Recognition of moving objects using feature signatures

An important application of machine vision systems is the recognition of known three-dimensional objects. A major difficulty arises when two or more objects project the same or similar two-dimensional image, often resulting in misclassification and degradation of system performance. The changes in images which result from the motion of objects provide a source of three-dimensional information which can greatly aid the classification process, but this three-dimensional analysis is computationally complex and subject to many sources of error. This work develops a methodology which utilizes the information derived from the apparent changes in object features over time to facilitate the recognition task, without the need to actually recover the three-dimensional structure of the objects under view. The basic approach is to generate a ``feature signature' by combining the feature measurements of the individual regions in a long sequence of images. The static information in the individual frames is analyzed along with the temporal information from the entire sequence. These techniques are particularly applicable in situations where static image processing methods cannot discriminate between ambiguous objects. Two example implementations are presented to illustrate the application of the techniques of object recognition using motion information.     

Carbonitriding at 700°C with subsequent hardening of the surface layer

Conclusion Carbonitriding at 700°C in a mixture of 70–30% of ammonia and 30–70% of endogas (or natural gas) is recommended for the production of a diffusion layer with a good set of operational properties (for example, with high wear resistance).Moreover, the use of a gaseous atmosphere based on commercial nitrogen (50–90%) with ammonia, natural-gas, oxygen, or carbon dioxide additives is promising for carbonitriding at 700°C. Here, this medium should not contain more 0.5–1.0% of O₂ and 3% of CO₂. The nitrogen-based gaseous atmospheres ensure the attainment of quality diffusion layers with decreased risk of explosion and saving of energy resources.Moscow Automobile Traffic and Highway Construction Institute. Nongovernmental Production Union "All-Union Scientific-Research and Experimental Design Institute for Trade Machine Construction. Translated from Metallovedenie i Termicheskaya Obrabotka Metallov, No. 5, pp. 32–36, May, 1987.     

Feature detection in 3-d medical images using shape information

Three methods for identifying the left ventricular apex in 3-D medical images of the heart called gated blood pool tomograms were investigated. The first method assumed a known orientation and positioning of the entire blood pool. The second and third methods used shapes described by quadratic surfaces, which are invariant to position and orientation. The first method performed best when the blood pool was accurately oriented, but as expected, could not handle blood pools in the wrong orientations. The quadratic surface methods performed well whether or not the blood pool was accurately oriented. The best quadratic surface method predicted the x, y, z value of the apex with correlations of 0.97, 0.98, 0.99.     

Limited Angle Reconstruction in Tomography via Squashing

We give a new algorithm for reconstructing a density f in the plane from its projections along those lines making an angle greater than a fixed delta > 0 with the x axis. Of course, the performance of the algorithm depends on delta and on the smoothness of f, but it appears to give a practical and simple solution to the problem whenever one exists. The basic idea, which seems to be new, is to make an affine (squashing) scale change of f to g for which the projections are then known at n equally spaced angles, so that we know how to find g, and then we obtain f from g by inverting the scale change.     

Sparse decompositions for exploratory pattern analysis

We define and verify the utility of a pattern analysis procedure called sparse decomposition. This technique involves sequentially ``peeling' sparse subsets of patterns from a pattern set, where sparse subsets are sets of patterns which possess a certain degree of regularity or compactness as measured by a compactness measure c. If this is repeated until all patterns are deleted, then the sequence of decomposition ``layers' derived by this procedure provides a wealth of information from which inferences about the original pattern set may be made. A statistic P is derived from this information and is shown to be powerful in detecting clustering tendency for data in reasonably compact sampling windows. The test is applied to both synthetic and real data.     

Parallel Algorithms for Image Template Matching on Hypercube SIMD Computers

This correspondence presents several parallel algorithms for image template matching on an SIMD array processor with a hypercube interconnection network. For an N by N image and an M by M window, the time complexity is reduced from O(N2M2) for the serial algorithm to O(M2/K2 + M * log2 N/K + log2 N * log2 K) for the N2K2-PE system (1 ? K ? M), or to O(N2M2/L2) for the L2-PE system (L ? N). With efficient use of the inter-PE communication network, each PE requires only a small local memory, many unnecessary data transmissions are eliminated, and the time complexity is greatly reduced.