Shovan Barma

An efficient image retrieval scheme for colour enhancement of embedded and distributed surveillance images

From the past few years, the size of the data grows exponentially with respect to volume, velocity, and dimensionality due to wide spread use of embedded and distributed surveillance cameras for security reasons. In this paper, we have proposed an integrated approach for biometric-based image retrieval and processing which addresses the two issues. The first issue is related to the poor visibility of the images produced by the embedded and distributed surveillance cameras, and the second issue is concerned with the effective image retrieval based on the user query. This paper addresses the first issue by proposing an integrated image enhancement approach based on contrast enhancement and colour balancing methods. The contrast enhancement method is used to improve the contrast, while the colour balancing method helps to achieve a balanced colour. Importantly, in the colour balancing method, a new process for colour cast adjustment is introduced which relies on statistical calculation. It adjusts the colour cast and maintains the luminance of the image. The integrated image enhancement approach is applied to the enhancement of low quality images produced by surveillance cameras. The paper addresses the second issue relating to image retrieval by proposing a content-based image retrieval approach. The approach is based on the three features extraction methods namely colour, texture and shape. Colour histogram is used to extract the colour features of an image. Gabor filter is used to extract the texture features and the moment invariant is used to extract the shape features of an image. The use of these three algorithms ensures that the proposed image retrieval approach produces results which are highly relevant to the content of an image query, by taking into account the three distinct features of the image and the similarity metrics based on Euclidean measure. In order to retrieve the most relevant images, the proposed approach also employs a set of fuzzy heuristics to improve the quality of the results further. The results show the proposed approaches perform better than the well-known existing approaches.

Quantitative measurement of split of the second heart sound (S2)

This study proposes a quantitative measurement of split of the second heart sound (S2) based on nonstationary signal decomposition to deal with overlaps and energy modeling of the subcomponents of S2. The second heart sound includes aortic (A2) and pulmonic (P2) closure sounds. However, the split detection is obscured due to A2-P2 overlap and low energy of P2. To identify such split, HVD method is used to decompose the S2 into a number of components while preserving the phase information. Further, A2s and P2s are localized using smoothed pseudo Wigner-Ville distribution followed by reassignment method. Finally, the split is calculated by taking the differences between the means of time indices of A2s and P2s. Experiments on total 33 clips of S2 signals are performed for evaluation of the method. The mean ± standard deviation of the split is 34.7 ± 4.6 ms. The method measures the split efficiently, even when A2-P2 overlap is ≤ 20 ms and the normalized peak temporal ratio of P2 to A2 is low (≥ 0.22). This proposed method thus, demonstrates its robustness by defining split detectability (SDT), the split detection aptness through detecting P2s, by measuring up to 96 percent. Such findings reveal the effectiveness of the method as competent against the other baselines, especially for A2-P2 overlaps and low energy P2.

Measurement of Duration, Energy of Instantaneous Frequencies, and Splits of Subcomponents of the Second Heart Sound

This paper presents an approach to measure the duration and the energy of instantaneous frequencies (EIFs) of the aortic (A2) and pulmonic (P2) valve closure sounds for the second heart sound (S2) based on analytic signal representation. In past studies, concepts were usually surrounded around the measurement of splits (i.e., delays between the A2 and the P2) in medical terms based only on visual inspections of the time-frequency representation of the S2s. The values were empirically estimated, and the two vital issues-A2 and P2 overlaps and low energies of P2s-were ignored. In this paper, such issues are addressed, and the relevant parameters are measured by identifying the starting and the ending positions of A2s and P2s. The diagnosis related to the duration and the EIFs of the A2 and the P2 is also examined. Furthermore, the proposed method explicitly guides to distinguish the normal/abnormal S2s and the types of S2 splits. The proposed method is characterized by the Hilbert transform-based IF estimation as well as the localization technique based on the reassignment of smoothed pseudo-Wigner-Ville distribution. To validate the idea, total 31 S2s collected from six healthy normal subjects were tested. The result computed by the proposed method shows that the mean ± standard deviation (SD) of the duration of A2s and P2s is 46.7 ± 2.5 and 41.8 ± 2.4 ms, respectively. The mean ± SD of the EIFs of A2s and P2s is 13.8 ± 2.4 and 10.5 ± 1.7, respectively. These estimated results accord with the evidence in the literature. Moreover, when the proposed method was applied to the abnormal S2s, collected from the database of the Texas Heart Institute, it could correctly distinguish the types of abnormal splits. The experimental result reveals that the accuracy rate of the proposed method is as high as up to 97%, thereby demonstrating the effectiveness of the proposed idea.

Game theory based no-reference perceptual quality assessment for stereoscopic images

In this paper, a no-reference perceptual quality assessment for stereoscopic image is proposed. Inspired by the binocular rivalry mechanism, the observation annoyance perception is explained as a bargain process. Game theory is exploited to model the rivalry of the left eye and right eye. The relation between annoyance perception with binocular disparity is further demonstrated and an annoyance map is calculated to simulate the observer perception. Then, with the consideration of the properties of HVS, the edge map and a saliency map are used to adjust the annoyance map. Finally, Minkowski pooling and multi-scale strategy are applied to compute the final score. We use the EPFL database to validate the proposed metric. The experimental results show that the final objective scores have a high degree of consistency with the subjective scores.

Frowning expression detection based on SOBEL filter for negative emotion recognition

This paper proposes a novel method to improve happiness status by detection negative emotional status based on frowning lines on face and a new term called facial expression factor (FEF). The FEF correlates the frowning and with emotional status. The frowning lines are detected using SOBEL filter and FEF factors are calculated from selected frowning lines to know the actual emotional status. Thus the negative emotional state are detected which could help to promote the happiness further. The experiment is conducted on 10 participants. In total 40 images (including 20 neutral and 20 frowning expression) are considered for experiment. The results show that the emotional status of 8 persons out of 10 participants is recognized correctly. Further, the wrong recognition results are corrected by tuning the threshold. Hence, the results depict the recognition accuracy up to 80%. The proposed work is based on simple training which also reduces the training time cost effectively. Furthermore, the proposed method is able to detect more complex facial expression (e.g., forced smile) using FEF. The tuning of threshold makes the method more effective. Therefore, such results show its effectiveness by detecting negative emotional state to promote the happiness.

Detection of the Third Heart Sound Based on Nonlinear Signal Decomposition and Time–Frequency Localization

This study presents a precise way to detect the third (S3) heart sound, which is recognized as an important indication of heart failure, based on nonlinear single decomposition and time- frequency localization. The detection of the S3 is obscured due to its significantly low energy and frequency. Even more, the detected S3 may be misunderstood as an abnormal second heart sound with a fixed split, which was not addressed in the literature. To detect such S3, the Hilbert vibration decomposition method is applied to decompose the heart sound into a certain number of subcomponents while intactly preserving the phase information. Thus, the time information of all of the decomposed components are unchanged, which further expedites the identification and localization of any module/section of a signal properly. Next, the proposed localization step is applied to the decomposed subcomponents by using smoothed pseudo Wigner-Ville distribution followed by the reassignment method. Finally, based on the positional information, the S3 is distinguished and confirmed by measuring time delays between the S2 and S3. In total, 82 sets of cardiac cycles collected from different databases including Texas Heart Institute database are examined for evaluation of the proposed method. The result analysis shows that the proposed method can detect the S3 correctly, even when the normalized temporal energy of S3 is larger than 0.16, and the frequency of those is larger than 34 Hz. In a performance analysis, the proposed method demonstrates that the accuracy rate of S3 detection is as high as 93.9%, which is significantly higher compared with the other methods. Such findings prove the robustness of the proposed idea for detecting substantially low-energized S3.

Direct Measurement of Elbow Joint Angle Using Galvanic Couple System

This paper proposes a simple approach to measure the elbow joint angle (EJA) using galvanic coupling system (GCS), directly; whereas, the traditional methods involved in either complex machine-learning task or arm movement models in which the consideration of model parameters are not accurate very often. First, a correlation between the EJA and GCS data has been established by defining a polynomial function based on a simple six-impedance model of human upper arm, where the EJA (

A review on heart sound modeling: Fluid dynamics and signal processing perspective

\theta

Keystroke Rhythm Analysis Based on Dynamics of Fingertips

) has been achieved by moving the forearm along the sagittal and transverse planes with different loads (empty hand, 1 and 2 kg). The coefficients of the polynomial are estimated based on the polynomial fit technique in which the actual angles (reference frame) are calculated by using motion data. In total, eleven subjects (seven males and four females) with the age of 30 ± 6 years have been considered during the experiment. However, the GCS data of eight subjects are used to derive the correlation, exclusively. Furthermore, the influence of muscle fatigue and different loads on the derived correlation has been studied. Next, based on the derived correlation, the EJA has been measured in two parts—inside and outside tests by considering six subjects. The results show that the proposed idea can measure the EJA very effectively with error up to ±0.11 rad (6°). Moreover, in a performance comparison, the proposed approach shows its compatibility by indicating low complexity, higher accuracy, and easy to measure.

Scalp Level Connectivity for Representative Channels in Emotional Status

Heart sounds are generated by the several mechanical movements, such as the heart valve leaflets, the blood flow in vessels etc., that can provide the useful information to understand the cardiovascular system in details. Auscultation (PCG) is a non-invasive, low cost and easily repeatable technique to observe the entire system behavior at a glance. Several works have been investigated to model the sounds from different aspects, including the signal processing, the fluid mechanic and the equivalent electrical analogy. However, all the works gave the limitations. Most of the signal processing literatures that are addressed the heuristic decision-based approach; on the other hand, the fluid mechanic-based works discussed the behavior of overall or particular part of the cardiovascular system. Besides, the acoustic signals from heart can be modeled with the help of behavior through the source generation. In this study, several modeling works of heart sound are inspected and categorized according to the literatures, thereafter the advantages and the limitations are discussed. An overall research direction will be conducted to combine the fluid mechanic, the acoustics and the signal processing in the study.