3D Model Assisted Image Segmentation

The problem of segmenting a given image into coherent regions is important in Computer Vision and many industrial applications require segmenting a known object into its components. Examples include identifying individual parts of a component for proces

The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey

The Internet of Things (IoT) is a dynamic global information network consisting of internet-connected objects, such as Radio-frequency identification (RFIDs), sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future internet. Over the last decade, we have seen a large number of the IoT solutions developed by start-ups, small and medium enterprises, large corporations, academic research institutes (such as universities), and private and public research organisations making their way into the market. In this paper, we survey over one hundred IoT smart solutions in the marketplace and examine them closely in order to identify the technologies used, functionalities, and applications. More importantly, we identify the trends, opportunities and open challenges in the industry-based the IoT solutions. Based on the application domain, we classify and discuss these solutions under five different categories: smart wearable, smart home, smart, city, smart environment, and smart enterprise. This survey is intended to serve as a guideline and conceptual framework for future research in the IoT and to motivate and inspire further developments. It also provides a systematic exploration of existing research and suggests a number of potentially significant research directions.Comment: IEEE Transactions on Emerging Topics in Computing 201

A Novel Illumination-Invariant Loss for Monocular 3D Pose Estimation

The problem of identifying the 3D pose of a known object from a given 2D image has important applications in Computer Vision. Our proposed method of registering a 3D model of a known object on a given 2D photo of the object has numerous advantages over existing methods. It does not require prior training, knowledge of the camera parameters, explicit point correspondences or matching features between the image and model. Unlike techniques that estimate a partial 3D pose (as in an overhead view of traffic or machine parts on a conveyor belt), our method estimates the complete 3D pose of the object. It works on a single static image from a given view under varying and unknown lighting conditions. For this purpose we derive a novel illumination-invariant distance measure between the 2D photo and projected 3D model, which is then minimised to find the best pose parameters. Results for vehicle pose detection in real photographs are presented

Energetic interactions within the solid-state spectrometer of the ASCA Satellite

Thesis (M.Eng. and B.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; and, (B.S.)--Massachusetts Institute of Technology, Dept. of Physics, 1995.Includes bibliographical references (p. 82-84).ASCA is a broad band (0.3 - 12 keV) x-ray astrophysical observatory in which various instruments operate with high throughput and high spectral resolution. Detailed examination of the method in which photoelectronic interactions occur in the Charge Coupled Devices (CCDs) of the Solid-State Imaging Spectrometer (SIS) on ASCA reveals a sudden variation in the nearly linear CCD signal output versus energy input curve near the silicon K-edge. The focus of this thesis is the modeling of the behavior of the SIS CCD at the silicon K-edge, first by theoretical analysis, and then by analysis of X-ray spectral data already acquired from the instrument. The relationship between CCD signal out and incoming photon energy at the silicon K-edge in the ASCA detectors is studied in detail in order to define and analyze the behavior of the CCD in this range. The results from this study showed that theoretically, a one to two percent deviation between incoming energy and output pulse-height should exist in the CCD. Although the nonlinearity was consistent with the available data, insufficient signal-to-noise in this data restricted the definite quantification of this disparity.by Srimal Wangu.B.S.M.Eng.and B.S

3D Model Assisted Image Segmentation

The problem of segmenting a given image into coherent regions is important in Computer Vision and many industrial applications require segmenting a known object into its components. Examples include identifying individual parts of a component for process control work in a manufacturing plant and identifying parts of a car from a photo for automatic damage detection. Unfortunately most of an object’s parts of interest in such applications share the same pixel characteristics, having similar colour and texture. This makes segmenting the object into its components a non-trivial task for conventional image segmentation algorithms. In this paper, we propose a “Model Assisted Segmentation ” method to tackle this problem. A 3D model of the object is registered over the given image by optimising a novel gradient based loss function. This registration obtains the full 3D pose from an image of the object. The image can have an arbitrary view of the object and is not limited to a particular set of views. The segmentation is subsequently performed using a level-set based method, using the projected contours of the registered 3D model as initialisation curves. The method is fully automatic and requires no user interaction. Also, the system does not require any prior training. We present our results on photographs of a real car

Image based automatic vehicle damage detection

Automatically detecting vehicle damage using photographs taken at the accident scene is very useful as it can greatly reduce the cost of processing insurance claims, as well as provide greater convenience for vehicle users. An ideal scenario would be where the vehicle user can upload a few photographs of the damaged car taken from a mobile phone and have the dam- age assessment and insurance claim processing done automatically. However, such a solution remains a challenging task due to a number of factors. For a start, the scene of the accident is typically an unknown and uncontrolled outdoor environment with a plethora of factors beyond our control including scene illumination and the presence of surrounding objects which are not known a priori. In addition, since vehicles have very reflective metallic bodies the photographs taken in such an uncontrolled environment can be expected to have a considerable amount of inter object reflection. Therefore, the application of standard computer vision techniques in this context is a very challenging task. Moreover, solving this task opens up a fascinating repertoire of computer vision problems which need to be addressed in the context of a very challenging scenario. This thesis describes research undertaken to address the problem of au- tomatic vehicle damage detection using photographs. A pipeline addressing a vertical slice of the broad problem is considered while focusing on mild vehicle damage detection. We propose to use 3D CAD models of undamaged vehicles which are used to obtain ground truth information in order to infer what the vehicle with mild damage in the photograph should have looked like, if it had not been damaged. To this end, we develop 3D pose estimation algorithms to register an undamaged 3D CAD model over a photograph of the known dam- aged vehicle. We present a 3D pose estimation method using image gradient information of the photograph and the 3D model projection. We show how the 3D model projection at the recovered 3D pose can be used to identify components of a vehicle in the photograph which may have mild damage. In addition, we present a more robust 3D pose estimation method by minimizing a novel illumination invariant distance measure, which is based on a Mahalanobis distance between attributes of the 3D model projection and the pixels in the photograph. In principle, image edges which are not present in the 3D CAD model projection can be considered to be vehicle damage. However, since the vehicle body is very reflective, there is a large amount of inter object reflection in the photograph which may be misclassified as damage. In order to detect image edges caused by inter object reflection, we propose to apply multi-view geometry techniques on two photographs of the vehicle taken from different view points. To this end, we also develop a robust method to obtain reliable point correspondences across the photographs which are dominated by large reflective and mostly homogeneous regions. The performance of the proposed methods are experimentally evaluated on real photographs using 3D CAD models of varying accuracy. We expect that the research presented in this thesis will provide the groundwork for designing an automatic photograph based vehicle damage de- tection system. Moreover, we hope that our method will provide the foundation for interesting future research

Modulation of rat peripheral polymorphonuclear leukocyte response by nitric oxide and arginine

The effect of nitric oxide (NO) on the luminol-dependent chemiluminescence (LCL) response of rat polymorphonuclear leukocytes (PMNLs) was analyzed by using sodium nitroprusside (SNP), a NO donor, and L-arginine (L-arg), a NO precursor. A significant reduction in the LCL intensity was observed in presence of SNP (100 μmol/L) or L-arg (5 or 10 mmol/L) in arachidonic acid (AA) phorbol ester (PMA) and formyl- methionyl-leucyl-phenylalanine stimulated PMNLs. However, opsonized zymosan-induced LCL was not attenuated significantly. Reduction in hydroxyl radical and superoxide generation was also observed in SNP- or L-arg-pretreated cells. D-Arg (10 mmol/L) pretreatment did not inhibit PMNLs' LCL response. Furthermore, methylene blue (5 μmol/L) and L-NG- mono methyl-L-arginine (100 or 300 μmol/L) significantly attenuated the LCL response, as induced by various agonists. Cyclic GMP did not alter the reactive oxygen species generation from rat PMNLs. In addition, AA-induced release of myeloperoxidase, a marker of azurophilic granules, was found to be enhanced in L-arg- (10 mmol/L) pretreated PMNLs. The results suggest that NO inhibits free radical generation from rat PMNLs

Development of an Electrochemical Method to Study Real-Time in Vivo Neurotransmitter Modulation

Histamine and serotonin are important biogenic amines that regulate vital brain functions. These two transmitters are thoughts to be involved in neurodegenerative diseases such as Parkinson’s and Alzheimer’s and affective disorders including depression. Histamine and serotonin are believed to regulate each other but their fundamental neuromodulation mechanisms are not well understood. This lack of understanding makes brain disorders implicating these two transmitters difficult to diagnose and treat. Our lab extensively investigates the serotonergic system to understand serotonin’s neurochemistry in the brain. However, histamine is relatively understudied with respect to other biogenic amines because of an absence of suitable analytical tools. This work introduces a strategic approach to overcome this analytical challenge and investigates the real-time neuromodulation of in vivo histamine and serotonin to understand physiological functions in healthy and disease states using fast-scan cyclic voltammetry (FSCV). First, we perform a proof-of-principle study of Copper (Cu(II)) analysis to characterize the adsorption driven FSCV response. Next, we employ FSCV to develop a novel voltammetric method to selectively and sensitively monitor real-time in vivo histamine and serotonin neurotransmissions in the posterior hypothalamus (PH). This study reveals that histamine inhibits serotonin via an H3 receptor mediated process, highlighting histamine’s roles in regulating serotonin release in the brain. Following that, we examine histamine’s reuptake mechanisms via monoamine transporter proteins and demonstrate that histamine uptake mechanism is mediated by organic cation transporters.Finally, we use our novel FSCV method to monitor histamine and serotonin neurotransmissions in HIV- 1 Tg rats, which exhibit neuroinflammation, to understand impaired neurochemical mechanisms in the disease state. Collectively, this dissertation showcases a novel and robust electroanalytical strategy to simultaneously monitor in vivo histamine and serotonin neuromodulation in real time. Innovative discoveries in this systematic investigation of the histaminergic regulation of serotonin in diverse neurochemical and pathophysiological processes will pave the way towards more efficient therapies for histamine and serotonin related brain disorders

Fluorescence studies on the interaction of some ligands with carcinoscorpin, the sialic acid specific lectin, from the horseshoe crab, Carcinoscorpius rotundacauda

The binding affinities of some ligands towards the sialic acid-specific lectin carcinoscorpin, from hemolymph of the horseshoe crab Carcinoscorpius rotundacauda have been determined by protein fluorescence quenching in presence of ligands. Among the ligands studied, the disaccharide O-(N-acetylneuraminyl)-(2→6)-2-acetamido-2-deoxy-D-galactitol has the highest Ka (l.15 × 106 M−1) for carcinoscorpin. Studies on the effect of pH on Ka values of disaccharide suggests the possible involvement of amino acid residues having pKa values around 6.0 and 9.0 in the binding activity of carcinoscorpin. There were distinct changes in the accessibility of the fluorescent tryptophan residues of carcinoscorpin by ligand-binding as checked through potassium iodide quenching