Abstract The task of natural image segmentation is one of the most researched topics of computer vision. There are mainly two principal approaches for the task, the statistical approach and the supervised approach. The proposed methodology segments natural images combining a set of statistical algorithms. First, the image is preprocessed to enhance the edges. Weighted average of the denoised image and its derivatives is the preprocessed output. Thereafter, an energy based super pixelation is applied to over segment the image. Finally, a connectivity graph is built where nodes correspond to super pixels and edges connect the adjacent super pixels. The adjacent super pixels are merged based on the confidence value defined in terms of their textural and colour similarity. Proposed methodology has been applied on the images of BSDS500 dataset. Performance of the proposed work has been compared with that of other works based on detected edge maps. Few works generate ultrametric contour maps (UCM). To compare the performance with those works, UCM is also generated by the proposed methodology. To do so images at multiple scales are considered. It is observed that the output of segmentation is better in case of the proposed methodology. Proposed methodology is much faster than others. Thus, makes it suitable for real time application in robot vision.

Microsoft Kinect has the huge potential to be used in home-based rehabilitation and clinical assessments for patients suffering from stroke or other neurological disorder, due to its affordability and unobtrusiveness in analysing joint kinematics. However, skeleton data obtained from Kinect Xbox 360 (Kinect 1) or Kinect Xbox One (Kinect 2) are usually noisy which affects accuracy of estimation of three dimensional joint locations. The noise profile varies for both stationary and dynamic postures and it affects anthropometric measurements of the body segments connecting any two joints. We propose a novel approach to constrain a standard Kalman filter, based on the dynamics of individual joints, in order to keep the distance between any two physically connected joints (namely bone length) constant over time. Our constrained Kalman filter method not only tracks the joints accurately but also reduces the variation in bone lengths by 92% and 94% for Kinect 2 and 1 respectively.

During an examination, the performance of a student not only depends on the preparation but also depends on cognitive and psychological factors. The present study aims at analysing the dynamics of the mental workload or stress and its impact on students' performance during an examination process. The experiment is designed using a set of multiple choice questions (MCQs) in one of the subject area namely data-structure. The MCQs are selected in three levels of complexity, which are rated by subject matter experts. A total of 13 right-handed graduate students in the specialization of Masters in Computer Application participated in the experiment. In order to get rid of any bias related to the intelligent quotient (IQ), students are taken such that the IQ varies from low to high. Experimental tasks are designed as interleaved sequences of the MCQs of varying complexity levels. During the tasks, Galvanic skin response (GSR) and pulse-oximeter (SPO2) signals are captured to estimate the stress of an individual from electro-dermal changes and heart rate variability (HRV). Apart from the tonic and phasic power of the GSR, fluctuation analysis is performed on the raw GSR signals. The standard deviation of normal-to-normal interval (SDNN) is computed to measure the HRV using the Photoplethysmogram (PPG) signal obtained from the SPO2 device. An estimation of the stress level is done using a score computed by the fluctuation analysis of the GSR signal. Out of the 13 students, the GSR sensor data for the 6 students are found to erroneous and hence only 7 data are analyzed. The methodology used to reject the erroneous data is also presented. Results indicate that there is a strong statistical correlation between the complexity of the MCQs and the GSR signals. Results of HRV analysis provide new insights to guess work during an assessment process.

We have presented a unified model for detecting different types of video shot transitions. Based on the proposed model, we formulate frame estimation scheme using the previous and the next frames. Unlike other shot boundary detection algorithms, instead of properties of frames, frame transition parameters and frame estimation errors based on global and local features are used for boundary detection and classification. Local features include scatter matrix of edge strength and motion matrix. Finally, the frames are classified as no change (within shot frame), abrupt change, or gradual change frames using a multilayer perceptron network. The proposed method is relatively less dependent on user defined thresholds and is free from sliding window size as widely used by various schemes found in the literature. Moreover, handling both abrupt and gradual transitions along with non-transition frames under a single framework using model guided visual feature is another unique aspect of the work.

For efficient indexing, browsing and retrieval of video data and also for video summarization, extraction of representative frames is essential. Once a video stream is segmented into shots, the representative frames or key-frames for the shot are selected. Automatic selection of suitable representatives for a wide variety of shots is still a challenge as the number of such frames in a shot may also vary depending on the variation in the content. In this work, we propose a novel scheme that relies on Wald-Wolfowitz runs test based hypothesis testing to detect the subshots within a shot and then for each subshot, the frame rendering the highest fidelity is extracted as the key-frame. Experimental result shows that the scheme works satisfactorily for a wide variety of shots.