Guoyou Wang

Efficient method for multiscale small target detection from a natural scene

According to the principle of human discrimination of small targets from a natural scene that there is a signature of discontinuity between the object and its neighboring regions, we develop an efficient method for multiscale small target detection using template matching based on a dissimilarity measure, which is called an average gray absolute difference maximum map (AGADMM), and infer the criterion of recognizing multiscale small objects from the properties of the AGADMM of the natural scene, which is a spatially independent and stable Gaussian random field. We explain how the AGADMM increases the ratio of the signal of object-to-background perturbations, improves the detectable probability, and keeps the false alarm probability very low. We analyze the complexity of computing an AGADMM and justify the validity and efficiency. Experiments with images of a natural scene such as a sky and sea surface have shown the great potential of the proposed method for distinguishing multiscale small objects from a natural scene.

Gray-scale morphology for small object detection

In this paper, we present morphological processing using median operation for small object detection. First, we perform median morphological operation on the gray-scale image with structuring element A and make all scene regions of size equal to the central As area or larger brighter (for bright objects) or darker (for dark objects) and other regions approximate invariably. Second, we perform median morphological operation on the gray-scale image with larger structuring element B and make all scene regions of size equal to the central Bs area or smaller darker (for bright objects) or brighter (for dark objects) and other regions approximate invariably. Third, we calculate the absolute difference of above two outputs. All object regions between the smallest and largest will be enhanced and all background regions will be weakened. Then a simple threshold can extract all objects with some smaller background regions. Finally, those smaller background regions whose areas are smaller than structuring element A can be eliminated by region labeling processing. We find that if (1) contrary to background, the object regions have the signature of discontinuity with their neighbor regions. (2) Each object concentrates relatively in a small region, which can be considered as a homogeneous compact region, our algorithm can achieve satisfactory detection performance.

Small-target predetection with an attention mechanism

We introduce the concept of predetection based on an attention mechanism to improve the efficiency of small-target detection by limiting the image region of detection. According to the characteristics of small-target detection, local contrast is taken as the only feature in predetection and a nonlinear sampling model is adopted to make the predetection adaptive to detect small targets with different area sizes. To simplify the predetection itself and decrease the false alarm probability, neighboring nodes in the sampling grid are used to generate a saliency map, and a short-term memory is adopted to accelerate the pop-out of targets. We discuss the fact that the proposed approach is simple enough in computational complexity. In addition, even in a cluttered background, attention can be led to targets in a satisfying few iterations, which ensures that the detection efficiency will not be decreased due to false alarms. Experimental results are presented to demonstrate the applicability of the approach.

Digital implementation of nonuniformity correction for IRFPAs

All IRFPAs require nonuniformity correction. Although two- point or multi-point correction algorithms may correct the nonuniformity of IRFPAs they can be limited by pixel nonlinearities and instabilities. So adaptive nonuniformity correction techniques are needed. Many researchers develop the methods of real-time correction based the scene being viewed. The nonuniformity correction process is completed in IRFPA sensor named smart FPA. However, the smart IRFPA is developing. The purpose of this paper is to describe a digital signal processing electronics for the nonuniformity correction. It includes ADSP21060 digital signal processor, 8751 chip and display circuit module. The image data from IRFPA are put into the dual RAM. The ADSP21060 DSP completes the nonuniformity correction function while the 8751 chip operates control function. At the same time, the correction results will be displayed on a monitor. The neural networks algorithms and the constant-statistics algorithm are tested in our digital implementations. When the image size is 60*97, processing time per frame is 14.75 millisecond for the neural network algorithm and it is 12.65 millisecond for the constant-statistics algorithm. Measured results show that digital processing system designed by us may achieve demand of real-time nonuniformity correction based the scene for small IRFPAs.

Ship detection with an attention mechanism applied

In this paper, An approach for fast ship detection in infrared (IR) images based on multi-resolution attention mechanism is proposed. In order to realize real-time image analysis, attention mechanism is indispensable to focus the computational resources on only regions or information related to the task at hand. This paper discusses topics of: sampling model; index information generating or areas of interest (AOI) searching; next fixation point determination and target detection. Variance of the neighboring nodes in the periphery is used to form a saliency map of the image. Node with higher saliency is of greater possibility to be engine or other hot parts on a ship, while a straight line near below can confirm the ship hypothesis. In the paper, the sampling model is introduced, and then the index region detection and the following saccade and analysis process are discussed. At the end of the paper, experimental results of the detection of ships with different size in infrared images are presented. Those demonstrate that our approach can find ship target effectively. Comparisons of performance with our approach and those of some other approaches are also presented.

Class of methods for representation and recognition of multiscale objects

When an airborne imagery sensor moves from far to near, for a non-zoom imaging system, the image sequence from a scene will vary with different scales dramatically, state-of-the-art methods based on a single invariant feature or a simple feature, such as moments invariant, shape specific points, topological features and Fourier descriptor, etc., are rendered useless for representing and recognizing a multiscale object in this specific image sequence. Even the image gray-pyramid technique, which has great potential for pattern recognition by template matching with different resolutions, can not provide satisfactory performance due to not knowing exactly the resolution of real images, so there is an increasing need for improvement in multiscale object rendering and recognition. In this paper, we develop a class of algorithms for representation and recognition of a multiscale object in the specific image sequence taken from a sensor moving from far to near, which is called hierarchy features model (HFM) and sequential object recognition algorithm (SORA) based on this hierarchy features model, respectively, and intended to represent a size-changing object and recognize it. Experimental results with many real visual and infrared images and simulated images have shown that when a non-zoom imagery sensor moves from far to near, the HFM is suitable to represent a multiscale object, and the SORA available to recognize it.

Small moving target indication based on linear-variant-coefficient-difference equation

The movement model of target limited by track-before-detect approaches is extended to that of target with constant acceleration in this paper. And an algorithm based on linear-variant-coefficient-difference-equation for moving target indication is proposed. Moreover, based on parametric models of target and background, this paper presents an analysis of its optimal SNR gain versus target and background characteristics as well as the sensitivity of this gain to mismatch.

Relaxation matching by the Hopfield neural network

A method that makes the Hopfield neural network perform the point pattern relaxation matching process is proposed. An advantage of this is that the relaxation matching process can be performed in real time with the massively parallel capability to process information of the neural network. Experimental results with large simulated images prove the effectiveness and feasibility to perform point relaxation matching by the Hopfield neural network.

Efficient small target detection algorithm

According to the principle of human discrimination of a small object from a natural scene in which there is the signature of discontinuity between the object and its neighbor regions, we develop an efficient algorithm for small object detection based on template matching by using a dissimilarity measure that is called average gray absolute difference maximum map (AGADMM), infer the criterion of recognizing a small object from the properties of the AGADMM of a natural scene that is a spatially independent and stable Gaussian random field, explain how the AGADMM improves the detectable probability and keeps the false alarm probability very low, analyze the complexity of computing AGADMM, and justify the validity and efficiency. Experiments with visual images of a natural scene such as sky and sea surface have shown the great potentials of the proposed method for distinguishing a small man-made object from natural scenes.

Simulation platform on scene matching

Scene matching technique is one of the most basic and important techniques in the modern information processing domain. Scene matching is the space registration process of two images taken from the same scene by two different sensors so that their relative displacement is gotten.