Aplikasi Deteksi Tepi pada Realtime Video Menggunakan Algoritma Canny Detection

Real-time video and image processing is used in a wide variety of applications from video surveillance and traffic management to medical imaging applications. This paper presents the implementation of an canny edge-detection using in realtime video from camera. The Canny algorithm uses an optimal edge detector based on a set of criteria which include finding the most edges by minimizing the error rate, marking edges as closely as possible to the actual edges to maximize localization, and marking edges only once when a single edge exists for minimal response

Face Tracking dan Distance Estimation pada Realtime Video Menggunakan 3d Stereo Vision Camera

Face Tracking dan Distance Estimation pada realtime video menggunakan 3D stereo vision camera yang diajukan dalam paper ini adalah sebuah sistem deteksi wajah dan pengukuran estimasi jarak obyek wajah yang terdeteksi menggunakan 3D stereo vision camera. Dalam penelitian ini dikembangkan sistem untuk deteksi wajah menggunakan Haar Cascade Classifier dan untuk pengukuran estimasi jarak wajah dengan kamera menggunakan proyeksi gambar 2D menjadi 3D. Data 3 dimensi pada stereo vision kamera yang digunakan dapat direkonstruksi menggunakan proyeksi 2 Dimensi dari 2 buah titik kamera pada stereo vision camera. Implementasi deteksi wajah (face tracking) dan estimasi jarak pada realtime video menggunakan stereo vision camera yang diusulkan dapat bekerja untuk mendeteksi setiap obyek wajah manusia dengan baik, dan mampu memberikan estimasi jarak antara obyek wajah yang ditangkap dengan stereo vision camera secara riil. Deteksi wajah dan estimasi jarak wajah yang optimal adalah pada kisaran jarak 51-200cm, dengan deteksi wajah dan estimasi jarak yang ideal adalah pada posisi frontal view. Dari percobaan yang dilakukan dapat dihasilkan sebuah sistem tracking wajah yang robust dan dapat diketahui akurasi perhitungan estimasi jarak dibandingkan dengan jarak riil wajah mencapai 94.74 %

KUALITAS PELAYANAN ADMINISTRASI DESA DI DESA GOLAN KECAMATAN SUKOREJO KABUPATEN PONOROGO TAHUN 2014

Di dalam penyelegaraan administrasi desa Tujuan dari penelitian ini adalah untuk mengetahui bagaimana Kualitas Pelayanan Administrasi Desa di Desa Golan Kecamatan Sukorejo dilaksanakan di Desa Golan Kecamatan Sukorejo Kabupaten Ponorogo. Informan dalam peneliti ini berjumlah 10 orang warga desa golan.Terdiri, Sekertaris desa, dua orang Kepala urusan, satu orang Ketua RT, satu orang ketua RW, dan lima orang tokoh masyarakat.hasil penelitian ini menyimpulkan bahwa kualitas pelayanan administrasi desa di Desa Golan Kecamatan Sukorejo Kabupaten Ponorogo belum bisa memuaskan. Kepala desa pun belum bisa memberikan sebuah perubahan terhadap proses pelayanan administasi terbukti dari hasil wawancara, banyaknya warga masyarakat yang kurang nyaman dalam memperoleh pelayanan di saat mengurus keperluannya. Selain itu waktu yang digunakan dalam proses pelayanan yang masih cenderung lama

Analysis of color features performance using support vector machine with multi-kernel for batik classification

Batik is a sort of cultural heritage fabric that originated in many areas of Indonesia. It can be traced back to many different parts of Indonesia. Each region, particularly Semarang in Central Java, Indonesia, has its Batik design. Unfortunately, due to a lack of knowledge, not all residents can recognize the types of Semarang batik. Therefore, this study proposed an automated method for classifying Semarang batik. Semarang batik was classified into five categories according to this method: Asem Arang, Blekok Warak, Gambang Semarangan, Kembang Sepatu, and Semarangan. It is required to analyze the color features based on the color space to develop discriminative features since color was able to differentiate these batik patterns. Color features were produced based on the RGB, HSV, YIQ, and YCbCr color spaces. Four different kernels were used to feed these features into the Support Vector Machine (SVM) classifier: linear, polynomial, sigmoid, and radial basis functions. The experiment was carried out using a local dataset of 1000 batik images classified into five classes (each class contains 200 images). A cross-validation test with a k-fold value of 10 was performed to analyze the method. In each of the SVM Kernels, the results showed that the proposed method achieved an accuracy value of 100% by utilizing the YIQ color space, which was reliable throughout all the tests

Asymmetrical Half-join Method on Dual Vision Face Recognition

This research proposes a model of face recognition using the method of joining two face images from left and right lens from a stereo vision camera namely half-join method. Half-join method is used in face image normalization processing. The proposed half-join method is a face images joining model, which is called asymmetrical half-join. In asymmetrical half-join method, a RoI (region of interest) of face image from left and right lenses are provided based on axis center of each eye in eye detection. The cropping of face image from asymmetrical half-join model has different width depends on eyes coordinate location. The proposed system shows that the asymmetrical half-join method can produce a better of face recognition rate. The experimental results show that the asymmetrical half-join method has a better recognition rate and computation time than single vision method and symmetrical half-join method

Robust Watermarking through Dual Band IWT and Chinese Remainder Theorem

CRT was a widely used algorithm in the development of watermarking methods. The algorithm produced good image quality but it had low robustness against compression and filtering. This paper proposed a new watermarking scheme through dual band IWT to improve the robustness and preserving the image quality. The high frequency sub band was used to index the embedding location on the low frequency sub band. In robustness test, the CRT method resulted average NC value of 0.7129, 0.4846, and 0.6768 while the proposed method had higher NC value of 0.7902, 0.7473, and 0.8163 in corresponding Gaussian filter, JPEG, and JPEG2000 compression test. Meanwhile the both CRT and proposed method had similar average SSIM value of 0.9979 and 0.9960 respectively in term of image quality. The result showed that the proposed method was able to improve the robustness and maintaining the image quality

Face Recognition Based on Symmetrical Half-Join Method using Stereo Vision Camera

The main problem in face recognition system based on half-face pattern is how to anticipate poses and illuminance variations to improve recognition rate. To solve this problem, we can use two lenses on stereo vision camera in face recognition system. Stereo vision camera has left and right lenses that can be used to produce a 2D image of each lens. Stereo vision camera in face recognition has capability to produce two of 2D face images with a different angle. Both angle of the face image will produce a detailed image of the face and better lighting levels on each of the left and right lenses. In this study, we proposed a face recognition technique, using 2 lens on a stereo vision camera namely symmetrical half-join. Symmetrical half-join is a method of normalizing the image of the face detection on each of the left and right lenses in stereo vision camera, then cropping and merging at each image. Tests on face recognition rate based on the variety of poses and variations in illumination shows that the symmetrical half-join method is able to provide a high accuracy of face recognition and can anticipate variations in given pose and illumination variations. The proposed model is able to produce 86% -97% recognition rate on a variety of poses and variations in angles between 0 °- 22.5 °. The variation of illuminance measured using a lux meter can result in 90% -100% recognition rate for the category of at least dim lighting levels (above 10 lux)

Anti-Cheating Presence System Based on 3WPCA- Dual Vision Face Recognition

To prevent counterfeit face image on face presence system, we can use dual vision camera in face recognition system. Dual vision camera is used to produce detectable face images from two positions of the left lens and the right lens. Image retrieval at the two corners of the left lens and the right lens can produce a merged face image database of left lens face image and right lens face image. The use of two sides of the face angle taking is used to avoid falsification of facial data such as the use of a face photo of a person or an image similar to a person's face. This research uses a dual-vision face recognition method on its preprocessing and uses 3WPCA (Three Level Wavelet Decomposition - Principal Component Analysis) as its feature extraction model. In dual-vision face recognition, we use half-join method to combine a half of the left image and a half of the right image into an image that is ready to be extracted using 3WPCA. This research can produce a presence system based on good face recognition and can be used to anticipate falsification of face data with recognition accuracy up to 98%