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Classification, object detection, object localization 2017 [52] M. Kragh et al. Daimler Monocular Pedestrian Detection dataset It is a dataset of pedestrians in urban environments. Pedestrians are box-wise labeled. Labeled part contains 15560 samples with pedestrians and 6744 samples without. Test set contains 21790 images without labels. Images
Objects detected with OpenCV's Deep Neural Network module (dnn) by using a YOLOv3 model trained on COCO dataset capable to detect objects of 80 common classes. Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. [1]
The program determines a suitable algorithm for pre-processing, segmenting, and post-processing a set of images for a specific application to distinguish crucial regions of interest within the image. CVIP-ATAT provides a graphical user interface (GUI) to input algorithms for testing and analysis. Users can define multiple processes to test at ...
Train/test splits, labeled images, 1360 Images, text Classification 2006 [315] [316] M-E Nilsback et al. Plant Seedlings Dataset 12 category dataset of plant seedlings. Labelled images, segmented images, 5544 Images Classification, detection 2017 [317] Giselsson et al. Fruits-360 Database with images of 131 fruits and vegetables.
The detection and description of local image features can help in object recognition. The SIFT features are local and based on the appearance of the object at particular interest points, and are invariant to image scale and rotation. They are also robust to changes in illumination, noise, and minor changes in viewpoint.
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.
In the fields of computer vision and image analysis, the Harris affine region detector belongs to the category of feature detection.Feature detection is a preprocessing step of several algorithms that rely on identifying characteristic points or interest points so to make correspondences between images, recognize textures, categorize objects or build panoramas.
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. In short, it consists of a sequence of classifiers.