thresholds which is shown to be important to get good efficiencies for specific physics event classes.

Thomson Scattering of Laser Light from a Relativistic Electron Beam

Thomson scattering of laser light was applied to a relativistic cooling electron beam. High-power laser pulses were necessary because of the very low scattering cross section. A spectrometer of high resolution and large background suppression factor in combination with time-gated photon counting served for signal detection. Analysis of Doppler shift and broadening of the backscattered light provided the determination of the electron beam energy with a precision of 2 × 10-3, and an upper limit of the longitudinal energy spread of 10-3 eV. A ratio of longitudinal to transverse electron temperature of 10-2 was found, indicating a flattened velocity distribution as is expected from the acceleration of the electrons.