Road network extraction with new vectorization and pruning from high-resolution RS images

With the increasing resolution of remote sensing images, road network can be displayed as continuous and homogeneity regions with a certain width rather than traditional thin lines. Therefore, road network extraction from large scale images refers to reliable road surface detection instead of road line extraction. In this paper, a novel automatic road network detection approach based on the combination of homogram segmentation and mathematical morphology is proposed, which includes three main steps: (i) the image is classified based on homogram segmentation to roughly identify the road network regions; (ii) the morphological opening and closing is employed to fill tiny holes and filter out small road branches; and (iii) the extracted road surface is further thinned by a thinning approach, pruned by a proposed method and finally simplified with Douglas-Peucker algorithm. Lastly, the results from some QuickBird images and aerial photos demonstrate the correctness and efficiency of the proposed process.

Extraction of road lanes from high-resolution stereo aerial imagery based on maximum likelihood segmentation and texture enhancement

Accurate road lane information is crucial for advanced vehicle navigation and safety applications. With the increasing of very high resolution (VHR) imagery of astonishing quality provided by digital airborne sources, it will greatly facilitate the data acquisition and also significantly reduce the cost of data collection and updates if the road details can be automatically extracted from the aerial images. In this paper, we proposed an effective approach to detect road lanes from aerial images with employment of the image analysis procedures. This algorithm starts with constructing the (Digital Surface Model) DSM and true orthophotos from the stereo images. Next, a maximum likelihood clustering algorithm is used to separate road from other ground objects. After the detection of road surface, the road traffic and lane lines are further detected using texture enhancement and morphological operations. Finally, the generated road network is evaluated to test the performance of the proposed approach, in which the datasets provided by Queensland department of Main Roads are used. The experiment result proves the effectiveness of our approach.

Understanding nutrient build-up on urban road surfaces

This paper discusses the outcomes of a research project on nutrients build-up on urban road surfaces. Nutrient build-up was investigated on road sites belonging to residential, industrial and commercial land use. Collected build-up samples were separated into five particle size ranges and were tested for total nitrogen (TN), total phosphorus (TP) and sub species of nutrients, namely, NO2-, NO3-, TKN and PO43-. Multivariate analytical techniques were used to analyse the data and to develop detailed understanding on build-up. Data analysis revealed that the solids loads on urban road surfaces are highly influenced by factors such as land use, antecedent dry period and traffic volume. However, the nutrient build-up process was found to be independent of the type of land use. It was solely dependent on the particle size of solids build-up. Most of the nutrients were associated with the particle size range <150 μm. Therefore, the removal of particles below 150 µm from road surfaces is of importance for the removal of nitrogen and phosphorus from road surface solids build-up. It is also important to consider the differences in the composition of nitrogen and phosphorus build-up in the context of designing effective stormwater quality mitigation strategies.

Off-world robotic excavation for Large-scale habitat construction and resource extraction

This paper describes technologies we have developed to perform autonomous large-scale off-world excavation. A scale dragline excavator of size similar to that required for lunar excavation was made capable of autonomous control. Systems have been put in place to allow remote operation of the machine from anywhere in the world. Algorithms have been developed for complete autonomous digging and dumping of material taking into account machine and terrain constraints and regolith variability. Experimental results are presented showing the ability to autonomously excavate and move large amounts of regolith and accurately place it at a specified location.

Cholecystokinin and leptin: their influence upon the eating behaviour and nutrient intake of dialysis patients

Background We have used serial visual analogue scores to demonstrate disturbances of the appetite profile in dialysis patients. This is potentially important as dialysis patients are prone to malnutrition yet have a lower nutrient intake than controls. Appetite disturbance may be influenced by accumulation of appetite inhibitors such as leptin and cholecystokinin (CCK) in dialysis patients. Methods Fasting blood samples were drawn from 43 controls, 50 haemodialysis (HD) and 39 peritoneal dialysis (PD) patients to measure leptin and CCK. Hunger and fullness scores were derived from profiles compiled using hourly visual analogue scores. Nutrient intake was derived from 3 day dietary records. Results Fasting CCK was elevated for PD (6.73 ± 4.42 ng/l vs control 4.99 ± 2.23 ng/l, P < 0.05; vs HD 4.43 ± 2.15 ng/l, P < 0.01). Fasting CCK correlated with the variability of the hunger (r = 0.426, P = 0.01) and fullness (r = 0.52, P = 0.002) scores for PD. There was a notable relationship with the increase in fullness after lunch for PD (r = 0.455, P = 0.006). When well nourished PD patients were compared with their malnourished counterparts, CCK was higher in the malnourished group (P = 0.004). Leptin levels were higher for the dialysis patients than controls (HD and PD, P < 0.001) with pronounced hyperleptinaemia evident in some PD patients. Control leptin levels demonstrated correlation with fullness scores (e.g. peak fullness, r = 0.45, P = 0.007) but the dialysis patients did not. PD nutrient intake (energy and protein intake, r = -0.56, P < 0.0001) demonstrated significant negative correlation with leptin. Conclusion Increased CCK levels appear to influence fullness and hunger perception in PD patients and thus may contribute to malnutrition. Leptin does not appear to affect perceived appetite in dialysis patients but it may influence nutrient intake in PD patients via central feeding centres.

Disturbed appetite and nutrient intake in peritoneal dialysis patients

OBJECTIVE Malnutrition is common among peritoneal dialysis (PD) patients. Reduced nutrient intake contributes to this. It has long been assumed that this reflects disturbed appetite. We set out to define the appetite profiles of a group of PD patients using a novel technique. DESIGN Prospective, cross-sectional comparison of PD patients versus controls. SETTING Teaching hospital dialysis unit. PATIENTS 39 PD patients and 42 healthy controls. INTERVENTION Visual analog ratings were recorded at hourly intervals to generate daily profiles for hunger and fullness. Summary statistics were generated to compare the groups. Food intake was measured using 3-day dietary records. MAIN OUTCOME MEASURES Hunger and fullness profiles. Derived hunger and fullness scores. RESULTS Controls demonstrated peaks of hunger before mealtimes, with fullness scores peaking after meals. The PD profiles had much reduced premeal hunger peaks. A postmeal reduction in hunger was evident, but the rest of the trace was flat. The PD fullness profile was also flatter than in the controls. Mean scores were similar despite the marked discrepancy in the profiles. The PD group had lower peak hunger and less diurnal variability in their hunger scores. They also demonstrated much less change in fullness rating around mealtimes, while the mean and peak fullness scores were little different. The reported nutrient intake was significantly lower for PD. CONCLUSION The data suggest that PD patients normalize their mean appetite perception at a lower level of nutrient intake than controls, suggesting that patient-reported appetite may be misleading in clinical practice. There is a loss of the usual daily variation for the PD group, which may contribute to their reduced food intake. The technique described here could be used to assess the impact of interventions upon the abnormal PD appetite profile.

Fingerprint analysis using wavelet transform with application to compression and feature extraction

The main goal of this research is to design an efficient compression al~ gorithm for fingerprint images. The wavelet transform technique is the principal tool used to reduce interpixel redundancies and to obtain a parsimonious representation for these images. A specific fixed decomposition structure is designed to be used by the wavelet packet in order to save on the computation, transmission, and storage costs. This decomposition structure is based on analysis of information packing performance of several decompositions, two-dimensional power spectral density, effect of each frequency band on the reconstructed image, and the human visual sensitivities. This fixed structure is found to provide the "most" suitable representation for fingerprints, according to the chosen criteria. Different compression techniques are used for different subbands, based on their observed statistics. The decision is based on the effect of each subband on the reconstructed image according to the mean square criteria as well as the sensitivities in human vision. To design an efficient quantization algorithm, a precise model for distribution of the wavelet coefficients is developed. The model is based on the generalized Gaussian distribution. A least squares algorithm on a nonlinear function of the distribution model shape parameter is formulated to estimate the model parameters. A noise shaping bit allocation procedure is then used to assign the bit rate among subbands. To obtain high compression ratios, vector quantization is used. In this work, the lattice vector quantization (LVQ) is chosen because of its superior performance over other types of vector quantizers. The structure of a lattice quantizer is determined by its parameters known as truncation level and scaling factor. In lattice-based compression algorithms reported in the literature the lattice structure is commonly predetermined leading to a nonoptimized quantization approach. In this research, a new technique for determining the lattice parameters is proposed. In the lattice structure design, no assumption about the lattice parameters is made and no training and multi-quantizing is required. The design is based on minimizing the quantization distortion by adapting to the statistical characteristics of the source in each subimage. 11 Abstract Abstract Since LVQ is a multidimensional generalization of uniform quantizers, it produces minimum distortion for inputs with uniform distributions. In order to take advantage of the properties of LVQ and its fast implementation, while considering the i.i.d. nonuniform distribution of wavelet coefficients, the piecewise-uniform pyramid LVQ algorithm is proposed. The proposed algorithm quantizes almost all of source vectors without the need to project these on the lattice outermost shell, while it properly maintains a small codebook size. It also resolves the wedge region problem commonly encountered with sharply distributed random sources. These represent some of the drawbacks of the algorithm proposed by Barlaud [26). The proposed algorithm handles all types of lattices, not only the cubic lattices, as opposed to the algorithms developed by Fischer [29) and Jeong [42). Furthermore, no training and multiquantizing (to determine lattice parameters) is required, as opposed to Powell's algorithm [78). For coefficients with high-frequency content, the positive-negative mean algorithm is proposed to improve the resolution of reconstructed images. For coefficients with low-frequency content, a lossless predictive compression scheme is used to preserve the quality of reconstructed images. A method to reduce bit requirements of necessary side information is also introduced. Lossless entropy coding techniques are subsequently used to remove coding redundancy. The algorithms result in high quality reconstructed images with better compression ratios than other available algorithms. To evaluate the proposed algorithms their objective and subjective performance comparisons with other available techniques are presented. The quality of the reconstructed images is important for a reliable identification. Enhancement and feature extraction on the reconstructed images are also investigated in this research. A structural-based feature extraction algorithm is proposed in which the unique properties of fingerprint textures are used to enhance the images and improve the fidelity of their characteristic features. The ridges are extracted from enhanced grey-level foreground areas based on the local ridge dominant directions. The proposed ridge extraction algorithm, properly preserves the natural shape of grey-level ridges as well as precise locations of the features, as opposed to the ridge extraction algorithm in [81). Furthermore, it is fast and operates only on foreground regions, as opposed to the adaptive floating average thresholding process in [68). Spurious features are subsequently eliminated using the proposed post-processing scheme.

GYAN: A methodology for rule extraction from artificial neural networks

Artificial neural network (ANN) learning methods provide a robust and non-linear approach to approximating the target function for many classification, regression and clustering problems. ANNs have demonstrated good predictive performance in a wide variety of practical problems. However, there are strong arguments as to why ANNs are not sufficient for the general representation of knowledge. The arguments are the poor comprehensibility of the learned ANN, and the inability to represent explanation structures. The overall objective of this thesis is to address these issues by: (1) explanation of the decision process in ANNs in the form of symbolic rules (predicate rules with variables); and (2) provision of explanatory capability by mapping the general conceptual knowledge that is learned by the neural networks into a knowledge base to be used in a rule-based reasoning system. A multi-stage methodology GYAN is developed and evaluated for the task of extracting knowledge from the trained ANNs. The extracted knowledge is represented in the form of restricted first-order logic rules, and subsequently allows user interaction by interfacing with a knowledge based reasoner. The performance of GYAN is demonstrated using a number of real world and artificial data sets. The empirical results demonstrate that: (1) an equivalent symbolic interpretation is derived describing the overall behaviour of the ANN with high accuracy and fidelity, and (2) a concise explanation is given (in terms of rules, facts and predicates activated in a reasoning episode) as to why a particular instance is being classified into a certain category.

Constraints of nutrient availability on primary production in two alpine tundra communities

A nutrient amendment experiment was conducted for two growing seasons in two alpine tundra communities to test the hypotheses that: (1) primary production is limited by nutrient availability, and (2) physiological and developmental constraints act to limit the responses of plants from a nutrient-poor community more than plants from a more nutrient-rich community to increases in nutrient availability. Experimental treatments consisted of N, P, and N+P amendments applied to plots in two physiognomically similar communities, dry and wet meadows. Extractable N and P from soils in nonfertilized control plots indicated that the wet meadow had higher N and P availability. Photosynthetic, nutrient uptake, and growth responses of the dominants in the two communities showed little difference in the relative capacity of these plants to respond to the nutrient additions. Aboveground production responses of the communities to the treatments indicated N availability was limiting to production in the dry meadow community while N and P availability colimited production in the wet meadow community. There was a greater production response to the N and N+P amendments in the dry meadow relative to the wet meadow, despite equivalent functional responses of the dominant species of both communities. The greater production response in the dry meadow was in part related to changes in community structure, with an increase in the proportion of graminoid and forb biomass, and a decrease in the proportion of community biomass made up by the dominant sedge Kobresia myosuroides. Species richness increased significantly in response to the N+P treatment in the dry meadow. Graminoid biomass increased significantly in the wet meadow N and N+P plots, while forb biomass decreased significantly, suggesting a competitive interaction for light. Thus, the difference in community response to nutrient amendments was not the result of functional changes at the leaf level of the dominant species, but rather was related to changes in community structure in the dry meadow, and to a shift from a nutrient to a light limitation of production in the wet meadow.

Automatic domain ontology extraction for context-sensitive opinion mining

Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline.