Deep learning models have demonstrated great success in various computer vision tasks such as image classification and object tracking. However, tracking the lumbar spine by digitalized video fluoroscopic imaging (DVFI), which can quantitatively analyze the motion mode of spine to diagnose lumbar instability, has not yet been well developed due to the lack of steady and robust tracking method. In this paper, we propose a novel visual tracking algorithm of the lumbar vertebra motion based on a Siamese convolutional neural network (CNN) model. We train a full-convolutional neural network offline to learn generic image features. The network is trained to learn a similarity function that compares the labeled target in the first frame with the candidate patches in the current frame. The similarity function returns a high score if the two images depict the same object. Once learned, the similarity function is used to track a previously unseen object without any adapting online. In the current frame, our tracker is performed by evaluating the candidate rotated patches sampled around the previous frame target position and presents a rotated bounding box to locate the predicted target precisely. Results indicate that the proposed tracking method can detect the lumbar vertebra steadily and robustly. Especially for images with low contrast and cluttered background, the presented tracker can still achieve good tracking performance. Further, the proposed algorithm operates at high speed for real time tracking.
With the latest improvements of microbolometer focal plane arrays (FPA), uncooled infrared (IR) cameras are becoming the most widely used devices in thermography, especially in handheld devices. However the influences derived from changing ambient condition and the non-uniform response of the sensors make it more difficult to correct the nonuniformity of uncooled infrared camera. In this paper, based on the infrared radiation characteristic in the TEC-less uncooled infrared camera, a novel model was proposed for calibration-based non-uniformity correction (NUC). In this model, we introduce the FPA temperature, together with the responses of microbolometer under different ambient temperature to calculate the correction parameters. Based on the proposed model, we can work out the correction parameters with the calibration measurements under controlled ambient condition and uniform blackbody. All correction parameters can be determined after the calibration process and then be used to correct the non-uniformity of the infrared camera in real time. This paper presents the detail of the compensation procedure and the performance of the proposed calibration-based non-uniformity correction method. And our method was evaluated on realistic IR images obtained by a 384x288 pixels uncooled long wave infrared (LWIR) camera operated under changed ambient condition. The results show that our method can exclude the influence caused by the changed ambient condition, and ensure that the infrared camera has a stable performance.