We analyze gestures for the initialization of HCI system, such as ‘hand-shaking’ and ‘hand-pushing’, especially in the aspects of robustness and sensing methods. For the detection of derived ‘hand-pushing’ action, temporal variation of depth histogram is used. The proposed method is empirically verified.
This paper presents a hardware that inspects defects on TFT-LCD cell modules and packed in a PCI-board equipped with FPGA and DSP processors. Images of TFT-LCD cell modules normally contain periodic cell patterns which make it difficult to detect defects. We propose an efficient and powerful algorithm for elimination of the cell pattern using magnitude spectrum analysis.
Since AlexNet, large deep convolutional neural networks (DCNNs) have been one of the major topics of interest in the field of computer vision, as well as bringing remarkable progress to the field. However, there has been little effort to use the DCNNs in realizing the mechanism of human memory. The human memory can be classified into three types: sensory memory, short-term memory and long-term memory. The short-term memory, also known as primary memory or active memory, is the information that humans are presently perceiving or thinking about, whereas the long-term memory refers to the persistent storage of information. In the mechanism of the human brain, the long-term memory enables the human vision to identify the actual color of an object effortlessly. In the computer vision, the DCNN-based illuminant estimation models are facing the long-term dependency problem as deeper networks encounter widening gaps between their earlier layers and later layers. Therefore, it is highly inspiring to apply the human long-term memory to the DCNN-based illuminant estimation models. The natural motivation of this article is to present a novel persistent memory residual network (PMRN) model which provides the DCNN with explicit access to persistent memory. The proposed PMRN architecture has two distinct units: a recursive unit and a gate unit. The two units combined serve to facilitate persistent memory access in a non-recursive fashion. The recursive unit has four residual blocks which are trained on the multiple-level image features on diverse receptive fields. The residual block outputs are concatenated and then fed into the gate unit. The proposed architecture keeps track of the recursive unit, deciding on how many of the earlier blocks to keep in reserve and how much of the image features to let the present block store. In this way, the proposed architecture contributes to solving the long-term dependency problem of conventional DCNNs. Comprehensive experiments support unparalleled performance of the proposed architecture in comparison to its counterparts and its potential to meet the needs of illumination estimation applications.
We have derived an analytic expression for the scalability of the video-overlay system in the FTTH network and shows that the scalability strongly depends on both the optical amplifier noise and the configuration of the distribution network.
An algorithm for measurement of the cutting blade of the ultrasonic vibration cutting machine used in an automation machine is proposed in this paper. The proposed algorithm, which is based upon a multi-step detection method, is developed for the accurate measurement of the cutting blade by exactly determining its rotation angle, length, and thickness. Instead of the commonly used Otsu method, we propose a new curvature-based adaptive binarization method, which provides more accurate details about the dimensions of the cutting blade. A region of interest containing the cutting blade from the acquired image is first extracted in the multi-step detection method, which is further processed to remove the noise, which increases the measurement reliability. An important feature of the proposed process is the restoration of the cutting blade’s tip data, which used to be lost during the fine noise-filtering process. The rotation angle and length are measured using the minimum rotated rectangle while the line fitting based upon the least square method is applied to increase the reliability of the thickness measurement. Experimental results validate the superiority of the proposed method over the conventional Otsu method.
The range of intensities that can be displayed on current display devices tend to be much smaller than dynamic range of real scene. Hence the need to compress real world data to fit the displayable range of such display device. For this reason, there are some methods for color rendition and dynamic range compression. However, after color rendering, image quality degradations, such as color changing and the dominated color, may occur. Accordingly, this paper presents a modified iCAM06 image appearance model using layering method in which image is divided into three components; base, middle, and detail layer image. The base layer image is obtained by using Gaussian filter, and then middle layer image is estimated through the JND-based adaptive filter. Finally, the detail layer image is estimated by dividing the original image by base and middle layer image. As mentioned above, the proposed method uses a layering method instead of a bilateral filter for an image decomposition method in iCAM06 color appearance model. The experimental results show that the proposed method yields better performance of HDR color rendition over the conventional method.
ABSTRACT Effective working memory (WM) training is often desired to improve WM. Recent studies have suggested that WM training is more successful when participants monitor scenes in three‐dimensional (3D) environments. Although previous neuroimaging studies have examined visuospatial WM in relation to a 3D scene or object, these studies did not investigate WM using stereoscopic 3D object stimuli. We used functional magnetic resonance imaging (fMRI) to identify brain activation during an N‐back task with 3D object stimuli, and determined the difference in activation pattern between stereoscopic versus shaded 3D objects. We found that the anterior insula, ventral striatum, and posterior orbitofrontal cortex showed greater activation during the 2‐back task with stereoscopic 3D objects than with shaded 3D objects. These regions have previously been associated with a salience network.
This paper presents a novel error concealment method using inter-layer correlation in multilayer video coding. The proposed method enhances the image quality by rejecting the high frequency component around the block boundary using the inter-block correlation of the reference layer and by preserving the original edge component considering the frequency features of neighbor blocks. The simulations show that the subjective quality and PSNR of the concealed image is high.
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