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Статті в журналах з теми "HAAR LIKE"

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Автор: Grafiati

Опубліковано: 11 вересня 2023

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1

Uddin, M. S., and A. Y. Akhi. "Horse Detection Using Haar Like Features." International Journal of Computer Theory and Engineering 8, no. 5 (October 2016): 415–18. http://dx.doi.org/10.7763/ijcte.2016.v8.1081.

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2

GOGYAN, SMBAT, and P. WOJTASZCZYK. "On weak non-equivalence of wavelet–like systems in L1." Mathematical Proceedings of the Cambridge Philosophical Society 144, no. 2 (March 2008): 499–510. http://dx.doi.org/10.1017/s0305004107000886.

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AbstractWe show that in $L_1(\R)$ the Haar wavelet basis is not equivalent to any permutation with any signs of the Strω wavelet basis. We also construct a Haar-type system in L1[0,1] which is not equivalent to any subsequence with signs of the classical Haar basis.

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3

Hahn, Sang-Il, and Hyung-Tai Cha. "Ear Detection using Haar-like Feature and Template." Journal of Broadcast Engineering 13, no. 6 (November 30, 2008): 875–82. http://dx.doi.org/10.5909/jbe.2008.13.6.875.

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4

Mustafa, Rashed, Yang Min, and Dingju Zhu. "Obscenity Detection Using Haar-Like Features and Gentle Adaboost Classifier." Scientific World Journal 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/753860.

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Large exposure of skin area of an image is considered obscene. This only fact may lead to many false images having skin-like objects and may not detect those images which have partially exposed skin area but have exposed erotogenic human body parts. This paper presents a novel method for detecting nipples from pornographic image contents. Nipple is considered as an erotogenic organ to identify pornographic contents from images. In this research Gentle Adaboost (GAB) haar-cascade classifier and haar-like features used for ensuring detection accuracy. Skin filter prior to detection made the system more robust. The experiment showed that, considering accuracy, haar-cascade classifier performs well, but in order to satisfy detection time, train-cascade classifier is suitable. To validate the results, we used 1198 positive samples containing nipple objects and 1995 negative images. The detection rates for haar-cascade and train-cascade classifiers are 0.9875 and 0.8429, respectively. The detection time for haar-cascade is 0.162 seconds and is 0.127 seconds for train-cascade classifier.

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5

Cheng, Ruzhong, Yongjun Zhang, Guoping Wang, Yong Zhao, and Rahmatulloev Khusravsho. "Haar-Like Multi-Granularity Texture Features for Pedestrian Detection." International Journal of Image and Graphics 17, no. 04 (October 2017): 1750023. http://dx.doi.org/10.1142/s0219467817500231.

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Pedestrian detection has been a significant problem for decades and remains a hot topic in computer vision. Pedestrian detection is one of the key algorithms for self-driving cars and some other functions in robotics, including driver support systems, road surveillance systems. In this paper, based on the characteristics of the human body and the Haar feature, the Haar-like multi-granularity local texture feature, i.e., multi-granularity Haar-like LBP (mgh-LBP), is proposed for pedestrian detection. The mgh-LBP feature combines four characteristics of the human body and their backgrounds to construct the Haar-like features, which can better describe human body texture and edge information. Compared with other texture features, including the rotation-invariant LBP feature, uniform LBP feature and basic-LBP feature, the proposed method greatly reduces the feature dimension and computational complexity, and obtains a higher pedestrian detection rate and robust detection performance.

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6

Essannouni, L., and D. Aboutajdine. "Correlation of robust Haar-like feature." Electronics Letters 47, no. 17 (2011): 961. http://dx.doi.org/10.1049/el.2011.1534.

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7

Mega Anjani, Nabila Dayu, Farida Farida, and Muchamad Kurniawan. "ANALISIS FITUR HAAR MENGGUNAKAN ALGORITMA HAAR-LIKE FEATURE PADA CITRA KENDARAAN BERMOTOR." Network Engineering Research Operation 5, no. 2 (December 14, 2020): 124. http://dx.doi.org/10.21107/nero.v5i2.187.

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8

Lee, Ching Min, and Yan Ming Li. "Implementation of an Embedded Facial Recognition System." Applied Mechanics and Materials 870 (September 2017): 283–88. http://dx.doi.org/10.4028/www.scientific.net/amm.870.283.

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In this paper, an embedded facial recognition system whose platform consists of pcDuono-V2 board with ARM-processor inside and a Linux-kernel-based operating system, Ubuntu, is implemented. A camera is set up on the platform to take human face images. A facial recognition program consisting of AdaBoost algorithm, Haar-like features, integral image method, and cascade classifiers is utilized to recognize images. The AdaBoost algorithm is a modified Boosting algorithm, which is a machine learning algorithm for training cascade stronger classifiers based on Haar-like features, where Haar-like features are the foundation of the recognition. An integral image method is used to speed up the calculation of corresponding rectangle feature values for Haar-like features. The whole facial recognition comprises facial training procedures and recognition procedures. In facial training procedures, sufficient amounts of positive and negative picture samples are necessary for getting Haar-like features to the recognition system. AdaBoost algorithm is then used to the system for training cascade stronger classifiers which are the detection tools in recognition procedures. While in facial recognition procedures, after getting the Haar-like features for the target images or pictures, cascade stronger classifiers work to detect and recognize. According to the experimental results, the resultant embedded system can recognize the experimental subjects in one second for every our considered situations, which assures the real-time performance.

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9

Akbar, Rafy Aulia, and Ricky Eka Putra. "Perbandingan Ekstraksi Fitur Haar-like dan Local Binary Pattern untuk Deteksi Wajah." Journal of Informatics and Computer Science (JINACS) 1, no. 01 (October 1, 2019): 1–8. http://dx.doi.org/10.26740/jinacs.v1n01.p1-8.

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Abstrak—Deteksi wajah manusia (face detection) adalah salah satu tahap awal yang sangat penting di dalam proses pengenalan wajah (face recognition). Karena sebelum memasuki proses tersebut deteksi wajah sangat mempengaruhi tingkat akurasi yang dihasilkan, karena potongan citra wajah dalam sebuah gambar ditentukan oleh deteksi wajah. Deteksi wajah dapat digunakan untuk melakukan pencarian dan pengindeksan data wajah dari citra atau video yang berisi wajah dengan berbagai ukuran, posisi, dan latar belakang. Penelitian ini mengevaluasi dua metode deteksi wajah berdasarkan tingkat hit deteksi dan waktu deteksi, dua metode itu adalah fitur Haar dan Local Binary Pattern (LBP). Pada percobaan menggunakan Haar menghasilkan total wajah yang terdeteksi benar adalah 11685 wajah dari 11745 wajah, sedangkan wajah yang terdeteksi salah adalah 103, sehingga memiliki hit rate 99,49%. Total dari waktu deteksi dari semua dataset adalah 1033 detik. Kemudian untuk percobaan menggunakan metode LBP total wajah yang terdeteksi benar adalah 11444 wajah dari 11745 wajah, sedangkan wajah yang terdeteksi salah adalah delapan, sehingga memiliki hit rate 97,48%. Total dari waktu deteksi dari semua dataset adalah 686 detik. Dari penelitian yang telah dilakukan, Haar memiliki keunggulan pada hit rate atau dapat mendeteksi wajah lebih banyak, sedangkan LBP memiliki keunggulan dalam waktu deteksi wajah yang jauh lebih singkat daripada Haar. LBP memiliki kelemahan pada hit rate, sedangkan Haar memiliki kelemahan pada waktu deteksi yang lebih lama dan kesalahan deteksi wajah yang lebih banyak daripada LBP.Kata Kunci— deteksi wajah; viola-jones; haar-like; local binary pattern; hit rate.

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10

Boscardín, Liliana Beatriz, Liliana Raquel Castro, and Silvia Mabel Castro. "Haar-LikeWavelets over Tetrahedra." Journal of Computer Science and Technology 17, no. 02 (October 1, 2017): e13. http://dx.doi.org/10.24215/16666038.17.e13.

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In this paper we define a Haar-like wavelets basis that form a basis for L2(T,S,μ), μ being the Lebesgue measure and S the σ -algebra of all tetrahedra generated from a subdivision method of the T tetrahedron. As 3D objects are, in general, modeled by tetrahedral grids, this basis allows the multiresolution representation of scalar functions defined on polyhedral volumes, like colour, brightness, density and other properties of an 3D object.

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11

Jabbar, Nahla Ibraheem. "Prediction Secondary Protein Structure from Images of Amino Acid by Using Harr - like Features in Support Vector Machine." Webology 19, no. 1 (January 20, 2022): 581–91. http://dx.doi.org/10.14704/web/v19i1/web19041.

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In this study Haar features are extracted from images of sub-sequences of amino acid and classified by Support Vector Machine (SVM). We apply a novel approach of integration Haar-like features extraction from primary protein structure to predication three states of secondary protein structure. The sequences of primary protein are divided into different slides windows for representation images and then Haar-like feature have been extracted from these images to classify three-category of secondary protein structure helix (H), strand (E) and coil (C). The final prediction results were generated from SVM overall per residue accuracies are: - accuracy of helix(H) reached 83.93%, accuracy of Sheet(E) is 85.15% and accuracy of the Coil (C) is 81.0126 %. Images are scanned from amino acid sequences are specified by the selection window sizes, when the size of window is small the important information of predicting secondary structure relay outside the window. It has taken only local sequence information. When the size of window has been increased the performance has been deteriorated. Haar-like gives a perfect input data of SVM with a huge amount of data, also for improvement of support vector machine are used varying cost parameters.

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12

Feng Tang, R. Crabb, and Hai Tao. "Representing Images Using Nonorthogonal Haar-Like Bases." IEEE Transactions on Pattern Analysis and Machine Intelligence 29, no. 12 (December 2007): 2120–34. http://dx.doi.org/10.1109/tpami.2007.1123.

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13

Kim, Man-Dong, and DaeEun Kim. "Local Visual Homing Navigation Using Gradient-Descent Learning of Haar-like Features." Transactions of The Korean Institute of Electrical Engineers 68, no. 10 (October 31, 2019): 1244–51. http://dx.doi.org/10.5370/kiee.2019.68.10.1244.

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14

Ding, You Dong, and Hai Bo Pang. "An Improved Algorithm of Hand-Gesture Recognition Based on Haar-Like Features and Adaboost." Advanced Materials Research 588-589 (November 2012): 1238–41. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.1238.

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In this paper, we proposed an improved algorithm of hand-gesture recognition based on Haar-like features and Adaboost. Initial, we calculated the Haar-like features of hand-gesture images by integral image. Then, we used the principal components analysis method to reduce the dimension of Haar-like features. At last, an Adaboost classifier performed the hand-gesture recognition task with the hand-gesture features. A dataset with large hand gestures (12 types, 600 hand-gesture images) was built, including some large pose-angle (about 40 deg.) hand-gesture images. Our experiment results demonstrated that our method could effectively recognize different hand gesture, and the best appropriate N was 12. In addition, the average processing time of the proposed method was about 0.05 second for every image.

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15

Chung, Byung Woo, Ki-Yeong Park, and Sun-Young Hwang. "A Fast and Efficient Haar-Like Feature Selection Algorithm for Object Detection." Journal of Korea Information and Communications Society 38A, no. 6 (June 30, 2013): 486–91. http://dx.doi.org/10.7840/kics.2013.38a.6.486.

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16

Vani, S., G. R. Suresh, T. Balakumaran, and Cross T. Ashawise. "EEG Signal Analysis for Automated Epilepsy Seizure Detection Using Wavelet Transform and Artificial Neural Network." Journal of Medical Imaging and Health Informatics 9, no. 6 (August 1, 2019): 1301–6. http://dx.doi.org/10.1166/jmihi.2019.2713.

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Electroencephalogram (EEG) measures electrical activity of the brain and proffers valuable insight of the brain dynamics. Accurate and careful analysis of EEG signal plays a prominent role in the diagnosis of brain diseases like epilepsy, brain tumor. EEG is the most significant method used for epilepsy monitoring, diagnosis and rehabilitation. A patient-specific seizure detection model has been developed using Haar wavelet and Artificial Neural Network. HAAR Wavelet decomposition of multi-channel EEG with five scales is made and three frequency bands of EEG selected for the consequent process. The conventional Haar wavelet transform (HWT) is replaced by a modified Haar wavelet transform whereas the number of multiplications and additions are reduced. The Haar wavelet reduces computational complexity from the existing Haar wavelet structure which consumes only 1–3 ms based on the decomposition level to detect epilepsy.

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17

Pal, Srikanta. "Human Face Detection Technique using Haar-like Features." International Journal of Computer Applications 175, no. 32 (November 17, 2020): 56–60. http://dx.doi.org/10.5120/ijca2020920883.

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18

Tam, W. Y. "Weighted Haar wavelet-like basis for scattering problems." IEEE Microwave and Guided Wave Letters 6, no. 12 (1996): 435–37. http://dx.doi.org/10.1109/75.544541.

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19

Septyanto, Moh Wahyu, Herry Sofyan, Herlina Jayadianti, Oliver Samuel Simanjuntak, and Dessyanto Boedi Prasetyo. "APLIKASI PRESENSI PENGENALAN WAJAH DENGAN MENGGUNAKAN ALGORITMA HAAR CASCADE CLASSIFIER." Telematika 16, no. 2 (January 9, 2020): 87. http://dx.doi.org/10.31315/telematika.v16i2.3182.

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AbstractPresence using face already widely adopted as a way of monitoring employee attendance. Research on using facial Presence never been done before by applying algorithms and algorithms Eigenface linear discriminant analysis (LDA). However, previous research has found that there are still weaknesses in the algorithms used. The weakness is that the process of identifying which takes a long time because the process of calculating the value carried on the overall image or image and the distance of the face of the webcam can affect the process of identifying faces. In this study, the algorithm used is haar cascade classifier algorithm. Haar classifier cascade or known by other names haar-like features are the rectangular features (square function), which gives an indication of the specifics on a picture or image. Principle Haar-like features are recognizing objects based on simple values of the features but not the pixel values of the object image. This method has the advantage that the computation is very fast, because it depends on the number of pixels in a square instead of each pixel value of an image. Haar classifier cascade also still be able to identify faces even if the distance face with the webcam is considerably due to the value of the facial features can still be identified. Results from this study that the system can identify the face with a good degree of accuracy. Tests carried out to 13 employees Starcross Store with each employee doing 30 times the experiment presence. Attendance successful has the success rate is 87% and 13% of the total failure of the experiment 390 times. Some absences failed to happen because there are several factors that can affect attendance as high luminance, uplifted head position, and the use of attributes (hats, glasses, etc.).Keywords : Presence, face recognition, Haar cascade classifier algorithmPresensi menggunakan wajah sudah banyak diterapkan sebagai cara untuk pemantauan kehadiran pegawai. Penelitian tentang presensi menggunakan wajah pernah dilakukan sebelumnya dengan menerapkan algoritma eigenface dan algoritma linear discriminant analysis (LDA). Namun dari penelitian sebelumnya telah ditemukan kelemahan yaitu pada proses pengidentifikasian yang membutuhkan waktu cukup lama dikarenakan proses perhitungan nilai dilakukan pada keseluruhan citra atau image dan jauhnya jarak wajah dari webcam dapat mempengaruhi proses pengidentifikasian wajah tersebut. Pada penelitian ini algoritma yang digunakan adalah algoritma haar cascade classifier. Haar cascade classifier atau yang dikenal dengan nama lain haar-like features merupakan rectangular features (fungsi persegi), yang memberikan indikasi secara spesifik pada sebuah gambar atau image. Prinsip Haar-like features adalah mengenali obyek berdasarkan nilai sederhana dari fitur tetapi bukan merupakan nilai piksel dari image obyek tersebut. Metode ini memiliki kelebihan yaitu komputasinya sangat cepat, karena hanya bergantung pada jumlah piksel dalam persegi bukan setiap nilai piksel dari sebuah image. Haar cascade classifier juga masih dapat mengidentifikasi wajah walaupun jarak wajah dengan webcam terbilang jauh dikarenakan nilai fitur wajah masih dapat diidentifikasi. Hasil dari penelitian ini bahwa sistem dapat mengidentifikasi wajah dengan tingkat akurasi baik. Pengujian dilakukan kepada 13 karyawan Starcross Store dengan masing-masing karyawan melakukan 30 kali percobaan presensi. Absensi yang berhasil memiliki nilai keberhasilan 87% dan 13% gagal dari total percobaan 390 kali. Beberapa absensi yang gagal terjadi karena ada beberapa faktor yang dapat mempengaruhi absensi seperti pencahayaan yang tinggi, posisi kepala yang mendongkak dan penggunaan atribut (topi, kacamata, dsb).Kata Kunci : Presensi, Pengenalan Wajah, Algoritma Haar Cascade Classifier

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20

Wang, Qing Wei, Zi Lu Ying, and Lian Wen Huang. "Face Recognition Algorithm Based on Haar-Like Features and Gentle Adaboost Feature Selection via Sparse Representation." Applied Mechanics and Materials 742 (March 2015): 299–302. http://dx.doi.org/10.4028/www.scientific.net/amm.742.299.

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This paper proposed a new face recognition algorithm based on Haar-Like features and Gentle Adaboost feature selection via sparse representation. Firstly, All the images including face images and non face images are normalized to size and then Haar-Like features are extracted . The number of Haar-Like features can be as large as 12,519. In order to reduce the feature dimension and retain the most effective features for face recognition, Gentle Adaboost algorithm is used for feature selection. Selected features are used for face recognition via sparse representation classification (SRC) algorithm. Testing experiments were carried out on the AR database to test the performance of the new proposed algorithm. Compared with traditional algorithms like NS, NN, SRC, and SVM, the new algorithm achieved a better recognition rate. The effect of face recognition rate changing with feature dimension showed that the new proposed algorithm performed a higher recognition rate than SRC algorithm all the time with the increasing of feature dimension, which fully proved the effectiveness and superiority of the new proposed algorithm.

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21

Park, Ki-Yeong, and Sun-Young Hwang. "An Improved Normalization Method for Haar-like Features for Real-time Object Detection." Journal of Korea Information and Communications Society 36, no. 8C (August 31, 2011): 505–15. http://dx.doi.org/10.7840/kics.2011.36c.8.505.

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22

Liu, Daxue, Kai Zang, and Jifeng Shen. "A Shallow–Deep Feature Fusion Method for Pedestrian Detection." Applied Sciences 11, no. 19 (October 3, 2021): 9202. http://dx.doi.org/10.3390/app11199202.

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In this paper, a shallow–deep feature fusion (SDFF) method is developed for pedestrian detection. Firstly, we propose a shallow feature-based method under the ACF framework of pedestrian detection. More precisely, improved Haar-like templates with Local FDA learning are used to filter the channel maps of ACF such that these Haar-like features are able to improve the discriminative power and therefore enhance the detection performance. The proposed shallow feature is also referred to as weighted subset-haar-like feature. It is efficient in pedestrian detection with a high recall rate and precise localization. Secondly, the proposed shallow feature-based detection method operates as a region proposal. A classifier equipped with ResNet is then used to refine the region proposals to judge whether each region contains a pedestrian or not. The extensive experiments evaluated on INRIA, Caltech, and TUD-Brussel datasets show that SDFF is an effective and efficient method for pedestrian detection.

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23

Chau, Khanh Ngan, and Nghi Thanh Doan. "DENSE SIFT FEATURE AND LOCAL NAIVE BAYES NEAREST NEIGHBOR FOR FACE RECOGNITION." Scientific Journal of Tra Vinh University 1, no. 28 (December 1, 2017): 56–63. http://dx.doi.org/10.35382/18594816.1.28.2017.46.

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Human face recognition is a technology which is widely used in life. There have been much effort on developing face recognition algorithms. In this paper, we present a new methodology that combines Haar Like Features - Cascade of Boosted Classifiers, Dense Scale-Invariant Feature Transform (DSIFT), Local Naive Bayes Nearest Neighbor (LNBNN) algorithm for the recognition of human face. We use Haar Like Features and the combination of AdaBoost algorithm and Cascade stratified model to detect and extract the face image, the DSIFT descriptors of the image are computed only for the aligned and cropped face image.Then, we apply the LNBNN algorithms for object recognition. Numerical testing on several benchmark datasets using our proposed method for facerecognition gives the better results than other methods. The accuracies obtained by LNBNN method is 99.74 %.

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24

Jirawatanakul, Yardnapa, and Saowaluk Watanapa. "Thai Face Cartoon Detection and Recognition Using Eigenface Model." Advanced Materials Research 931-932 (May 2014): 1412–16. http://dx.doi.org/10.4028/www.scientific.net/amr.931-932.1412.

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In this paper, an effective method for Thai face cartoon detection and recognition is used based on haar like feature and eigenface model. The basic idea of this method is to detection and recognition a cartoon Thai from the database based on a cartoon drawn by an artist. This method consists of three steps. We first manually, haar like feature is applied for Thai face cartoon detection. Second, those faces are extracted feature using eigenface. Final, those features are recognized using Euclidean distance. For experimental result, detection rate of 95% and recognition rate of 97%.

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25

Michaelis, Rune, H. Christian Hass, Svenja Papenmeier, and Karen H. Wiltshire. "Automated Stone Detection on Side-Scan Sonar Mosaics Using Haar-Like Features." Geosciences 9, no. 5 (May 11, 2019): 216. http://dx.doi.org/10.3390/geosciences9050216.

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Stony grounds form important habitats in the marine environment, especially for sessile benthic organisms. For the purpose of habitat demarcation and monitoring, knowledge of the position and abundance of individual stones is necessary. This is especially the case in areas with a scattered occurrence of stones in an environment which is otherwise characterized by relatively mobile sandy sediments. Exposed stones can be detected using side-scan sonar (SSS) data. However, apart from laborious manual identification, there is as yet no automated or semi-automated method available for a fast and spatially resolved detection of stones. In this study, a Haar-like feature detector was trained to identify individual stones on an SSS mosaic (~12 km2) showing heterogeneous sediment distribution. The results of this method were compared with those of manually derived stones. Our study shows that the Haar-like feature detector was able to detect up to 62% of the overall occurrence of stones within the study area. Even though the sheer number of correctly identified stones was influenced by, e.g., the type of sediments and the number of grey values of the mosaic, Haar-like feature detectors provide a relatively easy and fast method to identify stones on SSS mosaics when compared to the manual investigation.

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26

Kim, Soo-Jin, Sang-Kyun Park, Seon-Young Lee, and Kyeong-Soon Cho. "Design of High-performance Pedestrian and Vehicle Detection Circuit using Haar-like Features." KIPS Transactions:PartA 19A, no. 4 (August 31, 2012): 175–80. http://dx.doi.org/10.3745/kipsta.2012.19a.4.175.

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27

Imoize, Agbotiname Lucky, and Aanuoluwapo Eberechukwu Babajide. "Design and Implementation of an Infrared-Based Sensor for Finger Movement Detection." Journal of Biomedical Engineering and Medical Imaging 6, no. 4 (December 31, 2019): 29–44. http://dx.doi.org/10.14738/jbemi.64.7639.

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Анотація:

With the increasing interest in smart devices and convenient remote control, the need for accurate wireless means of control has become imperative. This gives rise to research in the field of gesture and finger movement detection. This design focuses on exploring techniques involved in hand and finger movement detection, using the depth-sensing infrared cameras embedded on Xbox Kinect Module. The generated 3-D images are first filtered along the z-axis, then two distinct techniques; Haar-Like Features, and Deep Learning using a Convolution Neural Network, are performed on the images to detect hands. Useful metrics like, Precision, Recall, F1-Score and Accuracy are then used to evaluate the efficiency of these techniques. The results show that while the deep learning model is the most accurate with a weighted accuracy of 1.0 (due to the absence of noise in the images) in contrast with 0.97 observed for the Haar-Like features, the Haar-like features technique runs faster due to its static nature. These findings point to the conclusion that the deep learning model is a better technique for detecting hands despite its longer running time.

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Wu, Hao, Yu Cao, Haiping Wei, and Zhuang Tian. "Face Recognition Based on Haar Like and Euclidean Distance." Journal of Physics: Conference Series 1813, no. 1 (February 1, 2021): 012036. http://dx.doi.org/10.1088/1742-6596/1813/1/012036.

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arma, Rahul Patel, Nidhi Daxini, Sachin Sh. "Real Time Animal Detection System using HAAR Like Feature." International Journal of Innovative Research in Computer and Communication Engineering 03, no. 06 (June 30, 2015): 5177–82. http://dx.doi.org/10.15680/ijircce.2015.0306032.

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30

Chau, Sugandi, Jepri Banjarnahor, Dikky Irfansyah, and Sinta Kumala. "Analisis Pendeteksian Pola Wajah Menggunakan Metode Haar-Like Feature." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 2, no. 2 (January 27, 2019): 69. http://dx.doi.org/10.31289/jite.v2i2.2133.

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Pencatatan kehadiran mahasiswa merupakan salah satu faktor penting dalam pengolahan kedisiplinan, kewajiban, dan ketaatan mahasiswa dalam mengikuti proses perkuliahan. Pencatatan kehadiran mahasiswa sebelumnya dilakukan dengan cara manual yaitu dengan menggunkan tanda tangan, pencatatan kehadiran dengan cara manual dapat menjadi penghambat pemantauan kedisiplinan, ketaatan mahasiswa dalam hal ketepatan waktu datang mahasiswa. Pencatatan kehadiran mahasiswa secara manual dapat diganti dengan pencatatan kehadiran mahasiswa secara terkomputerisasi yang menggunakan proses indentifikasi teknologi biometrik, untuk mengidentifikasi pola wajah mahasiswa digunakan metode <em>bilateral filter</em>, <em>canny edge detection</em>, <em>haar-like feature</em>, <em>integral image</em>, <em>cascade classifier adaboost</em>. Dari hasil pengujian, tingkat keberhasilan pencatatan kehadiran mahasiswa berdasarkan pola wajah dari masing-masing mahasiswa sebesar 70.43%.

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31

K. Youssef, I. "Memory Effects in Diffusion Like Equation Via Haar Wavelets." Pure and Applied Mathematics Journal 5, no. 4 (2016): 130. http://dx.doi.org/10.11648/j.pamj.20160504.17.

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32

Schwegmann, C. P., W. Kleynhans, and B. P. Salmon. "Synthetic Aperture Radar Ship Detection Using Haar-Like Features." IEEE Geoscience and Remote Sensing Letters 14, no. 2 (February 2017): 154–58. http://dx.doi.org/10.1109/lgrs.2016.2631638.

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33

Panning, A., A. K. Al-Hamadi, R. Niese, and B. Michaelis. "Facial expression recognition based on Haar-like feature detection." Pattern Recognition and Image Analysis 18, no. 3 (September 2008): 447–52. http://dx.doi.org/10.1134/s1054661808030139.

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34

Park, Ki-Yeong, and Sun-Young Hwang. "An improved Haar-like feature for efficient object detection." Pattern Recognition Letters 42 (June 2014): 148–53. http://dx.doi.org/10.1016/j.patrec.2014.02.015.

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35

Menezes, Paulo, José Carlos Barreto, and Jorge Dias. "Face tracking based on haar-like features and eigenfaces." IFAC Proceedings Volumes 37, no. 8 (July 2004): 304–9. http://dx.doi.org/10.1016/s1474-6670(17)31993-6.

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36

Taillefumier, Thibaud, and Marcelo O. Magnasco. "A Haar-like Construction for the Ornstein Uhlenbeck Process." Journal of Statistical Physics 132, no. 2 (April 25, 2008): 397–415. http://dx.doi.org/10.1007/s10955-008-9545-8.

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37

Nishimura, Jun, and Tadahiro Kuroda. "Versatile Recognition Using Haar-Like Feature and Cascaded Classifier." IEEE Sensors Journal 10, no. 5 (May 2010): 942–51. http://dx.doi.org/10.1109/jsen.2009.2038231.

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38

Sami Ur Rahman, Fakhre Alam, and Wajid Ali. "Gun Detection in CCTV Images using HAAR-Like Features." Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences 59, no. 4 (November 22, 2022): 1–11. http://dx.doi.org/10.53560/ppasa(59-4)749.

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Анотація:

Automated video-based surveillance is an important area of research to assist the security personnel to detect the incident of any abnormal events in the surroundings. The objective of this paper is to develop a framework for automatic gun detection using closed-circuit television (CCTV) images. The methodology presented in this paper involves the development of a framework for automatic gun detection using closed-circuit television (CCTV) images, with the aim of enhancing the surveillance of crime and improving human security. The proposed approach consists of a dataset of CCTV images containing instances of guns, as well as non-gun images for comparison. These images would be used to train the proposed algorithm to recognize and identify guns in future CCTV images. The proposed framework is designed for an indoor environment and uses Haar-like features for gun detection. The proposed system involves the installation of CCTV cameras in a suitable corner of an indoor environment for surveillance. The CCTV cameras capture the scene and the frames of the scene are compared with a predefined dataset for automatic gun detection. The proposed approach draws a bounding box and raises an alarm if it detects a gun in a frame extracted from a captured scene. This provides a visual indication of the presence of a gun, making it easier for relevant authorities to quickly identify and respond to the threat. The proposed system shows promising results in real-time applications and about 90% accuracy has been achieved.

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39

Sheetal and Monika Mittal. "A Haar Wavelet Based Approach for State Analysis of Disk Drive Read System." Applied Mechanics and Materials 592-594 (July 2014): 2267–71. http://dx.doi.org/10.4028/www.scientific.net/amm.592-594.2267.

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In this paper, computational savings by an Haar wavelet method for state analysis of disk-drive system is presented. Based upon useful properties of Haar functions like operational matrix of integration, analysis of disk drive system is done. Computational savings in system analysis achieved with the non-recursive operational matrix as compared to recursive operational matrix have been verified using MATLAB PROFILER for different resolutions.

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40

WATTANAPAN, JATURON, WATCHAREEPAN ATIPONRAT, SANTI TASENA, and TEERAPONG SUKSUMRAN. "Extension of Haar’s theorem." Carpathian Journal of Mathematics 38, no. 1 (November 15, 2021): 231–48. http://dx.doi.org/10.37193/cjm.2022.01.19.

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Haar’s theorem ensures a unique nontrivial regular Borel measure on a locally compact Hausdorff topological group, up to multiplication by a positive constant. In this article, we extend Haar’s theorem to the case of locally compact Hausdorff strongly topological gyrogroups. We simultaneously prove the existence and uniqueness of a Haar measure on a locally compact Hausdorff strongly topological gyrogroup, using the method of Steinlage. We then find a natural relationship between Haar measures on gyrogroups and on their related groups. As an application of this result, we study some properties of a convolution-like operation on the space of Haar integrable functions defined on a locally compact Hausdorff strongly topological gyrogroup

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41

Diantoro, Karno, and Dian Gustina. "Perancangan Sistem Deteksi Wajah Berbasis Gambar Menggunakan OPENCV." Jurnal Esensi Infokom : Jurnal Esensi Sistem Informasi dan Sistem Komputer 3, no. 2 (February 19, 2022): 48–53. http://dx.doi.org/10.55886/infokom.v3i2.336.

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Анотація:

Deteksi wajah adalah deteksi objek berupa wajah yang didalamnya terdapat fitur – fitur khusus yang merepresentasikan bentuk wajah pada umumnya. Salah satu metode deteksi wajah adalah dengan metode Viola Jones . Metode ini mempunyai empat proses utama yaitu, haar-like feature,citra integral , ada - boost , dan cascade classifier . Haar-like feature merupakan kumpulan fitur khusus yang merepresentasikan wajah dan citra integral adalah cara cepat menghitung haar feature . Sedangkan ada - boost adalah pembobotan secara statistik nilai – nilai fitur yang didapat dan di - filter menggunakan cascade classifier. Sistem deteksi wajah tersebut menggunakan java sebagai bahasa pemrograman, dan OpenCV sebagai librari deteksi objek. Hal ini dikarenakan librari OpenCV menerapkan metode Viola Jones kedalam sistem deteksinya, sehingga memudahkan dalam pembuatan sistem. Penelitian ini bertujuan untuk mengimplementasikan Viola Jones ke dalam sistem deteksi wajah sederhana dengan memanfaatkan library yang ada pada OpenCV dan memanfaatkan bahasa pemrograman java sebagai pondasi sistem. Setelah sistem selesai dibuat, dilakukan pengujian sistem terhadap karakteristik wajah yang dapat dideteksi

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42

Adeshina, Sirajdin Olagoke, Haidi Ibrahim, Soo Siang Teoh, and Seng Chun Hoo. "Custom Face Classification Model for Classroom Using Haar-Like and LBP Features with Their Performance Comparisons." Electronics 10, no. 2 (January 6, 2021): 102. http://dx.doi.org/10.3390/electronics10020102.

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Face detection by electronic systems has been leveraged by private and government establishments to enhance the effectiveness of a wide range of applications in our day to day activities, security, and businesses. Most face detection algorithms that can reduce the problems posed by constrained and unconstrained environmental conditions such as unbalanced illumination, weather condition, distance from the camera, and background variations, are highly computationally intensive. Therefore, they are primarily unemployable in real-time applications. This paper developed face detectors by utilizing selected Haar-like and local binary pattern features, based on their number of uses at each stage of training using MATLAB’s trainCascadeObjectDetector function. We used 2577 positive face samples and 37,206 negative samples to train Haar-like and LBP face detectors for a range of False Alarm Rate (FAR) values (i.e., 0.01, 0.05, and 0.1). However, the study shows that the Haar cascade face detector at a low stage (i.e., at six stages) for 0.1 FAR value is the most efficient when tested on a set of classroom images dataset with 100% True Positive Rate (TPR) face detection accuracy. Though, deep learning ResNet101 and ResNet50 outperformed the average performance of Haar cascade by 9.09% and 0.76% based on TPR, respectively. The simplicity and relatively low computational time used by our approach (i.e., 1.09 s) gives it an edge over deep learning (139.5 s), in online classroom applications. The TPR of the proposed algorithm is 92.71% when tested on images in the synthetic Labeled Faces in the Wild (LFW) dataset and 98.55% for images in MUCT face dataset “a”, resulting in a little improvement in average TPR over the conventional face identification system.

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MORIKAWA, Kenichiro, Daigo MURAMATSU, and Hiroyuki OGATA. "2P1-D22 Kana-kanji detection algorithm using Haar-like features." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2010 (2010): _2P1—D22_1—_2P1—D22_4. http://dx.doi.org/10.1299/jsmermd.2010._2p1-d22_1.

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44

Sugianto, Sugianto. "DETEKSI ALAT PELINDUNG KEPALA (HELM) MENGGUNAKAN METODE HAAR CASCADE CLASSIFIER." Joutica 4, no. 1 (March 1, 2019): 189. http://dx.doi.org/10.30736/jti.v4i1.283.

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Анотація:

Use of protective gear helmet head is often considered unimportant and trivial by workers. Whereas the use of protective headgear helmet is very important and affect the safety and health of workers. Kedisiplina workers to use protective gear head is still low so that the risk of accidents that could endanger workers large enough. In this research aims to detect protective equipment head helmet on video. In this study, the method used is the Haar Cascade Classifier. The system consists of two main processes, namely the process of training data and the detection process. This method of training process has four main processes, haar-like feature, integral image, no-boost and cascade classifier. Haar-like feature is a collection of special features presented the head, face and helmet. Citra is how to quickly calculate integrals haar feature. While no-boost are statistically weighted feature values are obtained and filtered using a cascade classifier. The detection process in this study there are two processes, the first detection process whether human or not, if the result of human detected will continue the process of detection of whether to use a helmet or not. Detection system testing is done individually using helmet colors red, blue and yellow. It obtained accuracy rate of 92%, while the testing group obtained the degree of accuracy of 71%.

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45

Kwak, Ju-Hyun, Il-Young Woen, and Chang-Hoon Lee. "Learning Algorithm for Multiple Distribution Data using Haar-like Feature and Decision Tree." KIPS Transactions on Software and Data Engineering 2, no. 1 (January 31, 2013): 43–48. http://dx.doi.org/10.3745/ktsde.2013.2.1.043.

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46

Anwar, Ridwan Suhair, Tasrif Hasanuddin, and Syahrul Mubarak Abdullah. "Sistem Keamanan Pintu Asrama Berbasis Pengenalan Wajah dengan Algoritma Haar Cascade." Buletin Sistem Informasi dan Teknologi Islam 3, no. 3 (August 31, 2022): 213–18. http://dx.doi.org/10.33096/busiti.v3i3.1197.

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Анотація:

Teknologi pada saat ini yang berkembang dengan sangat pesatnya dan merupakan salah satu bidang yang mempunyai peran yang sangat penting dibeberapa aspek kehidupan manusia, termasuk pada bidang security. Saat ini telah banyak dikembangkan sebuah sistem pengamanan akses masuk ke sebuah rumah atau ruangan dengan beberapa verifikasi identitas dengan sistem komputer, baik dengan menggunakan kunci, kartu, password, dan sebagainya. Namun metode ini masih memiliki kekurangan seperti keterbatasan manusia dalam mengingat benda dan kombinasi angka yang menyebabkan tidak dapatnya diakses pintu tersebut. Oleh sebab itu teknik untuk identifikasi ataupun verifikasi yang handal dan akurat dapat di rancang menggunakan teknologi pengenalan dan algoritma haar cascade agar menhasilkakn sistem keamanan yang lebih baik. Berdasarkan masalah yang ada, maka dibangun sebuah alat Sistem keamanan pintu asrama berbasis pengenalan wajah dengan Algoritma Haar Cascade dimana alat tersebut dapat membuka pintu secara otomatis ketika wajah yang di hadapkan ke kamera terdeteksi dan telah terdaftar dalam database maka pintu akan terbuak dan ketika wajah yang terdeteksi oleh kamera tidak terdaftar dalam database makan pintu tidak akan terbuka dan bazzer akan berbuanyi. Algoritma Haar cascade classifier atau yang dikenal dengan nama lain haar-like features merupakan rectangular features (fungsi persegi), yang memberikan indikasi secara spesifik pada sebuah gambar atau image. Prinsip Haar-like features adalah mengenali obyek berdasarkan nilai sederhana dari fitur tetapi bukan merupakan nilai piksel dari image obyek tersebut. Metode ini memiliki kelebihan yaitu komputasinya sangat cepat, karena hanya bergantung pada jumlah piksel dalam persegi bukan setiap nilai piksel dari sebuah image dan dapat bekerja dengan real time. Oleh karena itu dapat diperoleh kesimpulan bahwa alat ini dapat membantu sistem keamanan asrama pasangkayu dalam mengontrol setiap orang yang masuk didalam asrama, apakah dikenali atau tidak.

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47

Wang, Hong, Xian Li, and Shuang Liu. "The Design of a Car License Plate Identification System Based on AdaBoost Algorithm." Advanced Materials Research 181-182 (January 2011): 588–93. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.588.

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Design and implement a car license plate identification system with the applications of Viola and Jones algorithm. This algorithm which is based on the AdaBoost method is trained and optimized for the best performance using large database of car license plate images. The final license plate identification system obtained a cascade of classifiers consisting of 8 stages with 1310 Haar-like features. Once the license plates have sufficient visibility and there are no other objects similar to the plate in images, this system operates perfectly and shows high correct identification rate with low false positive rate. And as integral image allows the Haar-like features to be calculated very fast, the system also finished the identification rapidly.

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48

Liu, Chunsheng, Faliang Chang, and Chengyun Liu. "Cascaded split‐level colour Haar‐like features for object detection." Electronics Letters 51, no. 25 (December 2015): 2106–7. http://dx.doi.org/10.1049/el.2015.2092.

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49

Agarwal, Megha, and R. P. Maheshwari. "Co-occurrence of maximal Haar-like wavelet filters for CBIR." International Journal of Signal and Imaging Systems Engineering 8, no. 5 (2015): 316. http://dx.doi.org/10.1504/ijsise.2015.071956.

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50

Hapsari, Gita Indah, Giva Andriana Mutiara, and Husein Tarigan. "Face recognition smart cane using haar-like features and eigenfaces." TELKOMNIKA (Telecommunication Computing Electronics and Control) 17, no. 2 (April 1, 2019): 973. http://dx.doi.org/10.12928/telkomnika.v17i2.11772.

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