Harish Bhaskar

Live cell imaging: a computational perspective

One of the primary challenges in understanding complex living systems requires a good understanding of the interactions between cellular and molecular functional units. Live cell imaging is the process of non-invasively analyzing dynamic processes in living cells using state-of-the-art microscopy and computer vision techniques. Live cell imaging research provides exciting and novel insights into cell biology. In this paper, we present an overview of live cell imaging research and detail the role of computational image processing in live cell imaging.

Multiple particle tracking for live cell imaging with green fluorescent protein (GFP) tagged videos

Particle tracking is important for understanding the mobile behaviour of objects of varying sizes in a range of physical and biological science applications. In this paper we present a new algorithm for tracking cellular particles imaged using a confocal microscope. The algorithm performs adaptive image segmentation to identify objects for tracking and uses intelligent estimates of neighbourhood search, spatial relationship, velocity, direction estimates, and shape/size estimates to perform robust tracking. Our tracker is tested on three videos for vesicle tracking in GFP tagged videos. The results are compared to the popular Harvard tracker and we show that our tracking scheme offers better performance and flexibility for tracking.

Human body parts tracking using pictorial structures and a genetic algorithm

Tracking people and localising body parts is a challenging computer vision problem because people move unpredictably under circumstances of partial and full occlusions. In this work we focus on the problem of automatic detection and tracking of humans and we propose a combined background subtraction (BS) /foreground modeling and a matching technique based on a genetic algorithm. The developed architecture combines a self-adaptive cluster level BS scheme using a Gaussian mixture model (GMM) and an appearance learning model of the foreground with pictorial structures. The model of the human body parts is then matched with the background subtracted sequence using an efficient genetic algorithm. The efficiency of the designed technique is demonstrated over real video sequences.

Fast algorithm for restoration of foggy images

This paper describes a new fast method for foggy images restoration derived from the dark channel prior methodology. Based on a physical foggy image formation model, the parameters of the restoration algorithm are estimated from the saturation channel of a single colour image without any prior knowledge of the scene. The performance of the proposed method is evaluated on a set of real-world images and compared with other state-of-art fog removal and contrast enhancement methods. It is shown that the new method outperforms other tested techniques in terms of the execution speed and perceptual quality of the restored images.