In this thesis, we present an algorithm for vision based detection of moving objects observed by IR sensors on a moving platform. In addition we analyze the performance of different approaches in each step of the algorithm. The proposed algorithm is composed of preprocessing, feature detection, feature matching, homography estimation and difference image analysis steps. First, a global motion estimation based on planar homography model is performed in order to compensate the motion of the sensor and moving platform where the sensors are located. Then, moving objects are identified on difference images of consecutive video frames with global motion suppression. Performance of the proposed algorithm is shown on different IR image sequences.

The ultimate goal of the development of aerial photogrammery is to acquire rapidly and accurately the ground measurements. However, traditional photogrammetric technologies, particularly in the continuous digital images stitching technique, is still very limited. In the past, the ground control points were used as the references for the image registration, however, it is very time and resource consuming, as well as human visual capability constraint. Accuracy and efficiency are two key factors which need to be enhanced to meet the practical requirement for aerial imageries stitching. The SIFT (Sale Invariant Feature Transform) algorithm was used in the computer vision to perform feature extraction in good condition. The extracted SIFT features are invariant to image scale, rotation, noise and change in illumination, and it is a robust and abundant feature extraction algorithm. SIFT algorithm extracts feature points from multi-scale space. For a large scale aerial image containing huge amount of image contents, it will spend a lot of time to extract features from imagery. Therefore, this study proposes a new method, called Inter-Grid Down-Sampling (IGDS) method, to reduce the image size and relative amount of image information to improve the computing efficiency. The correspondent extracted features are matched in the adjacent images with additional RANSAC outlier removal procedure to select correct and characteristic feature points. Finally the Hugin-Panorama Photo Stitching software is used to stitch all the continuous photogrammetric images for producing a panorama imagery of all flight lines. The experiment results indicate that sub-pixel accuracy for extracted feature points can be obtained when the down-sampling factor 3 was selected for the IGDS method, and it only needs half of the computing time. Compared to the Nearest-Neighbor Interpolation and Cubic Interpolation methods to reduce the image size, the IGDS method can increase more feature extraction efficiency without scarifying the location accuracy. When threshold value for SIFT was set between 0.4 to 0.6, we can achieve the largest correct matching rate. In addition, the RANSAC outlier removal procedure can effectively select the best matching feature points both in numbers and locations. For image stitching, the Hugin-panorama photo stitching software can effectively be used to match feature points and do geometric correction and color adjustment to obtain a consistent panorama imagery. Finally, the proposed method in this study can derive a low-variant in resolution and measurements significance for a stitching image from continuous aerial images.

Localization is an important problem that must be resolved in order for a robot to make an estimation of its location based on observation and odometry updates. Relying on artificial landmarks such as the lines, circles, and goalposts in the robot soccer domain, current robot localization requires prior knowledge and suffers from uncertainty problems due to partial observation, and thus is less generalizable compared to human beings, who refer to their surroundings for complimentary information. To improve the certainty of the localization model, we propose a framework that recognizes orientation by actively using natural landmarks from the off-field surroundings, extracting these visual features from raw images. Our approach involves identifying visual features and natural landmarks, training with localization information to understand the surroundings, and prediction based on matching of features. This approach can increase the precision of robot orientation and improve localization accuracy by eliminating uncertain hypotheses, and in addition, it is also a general approach that can be extended and applied to other localization problems as well. / text

Whole grain is associated with rougher texture and off-flavours which has decreased consumer acceptance. Pasta drying is also critical during production because of influence on texture and quality. The effects of drying type and whole grain content on physicochemical and qualitative properties of pasta were investigated. Increasing whole grain content lowered paste viscosity and increased cooking loss while low temperature drying improved quality. The impact of these effects on pasta texture and flavour was further explored. While bitterness, branniness, and surface roughness positively correlated with whole grain content, drying type only affected firmness. Phenolic content, headspace, and textural analysis corroborated this data. Change in pasta flavour upon addition of sodium-reduced cheese sauce was also examined. Sodium not only enhances flavour of dishes, it also suppresses bitterness. While flavours were uniquely affected upon sauce addition, sodium content did not affect bitterness. Headspace analysis using SIFT-MS showed volatile concentration to reduce, likely caused by a barrier created from the sauce. / MITACS Accelerate, Kraft Mississauga Mill

The application of Selected Ion Flow Tube Mass Spectrometry (SIFT MS) to the analysis of olive oil shows several distinct advantages over more conventional analysis techniques. The two areas described in this thesis examining olive oil quality are the analysis of Volatile Organic Compounds (VOCs) and the assessment of antioxidant activity. VOCs are responsible for the aroma and much of the taste of olive oil, while antioxidants afford some protection from harmful reactions involving radical species inside the body by scavenging radicals when olive oil is ingested. The VOCs of olive oil are used by sensory panel judges to classify oils by their degree of suitability for human consumption. The major parameters used for this evaluation are the strengths of any defects and the degree of fruitiness. A defect is an indication of an undesired process which has occurred in the oil, while fruitiness is a fragile attribute which denotes a good quality oil and is easily masked by defects. SIFT MS was used to measure the strengths of the olive oil defects rancid, winey, musty, fusty and muddy. Great potential was demonstrated for all defects except musty and the concentrations of VOCs in olive oil head space were correlated with the peroxide value, a measure of the degree of oil oxidation. A study aimed at correlating the strength of the fruitiness attribute as determined by a sensory panel with the concentrations of VOCs in olive oil head space was unsuccessful. The SIFT MS Total Oxyradical Scavenging Capacity (TOSC) assay was used to measure olive oil antioxidants. This assay measures all antioxidants in oil, not only those removed by extraction with a solvent, as it is conducted in an emulsion. SIFT MS TOSC assay results were found to correlate well with those of the widely used Folin Ciocalteu assay and the total concentration of phenolic compounds present in olive oil. Discrepancies between the two assays were most likely due to hydrophobic antioxidants which are measured by the SIFT MS TOSC assay but not the other tests.

Data is presented for a range of experiments that have been performed using a selected ion flow tube (SIFT) instrument operated at room temperature (~ 298K) with carrier gas pressures typically in the range of 0.3 – 0.6 Torr. The majority of the experiments discussed are performed on a Voice100 instrument that has not been described in detail previously. The Voice100 is a novel instrument that has been designed particularly for quantitative trace gas analysis using the SIFT-MS technique. A mixture of helium and argon carrier gases are employed in the Voice100 flow tube. By mixing carrier gases, the flow dynamics and diffusion characteristics of a flow tube are altered when compared to classic single carrier gas models. Therefore firstly, optimal flow conditions for the operation of a Voice100 are characterised. The diffusion of an ion in a mixture of carrier gases is then characterised using theoretical models and experimental techniques. This research requires that a new parameter Mp be defined regarding the mass discrimination of an ion in the non-field-free region near the downstream ion sampling orifice. Furthermore, a new method is described for the simultaneous measurement of rate coefficients for the reactions of H₃O⁺.(H₂O)n (n = 1, 2, 3) ions with analytes. Rate coefficients and branching ratios for the reactions of SIFT-MS precursor ions with specific analytes related to four individual applications are presented. For each application, the kinetic parameters are determined so as to facilitate the quantitative detection of the analytes relevant to that application. The GeoVOC application involves the measurement of hydrocarbon concentrations in the headspace of soil and water across a range of humidities. Alkyl esters are investigated to allow for the quantitative detection of each compound in fruits and vegetables. Chemical warfare agents, their surrogates and precursor compounds are studied which allows for the quantitative or semi-quantitative detection of a range of highly toxic compounds. Finally, 17 compounds classified by the US-EPA as hazardous air pollutants are studied that enables SIFT-MS instruments to replicate sections of the TO-14A and TO-15 methods.

Selected Ion Flow Tube-Mass spectrometry (SIFT-MS) is an analytical measurement technology for the real-time quantification of volatile organic compounds in gaseous samples. This technology has current and potential applications in a wide variety of industries, although the focus of this research is in medical science. In this field, SIFT-MS has potential as a diagnostic device, capable of determining the presence of a particular disease or condition. In addition, SIFT-MS can be used to monitor the progression of a disease state, or predict deviations from expected behaviour. Lastly, SIFT-MS can be used for the identification of biomarkers of a particular disease state. All these possibilities are available non-invasively and in real-time, by analysing breath samples. SIFT-MS produces an extensive amount of data, requiring specific mathematical methods to identify biomarker masses that differ significantly between populations or time-points. Two classification methods are presented for the analysis of SIFT-MS mass scan data. The first method is a cross-sectional classification model, intended to differentiate between the diseased and non-diseased state. This model was validated in a simple test case. The second method is a longitudinal classification model, intended to identify key biomarkers that change over time, or in response to treatment. Both of these classification models were validated in 2 clinical trials, investigating renal function in humans and rats. The first clinical trial monitored changes in breath ammonia, TMA and acetone concentrations over the course of dialysis treatment. Correlations with the current gold standard plasma creatinine, and blood urea nitrogen were reported. Finally, biomarkers of renal function were identified that change predictably over the course of treatment. The second trial induced acute renal failure in rats, and monitored the change in renal function observed during recovery. For comparison and validation of the result, a 2-compartment model was developed for estimating renal function via a bolus injection of a radio-labelled inulin tracer, and was compared with the current gold standard plasma creatinine measurement, modified using the Cockcroft-Gault equation for rats. These two methods were compared with SIFT-MS monitoring of breath analytes, to examine the potential for non-invasive biomarkers of kidney function. Results show good promise for the non-invasive, real-time monitoring of breath analytes for diagnosis and monitoring of kidney function, and, potentially, other disease states.

The application of Selected Ion Flow Tube Mass Spectrometry (SIFT MS) to the analysis of olive oil shows several distinct advantages over more conventional analysis techniques. The two areas described in this thesis examining olive oil quality are the analysis of Volatile Organic Compounds (VOCs) and the assessment of antioxidant activity. VOCs are responsible for the aroma and much of the taste of olive oil, while antioxidants afford some protection from harmful reactions involving radical species inside the body by scavenging radicals when olive oil is ingested. The VOCs of olive oil are used by sensory panel judges to classify oils by their degree of suitability for human consumption. The major parameters used for this evaluation are the strengths of any defects and the degree of fruitiness. A defect is an indication of an undesired process which has occurred in the oil, while fruitiness is a fragile attribute which denotes a good quality oil and is easily masked by defects. SIFT MS was used to measure the strengths of the olive oil defects rancid, winey, musty, fusty and muddy. Great potential was demonstrated for all defects except musty and the concentrations of VOCs in olive oil head space were correlated with the peroxide value, a measure of the degree of oil oxidation. A study aimed at correlating the strength of the fruitiness attribute as determined by a sensory panel with the concentrations of VOCs in olive oil head space was unsuccessful. The SIFT MS Total Oxyradical Scavenging Capacity (TOSC) assay was used to measure olive oil antioxidants. This assay measures all antioxidants in oil, not only those removed by extraction with a solvent, as it is conducted in an emulsion. SIFT MS TOSC assay results were found to correlate well with those of the widely used Folin Ciocalteu assay and the total concentration of phenolic compounds present in olive oil. Discrepancies between the two assays were most likely due to hydrophobic antioxidants which are measured by the SIFT MS TOSC assay but not the other tests.

Data is presented for a range of experiments that have been performed using a selected ion flow tube (SIFT) instrument operated at room temperature (~ 298K) with carrier gas pressures typically in the range of 0.3 – 0.6 Torr. The majority of the experiments discussed are performed on a Voice100 instrument that has not been described in detail previously. The Voice100 is a novel instrument that has been designed particularly for quantitative trace gas analysis using the SIFT-MS technique. A mixture of helium and argon carrier gases are employed in the Voice100 flow tube. By mixing carrier gases, the flow dynamics and diffusion characteristics of a flow tube are altered when compared to classic single carrier gas models. Therefore firstly, optimal flow conditions for the operation of a Voice100 are characterised. The diffusion of an ion in a mixture of carrier gases is then characterised using theoretical models and experimental techniques. This research requires that a new parameter Mp be defined regarding the mass discrimination of an ion in the non-field-free region near the downstream ion sampling orifice. Furthermore, a new method is described for the simultaneous measurement of rate coefficients for the reactions of H₃O⁺.(H₂O)n (n = 1, 2, 3) ions with analytes. Rate coefficients and branching ratios for the reactions of SIFT-MS precursor ions with specific analytes related to four individual applications are presented. For each application, the kinetic parameters are determined so as to facilitate the quantitative detection of the analytes relevant to that application. The GeoVOC application involves the measurement of hydrocarbon concentrations in the headspace of soil and water across a range of humidities. Alkyl esters are investigated to allow for the quantitative detection of each compound in fruits and vegetables. Chemical warfare agents, their surrogates and precursor compounds are studied which allows for the quantitative or semi-quantitative detection of a range of highly toxic compounds. Finally, 17 compounds classified by the US-EPA as hazardous air pollutants are studied that enables SIFT-MS instruments to replicate sections of the TO-14A and TO-15 methods.

This thesis deals with tracking a predefined object in the movie. After a brief introduction describes the procedure suitable for the detection of an object in a video sequence, where the methods are also discussed in detail. There is dealt with issues of image preprocessing, image segmentation and object detection in the image. The main emphasis is laid on using detectors of interest points and descriptors of areas - SURF and SIFT. The second part deals with the practical implementation of a program suitable to monitor predefined object in the movie. First are analyzed libraries suitable for object tracking in a video sequence in an environment of Java, followed by a detailed description of the selected library OpenCV along with wrapper JavaCV. Further described is own application in terms of control and functionality are described key method. Outputs along with discussion and evaluation are presented at the end of work.