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Digital Image Processing is an emerging field finding applications in different domains of science and engineering. Image processing forms the basis for pattern recognition and machine learning technologies. Color Image Processing deals... more
Digital Image Processing is an emerging field finding applications in different domains of science and engineering. Image processing forms the basis for pattern recognition and machine learning technologies. Color Image Processing deals with color spaces and models for performing operations on an image of one type and transform it to another model for efficient analysis and feature manipulation. In this paper, a new image enhancement method is established for better visual perception and improving image quality. Histogram of an image is useful in determining the contents in the image and its distribution. Histogram equalization is the technique used to improve the dynamic range of an image and help in distribution of parameter wrights over the entire domain range rather than it getting concentrated over specific regions. This paper proposes an effective approach to enhance image quality by histogram equalization without compromising on mean brightness aspect of the image.
According to the embodiments provided herein, a trajectory determination device for geo-localization can include one or more relative position sensors, one or more processors, and memory. The one or more processors can execute machine... more
According to the embodiments provided herein, a trajectory determination device for geo-localization can include one or more relative position sensors, one or more processors, and memory. The one or more processors can execute machine readable instructions to receive the relative position signals from the one or more relative position sensors. The relative position signals can be transformed into a sequence of relative trajectories. Each of the relative trajectories can include a distance and directional information indicative of a change in orientation of the trajectory determination device. A progressive topology can be created based upon the sequence of relative trajectories; this progressive topology can be compared to map data. A geolocation of the trajectory determination device can be determined.
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A B S T R A C T Nuclear power plant (NPP) outages are challenging construction projects. Delays in NPP outage processes can cause significant economic losses. In typical NPP outages, extremely busy outage schedules, large crew sizes,... more
A B S T R A C T Nuclear power plant (NPP) outages are challenging construction projects. Delays in NPP outage processes can cause significant economic losses. In typical NPP outages, extremely busy outage schedules, large crew sizes, dynamic workspaces and zero tolerance of accidents pose challenges to ensuring the resilience of outage control, which should rapidly and proactively respond to delays, errors, or unexpected tasks added during outages. Two mutually interacting practical problems impede NPPs from achieving such resilient outage control: 1) how to control errors and wastes effectively during the " handoffs " between tasks, and 2) how to respond to numerous contingencies in NPP outage workflows in a responsive and proactive manner. A resilient NPP outage control system should address these two practical problems through " Human-Centered Automation (HCA), " which is improving the control process automation while fully considering human factors. Previous studies examined two categories of technologies that potentially enable HCA in outage control: 1) computational modeling and simulation methods for predicting states of field operations and workflows; 2) data collection and processing methods for capturing the reality and thus providing feedback to computational models. Unfortunately, limited studies systematically synthesize technological challenges related to these practical problems and underlying HCA principles. This paper identifies the domain requirements, challenges, and potential solutions of achieving the HCA system that effectively supports resilient NPP outage control. This proposed system aims at significantly improving the performance of handoff monitoring/control and responding to contingencies during the outage. Firstly, the authors identified information acquisition and modeling challenges of achieving human-center automation for outage control. The rest of the paper then synthesizes potential techniques available in the domains of computer science, cognitive science, system science, and construction engineering that can potentially address these challenges. The authors concluded this literature and technological review with a research roadmap for achieving HCA in construction.
Nuclear power plant (NPP) outages involve maintenance and repair activities of a large number of workers in limited workspaces, while having tight schedules and zero-tolerance for accidents. During an outage, thousands of workers will be... more
Nuclear power plant (NPP) outages involve maintenance and repair activities of a large number of workers in limited workspaces, while having tight schedules and zero-tolerance for accidents. During an outage, thousands of workers will be working around the NPP. Extremely high outage costs and expensive delays in maintenance projects (around $1.5 million per day) require tight outage schedules (typically 20 days). In such packed workspaces, real-time human behavior monitoring is critical for ensuring safe collaboration among workers, minimal wastes of time and resources due to the lack of situational awareness, and timely project control. Current methods for detailed human behavior monitoring on construction sites rely on manual imagery data collection and analysis, which is tedious and error-prone. This paper presents a framework of automatic imagery data analysis that enables real-time detection and diagnosis of anomalous human behaviors during outages, through the integration of 4D construction simulation and object tracking algorithms.
Images have become the most popular type of mul-timedia in the Big Data era. Widely used applications like automatic CBIR underscore the importance of image understanding, especially in terms of the semantically meaningful information.... more
Images have become the most popular type of mul-timedia in the Big Data era. Widely used applications like automatic CBIR underscore the importance of image understanding, especially in terms of the semantically meaningful information. Typically, high dimensional image descriptors are embedded to a subspace using a simple linear projection. However, semantic information has a complex distribution in feature space that requires a non-linear projection. We first estimate an intrinsic dimensionality of image data. Next we build a measure of visual information in embedded subspace. We compare several linear and non-linear projection methods. We use multiple image databases towards a comprehensive evaluation. We report results in terms of information content, consequent recognition rates, and computational cost. This paper is relevant for researchers interested in dimensionality reduction for large scale image understanding that is both quick and preserves semantically relevant information.
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Rapidly growing technologies like autonomous navigation require accurate geo-localization in both outdoor and indoor environments. GNSS based outdoor localization has limitation of accuracy, which deteriorates in urban canyons, forested... more
Rapidly growing technologies like autonomous navigation require accurate geo-localization in both outdoor and indoor environments. GNSS based outdoor localization has limitation of accuracy, which deteriorates in urban canyons, forested region and is unavailable indoors. Technologies like RFID, UWB, WiFi are used for indoor localization. These suffer limitations of high infrastructure costs, and signal transmission issues like multi-path, and frequent replacement of transciever batteries. We propose an alternative to localize an individual or a vehcile that is moving inside or outside a building. Instead of mobile RF transceivers, we utilize a sensor suite that includes a video camera and an inertial measurement unit. We estimate a motion trajectory of this sensor suite using Visual Odometery. Instead of pre-installed transceivers, we use GIS map for outdoors, or a BIM model for indoors. The transport layer in GIS map or navigable paths in BIM are abstracted as a graph structure. The geo-location of the mobile platform is inferred by first localizing its trajectory. We introduce an adaptive probabilistic inference approach to search for this trajectory in the entire map with no initialization information. Using an effective graph traversal spawn-and-prune strategy, we can localize the mobile platform in real-time. In comparison to other technologies, our approach requires economical sensors and the required map data is typically available in the public domain. Additionally, unlike other technologies which function exclusively indoors or outdoors, our approach functions in both environments. We demonstrate our approach on real world examples of both indoor and outdoor locations.
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Indoor positioning system is a rapidly emerging technology. Unlike outdoor positioning, which uses triangulation from satellites in line-of-sight, current indoor positioning methods attempt triangulation using Received Signal Strength... more
Indoor positioning system is a rapidly emerging technology. Unlike outdoor positioning, which uses triangulation from satellites in line-of-sight, current indoor positioning methods attempt triangulation using Received Signal Strength Indicator (RSSI) from indoor transmitters, like WiFi and RFID. These methods, however, are not accurate and suffer from issues like multi-path and absorption by walls and other objects. In this paper we propose an alternate and novel approach to indoor positioning, that combines signals from multiple sensors. In particular, we focus on visual and inertial sensors that are ubiquitously found in mobile devices. We utilize a Building Information Model (BIM) of the indoor environment as a guideline for navigable paths. The sensor suite signals are processed to generate a trajectory of device moving through the indoor environment. We compute features on this trajectory in real-time and data mine pre-computed features on BIM's navigable paths to determine the location of the device in real-time. We demonstrate our approach on BIM in our university campus. The key benefit of our approach is that unlike previous methods that require installation of a wireless sensor network of several transmitters spanning the indoor environment, we only require a floor-plan BIM and cheap ubiquitous sensor suite on board a mobile device for indoor positioning.
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The primary method for geo-localization is based on GPS which has issues of localization accuracy, power consumption, and unavailability. This paper proposes a novel approach to geo-localization in a GPS-denied environment for a mobile... more
The primary method for geo-localization is based on GPS which has issues of localization accuracy, power consumption, and unavailability. This paper proposes a novel approach to geo-localization in a GPS-denied environment for a mobile platform. Our approach has two principal components: public domain transport network data available in GIS databases or OpenStreetMap; and a trajectory of a mobile platform. This trajectory is estimated using visual odometry and 3D view geometry. The transport map information is abstracted as a graph data structure, where various types of roads are modeled as graph edges and typically intersections are modeled as graph nodes. A search for the trajectory in real time in the graph yields the geo-location of the mobile platform. Our approach uses a simple visual sensor and it has a low memory and computational footprint. In this paper, we demonstrate our method for trajectory estimation and provide examples of geolocalization using public-domain map data. With the rapid proliferation of visual sensors as part of automated driving technology and continuous growth in public domain map data, our approach has the potential to completely augment, or even supplant, GPS based navigation since it functions in all environments.
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This paper presents a novel adaptation of fuzzy clustering and feature encoding for image classification. Visual word ambiguity has recently been successfully modelled by kernel codebooks to provide improvement in classification... more
This paper presents a novel adaptation of fuzzy clustering and feature encoding for image classification. Visual word ambiguity has recently been successfully modelled by kernel codebooks to provide improvement in classification performance over the standard `Bag-of-Features'(BoF) approach, which uses hard partitioning and crisp logic for assignment of features to visual words. Motivated by this progress we utilize fuzzy logic to model the ambiguity and combine it with clustering to discover fuzzy visual words. The feature descriptors of an image are encoded using the learned fuzzy membership function associated with each word. The codebook built using this fuzzy encoding technique is demonstrated to provide superior performance over BoF. We use the Gustafson-Kessel algorithm which is an improvement over Fuzzy C-Means clustering and can adapt to local distributions. We evaluate our approach on several popular datasets and demonstrate that it consistently provides superior performance to the BoF approach.
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"Visual category recognition is a difficult task of significant interest to the machine learning and vision community. One of the principal hurdles is the high dimensional feature space. This paper evaluates several linear and non-linear... more
"Visual category recognition is a difficult task of significant interest to the machine learning and vision community. One of the principal hurdles is the high dimensional feature space. This paper evaluates several linear and non-linear dimensionality reduction techniques. A novel evaluation metric, the renyi entropy of the inter-vector Euclidean distance distribution, is introduced. This information theoretic measure judges the techniques on their preservation of structure in lower-dimensional sub-space. The popular dataset, Caltech-101 is utilized in the experiments. The results indicate that the techniques which preserve local neighbourhood structure performed best amongst the techniques evaluated in this paper.
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This paper presents a novel approach to learning a codebook for visual categorization, that resolves the key issue of intra-category appearance variation found in complex real world datasets. The codebook of visual-topics (semantically... more
This paper presents a novel approach to learning a codebook for visual categorization, that resolves the key issue of intra-category appearance variation found in complex real world datasets. The codebook of visual-topics (semantically equivalent descriptors) is made by grouping visual-words (syntactically equivalent descriptors) that are scattered in feature space. We analyze the joint distribution of images and visual-words using information theoretic co-clustering to discover visual-topics. Our approach is compared with the standard `Bag-of-Words' approach. The statistically significant performance improvement in all the datasets utilized (Pascal VOC 2006; VOC 2007; VOC 2010; Scene-15) establishes the efficacy of our approach.
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This paper presents a novel approach to learning a visual dictionary from sub-manifolds, using co-clustering, where each sub-manifold is associated with a semantically relevant part of a visual category. The standard dictionary learning... more
This paper presents a novel approach to learning a visual dictionary from sub-manifolds, using co-clustering, where each sub-manifold is associated with a semantically relevant part of a visual category. The standard dictionary learning technique, called `Bag-of-Features' is limited by problems of high-dimensionality, sparsity, and noise associated with affine invariant feature descriptors. Our approach draws inspiration from the relation between object part-based models; semantic topic models; non-negative matrix factorization of multivariate data; and sub-spaces in feature space, to resolve these issues in learning a dictionary. We use co-clustering, which performs simultaneous clustering and dimensionality reduction in an optimal way, to discover multiple semantically relevant sub-spaces. We use an information-theoretic and Euclidean divergence based co-clustering. Our approach is comprehensively evaluated on several popular datasets. This work constitutes a principled first step towards a semantically
meaningful dictionary, with regards to correspondence between object parts and multiple sub-manifolds, and is not intended to compete with state-of-the-art methods like sparse coding. It is specially pertinent for the future for learning a dictionary with increasing complexity of visual categories.
meaningful dictionary, with regards to correspondence between object parts and multiple sub-manifolds, and is not intended to compete with state-of-the-art methods like sparse coding. It is specially pertinent for the future for learning a dictionary with increasing complexity of visual categories.