We have proposed a new omnidirectional image sensor COPIS (COnic Projection Image Sensor) for guiding navigation of a mobile robot. Its feature is passive sensing of the omnidirectional image of the environment in real-time (at the frame rate of a TV camera) using a conic mirror. COPIS is a suitable sensor for visual navigation in real world environment with moving objects. This paper describes a method for estimating the location and the motion of the robot by detecting the azimuth of each object in the omnidirectional image. In this method, the azimuth is matched with the given environmental map. The robot can always estimate its own location and motion precisely because COPIS observes a 360 degree view around the robot even if all edges are not extracted correctly from the omnidirectional image. We also present a method to avoid collision against unknown obstacles and estimate their locations by detecting their azimuth changes while the robot is moving in the environment. Using the COPIS system, we performed several experiments in the real world.

A novel harmonic retrieval algorithm is proposed in this paper based on Hopfield's neural network. Frequencies can be retrieved with high accuracy and high resolution under low signal to noise ratio (SNR). Amplitudes and phases in harmonic signals can also be estimated roughly by an energy constrained linear projection approach as proposed in the algorithm. Only no less than 2q neurons are necessary in order to detect harmonic siglnals with q different frequencies, where q denotes the number of different frequencies in harmonic signals. Experimental simulations show fast convergence and stable solution in spite of low signal to noise ratio can be obtained using the proposed algorithm.

Topographic maps begin to be recognized as one of the major computational structures underlying neural computation in the brain. They provide dimension-reducing projections between feature spaces that seem to be established and maintained under the participation of selforganizing, adaptive processes. In this contribution, we investigate how well the structure of such maps can be replicated by simple adaptive processes of the kind proposed by Kohonen. We will particularly address the important issue, how the dimensionality of the input space affects the spatial organization of the resulting map.

In this paper, we present a new image compression scheme, Projection-VQ, based on reconstruction from vector quantized projections. We can easily deal with the blocks of larger size in Projection-VQ than in conventional VQ schemes, because the dimension of vectors in projection domain is, in general, much smaller than that in the spatial domain. In Projection-VQ, the image can be reconstructed without destroying edge sharpness in the process since the projection data having the edge information are preferentially transmitted. There are several good algorithms of reconstructing an image from projections. However, we use a new modified reconstruction algorithm suitable for a variable bit rate image coding system. We allocate the bits depending on the characteristics of the block images. Our simulation results show that the performances are superior to the ordinary VQ schemes in PSNR, and that the improvement in subjective image quality is substantial.