Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV arrays to initiate and develop the process of automatically identifying solar PV locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment. Machine-accessible metadata file describing the reported data (ISA-Tab format)

/pdf/distributed-solar-photovoltaic-array-location-and-extent-46upvu2u5n.pdf

Distributed solar photovoltaic array location and extent dataset for remote sensing object identification.

Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition The highly constrained nature of anatomical objects can be well captured with learning-based techniques However, in most recent and promising techniques such as CNN-based segmentation it is not obvious how to incorporate such prior knowledge State-of-the-art methods operate as pixel-wise classifiers where the training objectives do not incorporate the structure and inter-dependencies of the output To overcome this limitation, we propose a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end The new framework encourages models to follow the global anatomical properties of the underlying anatomy ( eg shape, label structure) via learnt non-linear representations of the shape We show that the proposed approach can be easily adapted to different analysis tasks ( eg image enhancement, segmentation) and improve the prediction accuracy of the state-of-the-art models The applicability of our approach is shown on multi-modal cardiac data sets and public benchmarks In addition, we demonstrate how the learnt deep models of 3-D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies

/pdf/anatomically-constrained-neural-networks-acnns-application-29u12in1oc.pdf

Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation

Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning based techniques. However, in most recent and promising techniques such as CNN based segmentation it is not obvious how to incorporate such prior knowledge. State-of-the-art methods operate as pixel-wise classifiers where the training objectives do not incorporate the structure and inter-dependencies of the output. To overcome this limitation, we propose a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end. The new framework encourages models to follow the global anatomical properties of the underlying anatomy (e.g. shape, label structure) via learned non-linear representations of the shape. We show that the proposed approach can be easily adapted to different analysis tasks (e.g. image enhancement, segmentation) and improve the prediction accuracy of the state-of-the-art models. The applicability of our approach is shown on multi-modal cardiac datasets and public benchmarks. Additionally, we demonstrate how the learned deep models of 3D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies.

Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation

The semantic image segmentation task consists of classifying each pixel of an image into an instance, where each instance corresponds to a class. This task is a part of the concept of scene understanding or better explaining the global context of an image. In the medical image analysis domain, image segmentation can be used for image-guided interventions, radiotherapy, or improved radiological diagnostics. In this review, we categorize the leading deep learning-based medical and non-medical image segmentation solutions into six main groups of deep architectural, data synthesis-based, loss function-based, sequenced models, weakly supervised, and multi-task methods and provide a comprehensive review of the contributions in each of these groups. Further, for each group, we analyze each variant of these groups and discuss the limitations of the current approaches and present potential future research directions for semantic image segmentation.

Deep semantic segmentation of natural and medical images: a review

Survey of machine learning techniques for malware analysis

This paper will have a survey on mobile computing. It involves software, hardware and mobile communication. Due to this, different types of mobile devices are talked and they are investigated in details. Then, existing operation systems that are most popular for mentioned devices are talked. Limitations of mobile computing is another issue that is concerning in this study. Key word: Mobile computing, mobile devices, mobile operating systems

https://www.it.iitb.ac.in/frg/wiki/images/3/38/Android_wifi_working_principles.pdf

Mobile Computing: Principles, Devices and Operating Systems

In most robot-assisted surgical interventions, multimodal fusion of pre- and intra-operative data is highly valuable, affording the surgeon a more comprehensive understanding of the surgical scene observed through the stereo endoscopic camera. More specifically, in the case of partial nephrectomy, fusing pre-operative segmentations of kidney and tumor with the stereo endoscopic view can guide tumor localization and the identification of resection margins. However, the surgeons are often unable to reliably assess the levels of trust they can bestow on what is overlaid on the screen. In this paper, we present the proof-of-concept of an uncertainty-encoded augmented reality framework and novel visualizations of the uncertainties derived from the pre-operative CT segmentation onto the surgeon’s stereo endoscopic view. To verify its clinical potential, the proposed method is applied to an ex vivo lamb kidney. The results are contrasted to different visualization solutions based on crisp segmentation demonstrating that our method provides valuable additional information that can help the surgeon during the resection planning.

Uncertainty-Encoded Augmented Reality for Robot-Assisted Partial Nephrectomy: A Phantom Study

Incorporating prior knowledge into image segmentation algorithms has proven useful for obtaining more accurate and plausible results. Two important constraints, containment and exclusion of regions, have gained attention in recent years mainly due to their descriptive power. In this paper, we augment the level set framework with the ability to handle these two intuitive geometric relationships, containment and exclusion, along with a distance constraint between boundaries of multi-region objects. Level set's important property of automatically handling topological changes of evolving contours/surfaces enables us to segment spatially-recurring objects (e.g., multiple instances of multi-region cells in a large microscopy image) while satisfying the two aforementioned constraints. In addition, the level set approach gives us a very simple and natural way to compute the distance between contours/surfaces and impose constraints on it. The downside, however, is a local optimization framework in which the final segmentation solution depends on the initialization. In fact, here, we sacrifice the optimizability (local instead of global solution) in exchange for lower space complexity (less memory usage) and faster runtime (especially for large microscopic images) as well as no grid artifacts. Nevertheless, the result from validating our method on several biomedical applications showed the utility and advantages of this augmented level set framework (even with rough initialization that is distant from the desired boundaries). We also compared our framework with its counterpart methods in the discrete domain and reported the pros and cons of each of these methods in terms of metrication error and efficiency in memory usage and runtime.

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Local optimization based segmentation of spatially-recurring, multi-region objects with part configuration constraints.

In this paper, we are going to introduce different types of steganography considering the cover data. As the first step, we will talk about text steganography and investigate its details. Then, image steganography and its techniques will be investigated. Some techniques including Least Significant Bits, Masking and filtering and Transformations will be subjected during image steganography. Finally, audio steganography which contains LSB Coding, Phase Coding, Spread Spectrum and Echo Hiding techniques will be described.

An introduction to steganography methods - TI Journals

This paper presents a new solution for automatic polygonal rooftop extraction (with no angular constraint) in satellite/ aerial imageries based on line intersections. The proposed approach uses edge definitions and their relationships with each other to create a set of potential vertices. Using a graph representation, the relationship between potential vertices are studied in an efficient way. Polygonal rooftops correspond to closed loops in this graph. The experimental results for images acquired from Google Earth show that this solution has a high precision in detecting polygonal rooftops.

Masoud Nosrati

Papers

Mobile Computing: Principles, Devices and Operating Systems

Uncertainty-Encoded Augmented Reality for Robot-Assisted Partial Nephrectomy: A Phantom Study

Local optimization based segmentation of spatially-recurring, multi-region objects with part configuration constraints.

An introduction to steganography methods - TI Journals

A novel approach for polygonal rooftop detection in satellite/aerial imageries