Optimizing the lateral beamforming step for filtered-delay multiply and sum beamforming to improve active contour segmentation using ultrafast ultrasound imaging

As an alternative to delay-and-sum beamforming, a novel beamforming technique called filtered-delay multiply and sum (FDMAS) was introduced recently to improve ultrasound B-mode image quality. Although a considerable amount of work has been performed to evaluate FDMAS performance, no study has yet focused on the beamforming step size, , in the lateral direction. Accordingly, the performance of FDMAS was evaluated in this study by fine-tuning to find its optimal value and improve boundary definition when balloon snake active contour (BSAC) segmentation was applied to a B-mode image in ultrafast imaging. To demonstrate the effect of altering in the lateral direction on FDMAS, measurements were performed on point targets, a tissue-mimicking phantom and in vivo carotid artery, by using the ultrasound array research platform II equipped with one 128-element linear array transducer, which was excited by 2-cycle sinusoidal signals. With 9-angle compounding, results showed that the lateral resolution (LR) of the point target was improved by 67.9% and 81.2%, when measured at −6 dB and −20 dB respectively, when was reduced from to . Meanwhile the image contrast ratio (CR) measured on the CIRS phantom was improved by 10.38 dB at the same reduction and the same number of compounding angles. The enhanced FDMAS results with lower side lobes and less clutter noise in the anechoic regions provides a means to improve boundary definition on a B-mode image when BSAC segmentation is applied

Multiple-model fully convolutional neural networks for single object tracking on thermal infrared video

The availability of affordable thermal infrared (TIR) camera has instigated its usage in various research fields, especially for the cases that require images to be captured in dark surroundings. One of the low-level tasks required by most TIR-based researches is the need to track an object throughout a video sequence. The main challenge posed by TIR camera usage is the lack of texture to differentiate two nearby objects of the same class. According to the VOT-TIR 2016 challenge, the best fully convolutional neural network (FCNN)-based tracker has only managed to obtain the third place. The discriminative ability of the FCNN tracker is not fully utilized because of the homogenous appearance pattern of the tracked object. This paper aims to improve FCNN-based tracker ability to predict object location through comprehensive sampling approach as well as better scoring scheme. Hence, a multiple-model FCNN is proposed, in which a small set of fully connected layers is updated on the top of pre-trained convolutional neural networks. The possible object locations are generated based on a two-stage sampling that combines stochastically distributed samples and clustered foreground contour information. The best sample is selected according to a combined score of appearance similarity, predicted location, and model reliability. The small set of appearance models is updated by using positive and negative training samples, accumulated from two periods of time which are the recent and parent node intervals. To further improve training accuracy, the samples are generated according to a set of adaptive variances that depends on the trustworthiness of the tracker output. The results show an improvement over TCNN, an FCNN-based tracker that won the VOT 2016 challenge with the expected average overlap increasing from 0.248 to 0.257. The performance enhancement is attributed to the better robustness with a 20% reduction in tracking failure rate compared to the TCNN

Multiple-model fully convolutional neural networks for single object tracking on thermal infrared video

The availability of affordable thermal infrared (TIR) camera has instigated its usage in various research fields, especially for the cases that require images to be captured in dark surroundings. One of the low-level tasks required by most TIR-based researches is the need to track an object throughout a video sequence. The main challenge posed by TIR camera usage is the lack of texture to differentiate two nearby objects of the same class. According to the VOT-TIR 2016 challenge, the best fully convolutional neural network (FCNN)-based tracker has only managed to obtain the third place. The discriminative ability of the FCNN tracker is not fully utilized because of the homogenous appearance pattern of the tracked object. This paper aims to improve FCNN-based tracker ability to predict object location through comprehensive sampling approach as well as better scoring scheme. Hence, a multiple-model FCNN is proposed, in which a small set of fully connected layers is updated on the top of pre-trained convolutional neural networks. The possible object locations are generated based on a two-stage sampling that combines stochastically distributed samples and clustered foreground contour information. The best sample is selected according to a combined score of appearance similarity, predicted location, and model reliability. The small set of appearance models is updated by using positive and negative training samples, accumulated from two periods of time which are the recent and parent node intervals. To further improve training accuracy, the samples are generated according to a set of adaptive variances that depends on the trustworthiness of the tracker output. The results show an improvement over TCNN, an FCNN-based tracker that won the VOT 2016 challenge with the expected average overlap increasing from 0.248 to 0.257. The performance enhancement is attributed to the better robustness with a 20% reduction in tracking failure rate compared to the TCNN

Robust Foreground Detection: A Fusion of Masked Grey World, Probabilistic Gradient Information and Extended Conditional Random Field Approach

Foreground detection has been used extensively in many applications such as people counting, traffic monitoring and face recognition. However, most of the existing detectors can only work under limited conditions. This happens because of the inability of the detector to distinguish foreground and background pixels, especially in complex situations. Our aim is to improve the robustness of foreground detection under sudden and gradual illumination change, colour similarity issue, moving background and shadow noise. Since it is hard to achieve robustness using a single model, we have combined several methods into an integrated system. The masked grey world algorithm is introduced to handle sudden illumination change. Colour co-occurrence modelling is then fused with the probabilistic edge-based background modelling. Colour co-occurrence modelling is good in filtering moving background and robust to gradual illumination change, while an edge-based modelling is used for solving a colour similarity problem. Finally, an extended conditional random field approach is used to filter out shadow and afterimage noise. Simulation results show that our algorithm performs better compared to the existing methods, which makes it suitable for higher-level applications