Ebook Object detection and recognition in digital images: Part 2
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Ebook Object detection and recognition in digital images: Part 2
4Object Detection and Tracking4.1IntroductionThis section is devoted to selected problems in object detection and tracking. Objects in this context ar Ebook Object detection and recognition in digital images: Part 2rc characterized by their salient features, such as color, shape, texture, or other trails. Then the problem is telling whether an image contains a defined object and. if so. then indicating its position in an image. If instead of a single image a video sequence is processed, then the task can be to Ebook Object detection and recognition in digital images: Part 2 track, or follow, the position and size of an object seen in the previous frame and so on. This assumes high correlation between consecutive frames iEbook Object detection and recognition in digital images: Part 2
n the sequence, which usually is the case. Eventually, an object will disappear from the sequence and the detection task can be started again.Detectio4Object Detection and Tracking4.1IntroductionThis section is devoted to selected problems in object detection and tracking. Objects in this context ar Ebook Object detection and recognition in digital images: Part 2hen the position of the object should be provided. Classification within a group of already detected objects is usually stated separately, however. In this case the question is formulated about what particular object is observed. Although the two groups arc similar, recognition methods arc left to t Ebook Object detection and recognition in digital images: Part 2he next chapter. Thus, examples of object detection in images are. for instance, detection of human faces, hand gestures, cars, and road signs in trafEbook Object detection and recognition in digital images: Part 2
fic scenes, or just ellipses in images. On the other hand, if we were to spot a particular person or a road sign. etc. we would call this recognition.4Object Detection and Tracking4.1IntroductionThis section is devoted to selected problems in object detection and tracking. Objects in this context ar Ebook Object detection and recognition in digital images: Part 2k. However, not least important is the proper selection of features that define an object. The main goal here is to choose features that are the most characteristic of a searched object or. in other words, that are highly discriminative, thus allowing an accurate response of a classifier. Finally, c Ebook Object detection and recognition in digital images: Part 2omputational complexity of the methods is also essential due to the usually high dimensions of lhe feature and search spaces. All these issues arc addEbook Object detection and recognition in digital images: Part 2
ressed in this section with a special stress on automotive applications.4.2Direct Pixel ClassificationColor conveys important information about the co4Object Detection and Tracking4.1IntroductionThis section is devoted to selected problems in object detection and tracking. Objects in this context ar Ebook Object detection and recognition in digital images: Part 2ishable from the background as possible to gain protection from predators.Object Detection and Recognition in Digital Images: Theory and Practice. First Edition. Bogustaw Cyganck.© 2013 John Wiley & Sons. Ltd. Published 2013 by John Wiley & Sons, Ltd.Object Detection and Tracking347The latter do the Ebook Object detection and recognition in digital images: Part 2 same to outwit their prey, and so on. Thus, objects can be segmented out from a scene based exclusively on their characteristic colors. This can be aEbook Object detection and recognition in digital images: Part 2
chieved with direct pixel classification into one of the two classes: objects and background. An object, or pixels potentially belonging to an object,4Object Detection and Tracking4.1IntroductionThis section is devoted to selected problems in object detection and tracking. Objects in this context ar Ebook Object detection and recognition in digital images: Part 2od as “all other values." Such a method is usually applied first in a chain on the computer vision system to sieve out the pixels of one object from all the others. For example Phung ft al. proposed a method for skin segmentation using direct color pixel classification [ 11. Road signs are detected Ebook Object detection and recognition in digital images: Part 2by direct pixel segmentation in the system proposed by Cyganck 121- Features other than color can also be used. For instance Viola and Jones propose uEbook Object detection and recognition in digital images: Part 2
sing Haar wavelets in a chain of simple classifiers to select from background pixels which can belong to human faces [31.Although not perfect, the met4Object Detection and Tracking4.1IntroductionThis section is devoted to selected problems in object detection and tracking. Objects in this context ar Ebook Object detection and recognition in digital images: Part 2Ground-Truth Data CollectionGround-truth data allow verification of performance of the machine learning methods. How ever. the process of its acquisition is tedious and time consuming, because of the high qualityrequirements of this type of data.Acquisition of ground-truth data can be facilitated by Ebook Object detection and recognition in digital images: Part 2 an application built for this purpose [4. 5J. Il allows different modes of point selection, such as individual point positions, as well as rectangleEbook Object detection and recognition in digital images: Part 2
and polynomial outlines of visible objects, as shown in Figure 4.1.An example of its operation for points marked inside the border of a road sign is d4Object Detection and Tracking4.1IntroductionThis section is devoted to selected problems in object detection and tracking. Objects in this context ar Ebook Object detection and recognition in digital images: Part 2ted image features, i.c. in this case it is color in the chosen color space. This tool was used to gather point samples for the pixel-based classification for human skin selection and road sign recognition, as will be discussed in the next sections.(a)Figure 4.1 A road sign manually outlined by a po Ebook Object detection and recognition in digital images: Part 2lygon defined by the points marked by an operator. This allow s selection of simple (a) and more complicated shapes (b). Selected points are saved asEbook Object detection and recognition in digital images: Part 2
metadata to an image with the help of a context menu. Color versions of this and subsequent images are available at WWW.wiley.com/go/cyganekobject.(b)4Object Detection and Tracking4.1IntroductionThis section is devoted to selected problems in object detection and tracking. Objects in this context ar Ebook Object detection and recognition in digital images: Part 2s of the selected points arc saved in the form of meta data to the original image. These can be used to obtain image features, such as color, in the indicated places.4.2.2CASE STUDY - Human Skin DetectionHuman skin detection gels much attention in computer Vision due to its numerous applications. Th Ebook Object detection and recognition in digital images: Part 2e most obvious is detection of human faces for their further recognition, human hands for gesture recognition.* or naked bodies for parental control sEbook Object detection and recognition in digital images: Part 2
ystems [6. 71. for instance.Detection of human skin regions in images requires the definition of characteristic parameters such as color and texture, 4Object Detection and Tracking4.1IntroductionThis section is devoted to selected problems in object detection and tracking. Objects in this context ar Ebook Object detection and recognition in digital images: Part 2lready discussed, a method for human skin segmentation based on a mixture of Gaussians was proposed by Jones and Rehg [81. Their model contains J = 16 Gaussians which were trained from almost one billion labeled pixels from the RGB images gathered mostly from the Internet. The reported detection ral Ebook Object detection and recognition in digital images: Part 2e is <80% with about 9% of false positives. A similar method based on MoG was undertaken by Yang and Ahuja in |9J.On the other hand. Jayaram er al. [Ebook Object detection and recognition in digital images: Part 2
101 report that the best results are obtained with histogram methods rather than using the Gaussian models. They also pointed out that different color4Object Detection and Tracking4.1IntroductionThis section is devoted to selected problems in object detection and tracking. Objects in this context ar Ebook Object detection and recognition in digital images: Part 2on is that in all color spaces directly partitioned into achromatic and chromatic components, performance was significantly better if the luminance component was employed in detection. Similar results, which indicate the positive influence of the illumination component and the poor performance of th Ebook Object detection and recognition in digital images: Part 2e Gaussian modeling, were reported b\ Phung el al. 11 J. They also found that the Bayesian classifier with the histogram technique, as well as the mulEbook Object detection and recognition in digital images: Part 2
tilayer perceptron, performs the best. The Bayes classifier operates in accordance w ith Equation (3.77), in which X is a color vector, Mt) denotes a 4Object Detection and Tracking4.1IntroductionThis section is devoted to selected problems in object detection and tracking. Objects in this context ar Ebook Object detection and recognition in digital images: Part 2 mixture of Gaussians. Therefore there is no unique “winner" and application of a specific detector can be driven by other factors such as the computational capabilities of target platforms.With respect to the color space, some authors advocate using perceptually uniform color spaces for object dete Ebook Object detection and recognition in digital images: Part 2ction based on pixel classification. Such an approach was undertaken by Wu et al. 1111 in their fuzzy face detection method. The front end of their deEbook Object detection and recognition in digital images: Part 2
tection constitutes1A method for gesture recognition is presented in Section 5.2.Object Detection and Tracking349Table 4.1 Fuzzy rules for skin detect4Object Detection and Tracking4.1IntroductionThis section is devoted to selected problems in object detection and tracking. Objects in this context arGọi ngay
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