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We introduce a novel technique for the automatic detection of word boundaries within continuous sentence expressions in Japanese Sign Language from three-dimensional body joint positions. First, the flow of signed sentence data within a temporal neighborhood is determined utilizing the spatial correlations between line segments of inter-joint pairs. Next, a frame-wise binary random forest classifier is trained to distinguish word and non-word frame content based on the extracted spatio-temporal features.

In this paper, we propose a simple yet efficient method termed as Progressive Filtering for Feature Matching, which is able to establish accurate correspondences between two images of common or similar scenes. Our algorithm first grids the correspondence space and calculates a typical motion vector for each cell, and then removes false matches by checking the consistency between each putative match and the typical motion vector in the corresponding cell, which is achieved by a convolution operation.

In this paper, we optimize the computations of third-order low-tubal-rank tensor operations on many-core GPUs. Tensor operations are compute-intensive and existing studies optimize such operations in a case-by-case manner, which can be inefficient and error-prone. We develop and optimize a BLAS-like library for the low-tubal-rank tensor model called cuTensor-tubal, which includes efficient GPU primitives for tensor operations and key processes. We compute tensor operations in the frequency domain and fully exploit tube-wise and slice-wise parallelisms.

Reliable collision avoidance is one of the main requirements for autonomous driving.
Hence, it is important to correctly estimate the states of an unknown number of static and dynamic objects in real-time.
Here, data association is a major challenge for every multi-target tracker.
We propose a novel multi-target tracker called Greedy Dirichlet Process Filter (GDPF) based on the non-parametric Bayesian model called Dirichlet Processes and the fast posterior computation algorithm Sequential Updating and Greedy Search (SUGS).

We evaluate the performance of objective quality metrics for high dynamic range (HDR) image coding that uses the transfer function (TF) of the Hybrid Log-Gamma (HLG) method. Previous evaluations of objective metrics for HDR image coding have studied which of them are reliable predictors of perceived quality; however, in those tests, all the non-linear transforms used both for encoding and by the best-performing metrics are essentially very similar and based on visual perception data of detection thresholds for lightness variations.

In this paper, we propose a single image super-resolution with limited number of filters based on RAISR. RAISR is well known as rapid and accurate super-resolution method which utilizes 864 filters for upscaling. This super-resolution idea utilizes the filter learned with sufficient training set. To get low cost of calculation and comparable image quality with other highly accurate super-resolution methods, the patch of input image is classified into classes by simple hash calculation.