Penggunaan Principal Component Analysis dan Minimum Distance Classifier untuk Pengenalan Citra Buah

Pengenalan buah dengan alat yang dapat diandalkan merupakan sebuah tantangan, walaupun manusia bisa mengenali buah-buahan hampir dengan tanpa USAha. Untuk mencapai tujuan tersebut, perlu dilakukan studi kepustakaan untuk memahami konsep dan landasan teori agar dapat memperkuat asumsi metode Principal Component Analysis dan Minimum Distance Classifier. Fokus utama dalam penelitian ini adalah bagaimana mendapatkan fitur dari setiap citra buah untuk membedakan buah satu sama lain dengan menerapkan metode Principal Component Analysis (PCA) sebagai ekstraksi ciri, dan Minimum Distance Classifier sebagai algoritma pengenalan sehingga didapatkan hasil pengenalan yang akurat. Hasil pengenalan dengan menggunakan data latih (training data set) mendapatkan keakuratan sebesar 100%, sedangkan hasil pengenalan menggunakan data uji (testing data set) mendapatkan keakuratan sebesar 84%. Sehingga dapat disimpulkan bahwa ekstraksi ciri menggunakan PCA dapat digunakan dalam penerapan algoritma Minimum Distance Classifier untuk pengenalan buah

An FPGA based Efficient Fruit Recognition System Using Minimum Distance Classifier

The paper deals with a simple yet effective fruit identification system developed on an FPGA, SPARTAN 3(XC3S200-5PQ208) platform .The fruits under consideration were apple, banana, sapodilla and strawberry. Out of these selected fruits there were four different classes of apples, two different classes of sapodillas and one class each of the other two fruits. A total of 800 color images, 200 images of each fruit of size 64x64 were used for training. The fruit identification success rate mainly depends on the feature vector and the Classifier used. The 3D feature vector incorporates two first order statistical features and the shape feature. Using the 3D feature vector the MATLAB analysis of The Minimum Distance Classifier (MID) fetched a success rate of 85%.The Verilog coded Hardware platform was developed by burning the COE file of a Test image generated by JAVA ECLIPSE IDE onto the IP core. The MATLAB results were verified using the Hardware Platform. Keywords: RGB image, feature vector, MID, Verilog, FPGA, IP core, COE file

Penggunaan Principal Component Analysis dan Minimum Distance Classifier untuk Pengenalan Citra Buah

Pengenalan buah dengan alat yang dapat diandalkan merupakan sebuah tantangan, walaupun manusia bisa mengenali buah-buahan hampir dengan tanpa usaha. Untuk mencapai tujuan tersebut, perlu dilakukan studi kepustakaan untuk memahami konsep dan landasan teori  agar dapat memperkuat asumsi metode Principal Component Analysis dan Minimum Distance Classifier. Fokus utama dalam penelitian ini adalah bagaimana mendapatkan fitur dari setiap citra buah untuk membedakan buah satu sama lain dengan menerapkan metode Principal Component Analysis (PCA) sebagai ekstraksi ciri, dan Minimum Distance Classifier sebagai algoritma pengenalan sehingga didapatkan hasil pengenalan yang akurat. Hasil pengenalan dengan menggunakan data latih (training data set) mendapatkan keakuratan sebesar 100%, sedangkan hasil pengenalan menggunakan data uji (testing data set) mendapatkan keakuratan sebesar 84%. Sehingga dapat disimpulkan bahwa ekstraksi ciri menggunakan PCA dapat digunakan dalam penerapan algoritma Minimum Distance Classifier untuk pengenalan buah

Multispectral Palmprint Recognition Using Textural Features

In order to utilize identification to the best extent, we need robust and fast algorithms and systems to process the data. Having palmprint as a reliable and unique characteristic of every person, we extract and use its features based on its geometry, lines and angles. There are countless ways to define measures for the recognition task. To analyze a new point of view, we extracted textural features and used them for palmprint recognition. Co-occurrence matrix can be used for textural feature extraction. As classifiers, we have used the minimum distance classifier (MDC) and the weighted majority voting system (WMV). The proposed method is tested on a well-known multispectral palmprint dataset of 6000 samples and an accuracy rate of 99.96-100% is obtained for most scenarios which outperforms all previous works in multispectral palmprint recognition.Comment: 5 pages, Published in IEEE Signal Processing in Medicine and Biology Symposium 201

Recognition of elementary upper limb movements in an activity of daily living using data from wrist mounted accelerometers

In this paper we present a methodology as a proof of concept for recognizing fundamental movements of the humanarm (extension, flexion and rotation of the forearm) involved in ‘making-a-cup-of-tea’, typical of an activity of daily-living (ADL). The movements are initially performed in a controlled environment as part of a training phase and the data are grouped into three clusters using k-means clustering. Movements performed during ADL, forming part of the testing phase, are associated with each cluster label using a minimum distance classifier in a multi-dimensional feature space, comprising of features selected from a ranked set of 30 features, using Euclidean and Mahalonobis distance as the metric. Experiments were performed with four healthy subjects and our results show that the proposed methodology can detect the three movements with an overall average accuracy of 88% across all subjects and arm movement types using Euclidean distance classifier

Robust classification by a nearest mean-median rule for generalized Gaussian pattern distributions

To provide stability of classification, a robust supervised minimum distance classifier based on the minimax (in the Huber sense) estimate of location is designed for the class of generalized Gaussian pattern distributions with a bounded variance. This classifier has the following low-complexity form: with relatively small variances, it is the nearest mean rule (NMean), and with relatively large variances, it is the nearest median rule (NMed). The proposed classifier exhibits good performance both under heavy-and short-tailed pattern distribution