A colonoscopy is a medical examination used to check disease or abnormalities in the large intestine. If necessary, polyps or adenomas would be removed through the scope during a colonoscopy. Colorectal cancer can be prevented through this. However, the polyp detection rate differs depending on the condition and skill level of the endoscopist. Even some endoscopists have a 90% chance of missing an adenoma. Artificial intelligence and robot technologies for colonoscopy are being studied to compensate for these problems. In this study, we propose a self-supervised monocular depth estimation using spatiotemporal consistency in the colon environment. It is our contribution to propose a loss function for reconstruction errors between adjacent predicted depths and a depth feedback network that uses predicted depth information of the previous frame to predict the depth of the next frame. We performed quantitative and qualitative evaluation of our approach, and the proposed FBNet (depth FeedBack Network) outperformed state-of-the-art results for unsupervised depth estimation on the UCL datasets.

Depth estimation using monocular camera sensors is an important technique in computer vision. Supervised monocular depth estimation requires a lot of data acquired from depth sensors. However, acquiring depth data is an expensive task. We sometimes cannot acquire data due to the limitations of the sensor. View synthesis-based depth estimation research is a self-supervised learning method that does not require depth data supervision. Previous studies mainly use the convolutional neural network (CNN)-based networks in encoders. The CNN is suitable for extracting local features through convolution operation. Recent vision transformers (ViTs) are suitable for global feature extraction based on multiself-attention modules. In this article, we propose a hybrid network combining the CNN and ViT networks in self-supervised learning-based monocular depth estimation. We design an encoder–decoder structure that uses CNNs in the earlier stage of extracting local features and a ViT in the later stages of extracting global features. We evaluate the proposed network through various experiments based on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) and Cityscapes datasets. The results showed higher performance than previous studies and reduced parameters and computations. Codes and trained models are available at https://github.com/fogfog2/manydepthformer .

Object tracking is a technique for tracking a specific object appearing in a video sequence while observing its features or changes. Recently, many algorithms showing high performance have emerged by applying the Siamese network to the object tracking field. A Siamese network is designed to learn the similarity between two images. In object tracking, the Siamese network tracks the object by finding the location most similar to the target image in the search image. Algorithms based on Siamese networks are vulnerable to partial and total occlusion of objects. In addition, since the object is tracked using only the similarity with the image obtained using the ground-truth bounding box of the first frame, if an object is missed once, then errors are accumulated, and a situation where the object drifts away from the object of interest frequently occurs. Therefore, in this paper, we propose a reinforcement learning model that can maximize the reward for tracking success after partial and total occlusion of an object. We also propose a dynamic template exchange method using a template that has been successfully tracked in a recent frame to solve the drift problem. When the proposed model is applied to the existing tracking models to evaluate the quantitative performance in representative object tracking benchmarks VOT2018 and OTB50, it is confirmed that the accuracy is improved, and the number of tracking failures decreases compared to the existing method. As a result, an accuracy of 0.618, robustness of 0.234, and expected average overlap (EAO) of 0.416 are achieved in VOT2018, and success of 0.673 and precision of 0.881 are achieved in OTB50.

Ji EK, et al. Korean J Phys Anthropol. 2001 Dec;14(4):333-338. https://doi.org/10.11637/kjpa.2001.14.4.333