Action Recognition by Hierarchical Mid-level Action Elements

Realistic videos of human actions exhibit rich spatiotemporal structures at multiple levels of granularity: an action can always be decomposed into multiple finer-grained elements in both space and time. To capture this intuition, we propose to represent videos by a hierarchy of mid-level action elements (MAEs), where each MAE corresponds to an action-related spatiotemporal segment in the video. We introduce an unsupervised method to generate this representation from videos. Our method is capable of distinguishing action-related segments from background segments and representing actions at multiple spatiotemporal resolutions. Given a set of spatiotemporal segments generated from the training data, we introduce a discriminative clustering algorithm that automatically discovers MAEs at multiple levels of granularity. We develop structured models that capture a rich set of spatial, temporal and hierarchical relations among the segments, where the action label and multiple levels of MAE labels are jointly inferred. The proposed model achieves state-of-the-art performance in multiple action recognition benchmarks. Moreover, we demonstrate the effectiveness of our model in real-world applications such as action recognition in large-scale untrimmed videos and action parsing

Visual Geo-Localization and Location-Aware Image Understanding

Geo-localization is the problem of discovering the location where an image or video was captured. Recently, large scale geo-localization methods which are devised for ground-level imagery and employ techniques similar to image matching have attracted much interest. In these methods, given a reference dataset composed of geo-tagged images, the problem is to estimate the geo-location of a query by finding its matching reference images. In this dissertation, we address three questions central to geo-spatial analysis of ground-level imagery: 1) How to geo-localize images and videos captured at unknown locations? 2) How to refine the geo-location of already geo-tagged data? 3) How to utilize the extracted geo-tags? We present a new framework for geo-locating an image utilizing a novel multiple nearest neighbor feature matching method using Generalized Minimum Clique Graphs (GMCP). First, we extract local features (e.g., SIFT) from the query image and retrieve a number of nearest neighbors for each query feature from the reference data set. Next, we apply our GMCP-based feature matching to select a single nearest neighbor for each query feature such that all matches are globally consistent. Our approach to feature matching is based on the proposition that the first nearest neighbors are not necessarily the best choices for finding correspondences in image matching. Therefore, the proposed method considers multiple reference nearest neighbors as potential matches and selects the correct ones by enforcing the consistency among their global features (e.g., GIST) using GMCP. Our evaluations using a new data set of 102k Street View images shows the proposed method outperforms the state-of-the-art by 10 percent. Geo-localization of images can be extended to geo-localization of a video. We have developed a novel method for estimating the geo-spatial trajectory of a moving camera with unknown intrinsic parameters in a city-scale. The proposed method is based on a three step process: 1) individual geo-localization of video frames using Street View images to obtain the likelihood of the location (latitude and longitude) given the current observation, 2) Bayesian tracking to estimate the frame location and video\u27s temporal evolution using previous state probabilities and current likelihood, and 3) applying a novel Minimum Spanning Trees based trajectory reconstruction to eliminate trajectory loops or noisy estimations. Thus far, we have assumed reliable geo-tags for reference imagery are available through crowdsourcing. However, crowdsourced images are well known to suffer from the acute shortcoming of having inaccurate geo-tags. We have developed the first method for refinement of GPS-tags which automatically discovers the subset of corrupted geo-tags and refines them. We employ Random Walks to discover the uncontaminated subset of location estimations and robustify Random Walks with a novel adaptive damping factor that conforms to the level of noise in the input. In location-aware image understanding, we are interested in improving the image analysis by putting it in the right geo-spatial context. This approach is of particular importance as the majority of cameras and mobile devices are now being equipped with GPS chips. Therefore, developing techniques which can leverage the geo-tags of images for improving the performance of traditional computer vision tasks is of particular interest. We have developed a location-aware multimodal approach which incorporates business directories, textual information, and web images to identify businesses in a geo-tagged query image

Effect of cutting parameters on cutting temperature of TiAL6V4 alloy

A Finite Element Modeling (FEM) and Simulation was Used to Investigate the Effect of Tool Rake Angle, Cutting Speed and Feed Rate on the Cutting Temperature of Tial6v4 Alloy. the Purpose of this Study was to Find Proper Cutting Parameters for Machining of Titanium Alloy where Cutting Temperature was Lowest. A FEM Based on ABAQUS Software which Involves Jonson-Cook Material Model and Coulomb’s Friction Law was Applied to Simulate an Orthogonal Cutting Process. in this Simulation Work, a Range of Tool Rake Angle from 0° to 10°, a Range of Cutting Speed from 300 m/min to 600 m/min and a Range of Feed Rate between 0.1 Rev/mm and 0.25 Rev/mm were Investigated. the Simulation Results Indicated that Increase in Rake Angle Reduces Cutting Temperature while Increasing Cutting Speed and Feed Rate Increase the Cutting Temperature

Effect of cutting parameters on tool-chip interface temperature in an orthogonal turning process

The aim of this paper is to investigate the effect of cutting speed and uncut chip thickness on cutting performance. A Finite Element Method (FEM) based on the ABAQUS explicit software which involves Johnson-Cook material mode and Coulombs friction law was used to simulate of High Speed Machining (HSM) of AISI 1045 steel. In this simulation work, feed rate ranging from 0.05 mm/rev to 0.13 mm/rev and cutting speed ranging from 200 m/min to 600 m/min at three different cutting speeds were investigated. From the simulation results it was observed that increasing feed rate and cutting speed lead to increase temperature and stress distribution at tool/chip interface. The results obtained from this study are highly essential to predict machining induced residual stresses and thermo-mechanical deformation related properties on the machined surface

Finite element analysis of aluminum-Kevlar/Epoxy pressure vessel

In this present work, the composite pressure vessel type three has been investigated by finite element method (FEM). The aluminum pressure vessel reinforced with Kevlar/Epoxy (Aramid 149) was analyzed under internal pressure to predict the ultimate failure pressure of the vessel. Also the optimum winding angle which provides the highest strength for the vessel was determined by applying Tsai-Wu and Tsai-Hill failure theories. The asymmetric fiber orientation for six different winding angles was utilized to reinforce the aluminum vessel. The commercial code ABAQUS/CAE was employed to analyze the composite vessel. Results obtained from the simulation were in good consistency with the analytical and the experimental outcomes

Finite element modeling and simulation of machining of titanium alloy and H13 tool steel using PCBN tool

This paper deals with finite element modeling (FEM) and simulation of machining of titanium alloy and H-13 tool steel. Titanium alloys are very suitable for airframe manufacture and aircraft as H-13 uses forging dies and machined die casting. The machinability of both metals was evaluated by high temperature and tool wear. Finite element simulation was performed with ABAQUS explicit software to predict cutting temperature and stress distribution during metal cutting process. The purpose of this study was evaluation the performance of PCBN cutting tool material on machining of titanium alloy and H-13. It was found that PCBN tool can resistant well against high thermal shocks, high temperature and stress distribution when machining difficult to cut materials. The results can give a better understanding of cutting tool material for metal cutting process

Failure analysis of aluminum reinforced composite vessel

At this paper attempts have been made to determine the effects of internal pressure on the reinforced composite pressure vessel. Finite element analysis (FEA) along with the Tsai-Wu failure criterion was utilized to predict the failure pressure of the vessel and the optimum fiber angle orientation. Six layers of E-glass/Epoxy and Graphite/Epoxyfibers have been selected to reinforce the aluminium vessel. Fibers were oriented with six different winding angles of 300, 450, 550, 600, 750 and 900 at asymmetric fiber orientation. The commercial code ABAQUS CAE was employed to simulate the model and analyse the structure. Results were revealed that Graphite/Epoxy has higher strength in comparison with E-glass/Epoxy fiber. Also it was observed that for both composite materials 550fiber angle is the optimum winding angle. Results were compared to the experimental ones and there was a good agreement between them

Finite element modeling of the effect of tool rake angle on cutting force and tool temperature during high speed machining of AISI 1045 steel

A finite element model (FEM) of an orthogonal metal-cutting process is used to study the influence of tool rake angle on the cutting force and tool temperature. The model involves Johnson-Cook material model and Coulomb’s friction law. A tool rake angle ranging from 0° to 20° and a cutting speed ranging from 300 to 600 m/min were considered in this simulation. The results of this simulation work are consistent optimum tool rake angle for high speed machining (HSM) of AISI 1045 medium carbon steel. It was observed that there was a suitable rake angle between 10° and 18° for cutting speeds of 300 and 433 m/min where cutting force and temperature were lowest. However, there was not optimum rake angle for cutting speeds of 550 and 600 m/min. This paper can contribute in optimization of cutting tool for metal cutting process