Image Segmentation from RGBD Images by 3D Point Cloud Attributes and High-Level Features

In this paper, an approach is developed for segmenting an image into major surfaces and potential objects using RGBD images and 3D point cloud data retrieved from a Kinect sensor. In the proposed segmentation algorithm, depth and RGB data are mapped together. Color, texture, XYZ world coordinates, and normal-, surface-, and graph-based segmentation index features are then generated for each pixel point. These attributes are used to cluster similar points together and segment the image. The inclusion of new depth-related features provided improved segmentation performance over RGB-only algorithms by resolving illumination and occlusion problems that cannot be handled using graph-based segmentation algorithms, as well as accurately identifying pixels associated with the main structure components of rooms (walls, ceilings, floors). Since each segment is a potential object or structure, the output of this algorithm is intended to be used for object recognition. The algorithm has been tested on commercial building images and results show the usability of the algorithm in real time applications

Gesture based Human Computer Interaction for Athletic Training

The invention of depth sensors for mobile devices, has led to availability of relatively inexpensive high-resolution depth and visual (RGB) sensing for a wide range of applications. The complementary nature of the depth and visual information opens up new opportunities to solve fundamental problems in object and activity recognition, people tracking, 3D mapping and localization, etc. One of the most interesting challenges that can be tackled by using these sensors is tracking the body movements of athletes and providing natural interaction as a result. In this study depth sensors and gesture recognition tools will be used to analyze the position and angle of an athlete’s body parts thought out an exercise. The goal is to assess the training performance of an athlete and decrease injury risk by giving warnings when the trainer is performing a high risk activity

Assessment of Levee Erosion using Image Processing and Contextual Cueing

Soil erosion is one of the most severe land degradation problems afflicting many parts of the world where topography of the land is relatively steep. Due to inaccessibility to steep terrain, such as slopes in levees and forested mountains, advanced data processing techniques can be used to identify and assess high risk erosion zones. Unlike existing methods that require human observations, which can be expensive and error-prone, the proposed approach uses a fully automated algorithm to indicate when an area is at risk of erosion; this is accomplished by processing Landsat and aerial images taken using drones. In this paper the image processing algorithm is presented, which can be used to identify the scene of an image by classifying it in one of six categories: levee, mountain, forest, degraded forest, cropland, grassland or orchard. This paper focuses on automatic scene detection using global features with local representations to show the gradient structure of an image. The output of this work counts as a contextual cueing and can be used in erosion assessment, which can be used to predict erosion risks in levees. We also discuss the environmental implications of deferred erosion control in levees

Optimizing the best play in basketball using deep learning

In a close game of basketball, victory or defeat can depend on a single shot. Being able to identify the best player and play scenario for a given opponent’s defense can increase the likelihood of victory. Progress in technology has resulted in an increase in the popularity of sports analytics over the last two decades, where data can be used by teams and individuals to their advantage. A popular data analytic technique in sports is deep learning. Deep learning is a branch of machine learning that finds patterns within big data and can predict future decisions. The process relies on a raw dataset for training purposes. It can be utilized in sports by using deep learning to read the data and provide a better understanding of where players can be the most successful. In this study the data used were on division I women’s basketball games of a private university in a conference featuring top 25 teams. Deep learning was applied to optimize the best offensive play in a game scenario for a given set of features. The system is used to predict the play that would lead to the highest probability of a made shot

Image processing for erosion control of open-pit mines

Soil erosion is one of the most severe land degradation problems afflicting many parts of the world where topography of the land is relatively steep. Owing to inaccessibility to this steep terrain, such as slopes in open-pit mines and forested mountains, advanced data processing techniques are required to identify and predict high risk erosion zones. Unlike existing methods that require human observations, which can be expensive and error-prone, the proposed approach uses a fully automated algorithm to indicate when an area is at risk of erosion; this is accomplished by processing Landsat and aerial images. In this paper, an image processing algorithm is presented to identify the scene of an image by classifying it in one of six categories: open-pit mine, mountain, forest, degraded forest, cropland, and grassland or orchard. This paper focuses on automatic scene detection using global features with local representations to show the gradient structure of an image. The output of this work counts as a contextual cueing and can be used in erosion detection, and for erosion control in a variety of applications; particularly, predicting erosion risks in open-pit mines. We also discuss the environmental implications of deferred erosion control in open-pit mines.

Machine Learning Approach for Pronominal Anaphora Resolution Based on Linguistic and Computational Features

Anaphora resolution is the problem of resolving references of pronouns to antecedents (previously mentioned noun phrases) in text documents. It is a fundamental preprocessing step in text understanding (semantic) applications, including dialogue and story understanding, document summarization, information extraction, machine translation, and recognizing entailment relations in text. We propose a set of computational and linguistic features to resolve the pronominal anaphora in text documents for a machine learning approach. The system was evaluated on the BBN Pronoun Coreference and Entity Type Corpus, and an F-measure of 89% was obtained. The system was also tested on different genre of document and the performance is compared with the result of the annotated corpus

Image Segmentation from RGBD Images by 3D Point Cloud Attributes and High-Level Features

In this paper, an approach is developed for segmenting an image into major surfaces and potential objects using RGBD images and 3D point cloud data retrieved from a Kinect sensor. In the proposed segmentation algorithm, depth and RGB data are mapped together. Color, texture, XYZ world coordinates, and normal-, surface-, and graph-based segmentation index features are then generated for each pixel point. These attributes are used to cluster similar points together and segment the image. The inclusion of new depth-related features provided improved segmentation performance over RGB-only algorithms by resolving illumination and occlusion problems that cannot be handled using graph-based segmentation algorithms, as well as accurately identifying pixels associated with the main structure components of rooms (walls, ceilings, floors). Since each segment is a potential object or structure, the output of this algorithm is intended to be used for object recognition. The algorithm has been tested on commercial building images and results show the usability of the algorithm in real time applications

Automatic Detection of Nominal Entities in Speech for Enriched Content Search

In this work, a methodology is developed to detect sentient actors in spoken stories. Meta-tags are then saved to XML files associated with the audio files. A recursive approach is used to find actor candidates and features which are then classified using machine learning approaches. Results of the study indicate that the methodology performed well on a narrative based corpus of children\u27s stories. Using Support Vector Machines for classification, an F-measure accuracy score of 86% was achieved for both named and unnamed entities. Additionally, feature analysis indicated that speech features were very useful when detecting unnamed actors

Assessment of Levee Erosion using Image Processing and Contextual Cueing

Soil erosion is one of the most severe land degradation problems afflicting many parts of the world where topography of the land is relatively steep. Due to inaccessibility to steep terrain, such as slopes in levees and forested mountains, advanced data processing techniques can be used to identify and assess high risk erosion zones. Unlike existing methods that require human observations, which can be expensive and error-prone, the proposed approach uses a fully automated algorithm to indicate when an area is at risk of erosion; this is accomplished by processing Landsat and aerial images taken using drones. In this paper the image processing algorithm is presented, which can be used to identify the scene of an image by classifying it in one of six categories: levee, mountain, forest, degraded forest, cropland, grassland or orchard. This paper focuses on automatic scene detection using global features with local representations to show the gradient structure of an image. The output of this work counts as a contextual cueing and can be used in erosion assessment, which can be used to predict erosion risks in levees. We also discuss the environmental implications of deferred erosion control in levees