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sven j. dickinson use .net framework code 128 code set a maker toincoporate uss code 128 in .net Microsoft Office Official Website example, image scale, Code 128C for .NET enabling you to evaluate the scale-invariance of your algorithm. Each successive suite, in turn, would test a different condition.

Moreover, each condition would be systematically parameterized, so that where you fail on a particular suite would tell you exactly how invariant you are to that suite s condition(s). Early databases, such as COIL-100 [168] and the Amsterdam Image Library [98] parameterized viewpoint and illumination. One recent database [65], created from the COIL-100 database, systematically parameterizes degree of occlusion.

As the suites progress toward the human vision suite, exemplars would give way to categories, rigid objects would give way to deformable objects, and uniform backgrounds would give way to cluttered backgrounds. Categories in earlier suites would exhibit very little within-class appearance or shape deformation, and in later suites would exhibit signi cant structural variability. Further suites could then combine conditions, leading to many subsets of conditions that might tease out limitations of particular algorithms.

To evaluate your algorithm would then amount to starting at suite-0 and reporting your results on each suite up to the conditions to which you claim to be invariant. Such a set of suites would need to be designed by a consortium with no disposition toward a particular recognition paradigm. In fact, to be paradigm-invariant, 3-D data of the imaged objects should also be provided, which would allow for the automatic construction of 3-D models, which some may prefer to view-based models.

The existence of such a set of suites would allow our algorithms to evolve in a clear direction, ever more invariant to increasingly challenging conditions but never losing sight of the need to address the fundamental conditions. Without the carefully designed intermediate suites, testing on only the most challenging suites, which combine many conditions (akin to today s popular databases), may contribute little to our understanding of categorization. If such databases become more performance- than diagnostic-oriented, they may, in fact, end up distracting the categorization community from focusing on those particular issues that deserve attention.

It is here that we can take a cue from our human vision colleagues, as the problem of designing proper experiments to test the performance of a vision system and to evaluate competing models has existed for a long time in the form of psychophysics. The rst formal presentation of psychophysical methods can be found in Fechner [80]. A recent review that emphasizes the use of signal detection theory can be found in Macmillan and Creelman [156], and examples of the application of psychophysical methodology to the study of 3-D shape perception is presented by Pizlo [184].

. 1.9 Conclusion The problem of object .net framework code 128 barcode categorization has been around since the early 1970s. The legacy left by that original community was a set of rich object representations that modeled the coarse, prototypical, 3-D shape of an object.

Although important concepts such as viewpoint invariance, hierarchical representations, structural variability, indexing, and symmetry are rooted in this early work, the lack of image abstraction mechanisms restricted these systems to contrived images of contrived scenes. Instead of incrementally building on these rich representational ideas, models became gradually stronger, rst in terms of shape and then appearance, thereby avoiding the need for image abstraction. the evolution of object categorization mechanisms. The result .net framework Code128 ing recognition systems began to be useful, rst solving real exemplar-based industrial recognition problems under tightly controlled conditions, and, more recently, solving real exemplar-based recognition problems in the real world.

Having made enormous progress on the problem of exemplar recognition, the community is now eager to return to the categorization problem. However, the gradual rede nition of the recognition problem from categories to exemplars, followed by a representational movement from shape to appearance, has unfortunately displaced a rich history of categorization from our community s memory. The sudden popularity of object recognition in the early 2000s is due in part to the fact that an image can now be mapped to a set of very distinctive local feature vectors without having to engage in the classical, unsolved problems of segmentation and grouping.

This has drawn a new generation of computer vision and machine learning researchers into the ring. Our progress will clearly bene t from both the increased popularity of the problem as well as the in ux of new techniques from other communities. However, a much smaller portion of this new community will have witnessed the evolution of categorization, which further serves to separate the categorization community from its roots.

Today s categorization community has moved quickly to apply exemplar-based appearance models to more categorical tasks. Ultimately, these are destined to fail, for local appearance is seldom generic to a category. This is re ected in a recent shift back to shape, along with a recent rediscovery of the importance of viewpoint invariance.

8 This is a very positive development, for our computers, our inference engines, our ability to deal with uncertain information, and our ability to learn a system s parameters rather than hand-code them represent enormous improvements over previous generations. As such, our return to earlier problems will lead to vastly more effective solutions. Without a doubt, we are heading in the right direction again.

One invariant, however, has survived the pendulum-like journey of our community: our tendency to avoid the dif cult problem of image (or shape) abstraction. Once we acknowledge this important problem, we must be patient and not expect results too soon. We must understand the history of the research in our community, build on important representational ideas and concepts from the past, and not dismiss earlier work just because it did not deal with real images.

Each generation of categorization researchers has made important contributions, and we must incrementally build on the foundations laid by our predecessors. When we do develop solutions, they must be carefully evaluated under controlled conditions that can provide us with the most constructive feedback. Finally, we must reconnect with our human vision colleagues, so that we can maximally bene t from their research on the most impressive categorization system of them all: the human vision system.

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