Kiyotaka Watanabe

Video Synthesis with High Spatio-Temporal Resolution Using Motion Compensation and Spectral Fusion

We propose a novel strategy to obtain a high spatio-temporal resolution video. To this end, we introduce a dual sensor camera that can capture two video sequences with the same field of view simultaneously. These sequences record high resolution with low frame rate and low resolution with high frame rate. This paper presents an algorithm to synthesize a high spatio-temporal resolution video from these two video sequences by using motion compensation and spectral fusion. We confirm that the proposed method improves the resolution and frame rate of the synthesized video.

Video synthesis with high spatio-temporal resolution using motion compensation and image fusion in wavelet domain

This paper presents a novel algorithm for obtaining a high spatio-temporal resolution video from two video sequences. These sequences are high resolution with low frame rate and low resolution with high frame rate. To this end, we introduce a dual sensor camera that can capture these sequences with the same field of view simultaneously. The proposed method observes motion information through the video with high frame rate. Moreover, the method conducts both motion compensation for the high resolution sequence and image fusion in the wavelet domain. We confirmed that the proposed method improves the resolution and frame rate of the synthesized video.

A fast algorithm of video super-resolution using dimensionality reduction by DCT and example selection

In this paper, we propose a novel learning-based video super resolution algorithm with less memory requirements and computational cost. To this end, we adopt discrete cosine transform (DCT) coefficients for feature vector components. Moreover, we design an example selection procedure to construct a compact database. We conducted evaluative experiments using MPEG test sequences to synthesize a high resolution video. Experimental results show that our method can improve effectiveness of super-resolution algorithm, while preserving the quality of synthesized image.

Video synthesis with high spatio-temporal resolution using spectral fusion

We propose a novel strategy to obtain a high spatio-temporal resolution video. To this end, we introduce a dual sensor camera that can capture two video sequences with the same field of view simultaneously. These sequences record high resolution with low frame rate and low resolution with high frame rate. This paper presents an algorithm to synthesize a high spatio-temporal resolution video from these two video sequences by using motion compensation and spectral fusion. We confirm that the proposed method improves the resolution and frame rate of the synthesized video.

Human specific activity retrieval from a surveillance image sequence

We propose an image processing system which searches the moving human and vehicles from the long-term surveillance video for intruder detection or parking lot monitoring, by comparing a series of retrieval queries like passing a certain area, moving direction, duration time and so on. In such a system, not only on-line detection from real-time video by pre-defined query but also quick re-search going back to the past when the user changed the query, and post-retrieval by interactive query change are required. In proposed method, we simplified the image retrieval meta-data to be described in a small and fixed length data however complicated the trajectory of moving object is. Moreover, the matching core function of image retrieval process is realized by a simple comparator. That enables fast image retrieval however complicated the series of the retrieval queries is.

Reliable image matching using binarized gradient features obtained with multi-flash camera

In this paper, we propose an object detection method using features describing information about a concavoconvex shape of an object that are obtained by using a small camera that controls the illumination direction. A feature image containing information about the shape of the object is generated by integrating images obtained by turning on, one by one, light emitting diodes (LEDs) annularly arranged around the camera. Our method can reliably detect a texture-less object by using this feature image in the matching process. Experiments using 200 actual images confirmed that the method achieves a 97.5% recognition success rate and a 4.62 sec processing time.

Construction Method of Efficient Database for Learning-Based Video Super-Resolution

There are two major problems with learning-based super-resolution algorithms. One is that they require a large amount of memory to store examples; while the other is the high computational cost of finding the nearest neighbors in the database. In order to alleviate these problems, it is helpful to reduce the dimensionality of examples and to store only a small number of examples that contribute to the synthesis of a high quality video. Based on these ideas, we have developed an efficient algorithm for learning-based video super-resolution. We introduce several strategies to construct an efficient database. Through the evaluation experiments we show the efficiency of our approach in improving super-resolution algorithms.