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Abnormal muscle tone and muscle weakness are related to gait asymmetry in stroke survivors. However, the internal muscle morphological changes that occur during walking remain unclear. To address this issue, this study investigated the muscle activity of the tibialis anterior (TA) and medial gastrocnemius (MG) of both the paretic and non-paretic sides during walking in nine stroke survivors, by simultaneously capturing electromyography (EMG), mechanomyography (MMG), and ultrasound images, and using a validated novel wearable ultrasound imaging and sensing system. Statistical analysis was performed to examine the test–retest reliability of the collected data, and both the main and interaction effects of each "side" (paretic vs. non-paretic) and "gait" factors, in stroke survivors. This study observed significantly good test–retest reliability in the collected data (0.794 ≤ ICC ≤ 0.985), and significant differences existed in both the side and gait factors of the average TA muscle thickness from ultrasound images, and in the gait factors of TA and MG muscle's MMG and EMG signals (p < 0.05). The muscle morphological characteristics also appeared to be different between the paretic and non-paretic sides on ultrasound images. This study uncovered significantly different internal muscle contraction patterns between paretic and non-paretic sides during walking for TA (7.2% ± 1.6%) and MG (5.3% ± 4.9%) muscles in stroke survivors. [ABSTRACT FROM AUTHOR]

“Seeing through” occluders is one of the most important effects that can be achieved with synthetic aperture imaging. As well, the occlusion problem, a challenging task for many computer vision applications, can be easily handled. Synthetic aperture imaging takes advantage of the property that only objects on the focal plane are sharp. The resulting image that is obtained by averaging images from different views consists of blurry objects away from the focal plane and sharp objects on the focal plane. Removing the blurriness caused by defocusing in synthetic aperture images to achieve an all-in-focus “seeing through” image is a challenging research problem. In this paper, we propose a novel method to improve the image quality of synthetic aperture imaging using image matting via energy minimization by estimating the foreground and the background. In particular, we first estimate the out-of-focus region by focusing on the background objects in each camera view using energy minimization. Next, we utilize a labeling method to create a sharp “see through” synthetic aperture image of the hidden objects. Then, image matting is used to extract the alpha matte of the hidden objects. Finally, by compositing the hidden objects with the estimated background regions, a sharp “see through” synthetic aperture image is created. The experimental results show that the proposed method outperforms the traditional synthetic aperture imaging method [1] as well as its improved versions [2]–[4], which simply dim and blur the area in the image that is out of focus, and a recent all-in-focus method [5]. We show that both the occluded objects and the background can be combined using our method to create a sharp synthetic aperture image. [ABSTRACT FROM PUBLISHER]

Abstract: Synthetic aperture imaging using an array of cameras, which has become popular recently, can easily handle the occlusion problem by “seeing through” occluders. Unfortunately, the resulting image is still blurry because it combines information not only from the region of interest but also from the occluding regions. Removing the blurriness of synthetic aperture images has become a challenging task for many computer vision applications. In this paper, we propose a novel method to improve the image quality of synthetic aperture imaging using energy minimization. Unlike the conventional synthetic aperture imaging method, which averages images from all the camera views, we reformulate the problem as a labeling problem. In particular, we use the energy minimization method to label each pixel in each camera view to decide whether or not it belongs to an occluder. After that, the focusing at the desired depth is by averaging pixels that are not labeled as occluder. The experimental results show that the proposed method outperforms the traditional synthetic aperture imaging method as well as its improved versions, which are simply dim and blur occluders in the resulting image. To the best of our knowledge, our proposed method is the first one for improving the results of synthetic aperture image without using a training set from the input sequence. As well, it is the first method that makes no assumptions on whether or not the objects in the scenes are static. [Copyright &y& Elsevier]

Abstract: This paper proposes a novel multi-object detection method using multiple cameras. Unlike conventional multi-camera object detection methods, our method detects multiple objects using a linear camera array. The array can stream different views of the environment and can be easily reconfigured for a scene compared with the overhead surround configuration. Using the proposed method, the synthesized results can provide not only views of significantly occluded objects but also the ability of focusing on the target while blurring objects that are not of interest. Our method does not need to reconstruct the 3D structure of the scene, can accommodate dynamic background, is able to detect objects at any depth using a new synthetic aperture imaging method based on a simple shift transformation, and can see through occluders. The experimental results show that the proposed method has a good performance and can synthesize objects located within any designated depth interval with much better clarity than that using an existing method. To our best knowledge, it is the first time that such a method using synthetic aperture imaging has been proposed and developed for multi-object detection in a complex scene with a significant occlusion at different depths. [Copyright &y& Elsevier]