OSC-CO\u3csup\u3e2\u3c/sup\u3e: Coattention and Cosegmentation Framework for Plant State Change with Multiple Features

Cosegmentation and coattention are extensions of traditional segmentation methods aimed at detecting a common object (or objects) in a group of images. Current cosegmentation and coattention methods are ineffective for objects, such as plants, that change their morphological state while being captured in different modalities and views. The Object State Change using Coattention-Cosegmentation (OSC-CO2) is an end-to-end unsupervised deep-learning framework that enhances traditional segmentation techniques, processing, analyzing, selecting, and combining suitable segmentation results that may contain most of our target object’s pixels, and then displaying a final segmented image. The framework leverages coattention-based convolutional neural networks (CNNs) and cosegmentation-based dense Conditional Random Fields (CRFs) to address segmentation accuracy in high-dimensional plant imagery with evolving plant objects. The efficacy of OSC-CO2 is demonstrated using plant growth sequences imaged with infrared, visible, and fluorescence cameras in multiple views using a remote sensing, high-throughput phenotyping platform, and is evaluated using Jaccard index and precision measures. We also introduce CosegPP+, a dataset that is structured and can provide quantitative information on the efficacy of our framework. Results show that OSC-CO2 out performed state-of-the art segmentation and cosegmentation methods by improving segmentation accuracy by 3% to 45%

Gaussian mixture model classifiers for detection and tracking in UAV video streams.

Masters Degree. University of KwaZulu-Natal, Durban.Manual visual surveillance systems are subject to a high degree of human-error and operator fatigue. The automation of such systems often employs detectors, trackers and classifiers as fundamental building blocks. Detection, tracking and classification are especially useful and challenging in Unmanned Aerial Vehicle (UAV) based surveillance systems. Previous solutions have addressed challenges via complex classification methods. This dissertation proposes less complex Gaussian Mixture Model (GMM) based classifiers that can simplify the process; where data is represented as a reduced set of model parameters, and classification is performed in the low dimensionality parameter-space. The specification and adoption of GMM based classifiers on the UAV visual tracking feature space formed the principal contribution of the work. This methodology can be generalised to other feature spaces. This dissertation presents two main contributions in the form of submissions to ISI accredited journals. In the first paper, objectives are demonstrated with a vehicle detector incorporating a two stage GMM classifier, applied to a single feature space, namely Histogram of Oriented Gradients (HoG). While the second paper demonstrates objectives with a vehicle tracker using colour histograms (in RGB and HSV), with Gaussian Mixture Model (GMM) classifiers and a Kalman filter. The proposed works are comparable to related works with testing performed on benchmark datasets. In the tracking domain for such platforms, tracking alone is insufficient. Adaptive detection and classification can assist in search space reduction, building of knowledge priors and improved target representations. Results show that the proposed approach improves performance and robustness. Findings also indicate potential further enhancements such as a multi-mode tracker with global and local tracking based on a combination of both papers

Automated interpretation of benthic stereo imagery

Autonomous benthic imaging, reduces human risk and increases the amount of collected data. However, manually interpreting these high volumes of data is onerous, time consuming and in many cases, infeasible. The objective of this thesis is to improve the scientific utility of the large image datasets. Fine-scale terrain complexity is typically quantified by rugosity and measured by divers using chains and tape measures. This thesis proposes a new technique for measuring terrain complexity from 3D stereo image reconstructions, which is non-contact and can be calculated at multiple scales over large spatial extents. Using robots, terrain complexity can be measured without endangering humans, beyond scuba depths. Results show that this approach is more robust, flexible and easily repeatable than traditional methods. These proposed terrain complexity features are combined with visual colour and texture descriptors and applied to classifying imagery. New multi-dataset feature selection methods are proposed for performing feature selection across multiple datasets, and are shown to improve the overall classification performance. The results show that the most informative predictors of benthic habitat types are the new terrain complexity measurements. This thesis presents a method that aims to reduce human labelling effort, while maximising classification performance by combining pre-clustering with active learning. The results support that utilising the structure of the unlabelled data in conjunction with uncertainty sampling can significantly reduce the number of labels required for a given level of accuracy. Typically 0.00001–0.00007% of image data is annotated and processed for science purposes (20–50 points in 1–2% of the images). This thesis proposes a framework that uses existing human-annotated point labels to train a superpixel-based automated classification system, which can extrapolate the classified results to every pixel across all the images of an entire survey

Automated interpretation of benthic stereo imagery

Autonomous benthic imaging, reduces human risk and increases the amount of collected data. However, manually interpreting these high volumes of data is onerous, time consuming and in many cases, infeasible. The objective of this thesis is to improve the scientific utility of the large image datasets. Fine-scale terrain complexity is typically quantified by rugosity and measured by divers using chains and tape measures. This thesis proposes a new technique for measuring terrain complexity from 3D stereo image reconstructions, which is non-contact and can be calculated at multiple scales over large spatial extents. Using robots, terrain complexity can be measured without endangering humans, beyond scuba depths. Results show that this approach is more robust, flexible and easily repeatable than traditional methods. These proposed terrain complexity features are combined with visual colour and texture descriptors and applied to classifying imagery. New multi-dataset feature selection methods are proposed for performing feature selection across multiple datasets, and are shown to improve the overall classification performance. The results show that the most informative predictors of benthic habitat types are the new terrain complexity measurements. This thesis presents a method that aims to reduce human labelling effort, while maximising classification performance by combining pre-clustering with active learning. The results support that utilising the structure of the unlabelled data in conjunction with uncertainty sampling can significantly reduce the number of labels required for a given level of accuracy. Typically 0.00001–0.00007% of image data is annotated and processed for science purposes (20–50 points in 1–2% of the images). This thesis proposes a framework that uses existing human-annotated point labels to train a superpixel-based automated classification system, which can extrapolate the classified results to every pixel across all the images of an entire survey

OSC-CO2: coattention and cosegmentation framework for plant state change with multiple features

Cosegmentation and coattention are extensions of traditional segmentation methods aimed at detecting a common object (or objects) in a group of images. Current cosegmentation and coattention methods are ineffective for objects, such as plants, that change their morphological state while being captured in different modalities and views. The Object State Change using Coattention-Cosegmentation (OSC-CO2) is an end-to-end unsupervised deep-learning framework that enhances traditional segmentation techniques, processing, analyzing, selecting, and combining suitable segmentation results that may contain most of our target object’s pixels, and then displaying a final segmented image. The framework leverages coattention-based convolutional neural networks (CNNs) and cosegmentation-based dense Conditional Random Fields (CRFs) to address segmentation accuracy in high-dimensional plant imagery with evolving plant objects. The efficacy of OSC-CO2 is demonstrated using plant growth sequences imaged with infrared, visible, and fluorescence cameras in multiple views using a remote sensing, high-throughput phenotyping platform, and is evaluated using Jaccard index and precision measures. We also introduce CosegPP+, a dataset that is structured and can provide quantitative information on the efficacy of our framework. Results show that OSC-CO2 out performed state-of-the art segmentation and cosegmentation methods by improving segementation accuracy by 3% to 45%

Using deep learning and Open Street Maps to find features in aerial images

Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona, Any: 2018, Tutor: Santi Seguí Mesquida i Jordi Vitrià i Marca[en] A great amount of the interesting information captured by aerial imagery is still not being used given how labour intensive the processing and annotation of these images is. Despite this, improvements in technology and advancements in the computer vision field have made available tools and techniques that can help make this process semi-automatized. In this project we focus on the use case of extracting roads from aerial imagery. For this purpose, we will study and compare models based on image segmentation using deep learning and RoadTracer, a revolutionary model proposed recently

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Two and three dimensional segmentation of multimodal imagery

The role of segmentation in the realms of image understanding/analysis, computer vision, pattern recognition, remote sensing and medical imaging in recent years has been significantly augmented due to accelerated scientific advances made in the acquisition of image data. This low-level analysis protocol is critical to numerous applications, with the primary goal of expediting and improving the effectiveness of subsequent high-level operations by providing a condensed and pertinent representation of image information. In this research, we propose a novel unsupervised segmentation framework for facilitating meaningful segregation of 2-D/3-D image data across multiple modalities (color, remote-sensing and biomedical imaging) into non-overlapping partitions using several spatial-spectral attributes. Initially, our framework exploits the information obtained from detecting edges inherent in the data. To this effect, by using a vector gradient detection technique, pixels without edges are grouped and individually labeled to partition some initial portion of the input image content. Pixels that contain higher gradient densities are included by the dynamic generation of segments as the algorithm progresses to generate an initial region map. Subsequently, texture modeling is performed and the obtained gradient, texture and intensity information along with the aforementioned initial partition map are used to perform a multivariate refinement procedure, to fuse groups with similar characteristics yielding the final output segmentation. Experimental results obtained in comparison to published/state-of the-art segmentation techniques for color as well as multi/hyperspectral imagery, demonstrate the advantages of the proposed method. Furthermore, for the purpose of achieving improved computational efficiency we propose an extension of the aforestated methodology in a multi-resolution framework, demonstrated on color images. Finally, this research also encompasses a 3-D extension of the aforementioned algorithm demonstrated on medical (Magnetic Resonance Imaging / Computed Tomography) volumes