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Large-Scale Discovery Of Visual Features For Object Recognition, Drew Linsley, Sven Eberhardt, Dan Shiebler, Thomas Serre 2017 Brown University

Large-Scale Discovery Of Visual Features For Object Recognition, Drew Linsley, Sven Eberhardt, Dan Shiebler, Thomas Serre

MODVIS Workshop

A central goal in vision science is to identify features that are important for object and scene recognition. Reverse correlation methods have been used to uncover features important for recognizing faces and other stimuli with low intra-class variability. However, these methods are less successful when applied to natural scenes with variability in their appearance.

To rectify this, we developed Clicktionary, a web-based game for identifying features for recognizing real-world objects. Pairs of participants play together in different roles to identify objects: A “teacher” reveals image regions diagnostic of the object’s category while a “student” tries to recognize the object ...


Target Detection With Neural Network Hardware, Hollis Bui, Garrett Massman, Nikolas Spangler, Jalen Tarvin, Luke Bechtel, Kevin Dunn, Shawn Bradford 2017 University of Tennessee, Knoxville

Target Detection With Neural Network Hardware, Hollis Bui, Garrett Massman, Nikolas Spangler, Jalen Tarvin, Luke Bechtel, Kevin Dunn, Shawn Bradford

University of Tennessee Honors Thesis Projects

No abstract provided.


Hierarchical Active Learning Application To Mitochondrial Disease Protein Dataset, James D. Duin 2017 University of Nebraska-Lincoln

Hierarchical Active Learning Application To Mitochondrial Disease Protein Dataset, James D. Duin

Computer Science and Engineering: Theses, Dissertations, and Student Research

This study investigates an application of active machine learning to a protein dataset developed to identify the source of mutations which give rise to mitochondrial disease. The dataset is labeled according to the protein's location of origin in the cell; whether in the mitochondria or not, or a specific target location in the mitochondria's outer or inner membrane, its matrix, or its ribosomes. This dataset forms a labeling hierarchy. A new machine learning approach is investigated to learn the high-level classifier, i.e., whether the protein is a mitochondrion, by separately learning finer-grained target compartment concepts and combining ...


Improving Deep Learning Image Recognition Performance Using Region Of Interest Localization Networks, AbdulWahab Kabani 2017 The University of Western Ontario

Improving Deep Learning Image Recognition Performance Using Region Of Interest Localization Networks, Abdulwahab Kabani

Electronic Thesis and Dissertation Repository

Deep Learning has been gaining momentum and achieving the state-of-the-art results on many visual recognition problems. The roots of this field can be traced back to the 1940s of the 20th century. The field has recently started delivering some interesting results on many image understanding problems. This is mainly due to the availability of powerful hardware that can accelerate the training process. In addition, the growth of the Internet and imaging devices such as mobile phones and cameras has contributed to the increase in the amount of data that can be used to train neural networks. All of these factors ...


Visual Transfer Learning In The Absence Of The Source Data, Shuang Ao 2017 The University of Western Ontario

Visual Transfer Learning In The Absence Of The Source Data, Shuang Ao

Electronic Thesis and Dissertation Repository

Image recognition has become one of the most popular topics in machine learning. With the development of Deep Convolutional Neural Networks (CNN) and the help of the large scale labeled image database such as ImageNet, modern image recognition models can achieve competitive performance compared to human annotation in some general image recognition tasks. Many IT companies have adopted it to improve their visual related tasks. However, training these large scale deep neural networks requires thousands or even millions of labeled images, which is an obstacle when applying it to a specific visual task with limited training data. Visual transfer learning ...


Semantic Description Of Activities In Videos, Fillipe Dias Moreira De Souza 2017 University of South Florida

Semantic Description Of Activities In Videos, Fillipe Dias Moreira De Souza

Graduate Theses and Dissertations

Description of human activities in videos results not only in detection of actions and objects but also in identification of their active semantic relationships in the scene. Towards this broader goal, we present a combinatorial approach that assumes availability of algorithms for detecting and labeling objects and actions, albeit with some errors. Given these uncertain labels and detected objects, we link them into interpretative structures using domain knowledge encoded with concepts of Grenander’s general pattern theory. Here a semantic video description is built using basic units, termed generators, that represent labels of objects or actions. These generators have multiple ...


Real-Time Vision-Based Lane Detection With 1d Haar Wavelet Transform On Raspberry Pi, Vikas Reddy Sudini 2017 Utah State University

Real-Time Vision-Based Lane Detection With 1d Haar Wavelet Transform On Raspberry Pi, Vikas Reddy Sudini

All Graduate Theses and Dissertations

Rapid progress is being made towards the realization of autonomous cars. Since the technology is in its early stages, human intervention is still necessary in order to ensure hazard-free operation of autonomous driving systems. Substantial research efforts are underway to enhance driver and passenger safety in autonomous cars. Toward that end GreedyHaarSpiker, a real-time vision-based lane detection algorithm is proposed for road lane detection in different weather conditions. The algorithm has been implemented in Python 2.7 with OpenCV 3.0 and tested on a Raspberry Pi 3 Model B ARMv8 1GB RAM coupled to a Raspberry Pi camera board ...


Detection And Recognition Of Traffic Signs Inside The Attentional Visual Field Of Drivers, Seyedjamal Zabihi 2017 The University of Western Ontario

Detection And Recognition Of Traffic Signs Inside The Attentional Visual Field Of Drivers, Seyedjamal Zabihi

Electronic Thesis and Dissertation Repository

Traffic sign detection and recognition systems are essential components of Advanced Driver Assistance Systems and self-driving vehicles. In this contribution we present a vision-based framework which detects and recognizes traffic signs inside the attentional visual field of drivers. This technique takes advantage of the driver's 3D absolute gaze point obtained through the combined use of a front-view stereo imaging system and a non-contact 3D gaze tracker. We used a linear Support Vector Machine as a classifier and a Histogram of Oriented Gradient as features for detection. Recognition is performed by using Scale Invariant Feature Transforms and color information. Our ...


Vision-Based Mobile Robotic Platform For Autonomous Landing Of Quadcopters, Timothy R. Joe 2017 University of Nebraska at Omaha

Vision-Based Mobile Robotic Platform For Autonomous Landing Of Quadcopters, Timothy R. Joe

Student Research and Creative Activity Fair

This project deals with the development of a vision-based control algorithm to assist quadcopters in the landing process. For demonstration purposes, the approach has been implemented in a mobile robotic platform (turtlebot). In this project, the objective is to use the mobile robot as a landing platform. The camera on-board the mobile robot detects the quadcopter (AprilTag attached to the flying robot) and keeps track of it. Based on this idea, the proposed approach estimates in real-time the landing zone. Once this zone is calculated, the mobile robot moves towards this area, stops under the quadcopter, and acts as a ...


Passive Chemical Detection System For Uavs, John Hare 2185222 2017 University of Nebraska at Omaha

Passive Chemical Detection System For Uavs, John Hare 2185222

Student Research and Creative Activity Fair

In this project we address the problem of autonomously detecting airborne gas particles using gas sensors that are mobilized using unmanned aerial vehicles (UAVs). The main hypothesis we investigate is whether a commercially available, off-the-shelf gas sensor can be suitably integrated on a UAV platform to detect ambient gas particles. The main challenges in this problem include addressing the weight constraints of the UAV’s payload and registering a consistent reading on the gas sensor in the presence of the turbulence in the air caused by the UAV’s rotors. To verify our hypothesis, we designed a passive funneling mechanism ...


A Modular Robotic System For Assessment And Exercise Of Human Movement, Mohan Sai Ambati 2017 University of Nebraska at Omaha

A Modular Robotic System For Assessment And Exercise Of Human Movement, Mohan Sai Ambati

Student Research and Creative Activity Fair

This project targets the problem of developing a wearable modular robotic system, for assessing human movement and providing different types of exercises for the user. The system attempts to provide not only a variety of exercises (concentric, eccentric, assisted and resisted), but also to assess the change in variability of the movement as the subject shows functional improvement. The system will not only be useful for patients with sensorimotor problem such as stroke, Parkinson’s, cerebral palsy, but also for special populations such as astronauts who spend long periods of time in space and experience muscle atrophy. In this work ...


Developing Predictive Models Of Driver Behaviour For The Design Of Advanced Driving Assistance Systems, Seyed Mohsen Zabihi 2017 The University of Western Ontario

Developing Predictive Models Of Driver Behaviour For The Design Of Advanced Driving Assistance Systems, Seyed Mohsen Zabihi

Electronic Thesis and Dissertation Repository

World-wide injuries in vehicle accidents have been on the rise in recent

years, mainly due to driver error. The main objective of this research is to

develop a predictive system for driving maneuvers by analyzing the cognitive

behavior (cephalo-ocular) and the driving behavior of the driver (how the vehicle

is being driven). Advanced Driving Assistance Systems (ADAS) include

different driving functions, such as vehicle parking, lane departure warning,

blind spot detection, and so on. While much research has been performed on

developing automated co-driver systems, little attention has been paid to the

fact that the driver plays an important role ...


Exploring Algorithms To Recognize Similar Board States In Arimaa, Malik Khaleeque Ahmed 2017 Rowan University

Exploring Algorithms To Recognize Similar Board States In Arimaa, Malik Khaleeque Ahmed

Theses and Dissertations

The game of Arimaa was invented as a challenge to the field of game-playing artificial intelligence, which had grown somewhat haughty after IBM's supercomputer Deep Blue trounced world champion Kasparov at chess. Although Arimaa is simple enough for a child to learn and can be played with an ordinary chess set, existing game-playing algorithms and techniques have had a difficult time rising up to the challenge of defeating the world's best human Arimaa players, mainly due to the game's impressive branching factor. This thesis introduces and analyzes new algorithms and techniques that attempt to recognize similar board ...


Malware Detection Using The Index Of Coincidence, Bhavna Gurnani 2017 San Jose State University

Malware Detection Using The Index Of Coincidence, Bhavna Gurnani

Master's Projects

In this research, we apply the Index of Coincidence (IC) to problems in malware analysis. The IC, which is often used in cryptanalysis of classic ciphers, is a technique for measuring the repeat rate in a string of symbols. A score based on the IC is applied to a variety of challenging malware families. We nd that this relatively simple IC score performs surprisingly well, with superior results in comparison to various machine learning based scores, at least in some cases.


Explorations Into Machine Learning Techniques For Precipitation Nowcasting, Aditya Nagarajan 2017 University of Massachusetts - Amherst

Explorations Into Machine Learning Techniques For Precipitation Nowcasting, Aditya Nagarajan

Masters Theses May 2014 - current

Recent advances in cloud-based big-data technologies now makes data driven solutions feasible for increasing numbers of scientific computing applications. One such data driven solution approach is machine learning where patterns in large data sets are brought to the surface by finding complex mathematical relationships within the data. Nowcasting or short-term prediction of rainfall in a given region is an important problem in meteorology. In this thesis we explore the nowcasting problem through a data driven approach by formulating it as a machine learning problem.

State-of-the-art nowcasting systems today are based on numerical models which describe the physical processes leading to ...


Robot Lives Matter?, Christopher Boolukos (Class of 2017) 2017 Sacred Heart University

Robot Lives Matter?, Christopher Boolukos (Class Of 2017)

Writing Across the Curriculum

It’s 2016 and slavery is still a brutal reality around the world and a crime against humanity. The human race has never been shy when it comes to enslaving fellow human beings, so with progress in robotics and AI, we will soon be able to enslave robots to do our bidding. This poses a serious moral dilemma as to what rights such entities would possess and what responsibility we have, if any, on how we use them in society. Should it make any difference whether an entity is made of silicon or carbon, or whether its brain uses semi-conductors ...


An Ensemble Learning Framework For Anomaly Detection In Building Energy Consumption, Daniel B. Araya, Katarina Grolinger, Hany F. ElYamany, Miriam AM Capretz, Girma T. Bitsuamlak 2017 Western University

An Ensemble Learning Framework For Anomaly Detection In Building Energy Consumption, Daniel B. Araya, Katarina Grolinger, Hany F. Elyamany, Miriam Am Capretz, Girma T. Bitsuamlak

Electrical and Computer Engineering Publications

During building operation, a significant amount of energy is wasted due to equipment and human-related faults. To reduce waste, today's smart buildings monitor energy usage with the aim of identifying abnormal consumption behaviour and notifying the building manager to implement appropriate energy-saving procedures. To this end, this research proposes a new pattern-based anomaly classifier, the collective contextual anomaly detection using sliding window (CCAD-SW) framework. The CCAD-SW framework identifies anomalous consumption patterns using overlapping sliding windows. To enhance the anomaly detection capacity of the CCAD-SW, this research also proposes the ensemble anomaly detection (EAD) framework. The EAD is a generic ...


Return On Investment Of The Cftp Framework With And Without Risk Assessment, Anne Lim Lee 2017 Walden University

Return On Investment Of The Cftp Framework With And Without Risk Assessment, Anne Lim Lee

Walden Dissertations and Doctoral Studies

In recent years, numerous high tech companies have developed and used technology roadmaps when making their investment decisions. Jay Paap has proposed the Customer Focused Technology Planning (CFTP) framework to draw future technology roadmaps. However, the CFTP framework does not include risk assessment as a critical factor in decision making. The problem addressed in this quantitative study was that high tech companies are either losing money or getting a much smaller than expected return on investment when making technology investment decisions. The purpose of this research was to determine the relationship between returns on investment before and after adding risk ...


On Relation Between Constraint Answer Set Programming And Satisfiability Modulo Theories, Yuliya Lierler, Benjamin Susman 2016 University of Nebraska at Omaha

On Relation Between Constraint Answer Set Programming And Satisfiability Modulo Theories, Yuliya Lierler, Benjamin Susman

Yuliya Lierler

Constraint answer set programming is a promising research direction that integrates answer set programming with constraint processing. It is often informally related to the field of Satisfiability Modulo Theories. Yet, the exact formal link is obscured as the terminology and concepts used in these two research areas differ. In this paper, by connecting these two areas, we begin the cross-fertilization of not only of the theoretical foundations of both areas but also of the existing solving technologies.


Constraint Answer Set Solver Ezcsp And Why Integration Schemas Matter, 2016 Selected Works

Constraint Answer Set Solver Ezcsp And Why Integration Schemas Matter

Yuliya Lierler

Researchers in answer set programming and constraint programming have spent significant efforts in the development of hybrid languages and solving algorithms combining the strengths of these traditionally separate fields. These efforts resulted in a new research area: constraint answer set programming. Constraint answer set programming languages and systems proved to be successful at providing declarative, yet efficient solutions to problems involving hybrid reasoning tasks. One of the main contributions of this paper is the first comprehensive account of the constraint answer set language and solver EZCSP, a mainstream representative of this research area that has been used in various successful ...


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