Category  · Edge Detection, Human Pose Detection

Labelling Enclosed Edges Using the Idea of a Cell Membrane

Below is a sample result of an experiment implemented and executed to attempt to efficiently label individual sets of edges. Labelling edges into separate sets is useful, specifically if one wants to have the edges grouped apart for purposes like featurization. Its also a lot faster and more memory efficient to featurize and label edges that are divided into small groups, as oppose to running algorithms on edges spread out across the entire image.

Category  · Edge Detection, Featurization, Human Pose Detection

Detecting Straight Edges

The following problem discussed here involves taking a set of labeled edges within an image, each enclosing an area of space within the image, and breaking them down into sections of straight lines. Such a technique is very useful when one wants to use the edges as a part of a set of features, to see where in the image parallel lines are being formed. Parallel lines are hard to detect unless the edges themselves within the image are found, clearly labelled and stored in memory. In this report, we will briefly explain our approach and show a few sample results of how these straight edges appear as part of a task in human pose detection.

Category  · Illumination, Gaussian Distributions, Vignetting

Correcting Illumination in Microscopy

When analyzing images taken under a microscope, it is important to have constant illumination captured throughout. Doing so allows each part of the image to appear the same, excluding the changing details that differ across the object of interest magnified. It also makes it easier for imaging software to detect and understand features. For example, uneven illumination within the image can confuse some software at falsely detecting object boundaries that are not there, due to a sudden and strong change in illumination within the image, making it appear as if there is an actual border between two separate colours.

Category  · OCT Imaging, Retina Analysis

Retinal Layer Detection for OCT Imaging

An automated method is presented to aid in the analysis and detection of retinal layers, using a combination of gradient filters and optimized modelling. Sample images are taken by the Optical Coherence Tomography (OCT) method.
The combined techniques will be discussed and presented separately. The steps for combining them will then be quickly briefed over. The aid in analysis is accomplished by outlining the layers within the retina. Once outlined, statistal data can be computed, such as measuring the distances between the layers and determining their uniformity.