Integration, Testing, And Analysis Of Multispectral Imager On Small Unmanned Aerial System For Skin Detection

Small Unmanned Aerial Systems (SUAS) have been utilized by the military, geological researchers, and first responders, to provide information about the environment in real time. Hyperspectral Imagery (HSI) provides high resolution data in the spatial and spectral dimension; all objects, including skin have unique spectral signatures. However, little research has been done to integrate HSI into SUAS due to their cost and form factor. Multispectral Imagery (MSI) has proven capable of dismount detection with several distinct wavelengths. This research proposes a spectral imaging system that can detect dismounts on SUAS. Also, factors that pertain to accurate dismount detection with an SUAS are explored. Dismount skin detection from an aerial platform also has an inherent difficulty compared to ground-based platforms. Computer vision registration, stereo camera calibration, and geolocation from autopilot telemetry are utilized to design a dismount detection platform with the Systems Engineering methodology. An average 5.112% difference in ROC AUC values that compared a line scan spectral imager to the prototype area scan imager was recorded. Results indicated that an SUAS-based Spectral Imagers are capable tools in dismount detection protocols. Deficiencies associated with the test expedient prototype are discussed and recommendations for further improvements are provided

Keyframe-based monocular SLAM: design, survey, and future directions

Extensive research in the field of monocular SLAM for the past fifteen years has yielded workable systems that found their way into various applications in robotics and augmented reality. Although filter-based monocular SLAM systems were common at some time, the more efficient keyframe-based solutions are becoming the de facto methodology for building a monocular SLAM system. The objective of this paper is threefold: first, the paper serves as a guideline for people seeking to design their own monocular SLAM according to specific environmental constraints. Second, it presents a survey that covers the various keyframe-based monocular SLAM systems in the literature, detailing the components of their implementation, and critically assessing the specific strategies made in each proposed solution. Third, the paper provides insight into the direction of future research in this field, to address the major limitations still facing monocular SLAM; namely, in the issues of illumination changes, initialization, highly dynamic motion, poorly textured scenes, repetitive textures, map maintenance, and failure recovery

Best Photo Selection

The rising of digital photography underlies a clear change on the paradigm of the photography management process by amateur photographers. Nowadays, taking one more photo comes for free, thus it is usual for amateurs to take several photos of the same subject hoping that one of them will match the quality standards of the photographer, namely in terms of illumination, focus and framing. Assuming that the framing issue is easily solved by cropping the photo, there is still the need to select which of the well framed photos, technically similar in terms of illumination and focus, are going to be kept (and in opposition which photos are going to be discarded). The process of visual observation, on a computer screen, in order to select the best photo is inaccurate and thus becomes a generator of insecurity feelings that may lead to no discarding of photos at all. In this work, we propose to address the issue of how to help the amateur photographer to select the best photo from a set of similar photos by analysing them in technical terms. The result is a novel workflow supported by a software package, guided by user input, which will allow the sorting of the similar photos accordingly to their technical characteristics (illumination and focus) and the user requirements. As a result, we expect that the process of choosing the best photo, and discarding of the remaining, becomes reliable and more comfortable

Long Range Automated Persistent Surveillance

This dissertation addresses long range automated persistent surveillance with focus on three topics: sensor planning, size preserving tracking, and high magnification imaging. field of view should be reserved so that camera handoff can be executed successfully before the object of interest becomes unidentifiable or untraceable. We design a sensor planning algorithm that not only maximizes coverage but also ensures uniform and sufficient overlapped camera’s field of view for an optimal handoff success rate. This algorithm works for environments with multiple dynamic targets using different types of cameras. Significantly improved handoff success rates are illustrated via experiments using floor plans of various scales. Size preserving tracking automatically adjusts the camera’s zoom for a consistent view of the object of interest. Target scale estimation is carried out based on the paraperspective projection model which compensates for the center offset and considers system latency and tracking errors. A computationally efficient foreground segmentation strategy, 3D affine shapes, is proposed. The 3D affine shapes feature direct and real-time implementation and improved flexibility in accommodating the target’s 3D motion, including off-plane rotations. The effectiveness of the scale estimation and foreground segmentation algorithms is validated via both offline and real-time tracking of pedestrians at various resolution levels. Face image quality assessment and enhancement compensate for the performance degradations in face recognition rates caused by high system magnifications and long observation distances. A class of adaptive sharpness measures is proposed to evaluate and predict this degradation. A wavelet based enhancement algorithm with automated frame selection is developed and proves efficient by a considerably elevated face recognition rate for severely blurred long range face images

Automatic Test Methods for Image and Video Verification

In this thesis four methods for automatic verification of images and video on mobile platforms are developed. Both the case of recording images and video and the case of viewing images and video on the mobile lcd screen are considered. The first method is used to test the zoom function of the camera. It uses SURF decriptors along with clustering and histograms to determine which of six discrete zoom levels the current frame belongs to. The second method identifies color effects and color anomalies using histograms. The third method determines if the autofocus works correctly by measuring the average length of edges in the image. The fourth method is an artifact detection scheme using a non-reference implementation of the SSIM metric, used in conjunction with a for this purpose specially designed test setup. Together these methods form a tool kit for detecting the mnost common errors to occur in images and video during the development stage of mobile platforms

Face recognition in the wild.

Research in face recognition deals with problems related to Age, Pose, Illumination and Expression (A-PIE), and seeks approaches that are invariant to these factors. Video images add a temporal aspect to the image acquisition process. Another degree of complexity, above and beyond A-PIE recognition, occurs when multiple pieces of information are known about people, which may be distorted, partially occluded, or disguised, and when the imaging conditions are totally unorthodox! A-PIE recognition in these circumstances becomes really “wild” and therefore, Face Recognition in the Wild has emerged as a field of research in the past few years. Its main purpose is to challenge constrained approaches of automatic face recognition, emulating some of the virtues of the Human Visual System (HVS) which is very tolerant to age, occlusion and distortions in the imaging process. HVS also integrates information about individuals and adds contexts together to recognize people within an activity or behavior. Machine vision has a very long road to emulate HVS, but face recognition in the wild, using the computer, is a road to perform face recognition in that path. In this thesis, Face Recognition in the Wild is defined as unconstrained face recognition under A-PIE+; the (+) connotes any alterations to the design scenario of the face recognition system. This thesis evaluates the Biometric Optical Surveillance System (BOSS) developed at the CVIP Lab, using low resolution imaging sensors. Specifically, the thesis tests the BOSS using cell phone cameras, and examines the potential of facial biometrics on smart portable devices like iPhone, iPads, and Tablets. For quantitative evaluation, the thesis focused on a specific testing scenario of BOSS software using iPhone 4 cell phones and a laptop. Testing was carried out indoor, at the CVIP Lab, using 21 subjects at distances of 5, 10 and 15 feet, with three poses, two expressions and two illumination levels. The three steps (detection, representation and matching) of the BOSS system were tested in this imaging scenario. False positives in facial detection increased with distances and with pose angles above ± 15°. The overall identification rate (face detection at confidence levels above 80%) also degraded with distances, pose, and expressions. The indoor lighting added challenges also, by inducing shadows which affected the image quality and the overall performance of the system. While this limited number of subjects and somewhat constrained imaging environment does not fully support a “wild” imaging scenario, it did provide a deep insight on the issues with automatic face recognition. The recognition rate curves demonstrate the limits of low-resolution cameras for face recognition at a distance (FRAD), yet it also provides a plausible defense for possible A-PIE face recognition on portable devices