SUMMARY1. The spatial and temporal frequency selectivity of 148 neurones in the striate cortex, VI, and of 122 neurones in the second visual cortical area, V2, of the macaque monkey were studied using sine-wave gratings of suprathreshold contrast drifting over the receptive field at the preferred orientation and direction.2. Neurones in VI and V2 were selective for different but partially overlapping ranges of the spatial frequency spectrum. At retinal eccentricities of 2-5 deg from the fovea, the spatial frequency preferences for neurones ranged from 0 5 to 8-0 cycles/deg in VI and from 0-2 to 2-1 cycles/deg in V2 and were on average almost 2 octaves lower in V2 than in VI. Spatial frequency full band widths in the two cortical areas were in the range 0 8-3 0 octaves, with a mean value of 1-8 octaves, in the parafoveal representation of both VI and V2, and 1-4 and 1-6 octaves respectively in the foveal representation of VI and V2.3. Most neurones in VI and some in V2 responded well at temporal frequencies up to 5-6-8-0 Hz before their responses dropped off at still higher frequencies. In VI, 68 % ofthe neurones exhibited low-pass temporal tuning characteristics and 32 % were very broadly tuned, with a mean temporal frequency full band width of 2-9 octaves.However, in V2 only 30 % of the neurones showed low-pass temporal selectivity and 70 % of the cells had bandpass temporal characteristics, with a mean full band width of 2-1 octaves. In V2 the minimal overlap of bandpass tuning curves across the temporal frequency spectrum suggests that there are at least two distinct bandpass temporal frequency mechanisms as well as neurones with low-pass temporal frequency tuning at each spatial frequency. K. H. FOSTER AND OTHERS for spatial frequency is essentially independent of the test temporal frequency; however, in V2 there was some tendency for temporal frequency peaks to shift slightly towards lower frequencies when non-optimum values of spatial frequency either above or below the preferred value were tested. 5. Neurones with pronounced directional selectivity were encountered over a wide range of spatial frequencies, although in both cortical areas there was a tendency for an increased incidence of directional selectivity among neurones which were selective for lower spatial frequencies and higher temporal frequencies.6. These results indicate that neurones in VI and V2 span partially overlapping but essentially different ranges of the spatial frequency spectrum yet have similar spatial frequency band widths, whereas neurones in VI and V2 span similar ranges of the temporal frequency spectrum but have different temporal frequency bandpass characteristics. Taken together, neurones in VI and V2 analyse spatially localized subdomains of the visual scene across an extended range of both the spatial frequency and temporal frequency spectrum.

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The authors present a method for designing and recording computer-generated holographic optical elements (HOES) for far-infrared radiation. The design method is based on minimising the mean-squared difference of the propagation vectors between the actual output wavefronts and the desired output wavefronts. This minimisation yields an analytic solution for the optimal grating vector. The design method is illustrated by recording a reflective off-axis focusing element with a laser scanner and lithographic techniques. The element is then tested, and the results indicate that diffraction-limited performance for a relatively large range of incidence angles can be obtained.

A system for inspecting metal parts in a production line at a rate of 300 parts per minute is described. During inspection, the parts are classified according to a wide range of predefined defect types, consisting of both structural defects (dents, bulges, scratches, splits), and textural defects (acid stains, paint, anneal, etc.).Each flaw has its own rejection criterion, which is not directly correlated to its size, shape or contrast.The image is modeled by utilizing a-priori information concerning the nature of the defects and the specific illumination configuration.We apply low level feature detection in several resolutions in order to derive the specific signature of defect. Classification is then done on the reduced feature space for flaw identification and severity decision.The algorithms are implemented with dedicated image processing hardware, working in a pipeline fashion on a dedicated synchronized video bus to achieve the high speed requirements of the system.