Although current computer vision systems are closer to the human intelligence when it comes to comprehending the visible world than previously, their performance is hindered when objects are partially occluded. Since we live in a dynamic and complex environment, we encounter more occluded objects than fully visible ones. Therefore, instilling the capability of amodal perception into those vision systems is crucial. However, overcoming occlusion is difficult and comes with its own challenges. The generative adversarial network (GAN), on the other hand, is renowned for its generative power in producing data from a random noise distribution that approaches the samples that come from real data distributions. In this survey, we outline the existing works wherein GAN is utilized in addressing the challenges of overcoming occlusion, namely amodal segmentation, amodal content completion, order recovery, and acquiring training data. We provide a summary of the type of GAN, loss function, the dataset, and the results of each work. We present an overview of the implemented GAN architectures in various applications of amodal completion. We also discuss the common objective functions that are applied in training GAN for occlusion-handling tasks. Lastly, we discuss several open issues and potential future directions.

Nowadays, autonomous navigation is getting more and more attention. The mobile robots need to have map of the close environment. The paper presents how can collect information about the environment for robot navigation. The sensors and the navigation system are installed on a mobile equipment platform. After this the article shows the different measurement systems, the generating virtual depth map and the navigation algorithm. At the end the Kinect sensor is compared with a sensor based on structured light.

Since the beginning of the coronavirus disease (COVID-19) pandemic several machine learning and deep learning methods had been introduced to detect the infected patients using the X-Ray or CT scan images. Numerous sophisticated data-driven methods had been introduced to improve the performance and the accuracy of the diagnosis models. This paper proposes an improved densely connected convolutional networks (DenseNet) method based on transfer learning (TL) to enhance the model performance. The results show promising model accuracy.

The content based image retrieval (CBIR) is one of the most popular, rising research areas of the digital image processing. Most of the available image search tools, such as Google Images and Yahoo! Image search, are based on textual annotation of images. In these tools, images are manually annotated with keywords and then retrieved using text-based search methods. The performances of these systems are not satisfactory. The goal of CBIR is to extract visual content of an image automatically, like color, texture, or shape. This paper aims to introduce the problems and challenges concerned with the design and the creation of CBIR systems, which is based on a free hand sketch (Sketch based image retrieval - SBIR). With the help of the existing methods, describe a possible solution how to design and implement a task spesific descriptor, which can handle the informational gap between a sketch and a colored image, making an opportunity for the efficient search hereby. The used descriptor is constructed after such special sequence of preprocessing steps that the transformed full color image and the sketch can be compared. We have studied EHD, HOG and SIFT. Experimental results on two sample databases showed good results. Overall, the results show that the sketch based system allows users an intuitive access to search-tools. The SBIR technology can be used in several applications such as digital libraries, crime prevention, photo sharing sites. Such a system has great value in apprehending suspects and indentifying victims in forensics and law enforcement. A possible application is matching a forensic sketch to a gallery of mug shot images. The area of retrieve images based on the visual content of the query picture intensified recently, which demands on the quite wide methodology spectrum on the area of the image processing.

This article describes an improved version of a fuzzy-based navigational system and compares the developed system with the Caversan [11] navigational method that was used as a basis of our approach. Our system (which uses a Mamdani-type fuzzy controller that has sixteen rules, three inputs and one output) has better algorithms for both the path planning and for the avoiding of obstacles. This is because our approach takes into consideration both the point-like obstacles in the front, the close walls, and the obstacles on the left as well; these are the aspects where the reference system makes a false assumption and the robot continues going straight forward. Our new system does a sharp turn if it detects an obstacle that is close, and goes on an almost ideal track if the obstacles are further away. The developed system was tested both in the MATLAB fuzzy simulator and using an autonomously moving robotic car as well; and it was determined that the robot is capable of navigation in real time while it avoids the obstacles. Since the evaluation of the environment is done using Kinect and infra-based distance-detection sensors, we introduced a new fuzzyfication method. Because of this, the parameters of the original method had to be adjusted to match the sensor's field of view and area of detection (this had to be done separately for the distances on the sides and in the front as well).

The main aim of this work is to show, how GPGPUs can facilitate certain type of image processing methods. The software used in this paper is used to detect special tissue part, the nuclei on (HE - hematoxilin eosin) stained colon tissue sample images. Since pathologists are working with large number of high resolution images - thus require significant storage space -, one feasible way to achieve reasonable processing time is the usage of GPGPUs. The CUDA software development kit was used to develop processing algorithms to NVIDIA type GPUs. Our work focuses on how to achieve better performance with coalesced global memory access when working with three-channel RGB tissue images, and how to use the on-die shared memory efficiently.