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Figure 13.5 The effect of pose. Age classification accuracy at various image resolutions for 12 sets of images with pose variations. The results for the performance of the classifier under pose variation are summarized in Figure 13.5. From this experiment it is easy to see that for all three classifiers at least 11 out of 12 sets of faces gave an accuracy of greater than or equal to 60 %. This result is significant as it points to a possibility of using the sampled classifier output for all the faces obtained in a given track to classify the person in the track. Figure 13.5 indicates that for all the resolutions, at least 11 of the 12 sets of images with pose variation gave an accuracy of greater than 50 %.



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The MovieClip class is a truly complex class, with several sets of duties and almost 100 properties and methods. Important classes like these would quickly become a mess if it weren t for inheritance. Remember: one purpose of classes is isolating responsibility. With inheritance, you can ensure that those responsibilities are limited to the most speci c possible interpretation of the class. All the more general responsibilities can be de ned in a more general superclass. Take the Seagull class, for example. Nowhere in its code did you have to de ne that it can lay an egg, that it has a beak and feathers, that it has two feet. These things are all true, of course, but they are really properties of birds. The Seagull class concerns itself with the speci c responsibilities of a seagull: the way in which it ies, the kinds of food it eats, and its call. If you were to complete this example, you might have Seagull extend Bird, Bird extend Vertebrate, and Vertebrate





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Aug 1, 2019 · Choose “Capture Screenshot” to take a screenshot of any content. Click on “OCR” button from the “Image” tab. Once the screenshot OCR process has done on Mac, copy text and past it to a text editor.

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extend Animal. Each class would be responsible for only its own speci c set of traits. Your inheritance chain might look something like Figure 4-4.

FIGURE 4-4

3 9 11 15

Does this idea sound familiar You already have an inheritance-based system for classifying the living things around you, in which the most speci c traits are described in a species, and the most generic traits are described in a kingdom. The real scienti c classi cation for the herring gull I have been describing looks like Figure 4-5, just in Latin instead of monospace.

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24 Jun 2019 ... Easy Screen OCR for Mac can help users to capture the screenshot and extract uncopiable text from images on Mac . Stop retyping, just drag ...

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Apr 18, 2019 · While the Preview app on macOS can handle basic editing of PDFs and other documents, it doesn't have OCR software built-in. ... Download PDFpen or PDFpenPro if you don't already have it (free trial available); After ...

In real-life scenarios, there are changes in the lighting conditions that can produce diverse intensity gradients across the facial images. The intensity gradients could be across the entire face or only across half the face, depending on the position of the light source. To test the performance of the classifier under such circumstances, gradual intensity gradients were introduced in the available set of images and tested with the available classifiers trained from the images without the brightness gradients. The purpose of the testing was to check the effectiveness of the preprocessing steps in taking care of such gradients. Figure 13.6 indicates that, as expected, the brightness gradient removal method for preprocessing worked best in the presence of vertical and horizontal gradients in the image. However, in the presence of diagonal gradients in the image, histogram equalization gave results equally as good as the results obtained using histogram equalization with brightness gradient removal as the preprocessing step.

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FIGURE 4-5

Scienti c classi cation of a herring gull (Larus argentatus)

which A35 = (0, 0, 0,O). Given such a pair, we will have found a collision and thereby have broken MD4. Below, we show that there is a computationally efficient method to generate messages M for which the differential condition (5.9) holds. As a result, an efficient attack exists for finding collisions. In [42], it is claimed that the actual siiccess probability of the differential phase of this MD4 attack is about 1/222, as opposed to 1/230, which is the probability indicated by the approximations in Table 5.5. Assuming the higher probability holds, if we can find 222 data values that satisfy the initial conditions of the differential attack, then we expect to find a collision. In fact, it is easy to show empirically that the higher probability does hold. For the differential attack in Table 5.5, there are no restrictions on M . In the remaining phases of the attack, we determine 111 so that A19 = (0,0,0,O) and the technical condition (5.10) holds, in which case the analysis in Table 5.5 is valid. This completes our description of the differential phase of the attack. In the next phase of the attack, we backup to step 12 and force the necessary differential condition at step 19 to hold. This next phase is the most complex part of the attack.

Species Argentatus Genus Larus Family Laridae Order Charadriiformes Class Aves Phylum Chordata Kingdom Animalia

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