Harish Bhaskar

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Machine learning is used in a large number of bioinformatics applications and studies. The application of machine learning techniques in other areas such as pattern recognition has resulted in accumulated experience as to correct and principled approaches for their use. The aim of this paper is to give an account of issues affecting the application of(More)
Detecting people or other articulated objects and localizing their body parts is a challenging computer vision problem as their movement is unpredictable under circumstances of partial and full occlusions. In this paper, a framework for human body parts tracking in video sequences using a self-adaptive cluster background subtraction (CBS) scheme is proposed(More)
Visual recognition of crowd dynamics has had a huge impact on several applications including surveillance, situation awareness, homeland security and intelligent environments. However, the state-of-the-art in crowd analysis has become diverse due to factors such as: a) the underlying definition of a crowd, b) the constituent elements of the crowd processing(More)
The field of remote sensing is associated with an increasing amount of imagery data, mission after mission. Such an increase makes the application of image fusion techniques in remote sensing important. However, the interrelationship between the two fields is not well-understood. In fact, the term “image fusion” in remote sensing has usually(More)
We describe a method for modelling and locating deformable objects using a combination of global and local shape models. An object is represented as a set of patches together with a geometric model of their relative positions. The geometry is modelled with a global pose and linear shape model, together with a Markov Random Field (MRF) model of local(More)
Detection is an inherent part of every advanced automatic tracking system. In this work we focus on automatic detection of humans by enhanced background subtraction. Background subtraction (BS) refers to the process of segmenting moving regions from video sensor data and is usually performed at pixel level. In its standard form this technique involves(More)
Background subtraction (BS) is an efficient technique for detecting moving objects in video sequences. A simple BS process involves building a model of the background and extracting regions of the foreground (moving objects) with the assumptions that the camera remains stationary and there exist no movements in the background. These assumptions restrict the(More)
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(More)
In this paper, we propose a smart video summarization technique that compiles a synopsis of event(s)-of-interest occurring within a segment of image frames in a video. The proposed solution space consists of extracting appropriate features that represent the dynamics of targets in surveillance environments using their motion trajectories combined with a(More)
In this paper, we propose a method for detecting variations in the Pulse Rate (PR) of infants undergoing the Hammersmith Infant Neurological Examinations (HINE) using video data. As in every other medical examination the measurement of the PR is critical to underpin the physiological state of living beings. During HINE, measuring the infant's PR is(More)