Exploring and establishing artificial neural networks with electrophysiological characteristics and high computational efficiency is a popular topic that has been explored for many years in the fields of pattern recognition and computer vision. Inspired by the working mechanism of the primary visual cortex, pulse-coupled neural networks (PCNNs) can exhibit the characteristics of synchronous oscillation, refractory period, and exponential decay. These characteristics empower the PCNN model to group pixels with similar spatiality and gray values and to process digital images without training. However, electrophysiological evidence shows that the neurons exhibit highly complex nonlinear dynamics when stimulated by external periodic signals. This chaos phenomenon, also known as the ‘butterfly effect,” cannot be explained by all PCNN models. In this work, we analyze the main obstacle preventing PCNN models from imitating a real primary visual cortex. We consider neuronal excitation as a stochastic process. We then propose a novel neural network of the primary visual cortex, called a continuous-coupled neural network (CCNN). Theoretical analysis indicates that the dynamic behavior of the CCNN is distinct from the PCNN. Numerical results show that the CCNN model exhibits periodic behavior under a DC stimulus, and exhibits chaotic behavior under an AC stimulus, which is consistent with the testing results of primary visual cortex neurons. Furthermore, the image and video processing mechanisms of the CCNN model are analyzed. For image processing tasks, this model encodes the pixel intensity as the frequency of output signals so that it can group pixels with similar gray values. This image processing method can reduce the local gray level difference of the image, and compensate for small local discontinuities in the image. For video processing tasks, the CCNN encodes changing pixels as non-periodic chaotic signals, and it encodes static pixels as periodic signals. It thusachieves the purpose of moving target object recognition by distinguishing the dynamic states corresponding to different neuron clusters in the video. Experimental results on image segmentation indicate that the CCNN model has better performance than the state-of-the-art of visual cortex neural network models.

The pulse-coupled neural network (PCNN) model is a third-generation artificial neural network without training that uses the synchronous pulse bursts of neurons to process digital images, but the lack of in-depth theoretical research limits its extensive application. By analyzing the working mechanism of the PCNN, we present an expression for the fire-extinguishing time of neurons that fire in the second iteration and an expression for the firing time of neurons that extinguish in the second iteration. In addition, we find a phenomenon of the PCNN and name it mathematically coupled fire extinguishing. Based on the above analysis, we propose a new working mode for the PCNN, where the refiring of fire-extinguishing neurons is only allowed when all firing neurons are extinguished. We also work out the constraint conditions of the parameter settings under this mode. Furthermore, we analyze the relationship between the network parameters and mathematically coupled fire extinguishing, the coupling of neighboring neurons, and the convergence rate of the PCNN, respectively. In addition, we demonstrate the essential regularity of extinguished neuron in the PCNN and then propose an optimal parameter setting to achieve the best comprehensive performance of the PCNN.

Speech augmentation is a common and effective strategy to avoid overfitting and improve on the robustness of an emotion recognition model.In this paper, we investigate for the first time the intrinsic attributes in a speech signal using the multi-resolution analysis theory and the Hilbert-Huang Spectrum, with the goal of developing a robust speech augmentation approach from raw speech data.Specifically, speech decomposition in a double tree complex wavelet transform domain is realized, to obtain sub-speech signals; then, the Hilbert Spectrum using Hilbert-Huang Transform is calculated for each sub-band to capture the noise content in unseen environments with the voice restriction to 100-4000 Hz; finally, the speechspecific noise that varies with the speaker individual, scenarios, environment, and voice recording equipment, can be reconstructed from the top two high-frequency sub-bands to enhance the raw signal.Our proposed speech augmentation is demonstrated using five robust machine learning architectures based on the RAVDESS database, achieving up to 9.3 % higher accuracy compared to the performance on raw data for an emotion recognition task.

In this paper, we mainly investigate subjects' food likability based on audio-related features as a contribution to EAT ? the ICMI 2018 Eating Analysis and Tracking challenge. Specifically, we conduct 4-level Double Tree Complex Wavelet Transform decomposition of an audio signal, and obtain five sub-audio signals with frequencies ranging from low to high. For each sub-audio signal, not only 'traditional' functional-based features but also deep learning-based features via pretrained CNNs based on SliCQ-nonstationary Gabor transform and a cochleagram map, are calculated. Besides, the original audio signals based Bag-of-Audio-Words features extracted by the openXBOW toolkit are used to enhance the model as well. Finally, the early fusion of all these three kinds of features can lead to promising results, yielding the highest UAR of 79.2 % by means of a leave-one-speaker-out cross-validation, which holds a 12.7 % absolute gain compared with the baseline of 66.5 % UAR.

This work describes the simplest chaotic system with a hyperbolic sine non‐linearity, accompanied by analysis of Lyapunov exponents, bifurcations, and stability. The corresponding simple chaotic circuit using only diodes and linear components is designed and implemented. Finally, an application of the system to spread spectrum communication based on differential chaos shift keying (DCSK) is presented. Since the hyperbolic sine is an odd function of its argument, the system is antisymmetric and exhibits symmetry breaking where the attractors split or merge as some bifurcation parameter is changed. The proposed system is especially simple both from the structure of the equations and in its electronic circuit realisation. Compared with the traditional DCSK scheme of a Chebyshev sequence, the system can reduce the bit error rate in the presence of noise.

Microscopic image processing is critical aspects to biomedical image analysis, and blood cell counts are very important role in medical diagnoses. Various dyeing methods and microscopes are used, so we need methods that can effectively count cells by adapting to such diversity. This paper presents a new method that extracts the contours of red blood cells based on the quality of a binary image that is preprocessed using PCNN. The method solves the various blood smear issues caused by the different cell dyeing methods. Moreover, it uses a self-adapting method for counting cells, using the circular Hough transform(CHT) for different amplifications. The experimental results show that the proposed method performed better in contrast variations between cells and background. The method is also much more efficient in segmentation on overlapped cells, and much more accurate in counting RBC results.

Blind image quality assessment (BIQA), which aims to estimate the perceptual quality of images without any reference information, is a very important yet challenging task. Although human visual system is sensitive to degradations on both spatial contrast and spatial distribution, most of the existing structural degradation based BIQA models consider only one of them. This study introduces a novel BIQA model by taking into account degradations on both contrast and spatial distribution. First, the authors construct a multi‐threshold local tetra pattern (MTLTrP) instead of local binary pattern to measure the changes on spatial distribution. Second, Weber–Laplacian of Gaussian (WLOG) operator, which responds to intensity contrast in a small spatial neighbourhood, is proposed to extract local contrast features. Finally, the joint statistics of MTLTrP and WLOG are utilised for BIQA model learning. Experimental results on three large benchmark databases demonstrate that the proposed model outperforms state‐of‐the‐art BIQA models, as well as with several well‐known full reference quality assessment methods.

Mammography is the most simple and effective technology for early detection of breast cancer. However, the lesion areas of breast are difficult to detect which due to mammograms are mixed with noise. This work focuses on discussing various multiresolution denoising techniques which include the classical methods based on wavelet and contourlet; moreover the emerging multiresolution methods are also researched. In this work, a new denoising method based on dual tree contourlet transform (DCT) is proposed, the DCT possess the advantage of approximate shift invariant, directionality and anisotropy. The proposed denoising method is implemented on the mammogram, the experimental results show that the emerging multiresolution method succeeded in maintaining the edges and texture details; and it can obtain better performance than the other methods both on visual effects and in terms of the Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structure Similarity (SSIM) values.

Based on the different strength of synaptic connections between actual neurons, this paper proposes a novel heterogeneous PCNN (HPCNN) algorithm to quantize images. HPCNN is constructed with traditional pulse coupled neural network (PCNN) models, which has different parameters corresponding to different image regions. It puts pixels of different gray levels to be classified broadly into two categories: the background regional ones and the object regional ones. Moreover, HPCNN also satisfies human visual characteristics (HVS). The parameters of HPCNN model are calculated automatically according to these categories and quantized results will be optimal and more suitable for human to observe. At the same time, the experimental results show the validity and efficiency of our proposed quantization method.

Recently, dictionary learning (DL) based methods have been introduced to compressed sensing magnetic resonance imaging (CS-MRI), which outperforms pre-defined analytic sparse priors. However, single-scale trained dictionary directly from image patches is incapable of representing image features from multi-scale, multi-directional perspective, which influences the reconstruction performance. In this paper, incorporating the superior multi-scale properties of uniform discrete curvelet transform (UDCT) with the data matching adaptability of trained dictionaries, we propose a flexible sparsity framework to allow sparser representation and prominent hierarchical essential features capture for magnetic resonance (MR) images. Multi-scale decomposition is implemented by using UDCT due to its prominent properties of lower redundancy ratio, hierarchical data structure, and ease of implementation. Each sub-dictionary of different sub-bands is trained independently to form the multi-scale dictionaries. Corresponding to this brand-new sparsity model, we modify the constraint splitting augmented Lagrangian shrinkage algorithm (C-SALSA) as patch-based C-SALSA (PB C-SALSA) to solve the constraint optimization problem of regularized image reconstruction. Experimental results demonstrate that the trained sub-dictionaries at different scales, enforcing sparsity at multiple scales, can then be efficiently used for MRI reconstruction to obtain satisfactory results with further reduced undersampling rate. Multi-scale UDCT dictionaries potentially outperform both single-scale trained dictionaries and multi-scale analytic transforms. Our proposed sparsity model achieves sparser representation for reconstructed data, which results in fast convergence of reconstruction exploiting PB C-SALSA. Simulation results demonstrate that the proposed method outperforms conventional CS-MRI methods in maintaining intrinsic properties, eliminating aliasing, reducing unexpected artifacts, and removing noise. It can achieve comparable performance of reconstruction with the state-of-the-art methods even under substantially high undersampling factors.

Fractal image compression (FIC) is one of the most widely approved image compression approaches for its high compression ratio and quality of retrieved images. However, FIC suffers from high computational cost in searching local self-similarities in natural image. Although many papers aiming at speeding up FIC have been published, they use pre-processing tools or approximation methods. Reducing the intrinsic computational complexity of FIC is still an open problem. Since quantum mechanics based Grover's quantum search algorithm (QSA) is able to achieve square-root speedup over classical algorithms in unsorted database searching, we propose an interdisciplinary approach by using Grover's QSA to reduce the intrinsic computational complexity of FIC in this letter. In particular, both domain blocks and range blocks are represented as quantum states, then Grover's QSA is employed to search the most similar domain block for each range block under the criterion of maximizing quantum fidelity between these two kinds of quantum states. Without sacrificing compression ratio, experimental results show that the execution time of the proposed method is 100 times shorter than that of the baseline FIC. Moreover, retrieved images from our proposal are also less distorted than those from other state-of-the-art FIC approaches.

In this paper, we propose a region-based object recognition (RBOR) method to identify objects from complex real-world scenes. First, the proposed method performs color image segmentation by a simplified pulse-coupled neural network (SPCNN) for the object model image and test image, and then conducts a region-based matching between them. Hence, we name it as RBOR with SPCNN (SPCNN-RBOR). Hereinto, the values of SPCNN parameters are automatically set by our previously proposed method in terms of each object model. In order to reduce various light intensity effects and take advantage of SPCNN high resolution on low intensities for achieving optimized color segmentation, a transformation integrating normalized Red Green Blue (RGB) with opponent color spaces is introduced. A novel image segmentation strategy is suggested to group the pixels firing synchronously throughout all the transformed channels of an image. Based on the segmentation results, a series of adaptive thresholds, which is adjustable according to the specific object model is employed to remove outlier region blobs, form potential clusters, and refine the clusters in test images. The proposed SPCNN-RBOR method overcomes the drawback of feature-based methods that inevitably includes background information into local invariant feature descriptors when keypoints locate near object boundaries. A large number of experiments have proved that the proposed SPCNN-RBOR method is robust for diverse complex variations, even under partial occlusion and highly cluttered environments. In addition, the SPCNN-RBOR method works well in not only identifying textured objects, but also in less-textured ones, which significantly outperforms the current feature-based methods.

Plant recognition recently becomes more and more attractive in computer vision and pattern recognition. Although some researchers have proposed several methods, their accuracy is not satisfactory. Therefore, a novel method of plant recognition based on leaf image is proposed in the paper. Both shape and texture features are employed in the proposed method Texture feature is extracted by intersecting cortical model, and shape feature is obtained by the representation of center distance sequence. Support vector machine is employed for the classifier. The leaf image is preprocessed to get better quality for extracting features, and then entropy sequence and center distance sequence are obtained by intersecting cortical model and center distance transform, respectively. Redundant data of entropy sequence vector and center distance are reduced by principal component analysis. Finally, feature vector is imported into the classifier for classification. In order to evaluate the performance, several existing methods are used to compare with the proposed method and three leaf image datasets are taken as test samples. The experimental result shows the proposed method gets the better accuracy of recognition than other methods.

This letter proposes a novel dual-output pulse coupled neural network model (DPCNN). The new model is applied to obtain a more stable texture description in the face of the geometric transformation. Time series, which are computed from output binary images of DPCNN, are employed as translation-, rotation-, scale-, and distortion-invariant texture features. In the experiments, DPCNN has been well tested by using Brodatz's album and the VisTex database. Several existing models are compared with the proposed DPCNN model. The experimental results, based on different testing data sets for images with different translations, orientations, scales, and affine transformations, show that our proposed model outperforms existing models in geometry-invariant texture retrieval. Furthermore, the robustness of DPCNN to noisy data is examined in the experiments.

An automatic parameter setting method of a simplified pulse coupled neural network (SPCNN) is proposed here. Our method successfully determines all the adjustable parameters in SPCNN and does not need any training and trials as required by previous methods. In order to achieve this goal, we try to derive the general formulae of dynamic threshold and internal activity of the SPCNN according to the dynamic properties of neurons, and then deduce the sub-intensity range expression of each segment based on the general formulae. Besides, we extract information from an input image, such as the standard deviation and the optimal histogram threshold of the image, and attempt to build a direct relation between the dynamic properties of neurons and the static properties of each input image. Finally, the experimental segmentation results of the gray natural images from the Berkeley Segmentation Dataset, rather than synthetic images, prove the validity and efficiency of our proposed automatic parameter setting method of SPCNN.

Based on the studies of existing local-connected neural network models, in this brief, we present a new spiking cortical neural networks model and find that time matrix of the model can be recognized as a human subjective sense of stimulus intensity. The series of output pulse images of a proposed model represents the segment, edge, and texture features of the original image, and can be calculated based on several efficient measures and forms a sequence as the feature of the original image. We characterize texture images by the sequence for an invariant texture retrieval. The experimental results show that the retrieval scheme is effective in extracting the rotation and scale invariant features. The new model can also obtain good results when it is used in other image processing applications.