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检索条件"主题词=Local binary pattern"

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Leaf Classification using local binary pattern and Histogram of Oriented Gradients 7

Leaf Classification using Local Binary Pattern and Histogram...

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7th International Conference on Smart Computing & Communications (ICSCC)

作者： Janahiraman, Tiagrajah, V Yee, Lim Khar Der, Chen Soon Aris, Hazleen Univ Tenaga Nas Dept Elect & Commun Engn Kajang Malaysia Univ Tenaga Nas Coll Grad Studies Kajang Malaysia Univ Tenaga Nas Inst Informat & Comp Energy Kajang Malaysia

ISBN: (纸本)9781728115573

Categorization of plant species is a significant process in studying the diversity of different plant species in order to utilize it as medical treatment and to keep track of invasive plant species to maintain the balance of the environment. However, plants have extremely complex structure and diverse with millions of species around the world which makes the classification process extremely tedious. This paper introduces a method which utilizes the combination of local binary pattern and Histogram Oriented Gradient as feature extractor for leaf classification which increases the accuracy during classification. Support Vector Machine was used as classifier of the leaf features. Two well-known datasets, Swedish Leaf Dataset and Flavia Dataset, were used to carry out the experimental studies. Our proposed method performed the best when compared to three other methods.

关键词： Leaf Classification local binary pattern Histogram Oriented Gradient Computer Vision Machine Learning

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Texture Image Retrieval Using Multiple Filters and Decoded Sparse local binary pattern 8th

Texture Image Retrieval Using Multiple Filters and Decoded S...

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8th International Conference on pattern Recognition and Machine Intelligence (PReMI)

作者： Hatibaruah, Rakcinpha Nath, Vijay Kumar Hazarika, Deepika Tezpur Univ Dept Elect & Commun Engn Tezpur Assam India

ISBN: (纸本)9783030348694;9783030348687

In local pattern based feature extraction, usually the raw spatial image provides limited information about the relationship between the pixels in a local neighborhood. A few recent methods address this issue by first filtering the images with bag of filters and then calculating local pattern over each filtered images. It is observed that the filtered images complement the discriminativeness of local pattern based features which enhances retrieval efficiency. Motivated by these approaches, a new approach based on multiple filters and decoded sparse local binary pattern (MF-DLBP) is proposed in this paper, wherein we first filter the raw spatial image with multiple filters to extract the low frequency and high frequency information. However, unlike previous approaches, we extract features from low pass and high pass filtered images adopting separate strategies, since characteristically they contain contrasting information. From each gradient filtered images, we compute sparse LBPs using LBP4hv (considering only horizontal and vertical neighbors) and LBP4d (considering only diagonal neighbors) techniques. To enhance the discriminativeness of the descriptor, two decoders are also used which compute the inter frequency relationship between the sparse local binary pattern maps of high pass filtered images only. The low pass filtered image is encoded with sign LBP (LBP S) and magnitude LBP (LBP M) to extract sign as well as magnitude information present in this image. The proposed approach is low dimensional and it shows highly competitive retrieval performance when tested on three benchmark texture databases which are Kylberg, Brodatz and STex.

关键词： local binary pattern Decoder Feature vector Image retrieval CBIR

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Wavelet-Energy-Weighted local binary pattern Analysis for Tire Tread pattern Classification 3

Wavelet-Energy-Weighted Local Binary Pattern Analysis for Ti...

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3rd International Conference on Imaging, Signal Processing and Communication (ICISPC)

作者： Liu, Ying Zhang, Shuai Wang, Fuping Lim, Kengpang Liu, Qiqi Lei, Yanbo Gong, Yanchao Lu, Jin Minist Publ Secur Natl Key Lab Elect Informat Proc Applicat Crime S Beijing Peoples R China Xian Univ Posts & Telecommun Ctr Image & Informat Proc Xian Peoples R China

ISBN: (纸本)9781728136639

Tire tread pattern image classification plays an important role in crime scene and traffic accident investigation. Due to the lack of standard test dataset, there is little work done in this area. For efficient texture feature description, inherent characteristic of tire patterns need to be considered. Leveraging on the directionality characteristics of tread patterns, a novel texture feature extraction algorithm is proposed based on adaptive weighted feature fusion with the weights defined by sub-band energy ratio. The proposed approach consists of: (1) discrete wavelet decomposition of tire tread image to obtain low frequency, horizontal, vertical and diagonal sub-bands;(2) extraction of rotation-invariant uniform local binary pattern features from the sub-band images;(3) concatenating the tread pattern directional features, weighted by their corresponding sub-band energies. Applying SVM for tire tread pattern classification, experimental results on real-world tire tread patterns show that the proposed texture feature extraction algorithm is outperforms other prior methods.

关键词： Tire tread pattern image classification texture directional feature local binary pattern adaptive weighting feature fusion

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Developing Wood Identification System by local binary pattern and Hough Transform Method 2

Developing Wood Identification System by Local Binary Patter...

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2nd International Conference on Data and Information Science (ICoDIS)

作者： Hadiwidjaja, M. Luthfi Gunawan, P. H. Prakasa, Esa Rianto, Yan Sugiarto, Bambang Wardoyo, Riyo Damayanti, Ratih Sugiyanto, Krisdianto Dewi, L. Mustika Astutiputri, Vidya Fatimah Telkom Univ Sch Comp Jalan Telekomunikasi 1 Terusan Buah Batu Bandung 40257 Indonesia Indonesian Inst Sci LIPI Res Ctr Informat Bandung Indonesia Forest Prod Res & Dev Ctr Bogor Indonesia

The aim of this paper is to develop the wood identification system using two methods, called, local binary pattern (LBP) and Hough Transform (HT). Here, 12 (twelve) varies of wood species form Indonesia will be used as the data sets. The wood species are taken from wood anatomy laboratory, Puslitbang Hasil Hutan (P3HH). Here, the classification method of this research uses Euclidean Distance (ED) to determine the distance of two images of wood. From the classification results, using LBP method is shown better than HT. The weakness of HT method in this paper is HT method can only detecting the circle shape rather than arbitrary shape. By using LBP method, 6 of 12 species are 100% accurate detected. Beside, using HT method, only one species (Cratoxylon formosum) has accuracy 90%.

关键词： local binary pattern

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Kinship verification using Compound local binary pattern and local Feature Discriminant Analysis 10

Kinship verification using Compound Local Binary Pattern and...

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10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

作者： Mukherjee, Moumita Meenpal, Toshanlal Natl Inst Technol Elect & Telecommun Engn Raipur Madhya Pradesh India

ISBN: (纸本)9781538659069

Automatic kinship verification system is designed to verify kinship relations between given pair of face images. Child adopts many characteristic such as similarity in appearance, likes and dislike, behavior, voice from his/her parents due to overlapping of genes. There are various existing algorithms that can verify whether a given pair of face images share kinship relation. This paper proposes a new method based on compound local binary pattern (CLBP) and local feature-based discriminate analysis (LFDA) to improve kinship verification accuracy. A well known texture feature extraction method is local binary pattern (LBP), but LBP performance deteriorates in flat images. To overcome this drawback, extraction of texture features from face images are calculated by compound local binary pattern (CLBP) technique. Extracted features mainly represent facial characteristics but may also contain some noises. Further, local feature-based discriminate analysis (LFDA) is used as a feature selection method to reduce these noises and choose the most relevant facial features. LFDA reduces inter-class similarity and increases intra-class similarity. Proposed method uses KNN classifier with 5-fold cross-validation. Kinship images are collected from KinFaceW-I and KinFaceW-II dataset. Best mean accuracy for proposed method on KinFaceW-I and KinFaceW-II are 82.825 % and 89.36% respectively. Experimental results also outperforms existing methods on these datasets.

关键词： Kinship verification local binary pattern compound local binary pattern local feature-based discriminate analysis

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Real time face detection using haar-like feature method and local binary pattern method 1

Real time face detection using haar-like feature method and ...

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1st Tarumanagara International Conference on the Applications of Technology and Engineering (TICATE)

作者： Junaidy, D. Wulandari, M. Tanudjaja, H. Univ Tarumanagara Fac Engn Dept Elect Engn Jakarta Indonesia Univ Katolik Indonesia Atma Jaya Fac Engn Dept Elect Engn Jakarta Indonesia

Face detection is one of the important roles in face identification. There are some difficulty factors in the process. Therefore, some methods are developed. This study aims to compare Haar-Like Feature method and local binary pattern (LBP) method for face detection. Samples are 25 people in the Electric Engineering Department. Based on the result, Haar-Like Feature method is more accurate than LBP method. Haar-Like method can detect 20 faces from 25 faces with success rate of 80% while LBP can detect 14 faces from 25 faces with success rate of 56%. From this research, it is taken that both methods are having in trouble detecting face from someone that use glasses, have dark skin, or having facial hair.

关键词： local binary pattern

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Skin Diseases Classification Using local binary pattern and Convolutional Neural Network 3

Skin Diseases Classification Using Local Binary Pattern and ...

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3rd International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM)

作者： Akmalia, Nurul Sihombing, Poltak Suherman Univ Sumatera Utara Fac Comp Sci & Informat Technol Dept Informat Technol Medan Indonesia Univ Sumatera Utara Fac Comp Sci & Informat Technol Dept Comp Sci Medan Indonesia Univ Sumatera Utara Fac Engn Dept Elect Engn Medan Indonesia

ISBN: (纸本)9781728124759

Skin disease is one of the diseases that are often found in tropical countries like Indonesia. Lack of knowledge about the types and prevention of skin diseases results a person suffering from acute skin diseases. Computer technology is expected to help detect disease early so that it can minimize the occurrence of more dangerous diseases. This paper proposes a method for introducing the shape, color, and texture of skin diseases in digital images and classifying the results of image analysis based on the type of disease in human skin. The method used is a combination of local binary pattern (LBP) and Convolutional Neural Network (CNN) methods which can later be used as sensors or vision for skin diseases automatically. The results of this study can help in the early identification of skin diseases, helping parties who want to know the image value of skin diseases by using LBP and classifying it based on the type of disease using CNN. This study shows the level of accuracy of combining LBP with CNN is quite high with an average value of 92%. In addition, this research can also be used as reference material for the development of further research in image processing that uses LBP and classification using CNN.

关键词： Image Classification Skin Disease local binary pattern Convolutional Neural Network Deep Learning

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Ball Detection using local binary pattern in Middle Size Robot Soccer (ERSOW) 2

Ball Detection using Local Binary Pattern in Middle Size Rob...

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2nd International Conference of Computer and Informatics Engineering (IC2IE) - Artificial Intelligence Roles in Industrial Revolution 4.0

作者： Wibowo, Iwan Kurnianto Haq, Muhammad Abdul Bachtiar, Mochamad Mobed Dewantara, Bima Sena Bayu Ihsan, Fadl Lul Hakim Politekn Elektronika Negeri Surabaya Dept Informat & Comp Engn Surabaya Indonesia

ISBN: (纸本)9781728123844

Detecting ball is the first competence that should be owned by robot soccer. In the case of wheeled-based robot soccer, the existence of a ball is commonly detected by using an omnidirectional camera and a monocular camera. Traditional wheeled-based robot soccer usually uses color thresholding technique to differentiate ball and other objects. However, the accuracy of the thresholding technique frequently decreasing due to the changing of lighting conditions. This condition causes the robot can't detect the ball correctly. In this paper, we address that problem by applying a local binary pattern (LBP) based ball detection system. Our experimental results show the performance of our system has percentages of success of 96.97% in bright lighting conditions, 80.69% insufficient lighting conditions, and 98.74% in dimmed lighting conditions when the ball moves slowly. Whereas the percentage of successful detection becomes 70.74% during bright lighting conditions, 60.42% for sufficient lighting conditions, and 83.79% for minimum lighting conditions when the ball moves quickly. LBP-based ball detection has 92.4% accuracy in measuring the distance between the ball and the robot.

关键词： ball detection local binary pattern lighting conditions

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Copy-Rotate-Move Forgery Detection using Complex Wavelet Transform and local binary pattern 10

Copy-Rotate-Move Forgery Detection using Complex Wavelet Tra...

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10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

作者： Dixit, Rahul Naskar, Ruchira Manipal Univ Jaipur Dept Comp & Commun Engn Jaipur 302007 Rajasthan India Indian Inst Engn Sci & Technol Dept Informat Technol Sibpur India

ISBN: (纸本)9781538659069

Image region duplication is a type of modification attack on digital images, where a region of the digital image is copied and pasted onto another location within itself. In the recent decade, various techniques have been presented for image region duplication detection based on investigation of matching pixel blocks. However, such forgery detection becomes a challenge when accompanied with geometrical operation such as rescaling, rotation on the forged portions of the image. In this work, we provide a solution of image region duplication detection in a rotation-invariant way. The proposed method is based on Complex Wavelet Transform, utilizes local binary pattern features, and is capable of detecting arbitrary degrees of rotation, unlike existing techniques. Mean-Variance method is used in order to optimize false matches resulting from naturally similar blocks in an image. According to our experimental analysis forgery detection efficiency of the presented methodology is higher, and its false positive rate is lower as compared to the existing methods.

关键词： Copy-Rotate-Move forgery Complex wavelet transforms Digital imageforensics local binary pattern Region duplication

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Analysis of local binary pattern for emphysema classification in lung CT image 11

Analysis of local binary pattern for emphysema classificatio...

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11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Tuba, Eva Strumberger, Ivana Bacanin, Nebojsa Tuba, Milan Singidunum Univ Belgrade Serbia

ISBN: (纸本)9781728116242

Medical digital images and methods for their processing and automatic analysis have been used for faster and more precise diagnosis. Computer-aided diagnosis systems are widely used by specialists as help for detecting and analyzing suspicions regions in medical digital images. Various types of medical digital images and numerous diseases that can be detected on them make this wide research field. One of the diseases that can be detected in lung CT images is chronic obstructive pulmonary disease or emphysema. In this paper we analyzed the capabilities of texture descriptors, local binary pattern, for detecting and classification of emphysema. Three different types of local binary pattern are used. Instead of using a whole local binary pattern operator output, statistical measurements have been used. Support vector machine optimized by elephant herding optimization algorithm was used for classification. Based on the obtained results, it can be concluded that six statistical information of uniform local binary pattern achieve the best classification accuracy.

关键词： emphysema detection texture features local binary pattern support vector machine swarm intelligence elephant herding optimization

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