人の動作の検出に関する研究 - 異常動作の特定に向けて -

九州工業大学博士学位論文（要旨） 学位記番号:工博甲第383号 学位授与年月日:平成27年3月25

Penghapus Derau Adaptif dengan Algoritma NLMS Ukuran Langkah Adaptasi Tetap dan Berubah

Penghapus derau adaptif adalah penapis optimal yang dapat diterapkan bila masukan referensi tersedia. Keuntungan metode ini adalah kemampuan adaptasi dan distorsi keluaran yang rendah. Penggunaan ukuran langkah yang tepat memberikan kecepatan konvergensi yang tinggi. Penelitian ini memodifikasi ANC klasik sehingga memberikan kecepatan konvergensi yang lebih tinggi dan distorsi sinyal keluaran yang lebih rendah. Seratus iterasi pertama menerapkan ukuran langkah tetap kemudian berubah-ubah untuk iterasi berikutnya. Ukuran langkah modifikasi ini berdasar pada estimasi SNR. Simulasi komputer dengan derau putih, menunjukkan ANC usulan mempunyai distorsi 10 hingga 20 dB dibawah ANC klasik

Abnormal motion detection in an occlusion environment

We present a motion classification approach to detect movements of interest (abnormal motion) based on optical flow. By tracking all feature points of a moving human in successive frames, we calculate the coordinate space and create feature space. This is done directly from the intensity information without explicitly computing the underlying motions. It requires no foreground segmentation, no prior learning of activities, no motion recognition and no object detection. First, we determine the abnormal scene and speed by using the velocity histogram. Then by using k-means clustering over velocity orientation and magnitude, we determine the abnormal direction. The performance of the proposed method is experimentally shown.SICE Annual Conference 2013 - International conference on Instrumentation, Control, Information Technology and System Integration September 14-17, 2013, Nagoya University, Nagoya, Japa

Breast Cancer Detection using Residual Convolutional Neural Network and Weighted Loss

This research presents a breast cancer detection system using deep learning method. Breast cancer detection in a large slide of biopsy image is a hard task because it needs manual observation by a pathologist to find the malignant region. The deep learning model used in this research is made up of multiple layers of the residual convolutional neural network, and instead of using another type of classifier, a multilayer neural network was used as the classifier and stacked together and trained using end-to-end training approach. The system is trained using invasive ductal carcinoma dataset from the Hospital of the University of Pennsylvania and The Cancer Institute of New Jersey. From this dataset, 80% and 20% were randomly sampled and used as training and testing data respectively. Training a neural network on an imbalanced dataset is quite challenging. Weighted loss function was used as the objective function to tackle this problem. We achieve 78.26% and 78.03% for Recall and F1-Score metrics, respectively which are an improvement compared to the previous approach.This research presents a breast cancer detection system using deep learning method. Breast cancer detection in a large slide of biopsy image is a hard task because it needs manual observation by a pathologist to find the malignant region. The deep learning model used in this research is made up of multiple layers of the residual convolutional neural network, and instead of using another type of classifier, a multilayer neural network was used as the classifier and stacked together and trained using end-to-end training approach. The system is trained using invasive ductal carcinoma dataset from the Hospital of the University of Pennsylvania and The Cancer Institute of New Jersey. From this dataset, 80% and 20% were randomly sampled and used as training and testing data respectively. Training a neural network on an imbalanced dataset is quite challenging. Weighted loss function was used as the objective function to tackle this problem. We achieve 78.26% and 78.03% for Recall and F1-Score metrics, respectively which are an improvement compared to the previous approach

Head Detection and Tracking for an Intelligent Room

We present a novel feature extraction method, which employs a histogram of transition feature, as an input to a SVM classifier. This feature relies on foreground extraction. We also evaluate some foreground extraction method. To evaluate the performance of this feature, we use it for head detection. Then, by applying a combination of the Harris corner detector and Lucas-Kanade tracker and motion pattern, we track the head position. The performance of the proposed method is experimentally shown.SICE Annual Conference 2014 - International conference on Instrumentation, Control, Information Technology and System Integration, September 9-12, 2014, Hokkaido University, Sapporo, Japa

Klasifikasi Citra Warna Daun Padi Menggunakan Metode Histogram of S-RGB dan Fuzzy Logic Berbasis Android

 The level of greenish leaves of rice plants is one indicator to analyze the nutrient needs of the rice plant nitrogen required. In the process, one recommended way to determine nitrogen needs for the rice plant is the use Leaf Color Chart (LCC). Given the need for efficiency of time and energy, and to avoid the perception of the color differences are observed, it is important to do the development of a system to facilitate the farmers in determining the nitrogen requirements for rice.This research aims to develop an Android-based system to determine nitrogen needs for the rice crop through image processing concept. The method used is of s-RGB Histograms and Fuzzy Logic. Method of s-RGB Histogram function to extract the characteristic color of rice leaves, while Fuzzy Logic is used to classify images based on 4 levels of rice leaf color on the LCC also to determine the dose of nitrogen necessary for the needs of rice plants.Tests carried out using Samsung's smartphone brands with a capacity of 8 MP camera. The test results and evaluation system using the Confusion Matrix for Multiple Classes showed that the accuracy of the system provide the requested information is considered good enough, that is 88.19%. The success of the system to find the information back to the recall level of 88.25%. Degree of proximity between the predicted value of the system to the actual value of 88.75%, and the level of specificity obtained at 62.12%. While the system achieved computational time average of 10:14 seconds. Keywords- Histogram of s-RGB, Fuzzy Logic, Leaf Color Chart, Confusion Matrix for Multiple Classe

Matching algorithm performance analysis for autocalibration method of stereo vision

Stereo vision is one of the interesting research topics in the computer vision field. Two cameras are used to generate a disparity map, resulting in the depth estimation. Camera calibration is the most important step in stereo vision. The calibration step is used to generate an intrinsic parameter of each camera to get a better disparity map. In general, the calibration process is done manually by using a chessboard pattern, but this process is an exhausting task. Self-calibration is an important ability required to overcome this problem. Self-calibration required a robust and good matching algorithm to find the key feature between images as reference. The purpose of this paper is to analyze the performance of three matching algorithms for the autocalibration process. The matching algorithms used in this research are SIFT, SURF, and ORB. The result shows that SIFT performs better than other methods

Sistem Layanan Informasi Dan Pemesanan Nomor Antrian Menggunakan Media SMS Berbasis Komunikasi Serial Asinkron Multipoint Standar RS-485

Sistem layanan informasi dan pemesanan nomor antrian terpusat melalui media handphone dapat dijadikan sebagai  salah satu solusi untuk mempermudah masyarakat dalam melakukan antrian sehingga aktivitas mereka bisa berjalan dengan baik dan waktu mereka tidak terbuang terlalu lama. Dengan menggunakan sistem ini, nasabah dapat dengan mudah melihat kondisi antrian saat ini dan memesan nomor antrian, yaitu dengan mengirimkan SMS berupa kata “daftar” untuk memesan nomor antrian dan kata “info” untuk mengetahui kondisi antrian ke handphone server. Personal komputer digunakan sebagai pusat pengendalian yang berfungsi untuk mengirim dan menerima data dari hanphone dan dari mikrokontroler pada unit slave. Komunikasi data antara komputer sentral dengan mikrokontroler berjalan dengan menggunakan komunikasi serial asinkron multipoint  dengan baudrate 57600 bps. Komunikasi serial antara komputer sentral dengan handphone berjalan dengan baudrate 19200 bps. Dari hasil pengujian menunjukkan bahwa sistem layanan informasi dan pemesanan nomor antrian dapat bekerja dengan baik. Informasi yang diberikan saat nasabah mendaftar nomor antrian  melalui handphone berupa nomor antrian dan password. Informasi yang diberikan komputer sentral saat nasabah meminta informasi kondisi antrian berupa berupa jumlah nasabah yang terdaftar pada sistem antrian saat ini, nomor antrian yang sedang dilayani pada masing-masing loket, waktu tutup antrian

Identifikasi Takaran Pupuk Nitrogen Berdasarkan Tingkat Kehijauan Daun Tanaman Padi Menggunakan Metode Histogram of s-RGB dan Fuzzy Logic

Abstrak – Analisis warna daun padi merupakan salah satu cara untuk mengidenfikasi kandungan unsur hara yang dibutuhkan sebagai dasar rekomendasi takaran pupuk untuk tanaman padi. Apabila kelebihan nitrogen, maka tanaman padi mudah terserang hama penyakit selain mencemari air tanah. Sebaliknya, jika kekurangan nitrogen, maka pertumbuhannya menjadi tidak normal. Tujuan penelitian ini adalah merancang sistem untuk mengidentifikasi takaran pupuk nitrogen berdasarkan tingkat kehijauan daun tanaman padi melalui konsep pengolahan citra menggunakan metode Histogram of s-RGB dan Fuzzy Logic berbasis android. Pada peneitian ini, Bagan Warna Daun (BWD) merupakan konsep dasar dalam proses pengembangan dan perancangan sistem ini. Sistem dirancang berdasarkan 4 skala warna sesuai level warna BWD agar dapat mengidentifikasi citra daun padi sebagai dasar rekomendasi takaran pupuk nitrogen. Berdasarkan hasil pengujian, diketahui bahwa rata-rata jarak terdekat (euclidean distance) nilai RGB citra daun padi yang dihasilkan sistem terhadap nilai RGB citra level warna BWD sebesar 14,28 pada smartphone 8 MP, sedangkan smartphone 5 MP sebesar 15,44. Hasil evaluasi Confusion Matrix for Multiple Classes menunjukkan bahwa ketepatan sistem memberikan informasi yang diminta pada smartphone 8 MP dinilai lebih baik, yaitu 93,03% dibanding pada smartphone 5 MP sebesar 87,18%. Keberhasilan sistem untuk menemukan informasi kembali pada smartphone 8 MP dinilai lebih unggul dengan tingkat recall sebesar 93,42%, dibanding sistem pada smartphone 5 MP sebesar 86,08%. Tingkat kedekatan antara nilai prediksi sistem dengan nilai aktual pada smartphone 8 MP sebesar 91,03%, sedangkan pada smartphone 5 MP mencapai 88,31%, namun keduanya memiliki specificity yang sama sebesar 66,67%.  Kata Kunci— Histogram of s-RGB, Fuzzy Logic, Euclidean Distance, Confusion Matrix for Multiple Classe