## 1 Introduction
The starting point for these techniques is the gradient of image intensity, which increases its robustness against changes in lighting, object colour and uncontrolled backgrounds. The gradient of the image highlights the edges in the image. Consequently, an edge analysis allows the extraction of hard features that are dependent on the object shape and independent of the colour of the pixels. For example, [PERSON] [12] used the histogram of oriented gradients technique to extract features and classify the gestures of bare hands with different skin colours and illumination. The orientation histogram is a technique developed by [PERSON] [13] and improved by [PERSON] and [PERSON] [14] in their work, which focused on human detection in images and videos. Another technique that may help to detect objects in sequential frames of video footage is the Kanade-Lucas-Tomasi algorithm [15]. These techniques can help with the object labelling process but they are not able to perform an accurate labelling process independently. With 2D computer vision algorithms, the accuracy is not as good as required in images with generic backgrounds, and the approach to object pixels proposed above is the starting point for manual labelling. Changes in backgrounds, foregrounds and object occlusion in images make the automatic and reliable detection of objects in generic images a very complex problem. Labelling with transfer learning uses neural network models for initial annotations of label-specific elements in an image. Some tools integrate third-party APIs, such as ImgLab, which is integrated with face++ API; faces can therefore be labelled as faces by marking the significant aspects of the image. Additionally, VIA [16; 17] offers face bounding box tracking with Faster RCNN [18]. Matlab Image Labeler App includes a built-in automation algorithm to detect and label people and vehicles using a pre-trained detector based on aggregate channel features. The Ground Truth Labeler of Automateddriving tool implements the Kanade-Lucas-Tomasi [19; 20; 21] algorithm to track features in successive frames of a video, labelling in bounding boxes only. Vicomtech [22] and Mask Editor [23] classify object parts into super-pixels, grouping nested pixels of a similar colour and considering that the borders of object parts have strong colour gradients. Afterwards, a group of super-pixels defining object parts are labelled manually as a complete object. If no shadows appear in the image, the accuracy in the borders with irregular shapes is considered better than a human-setting polygon vertex. Unfortunately, even after fine-tuning the parameters, the performance of automatic detection algorithms is disappointing due to the great variation in the perspectives of objects, as well as image conditions, backgrounds and foregrounds. Moreover, this strategy fails if a pre-trained model does not exist or if the pre-trained model does not classify the new image set with a good success rate. However, this transfer learning method could be a good starting point from which to manually drag and adjust the suggested area, therefore improving label accuracy. New images classified with a pre-trained model are the starting point of manual labelling, consisting of revising all work completed by the transfer learning process and fixing bad annotations if necessary. In addition, several authors use image synthesis to reduce the effort of manual annotations. [PERSON] et al. [24] localized text in natural images, synthetizing computer-generated texts and natural real images. [PERSON] et al. [25] and [PERSON] and [PERSON] [26] synthetized 3D CAD object models with real background images. [PERSON] et al. [27] generated synthetic structural magnetic resonance images for learning schizophrenia. [PERSON] et al. [28] recognized first-person reading activity by synthetizing computer-generated images and real background images.