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Cell Segmentation In Cancer Histopathology Images Using Convolutional Neural Networks, Viswanathan Kavassery Rajalingam
Cell Segmentation In Cancer Histopathology Images Using Convolutional Neural Networks, Viswanathan Kavassery Rajalingam
Computer Science and Engineering Theses
Cancer, the second most dreadful disease causing large scale deaths in humans is characterized by uncontrolled growth of cells in the human body and the ability of those cells to migrate from the original site and spread to distant sites. The major proportion of deaths in cancer is due to improper primary diagnosis that raises the need for Computer Aided Diagnosis (CAD). Digital Pathology is a technique that acts as second set of eyes to radiologists in delivering expert level preliminary diagnosis for cancer patients. Cell segmentation is a challenging step in digital pathology that identifies cell regions from micro-slide …
Convolutional And Recurrent Neural Networks For Pedestrian Detection, Vivek Arvind Balaji
Convolutional And Recurrent Neural Networks For Pedestrian Detection, Vivek Arvind Balaji
Computer Science and Engineering Theses
Pedestrian Detection in real time has become an interesting and a challenging problem lately. With the advent of autonomous vehicles and intelligent traffic monitoring systems, more time and money are being invested into detecting and locating pedestrians for their safety and towards achieving complete autonomy in vehicles. For the task of pedestrian detection, Convolutional Neural Networks (ConvNets) have been very promising over the past decade. ConvNets have a typical feed-forward structure and they share many properties with the visual system of the human brain. On the other hand, Recurrent Neural Networks (RNNs) are emerging as an important technique for image …
Deep Semantic Image Interpolation, Joshua D. Little
Deep Semantic Image Interpolation, Joshua D. Little
McKelvey School of Engineering Theses & Dissertations
Image datasets often live on a continuum: Images from an outdoor scene vary from day to night, across different weather conditions, and over the course of seasons. Faces age and exhibit different expressions. We consider the problem of taking individual images from these datasets and explicitly manipulating those images to change where they lie on the continuum. We focus on a version of this problem that requires as little input as possible, and we build off of previous work using CNN features to construct an intermediate image manifold on which to manipulate the images. We also investigate a novel way …
An Exercise And Sports Equipment Recognition System, Siddarth Kalra
An Exercise And Sports Equipment Recognition System, Siddarth Kalra
Electronic Thesis and Dissertation Repository
Most mobile health management applications today require manual input or use sensors like the accelerometer or GPS to record user data. The onboard camera remains underused. We propose an Exercise and Sports Equipment Recognition System (ESRS) that can recognize physical activity equipment from raw image data. This system can be integrated with mobile phones to allow the camera to become a primary input device for recording physical activity. We employ a deep convolutional neural network to train models capable of recognizing 14 different equipment categories. Furthermore, we propose a preprocessing scheme that uses color normalization and denoising techniques to improve …
Learning From Minimally Labeled Data With Accelerated Convolutional Neural Networks, Aysegul Dundar
Learning From Minimally Labeled Data With Accelerated Convolutional Neural Networks, Aysegul Dundar
Open Access Dissertations
The main objective of an Artificial Vision Algorithm is to design a mapping function that takes an image as an input and correctly classifies it into one of the user-determined categories. There are several important properties to be satisfied by the mapping function for visual understanding. First, the function should produce good representations of the visual world, which will be able to recognize images independently of pose, scale and illumination. Furthermore, the designed artificial vision system has to learn these representations by itself. Recent studies on Convolutional Neural Networks (ConvNets) produced promising advancements in visual understanding. These networks attain significant …