New Method for Optimization of License Plate Recognition system with Use of Edge Detection and Connected Component

License Plate recognition plays an important role on the traffic monitoring and parking management systems. In this paper, a fast and real time method has been proposed which has an appropriate application to find tilt and poor quality plates. In the proposed method, at the beginning, the image is converted into binary mode using adaptive threshold. Then, by using some edge detection and morphology operations, plate number location has been specified. Finally, if the plat has tilt, its tilt is removed away. This method has been tested on another paper data set that has different images of the background, considering distance, and angel of view so that the correct extraction rate of plate reached at 98.66%.Comment: 3rd IEEE International Conference on Computer and Knowledge Engineering (ICCKE 2013), October 31 & November 1, 2013, Ferdowsi Universit Mashha

Detection of major ASL sign types in continuous signing for ASL recognition

In American Sign Language (ASL) as well as other signed languages, different classes of signs (e.g., lexical signs, fingerspelled signs, and classifier constructions) have different internal structural properties. Continuous sign recognition accuracy can be improved through use of distinct recognition strategies, as well as different training datasets, for each class of signs. For these strategies to be applied, continuous signing video needs to be segmented into parts corresponding to particular classes of signs. In this paper we present a multiple instance learning-based segmentation system that accurately labels 91.27% of the video frames of 500 continuous utterances (including 7 different subjects) from the publicly accessible NCSLGR corpus (Neidle and Vogler, 2012). The system uses novel feature descriptors derived from both motion and shape statistics of the regions of high local motion. The system does not require a hand tracker

Pengenalan Sistem Isyarat Bahasa Indonesia Menggunakan Kombinasi Fitur Statis Dan Fitur Dinamis Lmc Berbasis L-gcnn

Jumlah karya ilmiah yang dihasilkan oleh akademisi dan peneliti di Indonesia semakin banyak, terutama setelah diterbitkannya surat edaran Dirjen DIKTI tahun 2012 dimana karya ilmiah dijadikan sebagai syarat kelulusan mahasiswa S1, S2 dan S3. Namun demikian, tidak semua karya ilmiah tersebut memiliki kualitas yang baik. Masih banyak karya ilmiah yang belum memenuhi standar baku Ejaan Yang Disempurnakan (EYD). Pada artikel ini, penulis mengembangkan sebuah kakas bantu untuk mendeteksi kesalahan tanda baca pada karya ilmiah, khususnya yang berbahasa Indonesia, sesuai dengan EYD. Aplikasi dirancang agar dapat mendeteksi kesalahan tanda baca pada tulisan karya ilmiah dengan format .doc atau .docx serta dapat menghasilkan keluaran berupa arsip Microsoft Word dengan tambahan hasil telaah pemeriksaan tanda baca yang dibangkitkan secara otomatis. Deteksi kesalahan tanda baca menggunakan metode pencarian kata dengan algoritma BoyerMoore. Aplikasi kakas bantu telah diuji coba dengan hasil rata-rata nilai presisi sistem sebesar 0,6806, recall sebesar 0,969 dan akurasi sistem sebesar 0,9636. Hasil tersebut menunjukkan bahwa aplikasi sudah mampu mendeteksi adanya kesalahan tanda baca meskipun masih ada keterbatasan deteksi karena tidak semua aturan tanda baca dicakup dalam pemeriksaannya

PENGENALAN SISTEM ISYARAT BAHASA INDONESIA MENGGUNAKAN KOMBINASI FITUR STATIS DAN FITUR DINAMIS LMC BERBASIS L-GCNN

Jumlah karya ilmiah yang dihasilkan oleh akademisi dan peneliti di Indonesia semakin banyak, terutama setelah diterbitkannya surat edaran Dirjen DIKTI tahun 2012 dimana karya ilmiah dijadikan sebagai syarat kelulusan mahasiswa S1, S2 dan S3. Namun demikian, tidak semua karya ilmiah tersebut memiliki kualitas yang baik. Masih banyak karya ilmiah yang belum memenuhi standar baku Ejaan Yang Disempurnakan (EYD). Pada artikel ini, penulis mengembangkan sebuah kakas bantu untuk mendeteksi kesalahan tanda baca pada karya ilmiah, khususnya yang berbahasa Indonesia, sesuai dengan EYD. Aplikasi dirancang agar dapat mendeteksi kesalahan tanda baca pada tulisan karya ilmiah dengan format .doc atau .docx serta dapat menghasilkan keluaran berupa arsip Microsoft Word dengan tambahan hasil telaah pemeriksaan tanda baca yang dibangkitkan secara otomatis. Deteksi kesalahan tanda baca menggunakan metode pencarian kata dengan algoritma BoyerMoore. Aplikasi kakas bantu telah diuji coba dengan hasil rata-rata nilai presisi sistem sebesar 0,6806, recall sebesar 0,969 dan akurasi sistem sebesar 0,9636. Hasil tersebut menunjukkan bahwa aplikasi sudah mampu mendeteksi adanya kesalahan tanda baca meskipun masih ada keterbatasan deteksi karena tidak semua aturan tanda baca dicakup dalam pemeriksaannya

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Artificial Intelligence for Multimedia Signal Processing

Artificial intelligence technologies are also actively applied to broadcasting and multimedia processing technologies. A lot of research has been conducted in a wide variety of fields, such as content creation, transmission, and security, and these attempts have been made in the past two to three years to improve image, video, speech, and other data compression efficiency in areas related to MPEG media processing technology. Additionally, technologies such as media creation, processing, editing, and creating scenarios are very important areas of research in multimedia processing and engineering. This book contains a collection of some topics broadly across advanced computational intelligence algorithms and technologies for emerging multimedia signal processing as: Computer vision field, speech/sound/text processing, and content analysis/information mining

Pengenalan Bahasa Isyarat SIBI Menggunakan Fitur Statis dan Dinamis LMC Berbasis RB-L-GCNN

Proses komunikasi antara penyandang tunarungu dan tunawicara dapat dipahami antar sesama dengan baik karena mereka sudah terbiasa sehari-harinya menggunakan bahasa isyarat. Namun sebagian besar orang normal akan kesulitan untuk memahami bahasa isyarat yang disampaikan oleh penyandang tunarungu dan tunawicara, begitu juga sebaliknya, penyandang tunarungu dan tunawicara akan kesulitan memahami bahasa yang disampaikan oleh orang normal. Untuk mengatasi masalah tersebut maka dibangun sebuah sistem pengenalan bahasa isyarat dengan menggunakan Leap Motion Controller (LMC). Pada penelitian sebelumnya, pengenalan bahasa isyarat American Sign Language (ASL) menggunakan LMC dengan menggunakan fitur yang bersifat statis berdasarkan pada KNN dan SVM memiliki akurasi pengenalan yang cukup baik.Namun metode tersebut hanya dapat mengenal bahasa isyarat yang bersifat statis. Padahal bahasa isyarat ada dua macam yaitu bahasa isyarat yang bersifat statis dan bahasa isyarat yang bersifat dinamis. Selain itu Logarithmic Learning for Generalized Classifier Neural Network (L-GCNN) merupakan metode yang handal dalam menangani klasifikasi data. Namun ketika L-GCNN digunakan pada data yang memiliki kelas yang banyak maka akan terjadi overfitting atau kesulitan dalam menentukan kelas pada data. Pada penelitian ini diusulkan pengenalan bahasa isyarat SIBI yang mengkombinasikan fitur statis dan fitur dinamis dari LMC berdasarkan Rule BasedL-GCNN (RB-L-GCNN). Dimana fitur statis dimanfaatkan untuk pengenalan bahasa isyarat yang bersifat statis, sedangkan fitur dinamis dimanfaatkan untuk mengenal bahasa isyarat yang bersifat dinamis. Rule based dimanfaatkan untuk mengurangi terjadinya overfitting pada metode klasifikasi LGCNN. Dari hasil pengujian yang dilakukan pengenalan bahasa isyarat SIBI dengan menggunakan kombinasi fitur statis dengan fitur dinamis dapat mengenal bahasa isyarat yang bersifat statis maupun bahasa isyarat yang bersifat dinamis. Sedangkan pembentukan rule based pada L-GCNN dapat meningkatkan akurasi pengenalan hingga 6.67%\ ====================================================================================================== The process of communication between the deaf and dumb people can be understood by each other well because they are already familiar to sign language. However, most of normal people will find it hard to understand sign language conveyed by the deaf and dumb people, and vice versa, the deaf and dumb people will have trouble to understand the language conveyed by normal people. To overcome these problems, we will develop a sign language recognition system using Leap Motion Controller (LMC). In previous research, the sign language recognition of American Sign Language (ASL) uses LMC that it uses the static features based on KNN and SVM that has recognition accuracy well. But, these methods can only recognize the static sign language. Where the sign language has two types, static sign language and dynamic sign language. Moreover Logarithmic Learning for Generalized Classifier Neural Network (L-GCNN) is a reliable methods to overcome data classification. But, when L-GCNN is used to data that have many classes, it will occur overfitting in determining the class of the data. In this study, we propose the SIBI sign language recognition which combines static and dynamic features of the LMC based on Rule Based L-GCNN (RB-L-GCNN). The static features is used for the recognition of static sign language, and the dynamic features is used to recognize the dynamic sign language. Rule based is used to reduce the occurrence of overfitting in L-GCNN classification methods. From the results of tests performed SIBIsign language recognition using a combination of static features with dynamic features can recognize static sign language or dynamic sign language. While the establishment of the rule based on L-GCNN can improve recognition accuracy up to 6.67

2D and 3D segmentation of medical images.

"Cardiovascular disease is one of the leading causes of the morbidity and mortality in the western world today. Many different imaging modalities are in place today to diagnose and investigate cardiovascular diseases. Each of these, however, has strengths and weaknesses. There are different forms of noise and artifacts in each image modality that combine to make the field of medical image analysis both important and challenging. The aim of this thesis is develop a reliable method for segmentation of vessel structures in medical imaging, combining the expert knowledge of the user in such a way as to maintain efficiency whilst overcoming the inherent noise and artifacts present in the images. We present results from 2D segmentation techniques using different methodologies, before developing 3D techniques for segmenting vessel shape from a series of images. The main drive of the work involves the investigation of medical images obtained using catheter based techniques, namely Intra Vascular Ultrasound (IVUS) and Optical Coherence Tomography (OCT). We will present a robust segmentation paradigm, combining both edge and region information to segment the media-adventitia, and lumenal borders in those modalities respectively. By using a semi-interactive method that utilizes "soft" constraints, allowing imprecise user input which provides a balance between using the user's expert knowledge and efficiency. In the later part of the work, we develop automatic methods for segmenting the walls of lymph vessels. These methods are employed on sequential images in order to obtain data to reconstruct the vessel walls in the region of the lymph valves. We investigated methods to segment the vessel walls both individually and simultaneously, and compared the results both quantitatively and qualitatively in order obtain the most appropriate for the 3D reconstruction of the vessel wall. Lastly, we adapt the semi-interactive method used on vessels earlier into 3D to help segment out the lymph valve. This involved the user interactive method to provide guidance to help segment the boundary of the lymph vessel, then we apply a minimal surface segmentation methodology to provide segmentation of the valve.