<p>Data embedding tools such as barcodes are very popular nowadays but not aesthetically pleasing. In this research, we propose a watermarking scheme and an image-based surface coding scheme using the grid points as fiducial markers and the shifted points as data-bearing features. Detecting and aligning point grids play a fundamental role in these applications. Joint determination of non-grid points and estimation of non-linear spatial distortions applied to the grid is a key challenge for grid alignment. We modify a SIFT-based surface feature detection method to eliminate as many spurious feature points as possible and propose a grid alignment algorithm that starts from a small nearly regular region found in the point set and then expands the list of candidate points included in the grid. Our method is tested on both synthetically generated and real samples. Furthermore, we extend some applications of the surface coding scheme to 3D space, including hyper-conformal mapping of the grid pattern onto the 3D models, 3D surface feature detection, and 3D grid points alignment. </p> <p>A document routing system is crucial to the concept of the smart office. We abstract it as an online class-incremental image classification problem. There are two kinds of classifiers to solve this problem: exemplar, and parametric classifiers. The architecture of exemplar-based classification is summarized here. We propose a one-versus-rest parametric classifier and four different updating algorithms based on the passive-aggressiveness algorithm. An adaptive thresholding method is also proposed to indicate the low-confidence prediction. We test our methods on 547 real document images that we collected and labeled and high cumulative accuracy is reported. </p>

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis proposes an automated grid-based alignment system utilizing lasers and an array of light-detecting photodiodes. The intent is to create an inexpensive and scalable alignment system for pick-and-place robotic systems. The system utilizes the transformation matrix, geometry, and trigonometry to determine the movements to align the robot with a grid-based array of photodiodes. The alignment system consists of a sending unit utilizing lasers, a receiving module consisting of photodiodes, a data acquisition unit, a computer-based control system, and the robot being aligned. The control system computes the robot movements needed to position the lasers based on the laser positions detected by the photodiodes. A transformation matrix converts movements from the coordinate system of the grid formed by the photodiodes to the coordinate system of the robot. The photodiode grid can detect a single laser spot and move it to any part of the grid, or it can detect up to four laser spots and use their relative positions to determine rotational misalignment of the robot. Testing the alignment consists of detecting the position of a single laser at individual points in a distinct pattern on the grid array of photodiodes, and running the entire alignment process multiple times starting with different misalignment cases. The first test provides a measure of the position detection accuracy of the system, while the second test demonstrates the alignment accuracy and repeatability of the system. The system detects the position of a single laser or multiple lasers by using a method similar to a center-of-gravity calculation. The intensity of each photodiode is multiplied by the X-position of that photodiode. The summed result from each photodiode intensity and position product is divided by the summed value of all of the photodiode intensities to get the X-position of the laser. The same thing is done with the Y-values to get the Y-position of the laser. Results show that with this method the system can read a single laser position value with a resolution of 0.1mm, and with a maximum X-error of 2.9mm and Y-error of 2.0mm. It takes approximately 1.5 seconds to process the reading. The alignment procedure calculates the initial misalignment between the robot and the grid of photodiodes by moving the robot to two distinct points along the robot’s X-axis so that only one laser is over the grid. Using these two detected points, a movement trajectory is generated to move that laser to the X = 0, Y = 0 position on the grid. In the process, this moves the other three lasers over the grid, allowing the system to detect the positions of four lasers and uses the positions to determine the rotational and translational offset needed to align the lasers to the grid of photodiodes. This step is run in a feedback loop to update the adjustment until it is within a permissible error value. The desired result for the complete alignment is a robot manipulator positioning within ±0.5mm along the X and Y-axes. The system shows a maximum error of 0.2mm in the X-direction and 0.5mm in the Y-direction with a run-time of approximately 4 to 5 minutes per alignment. If the permissible error value of the final alignment is tripled the alignment time goes down to 1 to 1.5 minutes and the maximum error goes up to 1.4mm in both the X and Y-directions. The run time of the alignment decreases because the system runs fewer alignment iterations.