Curvature scale space corner detector with adaptive threshold and dynamic region of support

Corners play an important role in object identification methods used in machine vision and image processing systems. Single-scale feature detection finds it hard to detect both fine and coarse features at the same time. On the other hand, multi-scale feature detection is inherently able to solve this problem. This paper proposes an improved multi-scale corner detector with dynamic region of support, which is based on Curvature Scale Space (CSS) technique. The proposed detector first uses an adaptive local curvature threshold instead of a single global threshold as in the original and enhanced CSS methods. Second, the angles of corner candidates are checked in a dynamic region of support for eliminating falsely detected corners. The proposed method has been evaluated over a number of images and compared with some popular corner detectors. The results showed that the proposed method offers a robust and effective solution to images containing widely different size features.published_or_final_versio

ANALISIS DAN IMPLEMENTASI DETEKSI SUDUT PADA CITRA DIGITAL MENGGUNAKAN METODE CURVATURE SCALE SPACE (CSS) YANG DIKOMBINASIKAN DENGAN DETEKSI SISI CANNY

ABSTRAKSI: Pendeteksian sudut pada citra digital bertujuan untuk menemukan semua sudut dalam suatu citra digital dan mengenali suatu pola yang terdapat pada citra itu. Dengan dikenalinya sudut pada sebuah citra maka akan mudah diperoleh informasi-informasi pada sebuah citra. Curvature Scale Space (CSS) merupakan salah satu metode dalam mendeteksi sudut pada sebuah citra digital yang memiliki keunggulan dalam menangani citra yang memiliki intensitas noise yang relatif tinggi dan T-junction. Pada tugas akhir ini, penulis akan melakukan analisis performansi metode Curvature Scale Space (CSS) pada beberapa nilai parameter berdasarkan detection rate dan error detection.Pengujian dan hasil analisis menunjukkan bahwa variasi nilai parameter seperti sigma, threshold, curvature, dan sudut memberikan pengaruh yang penting terhadap performansi CSS. Pada semua pengujian, citra hitam-putih memberikan hasil yang lebih baik dari citra greyscale. Pemilihan nilai parameter yang tepat dapat meningkatkan hasil deteksi sudut CSS.Kata Kunci : citra, noise, Canny, deteksi sudut, CSSABSTRACT: Corner detection of digital image aims to find all corners in an image and to recognize the pattern that belongs to it. If corners in an image are recognized, its information will be retrieved easily. Curvature Scale Space (CSS) is one of methods to detect corners in digital image which has excellence in handling images with high intensity noise and T-junction. This final project would analyze performance of Curvature Scale Space (CSS) with some parameters values based on detection rate and error detection.The tests and analysis results showed that variation of parameters value like sigma, threshold, curvature, and angle give important influences to CSS’s performance. In whole tests, black and white image gave better results than greyscale images. Choosing the correct parameters value can increase CSS’s performance.Keyword: image, noise, Canny, corner detection, CS

The Local Structure of Space-Variant Images

Local image structure is widely used in theories of both machine and biological vision. The form of the differential operators describing this structure for space-invariant images has been well documented (e.g. Koenderink, 1984). Although space-variant coordinates are universally used in mammalian visual systems, the form of the operators in the space-variant domain has received little attention. In this report we derive the form of the most common differential operators and surface characteristics in the space-variant domain and show examples of their use. The operators include the Laplacian, the gradient and the divergence, as well as the fundamental forms of the image treated as a surface. We illustrate the use of these results by deriving the space-variant form of corner detection and image enhancement algorithms. The latter is shown to have interesting properties in the complex log domain, implicitly encoding a variable grid-size integration of the underlying PDE, allowing rapid enhancement of large scale peripheral features while preserving high spatial frequencies in the fovea.Office of Naval Research (N00014-95-I-0409

Performance Assessment of Feature Detection Algorithms: A Methodology and Case Study on Corner Detectors

In this paper we describe a generic methodology for evaluating the labeling performance of feature detectors. We describe a method for generating a test set and apply the methodology to the performance assessment of three well-known corner detectors: the Kitchen-Rosenfeld, Paler et al. and Harris-Stephens corner detectors. The labeling deficiencies of each of these detectors is related to their discrimination ability between corners and various of the features which comprise the class of noncorners

Automatic landmark annotation and dense correspondence registration for 3D human facial images

Dense surface registration of three-dimensional (3D) human facial images holds great potential for studies of human trait diversity, disease genetics, and forensics. Non-rigid registration is particularly useful for establishing dense anatomical correspondences between faces. Here we describe a novel non-rigid registration method for fully automatic 3D facial image mapping. This method comprises two steps: first, seventeen facial landmarks are automatically annotated, mainly via PCA-based feature recognition following 3D-to-2D data transformation. Second, an efficient thin-plate spline (TPS) protocol is used to establish the dense anatomical correspondence between facial images, under the guidance of the predefined landmarks. We demonstrate that this method is robust and highly accurate, even for different ethnicities. The average face is calculated for individuals of Han Chinese and Uyghur origins. While fully automatic and computationally efficient, this method enables high-throughput analysis of human facial feature variation.Comment: 33 pages, 6 figures, 1 tabl