The purpose of this research is to carry out a virtual try-on of kimonos and dresses without time and effort. Virtual try-on based on PF-AFN can try on tight-fitting tops such as T-shirts, however, it has not yet been able to try on loose-fitting clothes or dresses. In this paper, we report on our attempt to extend the tops clothes region in PF-AFN to dresses using the adversarial attack concept. From the results, it appears that it is difficult to expand tops clothes region by our methods, so we will design a virtual try-on specifically for kimonos and dresses in the future.
Event data is the asynchronous acquisition of the increase or decrease in brightness of each pixel. Events are mainly generated near the edges along the motion of the subject and camera, and the event point clouds form a plane in the spatiotemporal 3D domain. Therefore, the surface formed by the event point clouds in the spatiotemporal domain can be regarded as the trajectory of motion (optical flow). In this paper, we propose a method for optical flow estimation using parameters of planes estimated from event point clouds in a segmented spatiotemporal subregion.
In recent years, the development of quanta image sensor (QIS), which can observe the amount of incident light intensity in units of photons, has been progressing. In QIS imaging, a large number of photon incident observations are performed in spatiotemporal direction, and multivalued images are obtained by reconstruction processing. In this observation, the binary value is output according to whether the number of incident photons exceeds a certain natural number of threshold preset in the minute photon detector (jot). In many existing methods for QIS imaging, a uniform threshold is set for all jots, the reconstructed multivalued image may be overexposed or underexposed. On the other hand, the method of setting an optimal threshold for each local region according to the scene requires time for adjustment, which leads to a decrease in temporal resolution. In this paper, we propose an imaging method that always accurately captures a wide range of light intensity from low to high by introducing a periodic pattern consisting of multiple thresholds. Since we do not adjust the threshold according to the scene, we can fundamentally avoid the degradation of temporal resolution. In addition, since the threshold applied to jots has a variety of values, it is possible to acquire a high-quality multivalued image even with a small number of photon incident observations. Our proposed method consists of three components: multivalued image reconstruction and noise reduction taking into account the characteristics of photon incident observation, and optimization of the periodic pattern.
We previously proposed a method of designing a 2D FIR filter that can maximize the well-known objective quality index called SSIM. The designed filter can be used as a post processing tool for lossy image coding methods to reduce coding artifacts. In this scenario, there is a trade-off between the amount of side information on filter coefficients and the obtained gain in image quality. In this paper, effectiveness of the designed filters on the rate-SSIM based coding performance is evaluated under different settings of the size and quantization precision of the filter coefficients. Moreover, we introduce symmetric constraints on the filter coefficients to reduce the side information.
We previously proposed a novel lossless image coding method that utilizes example search and adaptive prediction within a framework of probability model optimization. In this paper, the definition of the probability model as well as its optimization procedure are modified to reduce the encoding complexity. In addition, affine predictors used in the adaptive prediction are refined for accurate probability modeling. Simulation results indicate that our modification contributes not only to encoding time reduction, but also to coding efficiency improvement for all of the tested images.
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