Studi Akurasi Karakteristik Retina sebagai Future Identification dengan Euclidean Distance Metrics

Penelitian ini menghasilkan sistem keamanan menggunakan biometrik, dengan menggunakan retina sebagai identitas pengenalan yang akurat, serta efektif untuk meningkatkan proses identifikasi pada retina dimasa depan (future identification). Hal ini sangat penting untuk menentukan keakuratan sifat biometrik apa yang paling baik di dalam proses mengidentifikasi di masa depan, sekaligus membangun suatu sistem aplikasi atau tools yang dapat digunakan untuk mengetahui karakteristik distance meterics untuk mengukur akurasi retina sebagai identitas dimasa depan (future identification). Penggunaan retina dapat menjadi salah satu alternatif identifikasi manusia  seperti  untuk  pengganti  PIN  ATM  Bank,  Paspor  dan bidang-bidang lain yang memerlukan tingkat keamanan tinggi atau mustahil untuk dapat dipalsukan. Hasil dari penelitian ini ialah berbentuk pengujian untuk membuktikan tingkat akurasi CBIR dengan menggunakan citra query dengan dibangun database sebanyak 5.000 citra retina. Metode yang akan digunakan dalam menentukan similarity dan identification dengan menggunakan fitur warna. Histogram warna untuk pencarian citra dikerjakan dengan mengitung jumlah koefisien DCT dari setiap warna. Hasil penelitian menunjukan bahwa akurasi algoritma mendekati nilai 90%, akurasi ini cukup bagus di bidang image retrieval.  Di lihat dari kecepatan proses retrieval juga cukup cepat dimana rata –rata kecepatan proses dengan menggunakan 2.000 citra digital adalah kurang dari 10 detik

Towards Learning Representations in Visual Computing Tasks

abstract: The performance of most of the visual computing tasks depends on the quality of the features extracted from the raw data. Insightful feature representation increases the performance of many learning algorithms by exposing the underlying explanatory factors of the output for the unobserved input. A good representation should also handle anomalies in the data such as missing samples and noisy input caused by the undesired, external factors of variation. It should also reduce the data redundancy. Over the years, many feature extraction processes have been invented to produce good representations of raw images and videos. The feature extraction processes can be categorized into three groups. The first group contains processes that are hand-crafted for a specific task. Hand-engineering features requires the knowledge of domain experts and manual labor. However, the feature extraction process is interpretable and explainable. Next group contains the latent-feature extraction processes. While the original feature lies in a high-dimensional space, the relevant factors for a task often lie on a lower dimensional manifold. The latent-feature extraction employs hidden variables to expose the underlying data properties that cannot be directly measured from the input. Latent features seek a specific structure such as sparsity or low-rank into the derived representation through sophisticated optimization techniques. The last category is that of deep features. These are obtained by passing raw input data with minimal pre-processing through a deep network. Its parameters are computed by iteratively minimizing a task-based loss. In this dissertation, I present four pieces of work where I create and learn suitable data representations. The first task employs hand-crafted features to perform clinically-relevant retrieval of diabetic retinopathy images. The second task uses latent features to perform content-adaptive image enhancement. The third task ranks a pair of images based on their aestheticism. The goal of the last task is to capture localized image artifacts in small datasets with patch-level labels. For both these tasks, I propose novel deep architectures and show significant improvement over the previous state-of-art approaches. A suitable combination of feature representations augmented with an appropriate learning approach can increase performance for most visual computing tasks.Dissertation/ThesisDoctoral Dissertation Computer Science 201