Unsupervised domain adaptive object detection aims to adapt a well-trained detector from its original source domain with rich labeled data to a new target domain with unlabeled data. Previous works focus on improving the domain adaptability of region-based detectors,e.g., Faster-RCNN, through matching cross-domain instance-level features that are explicitly extracted from a region proposal network (RPN). However, this is unsuitable for region-free detectors such as single shot detector (SSD), which perform a dense prediction from all possible locations in an image and do not have the RPN to encode such instance-level features. As a result, they fail to align important image regions and crucial instance-level features between the domains of region-free detectors. In this work, we propose an adversarial module, namely, densely semantic enhancement module (DSEM), to strengthen the cross-domain matching of instance-level features for region-free detectors. Firstly, to emphasize the important regions of image, the DSEM learns to predict a transferable foreground enhancement mask that can be utilized to suppress the background disturbance in an image. Secondly, considering that region-free detectors recognize objects of different scales using multi-layer feature maps, the DSEM encodes multi-scale representations across different domains. Finally, the DSEM is pluggable into different region-free detectors, ultimately achieving the densely semantic feature matching via adversarial learning. Extensive experiments have been conducted on PASCAL VOC, Clipart, Comic, W atercolor, and FoggyCityscape benchmarks, and their results well demonstrate that the proposed approach not only improves the domain adaptability of region-free detectors but also outperforms existing domain adaptive region-based detectors under various domain shift settings.

For hyperspectral images (HSIs), the imbalance between the high dimensionality and the limited labeled samples has been a main obstacle to classification task. As a solution, semisupervised learning utilizing both labeled and unlabeled samples has shown its potential. In this letter, a novel semisupervised classification framework based on graph attention networks (GATs) for HSIs is proposed. Spatial-spectral joint measurement is designed for the graph model construction to make full use of spatial information. In the convolution process, different weights are assigned to different neighboring nodes according to their attention coefficients, avoiding designing connection weights artificially in previous graph convolution networks (GCNs). Experimental results on multiple hyperspectral data sets with various contexts and resolutions demonstrate that the proposed method outperforms several state-of-the-art graph-based methods.

Hyperspectral image classification with limited training samples has become a hot research topic recently. Though deep convolution neural network shows powerful ability for feature extraction, its good performance often relies on sufficient training data. In this paper, we propose a multitask learning framework based on siamese network and auto-encoder to fully exploit limited labeled samples’ information and obtain discriminative features for classification of hyperspectral images. A low intraclass and high interclass variability of features can be learned by metric learning using our framework. And superpixel-based 3D sample preprocessing is applied to improve the classification accuracy on the hyperspectral images’ boundaries. The experimental results demonstrate that our framework can achieve competitive results compared with the state-of-the-art methods.

The imbalance between high dimensionality and limited labeled samples has been a great challenge for classification task of hyperspectral images (HSIs). In this paper, a novel semi-supervised classification method for HSIs is proposed. This method contains two major parts: representation using spatial-spectral graph model and graph convolutional networks (GCN) using edge-conditioned convolution. For the proposed method, spatial-spectral information is considered simultaneously during the process of graph construction, and then GCN is used to extract the feature from input data and learn their topology relationships with edge label involved. Experimental results on multiple hyperspectral datasets with various contexts and resolutions demonstrate that the proposed classifier outperforms several graph-based methods.

The scarcity of labeled samples has been the main obstacle to the development of scene classification for remote sensing images. To alleviate this problem, the efforts have been dedicated to semisupervised classification which exploits both labeled and unlabeled samples for training classifiers. In this letter, we propose a novel semisupervised method that utilizes the effective residual convolutional neural network (ResNet) to extract preliminary image features. Moreover, the strategy of ensemble learning (EL) is adopted to establish discriminative image representations by exploring the intrinsic information of all available data. Finally, supervised learning is performed for scene classification. To verify the effectiveness of the proposed method, it is further compared with several state-of-the-art feature representation and semisupervised classification approaches. The experimental results show that by combining ResNet features with EL, the proposed method can obtain more effective image representations and achieve superior results.

The special characteristic of remote sensing (RS) images being large scale while only low number of labeled samples available in practical applications has been obstacle to the development of RS image classification. In this paper, a novel semi-supervised framework is proposed. The high-capacity convolutional neural networks (CNN) are adopted to extract preliminary image features. The strategy of ensemble learning is then utilized to establish discriminative image representations by exploring intrinsic information of available data. Plain supervised learning is finally performed to obtain classification results. To verify the efficacy of our work, we compare it with mainstream feature representation and semi-supervised approaches. Experimental results show that by utilizing CNN features and ensemble learning, our framework can obtain more effective image representations and achieve superior results compared with other paradigms of semi-supervised classification.

The geometrical features of airport line segments are seldom used by traditional methods for airport detection in panchromatic remote sensing images. This letter presents a novel method based on both bottom-up (BU) saliency and top-down saliency. Noticing that airport runways have features of vicinity and parallelity and that their lengths are among a certain range, we introduce the concept of near parallelity for the first time and treat it as prior knowledge that can fully exploit the geometrical relationship of airport runways. Meanwhile, a simplified graph-based visual saliency model is used to extract the BU saliency. Two-way results are combined, and candidate regions can be derived from it. Finally, a scale-invariant feature transform and a support vector machine are used to determine whether the regions contain airports or not. The proposed method is tested on an image data set composed of different kinds of airports. The experimental results show that the method outperforms other state-of-the-art models in terms of speed, the detection rate, and the false-alarm rate. In addition, the method is more robust to a complex background than the other methods.

In recent years, band selection is becoming a popular approach to reduce the dimensionality of hyperspectral data while preserving the desired information for target detection and classification analysis. This letter presents a new method for unsupervised band selection by transforming the hyperspectral data into complex networks. By analyzing the networks' topological feature corresponding to each band, one can easily evaluate the statistical characteristics and intrinsic properties of the signals. The proposed method searches for the network set which is most qualified for demarcating and identifying different substance signatures, and then, the network set's corresponding bands are regarded as the descried output results. This network measure is a new criterion for band selection. Experimental results demonstrate that the proposed method can acquire satisfactory results when compared with traditional methods.

This paper proposed a new approach to estimate the abundance of each endmember at each pixel using distance geometry concepts and distance geometry constraints. It improves current hyperspectral unmixing algorithms in several aspects. Firstly, denoting the distance relationship with Cayley-Menger matrix makes it easy to calculate the barycentric coordinates of observation pixels, and the computation is independent of number of bands. Secondly, by the distance geometry constraint, the geometric structure of dataset is considered to obtain the optimal result with least geometric deformation. The synthetic and real data experimental results demonstrate that this algorithm is a fast and accurate algorithm for the hyperspectral unmixing.

The hyperspectral bands are contiguous and highly correlated spectral bands. Band selection is often used to reduce the computational complexity for hyperspectral images. We proposed a new method for unsupervised band selection by using complex network to represent the spectral bands. The method completes the task with the objective of preserving the maximal information from original data in the selected bands. Both the divergences and connections between each hyperspectral band can be revealed from the topological characteristics of the generated network. We use the network topology as the criterion to identify the bands, and select the bands that can form the most approximate network comparing to the network of the original data. Experimental results demonstrate that, compared with traditional methods, the proposed algorithm can obtain accurate results with clear physical meaning and simple process.

In the linear unmixing of hyperspectral images, the observation pixels form a simplex whose vertices correspond to the endmembers, hence finding the endmembers is equivalent to extracting these vertices. A common technique for determining vertices is to analyze the simplex volume, but it usually has a high computational complexity, resulting from the exhaustive searching of volume in the large hyperspectral data. This problem limits the practicability and real-time application. In this paper, we utilize triangular factorization (TF) to calculate the volume, deducing a method named simplex volume analysis based on TF (SVATF). It requires just one comparison through the data to succeed in finding the global optimal solution for all the endmembers, thus improving the searching efficiency. Dimensionality reduction transformation is not necessary, which is another advantage of this method. Moreover, since TF is a broad conception including different methods, SVATF is a framework including various implementations. Based on TF, we also propose a fast learning algorithm named abundance quantification based on TF to estimate the abundances, which further saves the computation by utilizing the intermediate values involved in SVATF. The abundance estimation method can rectify possible errors in the given endmembers by utilizing two important constraints (abundance nonnegative constraint and abundance sum-to-one constraint) of the linear mixture model, so it is useful for the imagery without pure pixels. Experimental results on synthetic and real hyperspectral data demonstrate that the proposed methods can obtain accurate results with much lower computational complexity, with respect to other state-of-the-art methods.

Endmember extraction is a process to identify the spectra of materials from the hyperspectral scene. This paper presents a framework for endmember extraction by exploiting the ideas that: the endmembers are the vertices of the simplex, and the calculation of simplex volume can be simplified by triangular factorization. Triangular factorization is a broad conception including many methods, so the proposed framework is a group of methods including different implementations. Experimental results on both synthetic and real hyperspectral data demonstrate that the proposed algorithm can obtain the results with better accuracy and much lower complexity, comparing to other state-of-the-art approaches.

Nonnegative matrix factorization (NMF) has been recently applied to solve the hyperspectral unmixing problem because it ensures nonnegativity and needs no assumption for the presence of pure pixels. However, the algorithm has a large amount of local minima due to the obvious nonconvexity of the objective function. In order to improve its performance, auxiliary constraints can be introduced into the algorithm. In this paper, we propose a new approach named abundance separation and smoothness constrained NMF by introducing two constraints, namely, abundance separation and smoothness, into the NMF algorithm. These constraints are based on two properties of hyperspectral imagery. First, usually, every ground object presents dominance in a specific region of the entire image scene and the correlation is weak between different endmembers. Second, moving through various regions, ground objects usually vary slowly and abrupt changes rarely appear. We also propose a learning algorithm to further improve the performance of our method, from which the auxiliary constraints are removed at an appropriate time. The proposed algorithm retains all the advantages of NMF and effectively overcomes the shortcoming of local minima at the same time. Experimental results based on synthetic and real hyperspectral data show the superiority of the proposed algorithm with respect to other state-of-the-art approaches.

In recent years, independent component analysis (ICA) has been applied to unmix the hyperspectral data since it can perform without the prior knowledge of ground objects. The traditional ICA algorithm regards the extracted independent components as unmixing results, which is not reasonable for hyperspectral imagery, because different endmembers are not actually independent from each other. In order to solve this problem, a new approach, named as constrained ICA, is proposed, in which we consider “uncorrelation” instead of “independence.” Two constraints of the hyperspectral data (the abundance nonnegative and abundance sum-to-one constraints) are introduced to the ICA, changing its objective function based on independence assumption. Furthermore, we develop a technique, called as adaptive abundance modeling, to characterize the statistical distribution of the data. The model is automatically constructed according to the given data, which can encourage the algorithm that is applicable to various hyperspectral images with different statistical characteristics. The experimental results on both simulated and real hyperspectral data demonstrate that the proposed approach can obtain more accurate results with respect to existing algorithms. As an algorithm with no need of prior spectral knowledge, our method provides an effective solution for the blind unmixing of the hyperspectral data.

Traditional Nonnegative Matrix Factorization (NMF) algorithm is sensitive to the initial value when being applied to hyperspectral unmixing, because of the local minima in the objective function. In order to solve the problem, two constraints of abundance separation and smoothness are introduced into the NMF algorithm. The proposed algorithm retains the advantages of NMF, and effectively overcomes the shortcoming of local minima at the same time. Experimental results on simulated and real hyperspectral data demonstrate that the proposed approach can overcome the shortcoming of local minima, and obtain better results with respect to other state-of-art approaches. Meanwhile, the algorithm performs well for noisy data, and can also be used for the unmixing of hyperspectral data in which pure pixels do not exist.

In hyperspectral unmixing, endmember signals are not independent with each other, restricting the application of independent component analysis (ICA). We present a new algorithm to overcome this problem. By introducing abundance nonnegative and abundance sum-to-one constraints into objective function of ICA, the goal of our method is changed from “independence” to “uncorrelation”. We also develop an abundance modeling technique to describe the statistical distribution of hyperspectral data. The modeling approach is capable of self-adaptation, and can be applied to various images with different characteristics. Experimental results on both simulated and real hyperspectral data demonstrate that the proposed approach can obtain accurate results. As an algorithm with no need of spectral prior knowledge, our method provides an effective technique for hyperspectral unmixing.

Traditional spectral mixture analysis assumes that each endmember must have a constant spectral signature. However, endmember spectral variability always exists in practical situations, which results in reducing the accuracy of the decomposition of mixed pixels. In order to solve this problem, this letter proposes a new method based on Fisher discriminant null space (FDNS) for decomposition of mixed pixels in hyperspectral imagery. The FDNS searches a linear transformation of the spectra, which makes those endmember spectra to have no variability inside each endmember group but large differences among different endmember groups. Therefore, the negative impact caused by endmember spectral variability on unmixing accuracy can be decreased to a large extent by using the transformed spectra. Experimental results of both simulated and real hyperspectral images demonstrate that the proposed algorithm has a high accuracy for the decomposition of mixed pixels in hyperspectral imagery.

Compressive sampling is a novel framework in signal acquisition and reconstruction, which achieves sub-Nyquist sampling by exploiting the sparse nature of most signals of interest. In this letter, we propose a saliency-based compressive sampling scheme for image signals. The key idea is to exploit the saliency information of images, and allocate more sensing resources to salient regions but fewer to nonsalient regions. The scheme takes human visual attention into consideration because human vision would pay more attention to salient regions. Simulation results on natural images show that the proposed scheme improves the reconstructed image quality considerably compared to the case when saliency information is not used.

Salient areas in natural scenes are generally regarded as areas which the human eye will typically focus on, and finding these areas is the key step in object detection. In computer vision, many models have been proposed to simulate the behavior of eyes such as SaliencyToolBox (STB), Neuromorphic Vision Toolkit (NVT), and others, but they demand high computational cost and computing useful results mostly relies on their choice of parameters. Although some region-based approaches were proposed to reduce the computational complexity of feature maps, these approaches still were not able to work in real time. Recently, a simple and fast approach called spectral residual (SR) was proposed, which uses the SR of the amplitude spectrum to calculate the image's saliency map. However, in our previous work, we pointed out that it is the phase spectrum, not the amplitude spectrum, of an image's Fourier transform that is key to calculating the location of salient areas, and proposed the phase spectrum of Fourier transform (PFT) model. In this paper, we present a quaternion representation of an image which is composed of intensity, color, and motion features. Based on the principle of PFT, a novel multiresolution spatiotemporal saliency detection model called phase spectrum of quaternion Fourier transform (PQFT) is proposed in this paper to calculate the spatiotemporal saliency map of an image by its quaternion representation. Distinct from other models, the added motion dimension allows the phase spectrum to represent spatiotemporal saliency in order to perform attention selection not only for images but also for videos. In addition, the PQFT model can compute the saliency map of an image under various resolutions from coarse to fine. Therefore, the hierarchical selectivity (HS) framework based on the PQFT model is introduced here to construct the tree structure representation of an image. With the help of HS, a model called multiresolution wavelet domain foveation (MWDF) is proposed in this paper to improve coding efficiency in image and video compression. Extensive tests of videos, natural images, and psychological patterns show that the proposed PQFT model is more effective in saliency detection and can predict eye fixations better than other state-of-the-art models in previous literature. Moreover, our model requires low computational cost and, therefore, can work in real time. Additional experiments on image and video compression show that the HS-MWDF model can achieve higher compression rate than the traditional model.

The N-FINDR algorithm has been widely used in hyperspectral image analysis for endmember extraction due to its simplicity and effectiveness. However, there are several disadvantages of implementing the N-FINDR. This letter proposes an algorithm for decomposition of mixed pixels. It improves the N-FINDR in several aspects. First, an iterative Gram-Schmidt orthogonalization is applied in the endmember searching process to replace the matrix determinant calculation used in N-FINDR, which makes this algorithm run very fast and can also guarantee the stability of its final results. Second, with the set of orthogonal bases obtained by the Gram-Schmidt orthogonalization, the algorithm can also help to estimate the proper number of endmembers and unmix the original images by itself. In addition, unlike the N-FINDR, a dimensionality reduction transform is not necessary in this algorithm. Experimental results of both simulated images and practical remote sensing images demonstrate that this algorithm is a fast and accurate algorithm for the decomposition of mixed pixels.

There have been many computational models mimicking the visual cortex that are based on spatial adaptations of unsupervised neural networks. In this paper, we present a new model called neuronal cluster which includes spatial as well as temporal weights in its unified adaptation scheme. The "in-place" nature of the model is based on two biologically plausible learning rules, Hebbian rule and lateral inhibition. We present the mathematical demonstration that the temporal weights are derived from the delay in lateral inhibition. By training with the natural videos, this model can develop spatio-temporal features such as orientation selective cells, motion sensitive cells, and spatio-temporal complex cells. The unified nature of the adaptation scheme allows us to construct a multilayered and task-independent attention selection network which uses the same learning rule for edge, motion, and color detection, and we can use this network to engage in attention selection in both static and dynamic scenes.

Salient areas in natural scenes are generally regarded as the candidates of attention focus in human eyes, which is the key stage in object detection. In computer vision, many models have been proposed to simulate the behavior of eyes such as SaliencyToolBox (STB), neuromorphic vision toolkit (NVT) and etc., but they demand high computational cost and their remarkable results mostly rely on the choice of parameters. Recently a simple and fast approach based on Fourier transform called spectral residual (SR) was proposed, which used SR of the amplitude spectrum to obtain the saliency map. The results are good, but the reason is questionable.

In this paper, we propose a novel method of encoding 3D facial features for recognition. Facial Structural Angle (FSA) is proposed to characterize global geometric information of face. It’s elements are selected by Mutual Information Criterion. Local Region Map (LRM) is presented to describe salient parts around feature points on 3D face. Global (FSA) and local (LRM) descriptions are combined to play complementary roles in identification. Experimental results on GavabDB database indicate that the proposed method significantly improves recognition performance and is robust to facial expressions.

Image quality assessment (IQA) is of great importance for many image processing applications. Some IQA indexes proposed recently more or less try to boost their performance to accord with human subjective evaluation by simulating human visual system (HVS). However, they do not take global salient features into consideration, because of the lack of methods with low computational complexity for simulating visual attention mechanism. This paper proposes a simpler and faster method to extract a saliency map from the reference image, and inserts saliency factors into existing IQA indexes. Experimental results for a set of intuitive examples as well as validation from a database of 982 images with different distortion types show that our improved IQA indexes are much closer to human opinion.

Although 2D-based face recognition methods have made great progress in the past decades, there are also some unsolved problems such as PIE. Recently, more and more researchers have focused on 3D-based face recognition approaches. Among these techniques, facial feature point localization plays an important role in representing and matching 3D faces. In this paper, we present a novel feature point localization method on 3D faces combining global shape model and local surface model. Bezier surface is introduced to represent local structure of different feature points and global shape model is utilized to constrain the local search result. Experimental results based on comparison of our method and curvature analysis show the feasibility and efficiency of the new idea.

How to decompose mixed pixels effectively and precisely for remote sensing images is a critical issue for the quantitative remote sensing research. In this paper we propose a new method for decomposition of mixed pixels of multispectral or hyperspectral remote sensing images. The proposed method introduces the algorithm of Bayesian Self-Organizing Map (BSOM) into the problem of the decomposition of mixed pixels. It estimates Gaussian parameters by minimizing the Kullback- Leibler information metric, and finishes the unmixing with Gaussian Mixture Model (GMM). In order to obtain a high unmixing precision, we need to extend the range of Gaussian distributions, and thus propose the 3 σ variances adjustment method to solve this problem. In addition, the used unmixing model satisfies two constraints which are demanded for the problem of the decomposition of mixed pixels automatically: abundances non-negative constraint (ANC) and abundances summed-to-one constraint (ASC). Experimental results on simulated and practical remote sensing images demonstrate that the proposed method can get good unmixing results for the decomposition of mixed pixels, and is more robust to noise than other methods.

The simplex-based methods are a kind of the most important and widely used methods for the decomposition of mixed pixels in multispectral and hyperspectral remote sensing images, which need a strong basic hypothesis that there is at least one pure pixel for every endmember existing in the images. N- FINDR is of typical sense in this kind of methods, and it also needs this basic hypothesis. Unfortunately, the precision for the decomposition of mixed pixels will be seriously influenced if this hypothesis cannot be met in practice. This paper presents a new scheme based on Nonnegative Matrix Factorization (NMF) to solve this problem. In addition, some appropriate constrains are introduced into NMF for the decomposition of mixed pixels. Experimental results obtained from both artificial simulated and real-world remote sensing data demonstrate that the proposed scheme for decomposition of mixed pixels has excellent analytical performance.

A new algorithm for decomposition of mixed pixels based on orthogonal bases of data space is proposed in this paper. It extracts endmembers sequentially by adding a new vertex of the simplex with the maximum volume every time. It can avoid the dilemma in traditional simplex-based endmember extraction algorithms such as N-FINDR that it generally produces different sets of final endmembers if different initial conditions are used. Meanwhile, at each step of the searching process, the calculation of a simplex volume based on determinant of a matrix is replaced by the one based on the norm products of a set of orthogonal bases, which results in a tremendous reduction of computational complexity. Moreover, with this set of orthogonal bases, the proposed algorithm can also determine the proper number of endmembers and finish the unmixing of the original images which cannot be done by the traditional endmember extraction algorithms. Experimental results of practical remote sensing images demonstrate that the algorithm proposed in this paper is a fast and accurate algorithm for the decomposition of mixed pixels.

In this paper, an attention selection model with visual memory and online learning is proposed, which has three parts: Sensory Mapping (SM), Cognitive Mapping (CM) and Motor Mapping (MM). CM is the novelty of our model which incorporates visual memory and online learning. In order to mimic visual memory, we put forward an Amnesic Incremental Hierachical Discriminant Regression (AIHDR) Tree which has an amnesic function to guide the deletion of redundant information of the tree. Experimental results show that our AIHDR tree has better performance in retrieval speed and accuracy than IHDR/HDR tree. Self-Supervised Competition Neural Network (SSCNN) in CM has the characteristics of online learning since its connection weights can be updated in real time according to the change of environment. Eyeball Movement Prediction (EMP) mechanism is applied to estimate the movement of human eyeball so that attention can be focused on interested objects. Several applications such as object tracking and robot self-localization are realized by our proposed work.

This paper proposes a novel scheme to solve permutation ambiguity in frequency-domain for the separation of the convolutive mixing signals. We use sparseness of original acoustic signals to build a histogram of direction of arrival (DOA) for the original signals, and then use mask technique to get rough recovered signals with distortion but no order problem. An independent component analysis (ICA) is implemented to solve more accurate separation at each frequency bin. The permutation problem can easily be solved based on the rough recovered signals by mask of DOA histogram. Compared with the existing algorithms, the proposed algorithm has better performance than both ICA and time-frequency mask methods.

Manifold learning algorithms have been recently reported superior to classical dimensionality reduction techniques, such as PCA or MDS, in their ability to discover a more meaningful low-dimensional embedding of the high-dimensional samples. However, most of them encounter the problem of extension to novel samples. In this paper, we propose a regression model to extend three well-known manifold learning algorithms, i.e. Isomap, LLE, and Laplacian Eigenmap to novel samples by neural networks. We first examine these algorithms, and then show that the nonlinear dimensionality reduction ability can be acquired by neural networks, thus the extension problem is easily addressed. This model is very flexible and still preserves the nonlinear nature of the manifold leaning algorithms. Experimental results of data visualization and classification are reported, which validate the feasibility of the proposed model.

PCNN (pulse coupled neural network), a phenomenological model exhibiting synchronous pulse bursts, can be used in image processing efficiently by the specified algorithm corresponding to the specified application, but so far there has been no general design approach. This paper describes that the parallel pulse-spreading behavior of unit-linking PCNN for image processing, based on PCNN, is equal to the operation of mathematic morphology. Hereby we propose the general unit-linking PCNN design approach for binary image processing. In the meantime, in order to explain how to apply this new general design approach in the specified application, binary image denoising, binary image edge detection, binary image hole-filter based on unit-linking PCNN are analyzed respectively as examples.

This paper introduces an approach for image shadow removal by using pulse coupled neural network (PCNN), based on the phenomena of synchronous pulse bursts in the animal visual cortexes. Two shadow-removing criteria are proposed. These two criteria decide how to choose the optimal parameter (the linking strength beta). The computer simulation results of shadow removal based on PCNN show that if these two criteria are satisfied, shadows are removed completely and the shadow-removed images are almost as the same as the original nonshadowed images. The shadow removal results are independent of changes of intensities of shadows in some range and variations of the places of shadows. When the first criterion is satisfied, even if the second criterion is not satisfied, as to natural grey images that have abundant grey levels, shadows also can be removed and PCNN shadow-removed images retain the shapes of the objects in original images. These two criteria also can be used for color images by dividing a color image into three channels (R, G, B). For shadows varying drastically, such as the noisy points in images, these two criteria are still right, but difficult to satisfy. Therefore, this approach can efficiently remove shadows that do not include the random noise.

Two neural networks based on temporal coding are proposed in this paper to perform contour and motion matchings. Both of the proposed networks are three-dimensional (3-D) pulse-coupled neural networks (PCNNs). They are composed of simplified Eckhorn neurons and mimic the structure of the primary visual cortex. The PCNN for contour matching can segment from the background the object with a particular contour, which has been stored as prior knowledge and controls the network activity in the form of spike series; The PCNN for motion matching not only detects the motion in the visual field, but also extracts the object moving in an arbitrarily specified direction. The basic idea of these two models is to encode information into the timing of spikes and later to decode this information through coincidence detectors and synapse delays to realize the knowledge-controlled object matchings. The simulation results demonstrate that the temporal coding and the decoding mechanisms are powerful enough to perform the contour and motion matchings.

Scientists and experts have explored the mechanism of animal visual systems for decades for smart image processing and pattern recognition in order to satisfy sophisticated engineering applications. An application of image matching in navigation shows that the animal visual system method is at least 5% better than those classical methods. We apply independent component analysis (ICA) unsupervised learning to demonstrate the pre-attentive simple cells in animal vision. Our results confirm Blakemore and Cooper's (1970) experimental results about the growth of simple cells in the cat V1 area. Furthermore, by applying ICA methodology and the simplex algorithm, unsupervised neural synapse learning could simulate receptive fields in the visual cortex and growth of the visual cortex of a young animal in a special environment. These findings imply that an input image could be efficiently represented by ICA bases.

Due to interference caused by ground transmitters, so-called ghost noise occurs on images obtained from synthetic aperture radar (SAR). This letter proposes a scheme based on the wavelet packet transform to remove this ghost noise from the SAR image. The wavelet packet transform is employed to decompose the SAR image with the ghost noise both in the spatial and in the spatial frequency domain. To automatically detect and remove the ghost noise, an energy detection method is developed in this letter. The performance of the proposed scheme is demonstrated by some experimental results on real SAR images. In addition, we point out that the proposed scheme is also useful for other applications of image processing and analysis.