A new general registration method for images of different na- ture is presented. As gray levels or textures cannot be used for the registration of images from separate spectral bands, an edge-based method has been developed. Edge images are processed to extract straight linear segments, which are then grouped to form triangles. A set of candidate transformations is determined by matching triangles from the source and destination images. The transformations are then evalu- ated by matching the transformed set of source segments to the set of destination segments. As the coincidence of vertices or edge overlap- ping cannot be assumed in the registration of images of different nature, a new function for evaluating the matching quality between source and destination segments that does not rely on overlapping measures is pro- posed. Results and subjective evaluation of the registration of visual and thermal infrared images are presented. © 2000 Society of Photo-Optical Instru- mentation Engineers. (S0091-3286(00)03401-2)

Segment-based registration technique for visual-infrared images

Image registration is an ubiquitous task occurring in countless image analysis applications. A dedicated implementation of image registration algorithms is the best approach to meet the intensive computation requirements of implementing image registration schemes in real time. This paper presents an efficient VLSI architecture for real-time implementation of image registration algorithms using an exhaustive search method. Normalized cross correlation function, mean square error, and blue screen technique algorithms are implemented for image registration. The architecture is based on a data flow design that allows sequential inputs but performs parallel processing. Based on the architecture, a programmable chip can be designed for image registration. Chips can be cascaded to achieve better performance and sizes of both the search and the reference image which can vary with time from a small to a very large value.

A VLSI Architecture for Image Registration in Real Time

Digital images can suffer from periodic noise, resulting in the appearance of repetitive patterns on the image data and quality degradation. In order to effectively reduce the periodic noise effects, a novel adaptive Gaussian notch filter is proposed in this paper. In the presented method, the frequency regions that correspond to noise are determined by applying a segmentation algorithm on the spectral band of the noisy image using an adaptive threshold. Then, a region growing algorithm tries to determine the bandwidth of each periodic noise component separately. Subsequently, proper Gaussian notch filters are used to decrease the periodic noises only at the contaminated noise frequencies. The proposed filter and some other well-known filters including the frequency domain mean and median filters and also the traditional Gaussian notch filter are compared to evaluate the effectiveness of the approach. The results in different conditions show that the proposed filter gains higher performance both visually and quantitatively with lower computational cost. Furthermore, compared with the other methods, the proposed filter does not need any tuning and parameter adjustments.

A novel adaptive Gaussian restoration filter for reducing periodic noises in digital image

A real-time adaptive segmentation method based on new distance features is proposed for the centroid tracker. These novel features are distances from the center point of a predicted target to each pixel by a tracking filter in extraction of a moving target. The proposed method restricts clutters with target-like intensity from entering the tracking window with low computational complexity for real- time applications compared with other complex feature-based methods. Comparative experiments show that the proposed method is superior to the other segmentation methods based on the intensity feature only in target detection and tracking.

Robust centroid target tracker based on novel distance features in cluttered image sequences

Periodic noises are unwished and spurious signals that create repetitive pattern on images and decreased the visual quality. Firstly, this paper investigates various methods for reducing the effects of the periodic noise in digital images. Then an adaptive optimum notch filter is proposed. In the proposed method, the regions of noise frequencies are determined by analyzing the spectral of noisy image. Then, the repetitive pattern of the periodic noise is produced by applying the corresponding notch pass filter. Finally, an output image with reduced periodic noise is restored by an optimum notch filter method. The results of the proposed adaptive optimum notch filter are compared by the mean and the median filtering techniques in frequency domain. The results show that the proposed filter has higher performances, visually and statistically, and has lower computational cost. In spite of the other compared methods, the proposed filter does not need to tune any parameters.

https://eej.aut.ac.ir/article_94_befd8a642325852c3a0d41ece10b3feb.pdf

Adaptive optimum notch filter for periodic noise reduction in digital images

Autonomous fire and forget weapons have gained importance to achieve accurate first pass kill by hitting the target at an appropriate aim point. Centroid of the image presented by a target in the field of view (FOV) of a sensor is generally accepted as the aimpoint for these weapons. Centroid trackers are applicable only when the target image is of significant size in the FOV of the sensor but does not overflow the FOV. But as the range between the sensor and the target decreases the image of the target will grow and finally overflow the FOV at close ranges and the centroid point on the target will keep on changing which is not desirable. And also centroid need not be the most desired/vulnerable point on the target. For hardened targets like tanks, proper aimpoint selection and guidance up to almost zero range is essential to achieve maximum kill probability. This paper presents a centroid tracker realization. As centroid offers a stable tracking point, it can be used as a reference to select the proper aimpoint. The centroid and the desired aimpoint are simultaneously tracked to avoid jamming by flares and also to take care of the problems arising due to image overflow. Thresholding of gray level image to binary image is a crucial step in centroid tracker. Different thresholding algorithms are discussed and a suitable algorithm is chosen. The real-time hardware implementation of centroid tracker with a suitable thresholding technique is presented including the interfacing to a multimode tracker for autonomous target tracking and aimpoint selection. The hardware uses very high speed arithmetic and programmable logic devices to meet the speed requirement and a microprocessor based subsystem for the system control. The tracker has been evaluated in a field environment.

Centroid tracker and aimpoint selection

Area correlation tracking (ACT) is an effective solution for tracking targets that have neither prominent features nor high contrast with the background. The essential step in area correlation tracking is to find the position of best-fit between the reference image of the terrain surrounding the desired target to the real-time scene acquired by an imaging sensor. The image matching is accomplished by the basic mathematical correlation coefficient algorithm (CCA) or mean absolute difference (MAD) algorithm or other algorithms derived from these algorithms. The reference image has to be updated very frequently to circumvent the problems associated with image growth, aspect changes, rotation, etc. Otherwise the image tracking algorithm may become divergent with time thereby losing the track. This paper addresses the real-time implementation of area correlation algorithms for an on-board application. The implementation poses challenges because of the very large number of integer multiplications required by the correlation coefficient algorithm. In view of the space and power constraints in on board applications, the realization becomes all the more complex. To realize the system, a parallel pipelines architecture is adapted. Very high speed arithmetic devices are used for computations, programmable logic devices for the high speed control and a dual microprocessor based system for overall control. This hardware has been evaluated by integrating with a full fledged imaging seeker in captive flight trials. The results are presented.

Real-time hardware implementation for area correlation tracker

Image registration techniques have gained importance for many applications such as area correlation tracking, handing over recognized target scenes from one sensor to another sensor, tracking an area of interest before detection, tracking point targets, and the latest being multi- target handling capability for defense needs, scene stabilization, etc. No single image registration algorithm (IRA) can work satisfactorily for all applications and in all environments. This paper analyzes the suitability of image registration algorithms for infrared images. Infrared images are characterized by low contrast and sensor nonuniformities such as offset-errors, gain variations resulting in fixed pattern noise (FPN). Particularly, in focal plane arrays (FPA), the output of each detector is characterized by `ax + b' where `a' and `b' are gain and dc offset terms respectively, and `x' is photon flux level falling on the detectors. These parameters and especially their variations from detector element to detector element effect the performance of image registrations algorithms. In this paper, the basic IRAs are analyzed in this context of infrared images with low contrast and FPN. Simulated results using real world images are presented. Novel and inexpensive confidence and redundancy measures have been proposed to improve the performance by detecting misregistrations.

Analysis of image registration algorithms for infrared images

The performance of image registration algorithms for IR images depends on different system parameters such as SNR, gain, offset, image variance etc. in a very complex fashion. A statistical approach has been adopted to predict the performance of image registration algorithms , classical Correlation and Mean Absolute Difference algorithms, in terms of probability of correct registration as a function of different system parameters. Considering the complexity of the problem, some simplifying assumptions have been made regarding the random nature of different images as well as criteria function values which may not be strictly true for real environments. This analysis helps in understanding the sensitivity of different algorithms to different system parameters. This in turn helps to compare the performance of different registration algorithms in differe nt environments. A scheme for choosing adaptive threshold to meet constraints as probability of false hit and probability of miss, has also been proposed

Mathematical analysis of image registration algorithms for infrared images

Real-time hardware developed for image processing applications such as image enhancement, segmentation, image registration, pattern recognition, etc. can not be thoroughly debugged and analyzed for its performance by using conventional test equipment. To cite an example, the hardware for image registration may be failing intermittently or the registration point may be drifting even when the scene and the sensor are static. There is no way of knowing whether the malfunction is due to input data or any noise glitches. Therefore, for evaluating real-time image processing hardware, it is necessary to acquire image sequence data in real-time and also capture the status of the tracker in real-time. Test equipment like logic analyzer, microprocessor development systems do not have the capability to acquire and store image sequences. General purpose data acquisition systems on the IBM PC compatible computer are not suitable as the data rate required is about 500 kbytes/sec. the design of an interface card for achieving this data rate using IEEE-488 (GPIB) interface is described. Results on the evaluation of image processing hardware using this card are also presented.

Ronda Venkateswarlu

Papers

Centroid tracker and aimpoint selection

Real-time hardware implementation for area correlation tracker

Analysis of image registration algorithms for infrared images

Mathematical analysis of image registration algorithms for infrared images

Real-time evaluation techniques for real-time image processing hardware