Benthic and substrate cover data derived from a time series of photo-transect surveys for the Eastern Banks, Moreton Bay Australia, 2004-2015

This paper describes seagrass species and percentage cover point-based field data sets derived from georeferenced photo transects. Annually or biannually over a ten year period (2004-2015) data sets were collected using 30-50 transects, 500-800 m in length distributed across a 142 km**2 shallow, clear water seagrass habitat, the Eastern Banks, Moreton Bay, Australia. Each of the eight data sets include seagrass property information derived from approximately 3000 georeferenced, downward looking photographs captured at 2-4 m intervals along the transects. Photographs were manually interpreted to estimate seagrass species composition and percentage cover (Coral Point Count excel; CPCe). Understanding seagrass biology, ecology and dynamics for scientific and management purposes requires point-based data on species composition and cover. This data set, and the methods used to derive it are a globally unique example for seagrass ecological applications. It provides the basis for multiple further studies at this site, regional to global comparative studies, and, for the design of similar monitoring programs elsewhere.

DAEDALUS at ImageCLEF 2011 Plant Identification Task: Using SIFT Keypoints for Object Detection

This paper describes the participation of DAEDALUS at ImageCLEF 2011 Plant Identification task. The task is evaluated as a supervised classification problem over 71 tree species from the French Mediterranean area used as class labels, based on visual content from scan, scan-like and natural photo images. Our approach to this task is to build a classifier based on the detection of keypoints from the images extracted using Lowe’s Scale Invariant Feature Transform (SIFT) algorithm. Although our overall classification score is very low as compared to other participant groups, the main conclusion that can be drawn is that SIFT keypoints seem to work significantly better for photos than for the other image types, so our approach may be a feasible strategy for the classification of this kind of visual content.

Iwo Jima Photo Album, Image 2

Jack D. Cheesman signature and unit identification--V.A.C. (V Amphibious Corps)

Identification and characterization of shallow impurity states in gallium arsenide and indium phosphide using photothermal ionization spectroscopy

A model of far infrared (FIR) dielectric response of shallow impurity states in a semiconductor has been developed and is presented for the specific case of the shallow donor transitions in high purity epitaxial GaAs. The model is quite general, however, and should be applicable with slight modification, not only to shallow donors in other materials such as InP, but also to shallow acceptors and excitons. The effects of the enormous dielectric response of shallow donors on the FIR optical properties of reflectance, transmittance, and absorptance, and photoconductive response of high purity epitaxial GaAs films are predicted and compared with experimental photothermal ionization spectra. The model accounts for many of the peculiar features that are frequently observed in these spectra, one of which was the cause of erroneous donor identifications in the early doping experiments. The model also corrects some commonly held misconceptions concerning photo-thermal ionization peak widths and amplitudes and their relationships to donor and acceptor concentrations. These corrections are of particular relevance to the proper interpretation of photothermal ionization spectra in the study of impurity incorporation in high purity epitaxial material. The model also suggests that the technique of FIR reflectance, although it has not been widely employed, should be useful in the study of shallow impurities in semiconductors.

Texture for Script identification

The problem of determining the script and language of a document image has a number of important applications in the field of document analysis, such as indexing and sorting of large collections of such images, or as a precursor to optical character recognition (OCR). In this paper, we investigate the use of texture as a tool for determining the script of a document image, based on the observation that text has a distinct visual texture. An experimental evaluation of a number of commonly used texture features is conducted on a newly created script database, providing a qualitative measure of which features are most appropriate for this task. Strategies for improving classification results in situations with limited training data and multiple font types are also proposed.