Unsupervised segmentation using Gabor wavelets and statistical features in LIDAR data analysis

In this paper, we address issues in segmentation Of remotely sensed LIDAR (LIght Detection And Ranging) data. The LIDAR data, which were captured by airborne laser scanner, contain 2.5 dimensional (2.5D) terrain surface height information, e.g. houses, vegetation, flat field, river, basin, etc. Our aim in this paper is to segment ground (flat field)from non-ground (houses and high vegetation) in hilly urban areas. By projecting the 2.5D data onto a surface, we obtain a texture map as a grey-level image. Based on the image, Gabor wavelet filters are applied to generate Gabor wavelet features. These features are then grouped into various windows. Among these windows, a combination of their first and second order of statistics is used as a measure to determine the surface properties. The test results have shown that ground areas can successfully be segmented from LIDAR data. Most buildings and high vegetation can be detected. In addition, Gabor wavelet transform can partially remove hill or slope effects in the original data by tuning Gabor parameters.

Optimization algorithms in FMRF model-based segmentation for LIDAR data and co-registered bands

In this paper, a fuzzy Markov random field (FMRF) model is used to segment land-objects into free, grass, building, and road regions by fusing remotely, sensed LIDAR data and co-registered color bands, i.e. scanned aerial color (RGB) photo and near infra-red (NIR) photo. An FMRF model is defined as a Markov random field (MRF) model in a fuzzy domain. Three optimization algorithms in the FMRF model, i.e. Lagrange multiplier (LM), iterated conditional mode (ICM), and simulated annealing (SA), are compared with respect to the computational cost and segmentation accuracy. The results have shown that the FMRF model-based ICM algorithm balances the computational cost and segmentation accuracy in land-cover segmentation from LIDAR data and co-registered bands.

Non-word repetition in adolescents with Specific Language Impairment and Autism plus Language Impairments: a qualitative analysis

Non-word repetition (NWR) was investigated in adolescents with typical development, Specific Language Impairment (SLI) and Autism Plus language Impairment (ALI) (n = 17, 13, 16, and mean age 14;4, 15;4, 14;8 respectively). The study evaluated the hypothesis that poor NWR performance in both groups indicates an overlapping language phenotype (Kjelgaard & Tager-Flusberg, 2001). Performance was investigated both quantitatively, e.g. overall error rates, and qualitatively, e.g. effect of length on repetition, proportion of errors affecting phonological structure, and proportion of consonant substitutions involving manner changes. Findings were consistent with previous research (Whitehouse, Barry, & Bishop, 2008) demonstrating a greater effect of length in the SLI group than the ALI group, which may be due to greater short-term memory limitations. In addition, an automated count of phoneme errors identified poorer performance in the SLI group than the ALI group. These findings indicate differences in the language profiles of individuals with SLI and ALI, but do not rule out a partial overlap. Errors affecting phonological structure were relatively frequent, accounting for around 40% of phonemic errors, but less frequent than straight Consonant-for-Consonant or vowel-for-vowel substitutions. It is proposed that these two different types of errors may reflect separate contributory mechanisms. Around 50% of consonant substitutions in the clinical groups involved manner changes, suggesting poor auditory-perceptual encoding. From a clinical perspective algorithms which automatically count phoneme errors may enhance sensitivity of NWR as a diagnostic marker of language impairment. Learning outcomes: Readers will be able to (1) describe and evaluate the hypothesis that there is a phenotypic overlap between SLI and Autism Spectrum Disorders (2) describe differences in the NWR performance of adolescents with SLI and ALI, and discuss whether these differences support or refute the phenotypic overlap hypothesis, and (3) understand how computational algorithms such as the Levenshtein Distance may be used to analyse NWR data.

On-line handwritten segmentation in linear drawings

This paper addresses the problem of tracking line segments corresponding to on-line handwritten obtained through a digitizer tablet. The approach is based on Kalman filtering to model linear portions of on-line handwritten, particularly, handwritten numerals, and to detect abrupt changes in handwritten direction underlying a model change. This approach uses a Kalman filter framework constrained by a normalized line equation, where quadratic terms are linearized through a first-order Taylor expansion. The modeling is then carried out under the assumption that the state is deterministic and time-invariant, while the detection relies on double thresholding mechanism which tests for a violation of this assumption. The first threshold is based on an approach of layout kinetics. The second one takes into account the jump in angle between the past observed direction of layout and its current direction. The method proposed enables real-time processing. To illustrate the methodology proposed, some results obtained from handwritten numerals are presented.