We investigate the visibility of scrolling text presented nearby a dynamically moving image. In two experiments, we evaluate the subjective speed and readability of scrolled fake addresses presented immediately above a moving grating pattern that covers a large part of the visual field. The drift speed and direction of the grating were controlled to reveal the visibility of the text. The results show that the scrolling addresses exhibited slower subjective speed and better readability when the grating moved in the same direction as the scrolling addresses. On the contrary, faster subjective speed and worse readability of the scrolling addresses were raised by the grating moving in the opposite direction. The strength of these effects was dependent on the speed difference between the scrolling addresses and the grating. These results suggest that the visibility of the scrolling text, assessed in terms of subjective speed and readability, strongly depends on nearby moving images.

An image recognition system using NEURO4, a programmable parallel processor, is described. Optical flow is the velocity field that an observer detects on a two-dimensional image and gives useful information, such as edges, about moving objects. The processing time for detecting optical flow on the NEURO4 system was analyzed. Owing to the parallel computation scheme, the processing time on the NEURO4 system is proportional to the square root of the size of images, while conventional sequential computers need time in proportion to the size. This analysis was verified by experiments using the NEURO4 system. When the size of an image is 84 84, the NEURO4 system can detect optical flow in less than 10 seconds. In this case the NEURO4 system is 23 times faster than a workstation, Sparc Station 20 (SS20). The larger the size of images becomes, the faster the NEURO4 system can detect optical flow than conventional sequential computers like SS20. Furthermore, the paralleling effect increases in proportion to the number of connected NEURO4 chips by a ring expansion scheme. Therefore, the NEURO4 system is useful for developing moving image recognition algorithms which require a large amount of processing time.

A hierarchical relaxation method is presented for detecting local features in moving images. The relaxation processes are performed on the temporal-spatial pyramid, which is a multi-resolution data structure for the moving images. The accurate and high speed edge detection can be obtained by using infomation in the neighboring frames as well as the processed results in the higher layers of the pyramid.

In the design of 3-D filter detecting Linear Trajectory Signal (LTS), there may be paid little attention to the noise rejective characteristics. In this paper, we treat the noise rejection ability of the filter detecting LTS having margins both in its velocity and direction.