Digital Commons Network^™

A Robust Synthetic Basis Feature Descriptor Implementation And Applications Pertaining To Visual Odometry, Object Detection, And Image Stitching, Lindsey Ann Raven

Theses and Dissertations

Feature detection and matching is an important step in many object tracking and detection algorithms. This paper discusses methods to improve upon previous work on the SYnthetic BAsis feature descriptor (SYBA) algorithm, which describes and compares image features in an efficient and discreet manner. SYBA utilizes synthetic basis images overlaid on a feature region of interest (FRI) to generate binary numbers that uniquely describe the feature contained within the FRI. These binary numbers are then used to compare against feature values in subsequent images for matching. However, in a non-ideal environment the accuracy of the feature matching suffers due to …

Video Frame Interpolation Via Adaptive Separable Convolution, Simon Niklaus, Long Mai, Feng Liu

Computer Science Faculty Publications and Presentations

Standard video frame interpolation methods first estimate optical flow between input frames and then synthesize an intermediate frame guided by motion. Recent approaches merge these two steps into a single convolution process by convolving input frames with spatially adaptive kernels that account for motion and re-sampling simultaneously. These methods require large kernels to handle large motion, which limits the number of pixels whose kernels can be estimated at once due to the large memory demand. To address this problem, this paper formulates frame interpolation as local separable convolution over input frames using pairs of 1D kernels. Compared to regular 2D …

Region Based Convolutional Neural Networks For Object Detection And Recognition In Adas Application, Sachit Kaul

Mechanical and Aerospace Engineering Theses

Object Detection and Recognition using Computer Vision has been a very interesting and a challenging field of study from past three decades. Recent advancements in Deep Learning and as well as increase in computational power has reignited the interest of researchers in this field in last decade. Implementing Machine Learning and Computer Vision techniques in scene classification and object localization particularly for automated driving purpose has been a topic of discussion in last half decade and we have seen some brilliant advancements in recent times as self-driving cars are becoming a reality. In this thesis we focus on Region based …

Shadow Patching: Exemplar-Based Shadow Removal, Ryan Sears Hintze

Theses and Dissertations

Shadow removal is an important problem for both artists and algorithms. Previous methods handle some shadows well but, because they rely on the shadowed data, perform poorly in cases with severe degradation. Image-completion algorithms can completely replace severely degraded shadowed regions, and perform well with smaller-scale textures, but often fail to reproduce larger-scale macrostructure that may still be visible in the shadowed region. This paper provides a general framework that leverages degraded (e.g., shadowed) data to guide the image completion process by extending the objective function commonly used in current state-of-the-art image completion energy-minimization methods. This approach achieves realistic shadow …

Fast On-Line Kernel Density Estimation For Active Object Localization, Anthony D. Rhodes, Max H. Quinn, Melanie Mitchell

Computer Science Faculty Publications and Presentations

A major goal of computer vision is to enable computers to interpret visual situations—abstract concepts (e.g., “a person walking a dog,” “a crowd waiting for a bus,” “a picnic”) whose image instantiations are linked more by their common spatial and semantic structure than by low-level visual similarity. In this paper, we propose a novel method for prior learning and active object localization for this kind of knowledge-driven search in static images. In our system, prior situation knowledge is captured by a set of flexible, kernel-based density estimations— a situation model—that represent the expected spatial structure of the given situation. These …

Prediction Of Ground Reaction Forces And Moments Via Supervised Learning Is Independent Of Participant Sex, Height And Mass, William R. Johnson, Ajmal Mian, Cyril J. Donnelly, David Lloyd, Jacqueline Alderson

ISBS Proceedings Archive

Accurate multidimensional ground reaction forces and moments (GRF/Ms) can be predicted from marker-based motion capture using Partial Least Squares (PLS) supervised learning. In this study, the correlations between known and predicted GRF/Ms are compared depending on whether the PLS model is trained using the discrete inputs of sex, height and mass. All three variables were found to be accounted for in the marker trajectory data, which serves to simplify data capture requirements and importantly, indicates that prediction of GRF/Ms can be achieved without pre-existing knowledge of such participant specific inputs. This multidisciplinary research approach significantly advances machine representation of real-world …

Delving Into Salient Object Subitizing And Detection, Shengfeng He, Jianbo Jiao, Xiaodan Zhang, Guoqiang Han, Rynson W.H Lau

Research Collection School Of Computing and Information Systems

Subitizing (i.e., instant judgement on the number) and detection of salient objects are human inborn abilities. These two tasks influence each other in the human visual system. In this paper, we delve into the complementarity of these two tasks. We propose a multi-task deep neural network with weight prediction for salient object detection, where the parameters of an adaptive weight layer are dynamically determined by an auxiliary subitizing network. The numerical representation of salient objects is therefore embedded into the spatial representation. The proposed joint network can be trained end-to-end using backpropagation. Experiments show the proposed multi-task network outperforms existing …

Improved Scoring Models For Semantic Image Retrieval Using Scene Graphs, Erik Timothy Conser

Dissertations and Theses

Image retrieval via a structured query is explored in Johnson, et al. [7]. The query is structured as a scene graph and a graphical model is generated from the scene graph's object, attribute, and relationship structure. Inference is performed on the graphical model with candidate images and the energy results are used to rank the best matches. In [7], scene graph objects that are not in the set of recognized objects are not represented in the graphical model. This work proposes and tests two approaches for modeling the unrecognized objects in order to leverage the attribute and relationship models to …

Refining Bounding-Box Regression For Object Localization, Naomi Lynn Dickerson

Dissertations and Theses

For the last several years, convolutional neural network (CNN) based object detection systems have used a regression technique to predict improved object bounding boxes based on an initial proposal using low-level image features extracted from the CNN. In spite of its prevalence, there is little critical analysis of bounding-box regression or in-depth performance evaluation. This thesis surveys an array of techniques and parameter settings in order to further optimize bounding-box regression and provide guidance for its implementation. I refute a claim regarding training procedure, and demonstrate the effectiveness of using principal component analysis to handle unwieldy numbers of features produced …

Cross-Correlation-Based Structural System Identification Using Unmanned Aerial Vehicles., Hyungchul Yoon, Vedhus Hoskere, Jong-Woong Park, Billie F. Spencer

Michigan Tech Publications

Computer vision techniques have been employed to characterize dynamic properties of structures, as well as to capture structural motion for system identification purposes. All of these methods leverage image-processing techniques using a stationary camera. This requirement makes finding an effective location for camera installation difficult, because civil infrastructure (i.e., bridges, buildings, etc.) are often difficult to access, being constructed over rivers, roads, or other obstacles. This paper seeks to use video from Unmanned Aerial Vehicles (UAVs) to address this problem. As opposed to the traditional way of using stationary cameras, the use of UAVs brings the issue of the camera …

Formresnet: Formatted Residual Learning For Image Restoration, Jianbo Jiao, Wei-Chih Tu, Shengfeng He

Research Collection School Of Computing and Information Systems

In this paper, we propose a deep CNN to tackle the image restoration problem by learning the structured residual. Previous deep learning based methods directly learn the mapping from corrupted images to clean images, and may suffer from the gradient exploding/vanishing problems of deep neural networks. We propose to address the image restoration problem by learning the structured details and recovering the latent clean image together, from the shared information between the corrupted image and the latent image. In addition, instead of learning the pure difference (corruption), we propose to add a 'residual formatting layer' to format the residual to …

An Intelligent Multimodal Upper-Limb Rehabilitation Robotic System, Alexandros Lioulemes

Computer Science and Engineering Dissertations

A traffic accident, a battlefield injury, or a stroke can lead to brain or musculoskeletal injuries that impact motor and cognitive functions and can drastically change a person's life. In such situations, rehabilitation plays a critical role in the ability of the patient to partially or totally regain motor function, but the optimal training approach remains unclear. Robotic technologies are recognized as powerful tools to promote neuroplasticity and stimulate motor re-learning. Moreover, they deliver high-intensity, repetitive, active and task-oriented training; in addition, they provide objective measurements for patient evaluation. The primary focus of this research is to investigate the development …

Min Kao Drone Tour, Ethan Black, Holden Coppock, Victoria Florence, Elliot Greenlee, Caleb Mennen, Jacob Pollack

Chancellor’s Honors Program Projects

No abstract provided.

Understanding High Resolution Aerial Imagery Using Computer Vision Techniques, Fan Wang

Theses

Computer vision can make important contributions to the analysis of remote sensing satellite or aerial imagery. However, the resolution of early satellite imagery was not sufficient to provide useful spatial features. The situation is changing with the advent of very-high-spatial-resolution (VHR) imaging sensors. This change makes it possible to use computer vision techniques to perform analysis of man-made structures. Meanwhile, the development of multi-view imaging techniques allows the generation of accurate point clouds as ancillary knowledge.

This dissertation aims at developing computer vision and machine learning algorithms for high resolution aerial imagery analysis in the context of application problems including …

Deshadownet: A Multi-Context Embedding Deep Network For Shadow Removal, Liangqiong Qu, Jiandong Tian, Shengfeng He, Yandong Tang, Rynson W. H. Lau

Research Collection School Of Computing and Information Systems

Shadow removal is a challenging task as it requires the detection/annotation of shadows as well as semantic understanding of the scene. In this paper, we propose an automatic and end-to-end deep neural network (DeshadowNet) to tackle these problems in a unified manner. DeshadowNet is designed with a multi-context architecture, where the output shadow matte is predicted by embedding information from three different perspectives. The first global network extracts shadow features from a global view. Two levels of features are derived from the global network and transferred to two parallel networks. While one extracts the appearance of the input image, the …

The Emotional Impact Of Audio - Visual Stimuli, Titus Pallithottathu Thomas

Theses

Induced affect is the emotional effect of an object on an individual. It can be quantified through two metrics: valence and arousal. Valance quantifies how positive or negative something is, while arousal quantifies the intensity from calm to exciting. These metrics enable researchers to study how people opine on various topics. Affective content analysis of visual media is a challenging problem due to differences in perceived reactions. Industry standard machine learning classifiers such as Support Vector Machines can be used to help determine user affect. The best affect-annotated video datasets are often analyzed by feeding large amounts of visual and …

Motherbrain Swarm Robots, Tam Van, Mytch Johnson, Matthew Ng, Darius Holmgren

Computer Engineering

A trial in small-scale, cheap fleet autonomy with computer vision as the feedback controls system.

Ping Pong Trainer, Aaron Atamian

Computer Engineering

This is a ping pong trainer. It shoots out ping pong balls to red targets using computer vision techniques.

Computer Vision Based Route Mapping, Ryan S. Kehlenbeck, Zachary Cody

Computer Science and Software Engineering

The problem our project solves is the integration of edge detection techniques with mapping libraries to display routes based on images. To do this, we used the OpenCV library within an Android application. This application lets a user import an image from their device, and uses edge detection to pull out a path from the image. The application can find the user's location and uses it alongside the path data from the image to create a route using the physical roads near the location. The shape of the route matches the edges from the given image and the user can …

Using Computer Vision To Build A Predictive Model Of Fruit Shelf-Life, Nandan G. Thor

Master's Theses

Computer vision is becoming a ubiquitous technology in many industries on account of its speed, accuracy, and long-term cost efficacy. The ability of a computer vision system to quickly and efficiently make quality decisions has made computer vision a popular technology on inspection lines. However, few companies in the agriculture industry use computer vision because of the non-uniformity of sellable produce. The small number of agriculture companies that do utilize computer vision use it to extract features for size sorting or for a binary grading system: if the piece of fruit has a certain color, certain shape, and certain size, …

Bayesian Optimization For Refining Object Proposals, With An Application To Pedestrian Detection, Anthony D. Rhodes

Student Research Symposium

We devise an algorithm using a Bayesian optimization framework in conjunction with contextual visual data for the efficient localization of objects in still images. Recent research has demonstrated substantial progress in object localization and related tasks for computer vision. However, many current state-of-the-art object localization procedures still suffer from inaccuracy and inefficiency, in addition to failing to successfully leverage contextual data. We address these issues with the current research.

Our method encompasses an active search procedure that uses contextual data to generate initial bounding-box proposals for a target object. We train a convolutional neural network to approximate an offset distance …

Developing Computer Vision Technology To Automate Pitch Analysis In Baseball., Mahdi Moalla

Electronic Theses and Dissertations

Lokator is a baseball training system designed to document pitch location while teaching pitch command, selection and sequencing. It is composed of a pitching target and a smartphone app. The target is divided into a set of zones to identify the pitch location. The main limitation of the current system is its reliance on the user's feedback. After each throw, the pitcher or the coach needs to identify and report the target's zone that was hit by the ball by just relying on the naked eye. The purpose of this thesis is to investigate the possibility of using computer vision …

A Performance And Visualization Study On Inlet Geometries Of A Cross-Flow Fan., Yoel Tanquero

Electronic Theses and Dissertations

A study was conducted to characterize the flow-field in the suction region of different inlet geometries of a cross-flow fan. The characterization was accomplished by correlating the static performance curves measured for each fan-inlet configuration to the streamline plot obtained using a particle tracking velocimetry (PTV) measurement system at three constant flow rates (25, 40, and 55 CFM). The PTV measurement system used was developed by the author and uses helium bubbles as tracers, an LED light sheet, a slow motion camera, and a Matlab program. Four inlet geometry design variables were defined and independently studied to evaluate the effect …

Bayesian Optimization For Refining Object Proposals, Anthony D. Rhodes, Jordan Witte, Melanie Mitchell, Bruno Jedynak

Computer Science Faculty Publications and Presentations

We develop a general-purpose algorithm using a Bayesian optimization framework for the efficient refinement of object proposals. While recent research has achieved substantial progress for object localization and related objectives in computer vision, current state-of-the-art object localization procedures are nevertheless encumbered by inefficiency and inaccuracy. We present a novel, computationally efficient method for refining inaccurate bounding-box proposals for a target object using Bayesian optimization. Offline, image features from a convolutional neural network are used to train a model to predict an object proposal’s offset distance from a target object. Online, this model is used in a Bayesian active search to …

Evaluation Of The Parasight Platform For Malaria Diagnosis, Yochay Eshel, Arnon Houri-Yafin, Hagai Benkuzari, Natalie Lezmy, Mamta Soni, Malini Charles, Jayanthi Swaminathan, Hilda Solomon, Pavithra Sampathkumar, Zul Premji, Caroline Mbithi, Zaitun Nneka, Simon Onsongo, Daniel Maina, Sarah Levy-Schreier, Caitlin Lee Cohen, Dan Gluck, Joseph Joel Pollak, Seth J. Salpeter

Pathology, East Africa

The World Health Organization estimates that nearly 500 million malaria tests are performed annually. While microscopy and rapid diagnostic tests (RDTs) are the main diagnostic approaches, no single method is inexpensive, rapid, and highly accurate. Two recent studies from our group have demonstrated a prototype computer vision platform that meets those needs. Here we present the results from two clinical studies on the commercially available version of this technology, the Sight Diagnostics Parasight platform, which provides malaria diagnosis, species identification, and parasite quantification. We conducted a multisite trial in Chennai, India (Apollo Hospital [n = 205]), and Nairobi, Kenya …

Tandem 2.0: Image And Text Data Generation Application, Christopher J. Vitale

Dissertations, Theses, and Capstone Projects

First created as part of the Digital Humanities Praxis course in the spring of 2012 at the CUNY Graduate Center, Tandem explores the generation of datasets comprised of text and image data by leveraging Optical Character Recognition (OCR), Natural Language Processing (NLP) and Computer Vision (CV). This project builds upon that earlier work in a new programming framework. While other developers and digital humanities scholars have created similar tools specifically geared toward NLP (e.g. Voyant-Tools), as well as algorithms for image processing and feature extraction on the CV side, Tandem explores the process of developing a more robust and user-friendly …

Applications Of Machine Learning And Computer Vision For Smart Infrastructure Management In Civil Engineering, Shounak Mitra

Master's Theses and Capstones

Machine Learning and Computer Vision are the two technologies that have innovative applications in diverse fields, including engineering, medicines, agriculture, astronomy, sports, education etc. The idea of enabling machines to make human like decisions is not a recent one. It dates to the early 1900s when analogies were drawn out between neurons in a human brain and capability of a machine to function like humans. However, major advances in the specifics of this theory were not until 1950s when the first experiments were conducted to determine if machines can support artificial intelligence. As computation powers increased, in the form of …

Plantcv V2: Image Analysis Software For High-Throughput Plant Phenotyping, Malia A. Gehan, Noah Fahlgren, Arash Abbasi, Jeffrey C. Berry, Steven T. Callen, Leonardo Chavez, Andrew N. Doust, Max J. Feldman, Kerrigan B. Gilbert, John G. Hodge, J. Steen Hoyer, Andy Lin, Suxing Liu, César Lizárraga, Argelia Lorence, Michael Miller, Eric Platon, Monica Tessman, Tony Sax

Department of Agronomy and Horticulture: Faculty Publications

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major …

Thinking Outside The Box: Computing 3d Volume In 2d, Alexandra D. Morris

Senior Projects Fall 2017

This project explores how to compute 3D volume of cardboard boxes in 2D without a calibrated camera. Computer vision techniques to obtain 3D volume typically require camera calibration, the standard method for mapping 3D points to 2D. We created our own solution that doesn’t rely on camera calibration and obtains the areas of each box with unknown dimensions with the help of a chessboard pattern placed on each box side. The solution is a proportion that given the box area in pixels, chessboard pattern in pixels, and the chessboard pattern in inches, determines the box area in inches. We tested …

A Neural Network Approach To Visibility Range Estimation Under Foggy Weather Conditions, Hazar Chaabani, Faouzi Kamoun, Hichem Bargaoui, Fatma Outay, Ansar Ul Haque Yasar

All Works

© 2017 The Authors. Published by Elsevier B.V. The degradation of visibility due to foggy weather conditions is a common trigger for road accidents and, as a result, there has been a growing interest to develop intelligent fog detection and visibility range estimation systems. In this contribution, we provide a brief overview of the state-of-the-art contributions in relation to estimating visibility distance under foggy weather conditions. We then present a neural network approach for estimating visibility distances using a camera that can be fixed to a roadside unit (RSU) or mounted onboard a moving vehicle. We evaluate the proposed solution …