[PDF] Top 20 Multiple prediction combination and confidence measures for marine object detection

... confidence mapping curves, including the selected ‘best’ angles . . . . . 100 6.4 Summary of hill-climbing steps carried out during object detection . . . 102 6.5 Time in seconds taken to classify ... See full document

... The greedy matching algorithm repeatedly considers every possible pair of heads and bodies. It repeatedly finds the lowest-cost pairing of a head and body and, if neither item is already part of a pair, joins them. If ... See full document

... Few hill-climbing steps were needed in most cases. Half of the detections made only needed two hill-climbing step to maximise confidence, and none needed more than 9. Fig. 6.14(a) shows that the object to ... See full document

... In object detection, the Haar Classifier Cascade algorithm was considered in detail; its behaviour on rotated object detection was studied and it was extended to report numeric ... See full document

... IEEE International Conference on Computer Vision and Pattern Recognition CVPR’01, 511–518.. Robust real-time object detection.[r] ... See full document

... Figure B.5: ROC curves for seahorse segment detection on rotated images using binary detection followed by hill-climbing, varying the cascade random angle range... Appendix B Confidence-[r] ... See full document

... face detection true positive and false positive counts made by the three main methods compared in chapter ...other detection experiments on these images, although this thesis used its own annotations which ... See full document

... hand detection suggests that the positive training examples for a given angle should not be fixed at that angle, but should be perturbed by some random amount (Jones & Viola, 2003a; K¨olsch & Turk, 2004a), ... See full document

... Virtual attribute subsetting is a meta-classification technique which will be shown in chapter 6 to improve the performance of the confidence mapping technique described there. It was created in this work as a ... See full document

... for object detection, where a detector must pass over thousands of negative image regions for each true ...most object detection algorithms and count the true positives and false positives for ... See full document

... fish detection problem was part of a project in automated salmon farm ...many object detection environments, but the underwater images have poor contrast and are frequently crowded (Lines et ...fish ... See full document

... moving object from first frame up to last frame of the video. Object tracking is performed by using different types of techniques, such as blob matching tracking algorithm [3], Kalman filter [4], robust ... See full document

... A Support Vector Machine (SVM) is a discriminative classifier described by a dividing hyperplane. The key idea behind this classification method is to separate the classes with a plane that maximise the margin among ... See full document

... It is a Optimal Recursive Data Processing Algorithm. The Kalman Filter undergoes the restrictive probability density propagation. Kalman filter [12] is a set of mathematical equations which gives an efficient ... See full document

... Arrangements of moving items distinguished and track end over circumstances by individual sensors are combined to deliver the final rundown of tracks. Fusions at this level can decrease false tracks. Low level and guide ... See full document

... Confidence-Weighted linear classifiers (CW) and its successors were shown to perform well on binary and multiclass NLP prob- lems. In this paper we extend the CW ap- proach for sequence learning and show that it ... See full document

... the detection of object. The object detection is very ...the object detection many technologies are ...an object. It recognize a particular object not the noise or ... See full document

... similarity measures between documents by the cosine between their corresponding vectors or by their ...These measures can be used for clustering similar documents together to recognize “concepts” and ... See full document

... across multiple memory measures or a combination of defi cits in memory and nonmemory measures have less reversion to normal and faster rates of progression to dementia than those with single ... See full document

... Fig 4: Two of the total 32 test images and their three conspicuity maps extracted by basic visual attention model [7] As could be seen in the Fig.5, after combining three conspicuity map[r] ... See full document