Analysis and enhancement of the denoising depth data using kinect through iterative technique

Since the release of Kinect by Microsoft, the, accuracy and stability of Kinect data-such as depth map, has been essential and important element of research and data analysis. In order to develop efficient means of analyzing and using the kinnect data, researchers require high quality of depth data during the preprocessing step, which is very crucial for accurate results. One of the most important concerns of researchers is to eliminate image noise and convert image and video to the best quality. In this paper, different types of the noise for Kinect are analyzed and a unique technique is used, to reduce the background noise based on distance between Kinect devise and the user. Whereas, for shadow removal, the iterative method is used to eliminate the shadow casted by the Kinect. A 3D depth image is obtained as a result with good quality and accuracy. Further, the results of this present study reveal that the image background is eliminated completely and the 3D image quality in depth map has been enhanced

Kinect depth stream pre-processing for hand gesture recognition

Over the recent years there has been growing interest to propose a robust and efficient hand gesture recognition (HGR) system, using real-time depth sensors like Microsoft Kinect. The performance of such HGR systems have been affected by the low resolution, noise and quantization error in the depth stream. In this paper, we propose a method to pre-process Kinect depth stream in order to overcome some of these limitations. The design approach utilizes the hand tracker from OpenNI SDK to perform distance invariant segmentation of hand region depth stream. This is followed by the construction of three different projections of hand in XY, ZX and ZY planes. These projections are then further enhanced using a combination of morphological closing and simple averaging based interpolation. The evaluation results show above 80% similarity with ground truth, and 1.45-5.35% increase in accuracy for gestures with recognition accuracy less than 90%

Morphological Study of Granular-Granular Impact Craters through Time-of-Flight Cameras: from Concept to Automation in Python

Laboratory made granular-granular impact craters have been used as model analogues of planetary impact craters. These kind of craters have been observed and studied using profilometry techniques that allow to retrieve important morphologic features from the impacted surface. In this work, we propose to use a Time-of-Flight camera (Microsoft Kinect One) for the acquisition of depth data. We show comparisons between the typically used technique and the analysis derived from the Time-of-Flight data. We also release craterslab, a Python library developed to automate most of the tasks from the process of studying impact craters produced by granular projectiles hitting on the surface of granular targets. The library is able to acquire, identify, and measure morphological features of impacted surfaces through the reconstruction of 3D topographic maps. Our results show that using a Time-of-Flight camera and automating the data processing with a software library for the systematic study of impact craters can produce very accurate results while reducing the time spent on different stages of the process

Temporal Denoising of Kinect Depth Data

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