Search results
Results From The WOW.Com Content Network
CellCognition uses a computational pipeline which includes image segmentation, object detection, feature extraction, statistical classification, tracking of individual cells over time, detection of class-transition motifs (e.g. cells entering mitosis), and HMM correction of classification errors on class labels.
Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different view points, in many different sizes and scales or even when they are translated or rotated.
3-dimensional pen tip velocity trajectory matrix for each sample 2858 Text Handwriting recognition, classification 2008 [134] [135] B. Williams Chars74K Dataset Character recognition in natural images of symbols used in both English and Kannada: 74,107 Character recognition, handwriting recognition, OCR, classification 2009 [136] T. de Campos
The hash table is searched to identify all clusters of at least 3 entries in a bin, and the bins are sorted into decreasing order of size. Each of the SIFT keypoints specifies 2D location, scale, and orientation, and each matched keypoint in the database has a record of its parameters relative to the training image in which it was found.
It is widely used in computer vision tasks such as image annotation, [2] vehicle counting, [3] activity recognition, [4] face detection, face recognition, video object co-segmentation. It is also used in tracking objects, for example tracking a ball during a football match, tracking movement of a cricket bat, or tracking a person in a video.
Pyramid match kernel [13] is a fast algorithm (linear complexity instead of classic one in quadratic complexity) kernel function (satisfying Mercer's condition) which maps the BoW features, or set of features in high dimension, to multi-dimensional multi-resolution histograms. An advantage of these multi-resolution histograms is their ability ...
These events can be grouped into two main categories: Intrinsic Recognition and Extrinsic Recognition. [3] Intrinsic Recognition is when cells that are part of the same organism associate. [3] Extrinsic Recognition is when the cell of one organism recognizes a cell from another organism, like when a mammalian cell detects a microorganism in the ...
The Viola–Jones object detection framework is a machine learning object detection framework proposed in 2001 by Paul Viola and Michael Jones. [1] [2] It was motivated primarily by the problem of face detection, although it can be adapted to the detection of other object classes.