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A cellular automata approach to local patterns for texture recognition

EXPERT SYSTEMS WITH APPLICATIONS 2021年 179卷 115027-115027页

作者： Florindo, Joao B. Metze, Konradin Univ Estadual Campinas Inst Math Stat & Sci Comp Rua Sergio Buarque Holanda 651 BR-13083859 Campinas SP Brazil Univ Campinas UNICAMP Fac Med Sci Dept Pathol Rua Tessalia Vieira Camargo 126 BR-13083887 Campinas Brazil

Texture recognition is one of the most important tasks in computer vision and, despite the recent success of learning-based approaches, there is still need for model-based solutions. This is especially the case when the amount of data available for training is not sufficiently large, a common situation in several applied areas, or when computational resources are limited. In this context, here we propose a method for texture descriptors that combines the representation power of complex objects by cellular automata with the known effectiveness of local descriptors in texture analysis. The method formulates a new transition function for the automaton inspired by local binary descriptors. It counterbalances the new state of each cell with the previous state, in this way introducing an idea of "controlled deterministic chaos". The descriptors are obtained from the distribution of cell states. The proposed descriptors are applied to the classification of texture images both on benchmark data sets and a real-world problem, i.e., that of identifying plant species based on the texture of their leaf surfaces. Our proposal outperforms other classical and state-of-the-art approaches, especially in the real-world problem, thus revealing its potential to be applied in numerous practical tasks involving texture recognition at some stage.

关键词： Cellular automata Texture recognition local binary patterns Discrete dynamical system

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Reorganizing local image features with chaotic maps: an application to texture recognition

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第19期80卷 29177-29197页

作者： Florindo, Joao B. Univ Estadual Campinas Inst Math Stat & Sci Comp Rua Sergio Buarque de Holanda 651 BR-13083859 Campinas SP Brazil

Texture images are those where the focus of the analysis is on the spatial arrangement of pixels (primitives or textons) rather than on particular objects in the scene. The recognition of such images is naturally challenging as those primitives can be perceived in different manners depending on the image context. Despite the recent success of convolutional neural networks in texture recognition, model-based descriptors are still competitive, especially when we do not have access to large amounts of annotated data for training and the interpretation of the model is an important issue. Among the model-based approaches, fractal geometry has been one of the most popular, especially in biological applications. Nevertheless, fractals are part of a much broader family of models, which are the non-linear operators, studied in chaos theory. This scenario raises the question whether techniques from the broader area of chaos theory could be useful in texture modeling. Those techniques have been used for a long time in image cryptography, but studies on modeling in a broad sense have been scarce in the literature. In this context, we propose here a chaos-based local descriptor for texture recognition. More specifically, we map the image into the three-dimensional Euclidean space, iterate a chaotic map over this three-dimensional structure and convert it back to the original image. From such chaos-transformed image at each iteration we collect local descriptors (here we use local binary patters) and those descriptors compose the feature representation of the texture. The performance of our method was verified on the classification of benchmark databases and in the identification of Brazilian plant species based on the texture of the leaf surface. The achieved results confirmed our expectation of a competitive performance, even when compared with some learning-based modern approaches in the literature.

关键词： Chaotic maps Texture recognition Image descriptors local binary patterns

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Simultaneous multiclass retinal lesion segmentation using fully automated RILBP-YNet in diabetic retinopathy

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第PartB期86卷

作者： Pavani, Geetha Biswal, B. Gandhi, Tapan Kumar Indian Inst Technol Delhi Dept Elect Engn New Delhi India Sankar Fdn Eye Hosp & Inst Dept CQI & Res Visakhapatnam India Gayatri Vidya Parishad Coll Engn A Ctr Med Imaging Studies Dept ECE Visakhapatnam India

This paper presents a novel method for segmenting the multiple lesions like Microaneurysms, Haemorrhages, Hard and Soft exudates that occurs on the surface of the retina due to diabetes. If these lesions are left untreated in their early stage, they may cause irrevocable vision loss to diabetic patients. However, the manual assessment of these vision threatening diseases is prone to errors and time-consuming process. As a result, the proposed model, RILBP-YNet (Residual Inception local binary Pattern - YNetwork) is implemented as a dual encoder and single decoder architecture in which one encoder extracts the features from the fundus image. Whereas the parallel encoder extracts the textural features from local binary patterns (LBPs) of the corresponding fundus image simultaneously. The extracted features of the two encoders are concatenated at the end of every encoder level and fed as input to the next encoder. Finally, the concatenated features of the last encoder are transposed by performing the up-sampling operation and fed as input to the first decoder. Furthermore, at the end of every decoder, the features of the decoder and concatenated features of the respective encoder are fused and provided as input to the next decoder. As a result, at the end of the last decoder, segmented masks of each lesion are generated. The novelty of the proposed work lies in adding a parallel encoder to traditional encoder-decoder architecture to induce textural features to improve the segmentation results. The proposed model is trained and tested on the Indian Diabetic Retinopathy Image Dataset (IDRiD), and e-ophtha datasets and achieved competitive results compared to the existing methods in terms of AUC score. The proposed method is also tested on datasets like DRIVE, STARE, CHASE-DB1, and one locally real-time dataset.

关键词： Diabetic Retinopathy IDRiD dataset Image Segmentation local binary patterns Neural Networks Parallel encoder

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Lane Road Segmentation Based on Improved UNet Architecture for Autonomous Driving

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第7期14卷 217-225页

作者： Ngoc, Hoang Tran Nguyen, Huynh Vu Nhu Nguyen, Khang Hoang Quach, Luyl-Da FPT Univ Software Engn Dept Cantho City Vietnam

This paper introduces a real-time workflow for implementing neural networks in the context of autonomous driving. The UNet architecture is specifically selected for road segmentation due to its strong performance and low complexity. To further improve the model's capabilities, local binary Convolution (LBC) is incorporated into the skip connections, enhancing feature extraction, and elevating the Intersection over Union (IoU) metric. The performance evaluation of the model focuses on road detection, utilizing the IOU metric. Two datasets are used for training and validation: the widely used KITTI dataset and a custom dataset collected within the ROS2 environment. Simulation validation is performed on both datasets to assess the performance of our model. The evaluation of our model on the KITTI dataset demonstrates an impressive IoU score of 97.90% for road segmentation. Moreover, when evaluated on our custom dataset, our model achieves an IoU score of 98.88%, which is comparable to the performance of conventional UNet models. Our proposed method to reconstruct the model structure and provide input feature extraction can effectively improve the performance of existing lane road segmentation methods.

关键词： local binary patterns feature extraction UNet semantic segmentation

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A benchmark of dynamic versus static methods for facial action unit detection

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JOURNAL OF ENGINEERING-JOE 2021年第5期2021卷 252-266页

作者： Alharbawee, L. Pugeault, N. Univ Exeter Coll Engn Math & Phys Sci Exeter Devon England Univ Mosul Stat & Informat Dept Coll Comp Sci & Math Mosul Iraq Univ Glasgow Sch Comp Sci Glasgow Lanark Scotland

Action Units activation is a set of local individual facial muscle parts that occur in time constituting a natural facial expression event. AUs occurrence activation detection can be inferred as temporally consecutive evolving movements of these parts. Detecting AUs automatically can provide explicit benefits since it considers both static and dynamic facial features. Our work is divided into three contributions: first, we extracted the features from local binary patterns, local Phase Quantisation, and dynamic texture descriptor LPQTOP with two distinct leveraged network models from different CNN architectures for local deep visual learning for AU image analysis. Second, cascading the LPQTOP feature vector with Long Short-Term Memory is used for coding longer term temporal information. Next, we discovered the importance of stacking LSTM on top of CNN for learning temporal information in combining the spatially and temporally schemes simultaneously. Also, we hypothesised that using an unsupervised Slow Feature Analysis method is able to leach invariant information from dynamic textures. Third, we compared continuous scoring predictions between LPQTOP and SVM, LPQTOP with LSTM, and AlexNet. A competitive substantial performance evaluation was carried out on the Enhanced CK dataset. Overall, the results indicate that CNN is very promising and surpassed all other methods

关键词： Computer vision and image processing techniques distinct leveraged network models Long Short-Term Memory detecting AUs learning (artificial intelligence) AUs occurrence activation detection local binary patterns face recognition LPQTOP feature vector muscle temporally consecutive evolving movements longer term temporal information local deep visual learning Optical, image and video signal processing explicit benefits LSTM feature extraction natural facial expression event dynamic texture descriptor LPQTOP static features facial action unit detection unsupervised Slow Feature Analysis method dynamic versus static methods dynamic facial features local Phase Quantisation image texture invariant information local individual facial muscle parts AU image analysis neural nets unsupervised learning Action Units activation Image recognition dynamic textures image classification different CNN architectures support vector machines Other topics in statistics

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Efficient Handwritten Signatures Identification using Machine Learning

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第3期14卷 141-148页

作者： Alharbi, Ibraheem M. Univ Jeddah Coll Business MIS Dept Jeddah Saudi Arabia

Any agreement or contract between two or more parties requires at least one party to employ a signature as evidence of the other parties' identities and as a means of establishing the parties' intent. As a result, more people are curious about Signature Recognition than other biometric methods like fingerprint scanning. Utilizing both Fourier Descriptors and histogram of oriented gradients (HOG) features, this paper presents an efficient algorithms for signature recognition. The use of local binary patterns (LBP) features in a signature verification technique has been proposed. Using morphological techniques, the signature is encapsulated within a curve that is both symmetrical and a good match. Measured by the frequency with which incorrect patterns are confirmed by a given system, false acceptance rate (FAR) provides an indication of the effectiveness and precision of the proposed system. Using a local dataset of 60 test signature patterns, this investigation found that 10% were incorrectly accepted for FAR of 0.169. Experiments are conducted on signature photos from a local dataset. Verification of signatures has previously made use of KNN classifier. KNN classifier produced higher FARs and recognition accuracies than prior techniques.

关键词： K-nearest neighbor histogram of oriented gradients local binary patterns false acceptance rate Fourier descriptors

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Effective Face Recognition using Adaptive Multi-scale Transformer-based Resnet with Optimal Pattern Extraction

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第7期14卷 812-827页

作者： Shivaprakash, Santhosh Rajashekararadhya, Sannangi Viswaradhya Kalpataru Inst Technol Dept Elect & Commun Engn Tiptur 572201 Karnataka India

Human face is the major characteristic for identifying a person and it helps to differentiate each person. Face recognition methods are mainly useful for determining a person's identity with the help of biometric techniques. Face recognition methods are used in many practical applications like criminal identification, the phone unlocks systems and home security systems. It does not need any key and card, and it only requires facial images to provide high security over several applications. The interdependencies of the encryption methods are highly reduced in the deep learning-enabled face recognition models. Conventional methods did not satisfy the present demand due to poor recognition accuracy. Therefore, an advanced deep learning-based face recognition framework is implemented to authenticate the identity of individuals with high accuracy by using facial images. The required facial images are taken from the standard databases. The collected images are preprocessed using median filtering. The preprocessed facial images are subjected to spatial feature extraction, where the local binary patterns (LBP) and local Vector patterns (LVP) are utilized to extract the relevant optimal patterns from the facial images. Here, optimal pattern extraction is done with the facial recognition is done over the extracted optimal features with the usage of the implemented Adaptive Multi-scale transformerbased Resnet (AMT-ResNet), where the parameters in the recognition network are optimized by using the IRSO. The efficiency of the developed deep learning adopted face recognition model is validated through different heuristics algorithms, and baseline face recognition approaches.

关键词： Face recognition facial images optimal pattern extraction rate local binary patterns local vector patterns improved rat swarm optimization algorithm adaptive multi-scale transformer-based Resnet

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Detection method of power line under uneven lightness for flying-walking power line inspection robot

Detection method of power line under uneven lightness for fl...

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International Conference on Image, Video Processing, and Artificial Intelligence

作者： Li, Bo Qin, Xinyan Lei, Jin Zeng, Yujie Zhang, Jie Jia, Bo Li, Huidong Li, Zhaojun Shihezi Univ Coll Mech & Elect Engn 221 Beisi Rd Shihezi 832000 Xinjiang Peoples R China

ISBN: (数字)9781510650282

ISBN: (纸本)9781510650282;9781510650275

Due to the variation of sunlight conditions resulting in uneven lightness in images, the details of target objects tend to hide in the dark or bright regions, which is adverse to following image processing. To reliably land on the power line under the change of lightness, the flying-walking power line inspection robot (FPLIR) needs reliable detection for the power line. In this paper, a machine vision-based detection method of power line is proposed to adapt different lightness. Firstly, a visual system of the FPLIR is designed to collect and process power line images. Secondly, the multi-scale retinex (MSR) algorithm is used to reduce the influence of lightness. Then, the local binary pattern (LBP) map of power line image is generated by the LBP operator and is divided into many blocks. An LBP histogram vector is calculated for every block, then the first-order entropy and second-order entropy of every histogram vector are calculated. Finally, the first-order entropy, the second-order entropy, and the edge density of power line image are used as the feature vector of fuzzy c-means (FCM) to obtain the power line region. The experimental result shows that the accuracy of the proposed method is 82.6%, which is 9.3% more than the method without image enhancement. Thus, the proposed method can effectively detect power line, improving the robustness and accuracy of power line detection (PLD) during the FPLIR landing.

关键词： Uneven Lightness power line detection power line inspection robot multi-scale retinex local binary patterns fuzzy c-means

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Covid-19 detection from radiographs by feature-reinforced ensemble learning

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第23期34卷 e7179-e7179页

作者： Elen, Abdullah Bandirma Onyedi Eylul Univ Fac Engn & Nat Sci Dept Software Engn TR-10200 Bandirma Balikesir Turkey

The coronavirus (Covid-19) epidemic continues to have a negative influence on the global population's well-being and health. Scientists in many fields around the world are working non-stop to find a solution to the prevention of this epidemic. In the field of computer science, this struggle is supported by studies on especially the analysis of X-ray and CT images with artificial intelligence. In this study, two different ensemble learning models, including deep learning and a combination of machine learning methods, are presented for the detection of SARS-CoV-2 infection from X-ray images. The main purpose of this study is to increase the classification ability of Residual Convolutional Neural Network (ResCNN), which is used as a deep learning method, with the assist of machine learning algorithms and extracted features from images. The proposed models were validated on a total of 5228 chest X-ray images categorized as Normal, Pneumonia, and Covid-19. The images in the dataset were sized in four different ways, 32 x 32, 64 x 64, 128 x 128, and 256 x 256, in order to analyze the validity of the proposed models in more detail. These four datasets were partitioned with the 10-fold cross-validation technique and converted into a total of 40 training and test data. Both proposed models use features derived from the ResCNN as the basis and test a certain number of machine learning algorithms with a majority voting technique by dividing them into subsets. In the architecture of the second model, it combines the features extracted from the local binary patterns (LBP) and Histogram of Oriented Gradients (HOG) methods in addition to the features obtained from the ResCNN. It has been seen that the classification ability of both proposed models is better than the ResCNN in the experiments. In particular, the second model gives a similar classification score even though it is tested with images four-times smaller (e.g., 32 x 32 vs. 128 x 128) than those used in the ResCNN. This

关键词： convolutional neural network Covid-19 histogram-oriented gradients local binary patterns machine learning X-ray images

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FUSION OF COMPLEX NETWORKS-BASED GLOBAL AND local FEATURES FOR FEATURE REPRESENTATION 20

FUSION OF COMPLEX NETWORKS-BASED GLOBAL AND LOCAL FEATURES F...

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20th International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Huang, Zheng-Rui Fuzhou Univ Acad Digital China Fujian Fuzhou 350108 Peoples R China Fuzhou Univ Minist Educ Key Lab Spatial Data Min & Informat Sharing Fuzhou 350108 Peoples R China

ISBN: (纸本)9781665466080

To realize effective feature representation, this paper proposes a new complex networks (CNs)-based feature fusion scheme to recognize texture images. Specifically, we propose two feature extractors to detect the global and local features of texture images respectively. To capture the global features, we first map a texture image as an undirected graph based on pixel location and intensity, and three feature measurements are selected to further decipher the image features, which retains as much image information as possible. Next, given the original band images (Bis) and the generated feature images, we encode them using local binary patterns (LBPs). Thus, the global feature vector is obtained by concatenating four spatial histograms. To decipher the local features, we jointly transfer and fine-tune a pre-trained VGGNet-16 model. After then, we fuse the middle outputs of max-pooling layers (MPs) and generate the local feature vector based on a global average pooling layer (GAP). Finally, the global and local feature vectors are connected to form the final feature representation of texture images. Experiment results show that the proposed scheme outperforms the state-of-the-art statistical descriptors and several deep convolutional neural network (CNN) models.

关键词： Feature representation Feature fusion Complex networks local binary patterns

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