Aspect angle estimation of targets in forward looking infrared images using the model-based vision approach

We develop a model-based vision algorithm to estimate the aspect angle of a target in a forward-looking infrared (FLIR) image. In the preprocessing stage of the algorithm, a set of 3-D voxel-based models is created using a CAD/CAM package. These models are rotated about the vertical axis through a series of predetermined angles and then projected onto the horizontal plane. This gives us a database library of 2-D images. We select as signature from a given FLIR image and attempt to match it with the various images in the given database library of images using the normalized cross-correlation method. The angle of rotation corresponding to the image in the database library giving the best possible match is estimated to be the aspect angle of the signature (target). We use an algebraic approach to represent images and the process involves certain algebraic operations on the polynomials. An advantage of the algebraic approach is that a high speedup in the run time is possible if the fast Fourier transform is used to compute the polynominal multiplications involved in the processing

Object Detection and Classification in the Visible and Infrared Spectrums

The over-arching theme of this dissertation is the development of automated detection and/or classification systems for challenging infrared scenarios. The six works presented herein can be categorized into four problem scenarios. In the first scenario, long-distance detection and classification of vehicles in thermal imagery, a custom convolutional network architecture is proposed for small thermal target detection. For the second scenario, thermal face landmark detection and thermal cross-spectral face verification, a publicly-available visible and thermal face dataset is introduced, along with benchmark results for several landmark detection and face verification algorithms. Furthermore, a novel visible-to-thermal transfer learning algorithm for face landmark detection is presented. The third scenario addresses near-infrared cross-spectral periocular recognition with a coupled conditional generative adversarial network guided by auxiliary synthetic loss functions. Finally, a deep sparse feature selection and fusion is proposed to detect the presence of textured contact lenses prior to near-infrared iris recognition

Design and performance assessment of correlation filters for the detection of objects in high clutter thermal imagery

The research reported in this thesis has examined means of enhancing the performance of the Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter for target detection in Forward Looking Infra-Red (FLIR) imagery acquired from a helicopter and border security FLIR camera in northern Kuwait. The data acquired with these FLIR sensors allows real-world evaluation of the comparative performance of the various filters that have been developed in the thesis. The results obtained have been quantified using well known performance measures such as Peak to Side-lobe Ratio (PSR) and Total Detection Error (TDE). The initial focus was to study the effect of modifying the OT-MACH parameters on the correlation metrics. A new optimisation technique has been presented, which computes statistically the filter alpha parameter associated with controlling the response of the filter to clutter noise. A further modification of the OT-MACH filter performance using the Difference of Gaussian bandpass filter (named the D-MACH filter) as a pre-processing stage has been described. The D-MACH has been applied to several test images containing single and multiple targets in the scene. Enhanced performance of the modified filter is demonstrated with improved metrics being obtained with less false side peaks in the correlation plane, especially when multiple targets are present in the test images. A further pre-processing technique was investigated using the Rayleigh distribution as a pre-processing filter (named the R-MACH filter). The R-MACH filter has been applied to multiple target types with tests conducted across various image data sets. The filter demonstrated an improvement over the Difference of Gaussian filter in terms of 6 reducing the number of parameters needing to be tuned whilst producing further enhanced correlation plane metrics. Finally, recommendations for future work has been made to improve the use of the OT-MACH filter in target detection and identification. A novel training image representation is proposed for further investigation, which will minimise the computational intensity of using the MACH filter for unconstrained object recognition

IDENTIFICATION OF ULTRASTRUCTURAL AND BIOCHEMICAL MARKERS OF FROST AVOIDANCE IN THE CUTICULAR LAYER OF CORN

Abiotic stresses are a critical factor in the reduction of yield. Corn has been identified as a highly economically important yet, frost sensitive crop. Climate change trends are show increased frost damage. The global need for food production is increasing and current production will not meet demand. Corn is killed at the moment of freezing and therefore, developing frost avoidance is essential. The primary obstacle limiting production of new more cold sensitive crops in the Canadian prairies is the cooler climate and early frost events in both spring and fall which are preventing widespread expansion. While many studies have examined corn chilling and frost sensitivity, the impact of simulated autumn temperatures (termed chilling pre-treatment) preceding a frost has not been reported. The effect of chilling pre-treatment, on subsequent freezing avoidance was studied in mature hybrid grain corn of four contrasting genotypes (256 and 675 [chilling sensitive]; 884 and 959 [chilling resistant]). Chilling pre-treatment (18°C/6°C, 10 days) induced physical and biochemical changes in the cuticular wax layer in all four genotypes. These changes were measured using a suite of complementary techniques including: thermal imaging, hydrophobicity, Confocal Laser Scanning Microscopy (CLSM), Attenuated Total internal Reflectance (ATR-FTIR), and Gas Chromatography Mass Spectrometry (GC-MS). In all corn genotypes studied, chilling pre-treatment induced a warmer freezing temperature than non-chilled. No significant genotypic differences were observed, however, genotypes 675 and 959 were least responsive to the stressor which resulted in the smallest change in freezing temperature induced by chilling pre-treatment. Hydrophobicity was reduced following chilling pre-treatment in all genotypes with the most significant effect observed in genotype 675. Cuticular thickness (μ=3.25 μm) remained unchanged over the ten-day chilling pre-treatment under controlled environment conditions. By contrast, over the five-week field conditions, cuticle thickness increased in all genotypes. Genotype 256 had a significantly thinner cuticle (-0.25 μm) than the other genotypes indicating genotypic variation is accentuated under field conditions and sensitive lines may have a thinner cuticle. In the growth chamber, chilling treatment induced increasing cutan, cutin, & cuticular wax only in Region 1 (CH3 functional group) according to ATR-FTIR within 2 μm of the adaxial surface layer. By contrast, field treatment induced a reduction in cutan, cutin, and cuticular wax in all regions (1, 2, & 3) (CH3, Asymmetrical CH2, Symmetrical CH2) to the same 2 μm depth of ATR sampling. A primary challenge of proofing cuticle based studies in the field is the extremely strong environmental influence (high light intensity, wind abrasion, insects, temperature fluctuations) which induce modifications on the cuticle. Using GC-MS analysis, 142 known compounds were identified in both controlled environment (chilling treatment) and field samples from the adaxial cuticular wax extraction of mature grain hybrid grain corn. Of those identified compounds, 28 were found to represent significant (P<0.05) variation between chilling treated and non-chilled treatments under both growth chamber and field conditions. This variation represented 5 Classes of key compounds (Alkane, Alcohol, Fatty Acid, Triterpenes and other). It is clear that chilling treatment modifies both physical and biochemical properties of the cuticular layer. The degree and rate of detectable chemical changes induced by chilling treatment indicate physical cuticular modifications likely are contingent on biochemical changes. This may be due to the great number of chemical modifications and signals needed to induce a physical modification. ATR applications are more reflective of the cuticular composition in cases where the entire cuticular thickness is within the depth of sampling (2 μm). The investigation of the dynamic process of cuticular wax modification following chilling treatment using complementary techniques in Zea mays appears to be a useful system with practical applications for evaluating the correlation between the cuticle as a barrier to abiotic stress and chilling treatment in a whole plant system

Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated by OKTAL-SE.1146Ysciescopu

Simulated marine plastics pollution weathering : a novel laboratory system for weathering plastics

Over the last three years there has been a call for more environmentally relevant laboratory weathering experiments. These experiments would require well-defined reaction conditions and standardised reporting of both the rate of degradation and methods used. Several new designs have been proposed, however, no design has yet been developed that meets these requirements. This thesis critically reviews the current plastic weathering methodologies and presents a novel method that produces well-defined conditions, omitted by other works. The method presented here aims to implement irradiance of ultraviolet radiation (UVR), temperature and saltwater parameters in a standardised way, as the three most influential variables of marine plastic weathering. In doing so, the practice of reporting single irradiance values is questioned due to its shortfall when comparing studies. The performance of the proposed method was assessed by weathering five plastics for 18 days before the experiment was stopped due to the Covid-19 Pandemic. The degradation of the plastics was measured using a trio of FTIR interfaces (ATR, Diffuse and Specular). A novel introduction of saltwater to the plastic samples provides stable simulated marine conditions to replicate marine weathering. The weathering characteristics from this method were found to be similar to those reported in outdoor weathering studies, showing that the laboratory method presented here is able to simulate environmental weathering. Despite promising results, the performance of the system could still be improved. The ultraviolet irradiance spectrum produced by the weather-o-meter, failed to match solar irradiance over the UVR range from 310 nm – 350 nm, despite having an overall irradiance which matched solar levels. Given the wavelength specific nature of plastic degradation, future work should aim to report the spectrum used, alongside the total irradiance

Development of a Transparent Thermal Reflective Thin Film Coating for Accurate Separation of Food-Grade Plastics in Recycling Process via AI-Based Thermal Image Processing

This paper presents the development of a specific thin film coating designed to address the challenge of accurately separating food-grade plastics in the recycling process. The coating, created using a plasma sputtering process, is transparent to the visible spectrum of light while effectively reflecting infrared emissions above 1500 nm. Composed of a safe metal oxide formulation with a proprietary composition, the coating is applied to packaging labels. By employing thermal imaging and a computer vision AI model, the coated labels enable precise differentiation of plastics associated with food packaging in the initial stage of plastic recycling. The proposed system achieved a remarkable 100% accuracy in separating food-grade plastics from other types of plastics. This innovative approach holds great potential for enhancing the efficiency and effectiveness of plastic recycling processes, ensuring the recovery of food-grade plastics for future use

Concave-convex local binary features for automatic target recognition in infrared imagery

This paper presents a novel feature extraction algorithm based on the local binary features for automatic target recognition (ATR) in infrared imagery. Since the inception of the local binary pattern (LBP) and local ternary pattern (LTP) features, many extensions have been proposed to improve their robustness and performance in a variety of applications. However, most attentions were paid to improve local feature extraction with little consideration on the incorporation of global or regional information. In this work, we propose a new concave-convex partition (CCP) strategy to improve LBP and LTP by dividing local features into two distinct groups, i.e., concave and convex, according to the contrast between local and global intensities. Then two separate histograms built from the two categories are concatenated together to form a new LBP/LTP code that is expected to better reflect both global and local information. Experimental results on standard texture images demonstrate the improved discriminability of the proposed features and those on infrared imagery further show that the proposed features can achieve competitive ATR results compared with state-of-the-art methods.Peer reviewedElectrical and Computer Engineerin