Synthetic Discriminant Function Research Articles

The Correlation Recognition of Distorted Images Based on Synthetic Discriminant Function

The Correlation Recognition of Distorted Images Based on Synthetic Discriminant Function

Fast Recursive Computation of Composite Correlation Filters

Abstract—Algorithms for tracking multiple objects using correlation filters require calculation of a composite filter based on synthetic discriminant functions in order to increase the robustness to changes in posture, partial overlapping of objects by other objects, scaling, rotation, nonuniform illumination, and complex background. The calculation algorithm has large computational complexity. In this paper, we propose recursive calculation of a composite filter using algorithms for rapid inversion of matrices to accelerate synthesis of the filter. Computer simulation results on calculation of a composite correlation filter are presented and discussed in terms of the computation accuracy and the rate of calculation.

Research on Distortion Invariant Recognition of a Planar Integrated Optical Correlator

In this paper, an optical correlator which adopts planar integrated 2F structure and optimal tradeoff synthetic discriminant function filter is proposed to improve the system integration and the recognition ability. The specialized digital microlens is designed for the correlator to replace the physical lens. By the theoretical analysis, the structural design conditions and parameters of the correlator are given, and the system volume is 63.1 cm3 which is only half of the planar integrated 4F optical correlator. Moreover, simulations and experimental research works reveal that the correlator can achieve stable recognition to the input object of distortion with scaling (52%, 150%), rotation (–50°, +42°), and existed scaling (72%, 130%) and rotation (–36°, +38°) simultaneously. Theoretical analysis and experimental results indicate that the planar integrated optical correlator with compact structure and high integration has good performance of distortion invariant recognition.

Target tracking with composite linear filters on noisy scenes

A tracking system using a bank of adaptive linear filters is proposed. Tracking is carried out by means of multiple target detections. The linear filters are designed from multiple views of a target using synthetic discriminant functions. For each view an optimum filter is derived from noisy reference image and disjoint background model. An iterative algorithm is used to improve the performance of the synthesized filters. The number of filters in the bank can be controlled to guarantee a prescribed tracking accuracy. Computer simulation results show that the proposed algorithm is able to precisely track a target.

大尺度混合畸变的目标识别技术

In this paper, area-polar algorithm is firstly proposed to solve the problem of hybrid-distortion target recognition technology. This method makes area transform and polar coordinate transform for both target and template, based on the surjective function relationship between the scale and the area of similar figures and the invariance of rotation of polar coordinate. Applied on hybrid optoelectronic joint transform correlator, a combined method can achieve large-scale hybrid-distortion target recognition, which includes area-polar algorithm and edge extracting process based on morphological dilation. Presented with examples of planes, The optical correlation experiments show that this method can enhance the brightness of the correlation peaks for hybrid-distortion target, which possesses large hybrid-distortion tolerance (with 0~100 rotation distortion tolerance and 100%~200% scale distortion tolerance), and expands the scope of target recognition. The hybrid-distortion target recognition capability by this method is better than by Log-polar transform algorithm, and also better than by synthetic discriminant function and by derivative methods, besides its time complexity is far less than synthetic discriminant function's and derivative methods'.

Distortion tolerant correlation filter design

This paper introduces a computationally efficient algorithm for synthesis of a distortion tolerant correlation filter and associated threshold, denoted collectively as the enhanced matched filter (EMF). Application areas of EMF include imagery based automatic target detection and recognition and biometrics. The EMF is synthesized from a set of training images characterizing the target of interest within the expected distortion range. A distinguishing feature of EMF is the ascribed threshold, which is a byproduct of the filter computation process and does not rely on nontarget trainers. The EMF performance is compared to that of the synthetic discriminant function using realistic test scenarios.

Three-dimensional object rotation-tolerant recognition for integral imaging using synthetic discriminant function

This paper presents a novel approach of three-dimensional object rotation-tolerant recognition that combines the merits of Integral Imaging (II) and Synthetic Discriminant Function (SDF). SDF aims at filters and distortion-tolerant recognition, and we use it for three-dimensional (3-D) rotation-tolerant recognition with II system. Using high relevancy of elemental images of II, the approach can not only realize 3-D rotation-tolerant recognition, but also reduce computational complexity. The correctness has been validated by experimental results.

Multiclass pattern recognition using adaptive correlation filters with complex constraints

An efficient method for reliable multiclass pattern recognition using a bank of adaptive correlation filters is proposed. The method can recognize and classify multiple targets from an input scene by using both the intensity and phase distributions of the output complex correlation plane. The adaptive filters are synthesized with the help of an iterative algorithm based on synthetic discriminant functions with complex constraints. The algorithm optimizes the discrimination capability of the adaptive filters and determines the minimum number of filters in a bank to guarantee a desired classification efficiency. As a result, the computational complexity of the proposed system is low. Computer simulation results obtained with the proposed approach in cluttered and noisy scenes are discussed and compared with those obtained through existing methods in terms of recognition performance, classification efficiency, and computational complexity.

Distortion-invariant recognition of volume holographic correlator based on morphological algorithm and synthetic discriminant function

We proposed a novel method based on mathematical morphological algorithm and space domain synthetic discriminant function (SDF) for distortion-invariant recognition of volume holographic correlator (VHC). To suppress the effect of pattern-dependent behavior, a new morphological algorithm is introduced, which can process the gray-scale images into strong and clean edge maps without redundancy and irrelevant detail. Using these edge maps, SDF filters are synthesized for detecting distorted targets on VHC system. Experimental results demonstrate that the proposed method can achieve both rotation and scale invariant recognition.

水下光束漂移激光图像抗畸变识别

Beam drift exists in underwater laser propagation. Influence of the beam drift on the images correlation recognition and the irrelevance between the random noise of inter-frame images were analyzed. The distortion-invariant matched filter design was studied, a phase-matching extraction of common features between distorted images was proposed, and the common features were used as the training samples of the synthetic discriminant function, based on which anti-distortion matching filter was designed and verified by a simulation. The Matlab simulation results show that the design of anti-distortion synthetic discriminant matching filter, combined with the pre-filtering of the image to be recognized, can effectively improve the PCE value (peak- the correlation energy ratio) of the correlation peak output and the disk values (peak identification rate).

Robust Classification Method of Tumor Subtype by Using Correlation Filters

Tumor classification based on gene expression profiles, which is of great benefit to the accurate diagnosis and personalized treatment for different types of tumor, has drawn a great attention in recent years. This paper proposes a novel tumor classification method based on correlation filters to identify the overall pattern of tumor subtype hidden in differentially expressed genes. Concretely, two correlation filters, i.e., Minimum Average Correlation Energy (MACE) and Optimal Tradeoff Synthetic Discriminant Function (OTSDF), are introduced to determine whether a test sample matches the templates synthesized for each subclass. The experiments on six publicly available datasets indicate that the proposed method is robust to noise, and can more effectively avoid the effects of dimensionality curse. Compared with many model-based methods, the correlation filter based method can achieve better performance when balanced training sets are exploited to synthesize the templates. Particularly, the proposed method can detect the similarity of overall pattern while ignoring small mismatches between test sample and the synthesized template. And it performs well even if only few training samples are available. More importantly, the experimental results can be visually represented, which is helpful for the further analysis of results.

Distortion-invariant pattern recognition using synthetic discriminant function based multiple phase-shifted-reference fringe-adjusted joint transform correlation

This paper proposes a novel pattern recognition system for invariance to noise and distortions. The technique first generates a synthetic discriminant function of the target image from its different distorted versions. It then takes four different phase-shifted versions of the reference image, which are individually joint transform correlated with the given input scene. Thus the proposed algorithm produces a single cross-correlation signal corresponding to each potential target. Also a fringe-adjusted filter is designed to generate a delta-like correlation peak with high discrimination between the signal and the noise. The pattern recognition system is also designed for the identification of multiple targets belonging to multiple reference objects simultaneously in a given input scene. The proposed technique is investigated using computer simulation including real-life images in different complex environments.

Content-addressable holographic data storage system for invariant pattern recognition of gray-scale images

Conventionally a holographic data storage system uses binary digital data as the input pages. We propose and demonstrate the use of a holographic data storage system for the purpose of invariant pattern recognition of gray-scale images. To improve the correlation accuracy for gray-scale images, we present a coding technique, phase Fourier transform (phase-FT) coding, to code a gray-scale image into a random and balanced digital binary image. In addition to the fact that a digital data page is obtained for incorporation into a holographic data storage system, this phase-FT coded image produces dc-free homogenized Fourier spectrum. This coded image can also be treated as an image for further processing, such as synthesis of distortion-invariant filters for invariant pattern recognition. A space-domain synthetic discriminant function (SDF) filter has been synthesized using these phase-FT coded images for rotation-invariant pattern recognition. Both simulation and experimental results are presented. The results show good correlation accuracy in comparison to correlation results obtained for SDF filter synthesized using the original gray-scale images themselves.

Distortion-invariant pattern recognition using synthetic discriminant function-based shifted phase-encoded joint transform correlator

A novel pattern recognition technique is proposed that employs shifted phase-encoded fringe-adjusted joint transform correlation (SPFJTC) for efficient real-time application and synthetic discriminant function for invariance to distortions. The proposed technique produces a single delta-like correlation for a potential target in the input scene, and performs successfully even in a noisy environment. The simple architecture of the proposed technique can be implemented easily on optoelectronics for very high-speed operation. Computer simulation results verify the efficient performance of the technique under different variations of the input scene and the environment.

Optoelectronic 3-D Object Classification From 2-D Images

The task of object classification is complicated by variations in the 3-D object, which translate into distortions in 2-D images. The pattern matching for 3-D invariance classification requires a large amount of data and computation time. Proposed here is an efficient 3-D object classification algorithm for real-time fringe-adjusted joint transform correlator (FJTC)-based automatic target recognition (ATR) system. The proposed classification technique employed a fragment-based recognition approach and a new type of synthetic discriminant function filter in the generation of distortion-invariant correlation filter sets. The optoelectronic FJTC is then used to provide correlation of the filter sets with the input under a proper arrangement. This classification method is simple and fast, hence is suitable to be in use by real-time ATR systems. For the conclusion, simulation results are provided to prove the effectiveness of the proposed system in the classification of objects invariant to 3-D out-of-plane rotation distortion.

Adaptive composite filters for pattern recognition in linearly degraded and noisy scenes

New adaptive correlation filters for reliable recognition of geometrically distorted objects in blurred and noisy scenes are proposed. The filters are based on modified synthetic discriminant functions. The information about objects to be recognized, false objects, disjoint background, additive noise, and expected degradations of targets and input scenes are utilized in an iterative training algorithm. The algorithm is used to design a correlation filter with a specified discrimination capability. Computer simulation results obtained with the proposed adaptive filters in test scenes are discussed and compared with those of various correlation filters in terms of discrimination capability and location errors.

Adaptive phase-input joint transform correlator

An adaptive phase-input joint transform correlator for pattern recognition is presented. The input of the system is two phase-only images: input scene and reference. The reference image is generated with a new iterative algorithm using phase-only synthetic discriminant functions. The algorithm takes into account calibration lookup tables of all optoelectronics elements used in optodigital experiments. The designed adaptive phase-input joint transform correlator is able to reliably detect a target and its distorted versions embedded into a cluttered background. Computer simulations are provided and compared with those of various existing joint transform correlators in terms of discrimination capability, tolerance to input additive noise, and to small geometric image distortions. Experimental optodigital results are also provided and discussed.

Pattern recognition with an adaptive joint transform correlator

An adaptive joint transform correlator for real-time pattern recognition is presented. A reference image for the correlator is generated with a new iterative algorithm. The training algorithm is based on synthetic discriminant functions. The obtained reference image contains the information needed to reliably discriminate a target against known false objects and a cluttered background. Calibration lookup tables of all optoelectronics elements used are included in the design of the adaptive joint transform correlator. Two methods for the implementation of the proposed joint transform correlator in an optodigital setup are considered. Experimental results are provided and compared with those of computer simulations.

Adaptive correlation filters for pattern recognition

Adaptive correlation filters based on synthetic discriminant functions (SDFs) for reliable pattern recognition are proposed. A given value of discrimination capability can be achieved by adapting a SDF filter to the input scene. This can be done by iterative training. Computer simulation results obtained with the proposed filters are compared with those of various correlation filters in terms of recognition performance.

Adaptive synthetic discriminant function filters for pattern recognition

New adaptive correlation filters based on a conventional synthetic discriminant function (SDF) for reliable recognition of an object in cluttered background are proposed. The information about an object to be recognized, false objects, and a background to be rejected is utilized in an iterative training procedure to design a correlation filter with a given value of discrimination capability. Computer simulation results obtained with the proposed adaptive filter in test scenes are discussed and compared with those of various correlation filters in terms of discrimination capability, tolerance to input additive noise that is always present in image sensors, and to small geometric image distortions.