Computer Vision Algorithms for Mobile Camera Applications

Wearable and mobile sensors have found widespread use in recent years due to their ever-decreasing cost, ease of deployment and use, and ability to provide continuous monitoring as opposed to sensors installed at fixed locations. Since many smart phones are now equipped with a variety of sensors, including accelerometer, gyroscope, magnetometer, microphone and camera, it has become more feasible to develop algorithms for activity monitoring, guidance and navigation of unmanned vehicles, autonomous driving and driver assistance, by using data from one or more of these sensors. In this thesis, we focus on multiple mobile camera applications, and present lightweight algorithms suitable for embedded mobile platforms. The mobile camera scenarios presented in the thesis are: (i) activity detection and step counting from wearable cameras, (ii) door detection for indoor navigation of unmanned vehicles, and (iii) traffic sign detection from vehicle-mounted cameras. First, we present a fall detection and activity classification system developed for embedded smart camera platform CITRIC. In our system, the camera platform is worn by the subject, as opposed to static sensors installed at fixed locations in certain rooms, and, therefore, monitoring is not limited to confined areas, and extends to wherever the subject may travel including indoors and outdoors. Next, we present a real-time smart phone-based fall detection system, wherein we implement camera and accelerometer based fall-detection on Samsung Galaxy S™ 4. We fuse these two sensor modalities to have a more robust fall detection system. Then, we introduce a fall detection algorithm with autonomous thresholding using relative-entropy within the class of Ali-Silvey distance measures. As another wearable camera application, we present a footstep counting algorithm using a smart phone camera. This algorithm provides more accurate step-count compared to using only accelerometer data in smart phones and smart watches at various body locations. As a second mobile camera scenario, we study autonomous indoor navigation of unmanned vehicles. A novel approach is proposed to autonomously detect and verify doorway openings by using the Google Project Tango™ platform. The third mobile camera scenario involves vehicle-mounted cameras. More specifically, we focus on traffic sign detection from lower-resolution and noisy videos captured from vehicle-mounted cameras. We present a new method for accurate traffic sign detection, incorporating Aggregate Channel Features and Chain Code Histograms, with the goal of providing much faster training and testing, and comparable or better performance, with respect to deep neural network approaches, without requiring specialized processors. Proposed computer vision algorithms provide promising results for various useful applications despite the limited energy and processing capabilities of mobile devices

Application of Periodogram-Based Cointegration Test for the Analysis of the Services and Goods Sector Inflations

The differing dynamics of the inflations of the services and goods sectors has been of major concern in Turkey. The persistence of the services sector inflation during disinflation periods hampered the efforts of the Central Bank of Turkey of hitting inflation targets in a country with long-lasting high inflation experience. In search of a possible long-run relationship between the services and goods sectors’ inflations, this paper employs a method based on periodograms of the series in addition to time series tools. A periodogram-based test has pros over conventional tests; this test is model-free, seasonally robust and mean invariant. Empirical findings obtained from the methods employed in this study, Engle-Granger’s and Johansen’s conventional long-run time series tools as well as periodogram based test, suggest that services and goods sector inflations in Turkey are not cointegrated.Cointegration, Periodogram, Time-Series Analysis, Inflation, Services Sector

Application of Periodogram-Based Cointegration Test for the Analysis of the Services and Goods Sector Inflations

The differing dynamics of the inflations of the services and goods sectors has been of major concern in Turkey. The persistence of the services sector inflation during disinflation periods hampered the efforts of the Central Bank of Turkey of hitting inflation targets in a country with long-lasting high inflation experience. In search of a possible long-run relationship between the services and goods sectors’ inflations, this paper employs a method based on periodograms of the series in addition to time series tools. A periodogram-based test has pros over conventional tests; this test is model-free, seasonally robust and mean invariant. Empirical findings obtained from the methods employed in this study, Engle-Granger’s and Johansen’s conventional long-run time series tools as well as periodogram based test, suggest that services and goods sector inflations in Turkey are not cointegrated

Glucose Control, Sleep, Obesity, and Real-World Driver Safety at Stop Intersections in Type 1 Diabetes

Background: Diabetes is associated with obesity, poor glucose control and sleep dysfunction which impair cognitive and psychomotor functions, and, in turn, increase driver risk. How this risk plays out in the real-world driving settings is terra incognita. Addressing this knowledge gap requires comprehensive observations of diabetes driver behavior and physiology in challenging settings where crashes are more likely to occur, such as stop-controlled traffic intersections, as in the current study of drivers with Type 1 Diabetes (T1DM). Methods: 32 active drivers from around Omaha, NE participated in 4-week, real-world study. Each participant's own vehicle was instrumented with an advanced telematics and camera system collecting driving sensor data and video. Videos were analyzed using computer vision models detecting traffic elements to identify stop signs. Stop sign detections and driver stopping trajectories were clustered to geolocate and extract driver-visited stop intersections. Driver videos were then annotated to record stopping behavior and key traffic characteristics. Stops were categorized as safe or unsafe based on traffic law. Results: Mixed effects logistic regression models examined how stopping behavior (safe vs. unsafe) in T1DM drivers was affected by 1) abnormal sleep, 2) obesity, and 3) poor glucose control. Model results indicate that one standard deviation increase in BMI (~7 points) in T1DM drivers associated with a 14.96 increase in unsafe stopping odds compared to similar controls. Abnormal sleep and glucose control were not associated with increased unsafe stopping. Conclusion: This study links chronic patterns of abnormal T1DM driver physiology, sleep, and health to driver safety risk at intersections, advancing models to identify real-world safety risk in diabetes drivers for clinical intervention and development of in-vehicle safety assistance technology.Comment: 23 pages, 7 figures, 10 table

All-optical image denoising using a diffractive visual processor

Image denoising, one of the essential inverse problems, targets to remove noise/artifacts from input images. In general, digital image denoising algorithms, executed on computers, present latency due to several iterations implemented in, e.g., graphics processing units (GPUs). While deep learning-enabled methods can operate non-iteratively, they also introduce latency and impose a significant computational burden, leading to increased power consumption. Here, we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images - implemented at the speed of light propagation within a thin diffractive visual processor. This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features, causing them to miss the output image Field-of-View (FoV) while retaining the object features of interest. Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of ~30-40%. We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum. Owing to their speed, power-efficiency, and minimal computational overhead, all-optical diffractive denoisers can be transformative for various image display and projection systems, including, e.g., holographic displays.Comment: 21 Pages, 7 Figure

The role of primary audible cues in sound localisation

Sound localisation is a complex perceptual process that involves the integration of information derived from multiple audible cues. Human sound localisation is mediated by these audible cues. The primary audible cues are the interaural time difference (ITD), phase difference (IPD) and intensity difference (IID) that result from diffraction of sound waves around the head and pinnae. The ITD and the IID have received much attention in the literature as they are considered to be the most important binaural cues. These cues result from a neural comparison between the signals at both ears. Although these cues are signal-dependent, they have not been investigated using day-to-day life signals due to the experimentation complexity. Many research studies employed either single frequency or unnatural signals to draw conclusions. Further, the IPD could not be distinguished from the ITD for multiple frequency signals. This thesis presents new experimental techniques which are developed to investigate the role of primary audible cues in sound localisation. These techniques independently control and investigate the cues with multiple frequency signals. Listening experiments are initially completed with single cues which are followed by experiments with cues in conflict. The stimuli are chosen to include representative day-to-day life signals. Two audible cues at a time are used simultaneously in conflict so that their relative importance can be determined. Experimental techniques included a mathematical approach to manipulate the phase of all the component frequencies in a multiple frequency signal, whilst leaving the amplitude structure unchanged. This approach is adopted to distinguish the IPD from an equivalent ITD for multiple frequency signals. It is therefore possible to investigate the IPD independently with multiple frequency signals. The experimental results indicate that the effect of the IPD can be compensated for by an appropriate opposing ITD. In general, localisations become centrally diffuse when either the ITD or the IPD is placed in conflict with the IID. A localisation model is developed to provide predictions for the effect of one cue against an alternative cue, similar to the experiments. The results from the model are in agreement with the experiments. It is not evident whether the ITD or the IPD is more effective. The new techniques and the localisation model provide the opportunity to investigate the primary audible cues with representative day-to-day life signals. It has become possible to independently assess these cues and to draw conclusions based on their role in sound localisation

The Settlement Typologies in Anatoliaduring Seljuk Period -I- Fairgrounds orMarket Places

It is considered that fairgrounds and bazaars in Anatolia during the Seljuk period were the spatial reflections of the economical policies based on the international trade. The paper is aimed to determine the settlement typology and analyze demographical-economical size of bazaars or fairgrounds developed by means of the monumental-public buildings that were founded by Seljuk Sultans or Emirs using waqfs. Another dimension of the study is to examine the historical backgrounds of the bazaar or fairground tradition in Anatolia during Seljuk period. In this study, amethodology is considered that based on the plans and maps transferred from the historical sources such as waqffiyye or chronicles and archaeological- architectural inheritances.Anatolia, Seljuk period, settlement typology, demographical-economical analysis, bazaar or fairgrounds.