Detection and tracking of objects in a low resolution grayscale image

Tracking is the technique of following an object in motion. We use tracking technology on an everyday basis through Navigation GPS in cars or smart phones. Many of these units also feature touch displays, which allow interaction using a finger or touch stylus. This master thesis focuses on with tracking fingers on touch displays, which appear as objects on a low-resolution image. Touch displays often use a low resolution sensor grid, which requires subpixel estimation prior to tracking. The coordinates produced by the tracking system is used by an application level not included in this project, which analyzes the input location and motion. With higher accuracy of the position and tracking, the smaller symbols can be utilized on the display, and the more sophisticated motions are possible to interpret for the application level. The tracker system is designed to work in real time for any touch display and tracks up to two fingers of any size. High tracking accuracy was achieved using digital signal processing techniques. A signal processing model was created initially to define the system. The tracker system created consists of two modules: A scanner and a tracker. The scanner analyzes the data sets individually, and produces a high-resolution two-dimensional coordinate for each input. The tracker analyzes these observations collectively, validates that these are not caused by noise, and filters the positions through a (Kalman) tracking filter. Evaluation of the system was performed using data sets from a real touch display as well as simulated data sets. Some assumptions and limitations had to be made to successfully handle all situations found in the data sets. Pressing hard on the touch displays creates large objects, and a size estimation algorithm was made. This was based on a sensor value threshold, but this makes the program non-ideal for displays with low signal to noise ratio. Two close objects might also be falsely identified as one large object, but this was solved in this project using previous knowledge and adjusting the sensor values to force the scanner to produce two outputs. Lastly, the tracking filter smoothed the trajectory of the objects, but it did not always provide better accuracy. A tracking filter should be considered based on the application intended. All situations in the real data sets were handled, albeit with reduced accuracy for large objects and for two close objects. Further research involves handling three or more inputs, performing a running cost analysis of the algorithm and implementing this on a real tablet. Implementation will require an adjustment of the different thresholds and settings to match the touch display with regards to node resolution, sampling rate and sensor noise. This can be solved by the application level or further development of the tracker system

Multiplex Quantitative Real-Time Reverse Transcriptase PCR for F\u3csup\u3e+\u3c/sup\u3e-Specific RNA Coliphages: a Method for Use in Microbial Source Tracking

It is well documented that microbial contamination of coastal waters poses a significant risk to human health through recreational exposure and consumption of shellfish. Identifying the source of microbial contamination (microbial source tracking) plays a dominant role in enabling effective management and remediation strategies. One method used to determine the source of the contamination is quantification of the ratio of the four subgroups of F+-specific RNA coliphages (family Leviviridae) in impacted water samples. Because of typically low concentrations in the environment, enrichment assays are performed prior to detection, even though differential replication rates have been reported. These assays are also compromised by differential loss of phage infectivity among subgroups after release into the environment, thus obscuring the initial ratio. Here, a culture-independent multiplex real-time reverse transcriptase-PCR (RT-PCR) protocol for the simultaneous quantification of all four subgroups of F+-specific RNA coliphages using novel primer sets and molecular beacons is presented. This assay is extremely sensitive, achieving detection with as few as 10 copies of isolated coliphage RNA, and is linear for a minimum of six orders of magnitude. During survival experiments, the real-time RT-PCR technique was able to quantify coliphages in seawater when culture-based double agar layer assay failed. While infectivity was lost at different rates at the subgroup level, decay constants in seawater, calculated using the real-time RT-PCR estimates, did not vary among subgroups. The accurate determination of the in situ concentration of F+-specific RNA coliphages using this method will facilitate more effective remediation strategies for impacted environments

Generative modeling of living cells with SO(3)-equivariant implicit neural representations

Data-driven cell tracking and segmentation methods in biomedical imaging require diverse and information-rich training data. In cases where the number of training samples is limited, synthetic computer-generated data sets can be used to improve these methods. This requires the synthesis of cell shapes as well as corresponding microscopy images using generative models. To synthesize realistic living cell shapes, the shape representation used by the generative model should be able to accurately represent fine details and changes in topology, which are common in cells. These requirements are not met by 3D voxel masks, which are restricted in resolution, and polygon meshes, which do not easily model processes like cell growth and mitosis. In this work, we propose to represent living cell shapes as level sets of signed distance functions (SDFs) which are estimated by neural networks. We optimize a fully-connected neural network to provide an implicit representation of the SDF value at any point in a 3D+time domain, conditioned on a learned latent code that is disentangled from the rotation of the cell shape. We demonstrate the effectiveness of this approach on cells that exhibit rapid deformations (Platynereis dumerilii), cells that grow and divide (C. elegans), and cells that have growing and branching filopodial protrusions (A549 human lung carcinoma cells). A quantitative evaluation using shape features, Hausdorff distance, and Dice similarity coefficients of real and synthetic cell shapes shows that our model can generate topologically plausible complex cell shapes in 3D+time with high similarity to real living cell shapes. Finally, we show how microscopy images of living cells that correspond to our generated cell shapes can be synthesized using an image-to-image model.Comment: Medical Image Analysis 2023 (Submitted

Hierarchical fuzzy logic based approach for object tracking

In this paper a novel tracking approach based on fuzzy concepts is introduced. A methodology for both single and multiple object tracking is presented. The aim of this methodology is to use these concepts as a tool to, while maintaining the needed accuracy, reduce the complexity usually involved in object tracking problems. Several dynamic fuzzy sets are constructed according to both kinematic and non-kinematic properties that distinguish the object to be tracked. Meanwhile kinematic related fuzzy sets model the object's motion pattern, the non-kinematic fuzzy sets model the object's appearance. The tracking task is performed through the fusion of these fuzzy models by means of an inference engine. This way, object detection and matching steps are performed exclusively using inference rules on fuzzy sets. In the multiple object methodology, each object is associated with a confidence degree and a hierarchical implementation is performed based on that confidence degree.info:eu-repo/semantics/publishedVersio

Towards a Scalable Hardware/Software Co-Design Platform for Real-time Pedestrian Tracking Based on a ZYNQ-7000 Device

Currently, most designers face a daunting task to research different design flows and learn the intricacies of specific software from various manufacturers in hardware/software co-design. An urgent need of creating a scalable hardware/software co-design platform has become a key strategic element for developing hardware/software integrated systems. In this paper, we propose a new design flow for building a scalable co-design platform on FPGA-based system-on-chip. We employ an integrated approach to implement a histogram oriented gradients (HOG) and a support vector machine (SVM) classification on a programmable device for pedestrian tracking. Not only was hardware resource analysis reported, but the precision and success rates of pedestrian tracking on nine open access image data sets are also analysed. Finally, our proposed design flow can be used for any real-time image processingrelated products on programmable ZYNQ-based embedded systems, which benefits from a reduced design time and provide a scalable solution for embedded image processing products