On boundary detection

Given a sample of a random variable supported by a smooth compact manifold $M\subset \mathbb{R}^d$, we propose a test to decide whether the boundary of $M$ is empty or not with no preliminary support estimation. The test statistic is based on the maximal distance between a sample point and the average of its $k_n$-nearest neighbors. We prove that the level of the test can be estimated, that, with probability one, its power is one for $n$ large enough, and that there exists a consistent decision rule. Heuristics for choosing a convenient value for the $k_n$ parameter and identifying observations close to the boundary are also given. We provide a simulation study of the test

SEGMENTASI CITRA DIGITAL MENGGUNAKAN BOUNDARY DETECTION (IMAGE SEGMENTATION USING BOUNDARY DETECTION)

Penggunaan mobile devices yang semakin pesat, mendorong adanya penyediaan layanan yang dapat diakses dengan perangkat tersebut dengan merancang suatu aplikasi wireless, yaitu aplikasi yang dikembangkan untuk digunakan pada mobile devices. Dalam Tugas Akhir ini dirancang suatu sistem yang menyediakan layanan informasi tagihan terpadu, yaitu tagihan telepon rumah, listrik, dan Perusahaan Daerah Air Minum (PDAM) untuk mobile devices, yaitu handphone dengan teknologi J2ME. Pada sistem ini, pelanggan terlebih dahulu harus memasukkan lokasi pengaksesan sistem, yang kemudian akan ditampilkan pilihan nama kota yang mirip dengan nama kota yang diinputkannya, yang dapat dipilih sesuai dengan yang dimaksud dan sistem akan menampilkan id server dan id kota. Selanjutnya pelanggan harus melakukan proses registrasi id ke sistem dengan memasukkan user id dan password sesuai yang diinginkan. Data registrasi yang meliputi id server, id kota, user id (disimpan menjadi satu field) dan password akan disimpan pada server database berdasarkan id server. Proses selanjutnya adalah pendaftaran nomor rekening agar dapat diketahui besar tagihannya sesuai dengan jenis tagihan yang diinginkan, yang akan disimpan dalam server database yang sama dengan data registrasi id. Pelanggan dapat menjaga kerahasiaan data tagihannya dengan menentukan status nomor rekening yang didaftarkannya, yaitu dengan memilih status public (nomor rekening dapat didaftarkan lagi oleh pelanggan lain) atau private (nomor rekening tidak dapat didaftarkan lagi oleh pelanggan lain). Tagihan dan nomor rekening yang didaftarkan bisa lebih dari satu dan mempunyai kode area yang berbeda. Kemudian untuk mengetahui berapa besar tagihannya, pelanggan dapat memilih nomor rekening sesuai dengan yang telah didaftarkannya yang ditampilkan setelah proses login. Akhirnya sistem akan menampilkan jumlah tagihan beserta perinciannya (jika pelanggan menghendaki), serta informasi bank mana saja yang telah ditunjuk sebagai tempat pembayaran dikota dia berada. Dengan adanya sistem ini, diharapkan dapat menghasilkan layanan informasi yang dapat memudahkan pelanggan dalam mengetahui informasi tagihannya. mobile devices, aplikasi wireless , tagihan terpadu, J2ME

Detection boundary in sparse regression

We study the problem of detection of a p-dimensional sparse vector of parameters in the linear regression model with Gaussian noise. We establish the detection boundary, i.e., the necessary and sufficient conditions for the possibility of successful detection as both the sample size n and the dimension p tend to the infinity. Testing procedures that achieve this boundary are also exhibited. Our results encompass the high-dimensional setting (p>> n). The main message is that, under some conditions, the detection boundary phenomenon that has been proved for the Gaussian sequence model, extends to high-dimensional linear regression. Finally, we establish the detection boundaries when the variance of the noise is unknown. Interestingly, the detection boundaries sometimes depend on the knowledge of the variance in a high-dimensional setting

High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and its Applications to High-Level Vision

Most of the current boundary detection systems rely exclusively on low-level features, such as color and texture. However, perception studies suggest that humans employ object-level reasoning when judging if a particular pixel is a boundary. Inspired by this observation, in this work we show how to predict boundaries by exploiting object-level features from a pretrained object-classification network. Our method can be viewed as a "High-for-Low" approach where high-level object features inform the low-level boundary detection process. Our model achieves state-of-the-art performance on an established boundary detection benchmark and it is efficient to run. Additionally, we show that due to the semantic nature of our boundaries we can use them to aid a number of high-level vision tasks. We demonstrate that using our boundaries we improve the performance of state-of-the-art methods on the problems of semantic boundary labeling, semantic segmentation and object proposal generation. We can view this process as a "Low-for-High" scheme, where low-level boundaries aid high-level vision tasks. Thus, our contributions include a boundary detection system that is accurate, efficient, generalizes well to multiple datasets, and is also shown to improve existing state-of-the-art high-level vision methods on three distinct tasks

Multispectral processing based on groups of resolution elements

Several nine-point rules are defined and compared with previously studied rules. One of the rules performed well in boundary areas, but with reduced efficiency in field interiors; another combined best performance on field interiors with good sensitivity to boundary detail. The basic threshold gradient and some modifications were investigated as a means of boundary point detection. The hypothesis testing methods of closed-boundary formation were also tested and evaluated. An analysis of the boundary detection problem was initiated, employing statistical signal detection and parameter estimation techniques to analyze various formulations of the problem. These formulations permit the atmospheric and sensor system effects on the data to be thoroughly analyzed. Various boundary features and necessary assumptions can also be investigated in this manner