Real-Time Anisotropic Diffusion using Space-Variant Vision

Many computer and robot vision applications require multi-scale image analysis. Classically, this has been accomplished through the use of a linear scale-space, which is constructed by convolution of visual input with Gaussian kernels of varying size (scale). This has been shown to be equivalent to the solution of a linear diffusion equation on an infinite domain, as the Gaussian is the Green's function of such a system (Koenderink, 1984). Recently, much work has been focused on the use of a variable conductance function resulting in anisotropic diffusion described by a nonlinear partial differential equation (PDF). The use of anisotropic diffusion with a conductance coefficient which is a decreasing function of the gradient magnitude has been shown to enhance edges, while decreasing some types of noise (Perona and Malik, 1987). Unfortunately, the solution of the anisotropic diffusion equation requires the numerical integration of a nonlinear PDF which is a costly process when carried out on a fixed mesh such as a typical image. In this paper we show that the complex log transformation, variants of which are universally used in mammalian retino-cortical systems, allows the nonlinear diffusion equation to be integrated at exponentially enhanced rates due to the non-uniform mesh spacing inherent in the log domain. The enhanced integration rates, coupled with the intrinsic compression of the complex log transformation, yields a seed increase of between two and three orders of magnitude, providing a means of performing real-time image enhancement using anisotropic diffusion.Office of Naval Research (N00014-95-I-0409

Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping

This work investigates the use of semantic information to link ground level occupancy maps and aerial images. A ground level semantic map, which shows open ground and indicates the probability of cells being occupied by walls of buildings, is obtained by a mobile robot equipped with an omnidirectional camera, GPS and a laser range finder. This semantic information is used for local and global segmentation of an aerial image. The result is a map where the semantic information has been extended beyond the range of the robot sensors and predicts where the mobile robot can find buildings and potentially driveable ground

Effective Cloud Detection and Segmentation using a Gradient-Based Algorithm for Satellite Imagery; Application to improve PERSIANN-CCS

Being able to effectively identify clouds and monitor their evolution is one important step toward more accurate quantitative precipitation estimation and forecast. In this study, a new gradient-based cloud-image segmentation technique is developed using tools from image processing techniques. This method integrates morphological image gradient magnitudes to separable cloud systems and patches boundaries. A varying scale-kernel is implemented to reduce the sensitivity of image segmentation to noise and capture objects with various finenesses of the edges in remote-sensing images. The proposed method is flexible and extendable from single- to multi-spectral imagery. Case studies were carried out to validate the algorithm by applying the proposed segmentation algorithm to synthetic radiances for channels of the Geostationary Operational Environmental Satellites (GOES-R) simulated by a high-resolution weather prediction model. The proposed method compares favorably with the existing cloud-patch-based segmentation technique implemented in the PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network - Cloud Classification System) rainfall retrieval algorithm. Evaluation of event-based images indicates that the proposed algorithm has potential to improve rain detection and estimation skills with an average of more than 45% gain comparing to the segmentation technique used in PERSIANN-CCS and identifying cloud regions as objects with accuracy rates up to 98%

Ekstraksi Jalan Raya Berdasarkan Pendeteksian Zebra Cross Dari Foto Udara Beresolusi Sangat Tinggi Dan Data DSM

Terdapat beberapa strategi pada proses ekstraksi jalan seperti segmentasi citra, deteksi garis, evolusi kurva, multi-resolution, kecerdasan buatan, deteksi tepi, roadtracking, operasi morfologi, pengenalan objek. Semua strategi dari ekstraksi jalan sangat tergantung pada karakteristik data. Foto udara dan data ketinggian menjadi data pada tugas akhir ini. Mengintegrasikan foto udara dan data ketinggian akan menyelesaikan kelemahan masing-masing data dalam pengekstraksian jalan. Pada Tugas Akhir ini digunakan pendeteksian zebra cross sebagai langkah awal untuk mengenali jalan, dilanjutkan dengan threshold, region growing, dan yang terakhir, road line filtering untuk mengekstraksi jalan. Dari hasil uji coba didapatkan hasil quality pengekstraksian jalan yang dilakukan terbaik sebesar 42,2%., persentase jalan yang terdeteksi sebesar 86,5%, dan persentase objek bukan jalan yang terdeteksi sebesar 71,1% yang menggunakan metode threshold dan road line filtering. ================================================================================================================== There are some strategies in term of road extraction process such as: Image segmentation, edge detection, curve evolution, multi-resolution, artificial intelligent, line detection, road tracking, morphology operation, object recognition. All strategies of road extraction are very depend on the characteristic of the data. RGB aerial image and DSM data be the input data in this undergraduate thesis. Integrating the aerial image and elevation based data will overcome the shortcomings and weaknesses of each type of data in road extraction. In this undergraduate thesis, to recognize the road used zebra cross detection as initial step, and then used threshold, region growing, and the final step is road line filtering to extract the road. From experimental results, the quality of road extraction is 42,2%, percentage of detected road is 86,5%, and percentage of detected another object (non road) is 71,1% which used threshold and region growing method

On Using Physical Analogies for Feature and Shape Extraction in Computer Vision

There is a rich literature of approaches to image feature extraction in computer vision. Many sophisticated approaches exist for low- and high-level feature extraction but can be complex to implement with parameter choice guided by experimentation, but impeded by speed of computation. We have developed new ways to extract features based on notional use of physical paradigms, with parameterisation that is more familiar to a scientifically-trained user, aiming to make best use of computational resource. We describe how analogies based on gravitational force can be used for low-level analysis, whilst analogies of water flow and heat can be deployed to achieve high-level smooth shape detection. These new approaches to arbitrary shape extraction are compared with standard state-of-art approaches by curve evolution. There is no comparator operator to our use of gravitational force. We also aim to show that the implementation is consistent with the original motivations for these techniques and so contend that the exploration of physical paradigms offers a promising new avenue for new approaches to feature extraction in computer vision

A high-efficiency spin-resolved phototemission spectrometer combining time-of-flight spectroscopy with exchange-scattering polarimetry

We describe a spin-resolved electron spectrometer capable of uniquely efficient and high energy resolution measurements. Spin analysis is obtained through polarimetry based on low-energy exchange scattering from a ferromagnetic thin-film target. This approach can achieve a similar analyzing power (Sherman function) as state-of-the-art Mott scattering polarimeters, but with as much as 100 times improved efficiency due to increased reflectivity. Performance is further enhanced by integrating the polarimeter into a time-of-flight (TOF) based energy analysis scheme with a precise and flexible electrostatic lens system. The parallel acquisition of a range of electron kinetic energies afforded by the TOF approach results in an order of magnitude (or more) increase in efficiency compared to hemispherical analyzers. The lens system additionally features a 90{\deg} bandpass filter, which by removing unwanted parts of the photoelectron distribution allows the TOF technique to be performed at low electron drift energy and high energy resolution within a wide range of experimental parameters. The spectrometer is ideally suited for high-resolution spin- and angle-resolved photoemission spectroscopy (spin-ARPES), and initial results are shown. The TOF approach makes the spectrometer especially ideal for time-resolved spin-ARPES experiments.Comment: 16 pages, 11 figure

HST and Spitzer Observations of the HD 207129 Debris Ring

A debris ring around the star HD 207129 (G0V; d = 16.0 pc) has been imaged in scattered visible light with the ACS coronagraph on the Hubble Space Telescope and in thermal emission using MIPS on the Spitzer Space Telescope at 70 microns (resolved) and 160 microns (unresolved). Spitzer IRS (7-35 microns) and MIPS (55-90 microns) spectrographs measured disk emission at >28 microns. In the HST image the disk appears as a ~30 AU wide ring with a mean radius of ~163 AU and is inclined by 60 degrees from pole-on. At 70 microns it appears partially resolved and is elongated in the same direction and with nearly the same size as seen with HST in scattered light. At 0.6 microns the ring shows no significant brightness asymmetry, implying little or no forward scattering by its constituent dust. With a mean surface brightness of V=23.7 mag per square arcsec, it is the faintest disk imaged to date in scattered light.Comment: 28 pages, 8 figure