Edge Detection in Natural Scenes Inspired by the Speed Drawing Challenge

Edge detection is a major step in several computer vision applications. Edges define the shape of objects to be used in a recognition system, for example. In this work, we introduce an approach to edge detection inspired by a challenge for artists: the Speed Drawing Challenge. In this challenge, a person is asked to draw the same figure in different times (as 10[Formula: see text]min, 1[Formula: see text]min and 10[Formula: see text]s); at each time, different levels of details are drawn by the artist. In a short time stamp, just the major elements remain. This work proposes a new approach for producing images with different amounts of edges representing different levels of relevance. Our method uses superpixel to suppress image details, followed by Globalized Probability of Boundary (gPb) and Canny edge detection algorithms to create an image containing different number of edges. After that, an edge analysis step detects whose edges are the most relevant for the scene. The results are presented for the BSDS500 dataset and they are compared to other edge and contour detection algorithms by quantitative and qualitative means with very satisfactory results.

Human Motion Posture Detection Algorithm Using Deep Reinforcement Learning

To address problems of serious loss of details and low detection definition in the traditional human motion posture detection algorithm, a human motion posture detection algorithm using deep reinforcement learning is proposed. Firstly, the perception ability of deep learning is used to match human motion feature points to obtain human motion posture features. Secondly, normalize the human motion image, take the color histogram distribution of human motion posture as the antigen, search the region close to the motion posture in the image, and take its candidate region as the antibody. By calculating the affinity between the antigen and the antibody, the feature extraction of human motion posture is realized. Finally, using the training characteristics of deep learning network and reinforcement learning network, the change information of human motion posture is obtained, and the design of human motion posture detection algorithm is realized. The results show that when the image resolution is 384 × 256 px, the motion pose contour detection accuracy of this algorithm is 87%. When the image size is 30 MB, the recognition time of this method is only 0.8 s. When the number of iterations is 500, the capture rate of human motion posture details can reach 98.5%. This shows that the proposed algorithm can improve the definition of human motion posture contour, improve the posture detailed capture rate, reduce the loss of detail, and have better effect and performance.

Depth Image Selection Based on Posture for Calf Body Weight Estimation

We are developing a system to estimate body weight using calf depth images taken in a loose barn. For this purpose, depth images should be taken from the side, without calves overlapping and without their backs bent. However, most of the depth images that are taken successively and automatically do not satisfy these conditions. Therefore, we need to select only the depth images that match these conditions, as to take many images as possible. The existing method assumes that a calf standing sideways and upright in front of cameras is in a suitable pose. However, since such cases rarely occur, not many images were selected. This paper proposes a new depth image-selection method, focusing on whether a calf is sideways, and the back is not bent, regardless of whether the calf is still or walking. First, depth images including only a single calf are extracted. The calf was identified using radio frequency identification (RFID) when its depth image was taken. Then, the calf area was extracted by background subtraction and contour detection with a depth image. Finally, to judge the usable depth images, we detected and evaluated the calf’s posture, such as the angle of the calf to the camera and the slope of the dorsal line. We used the mean absolute percentage error (MAPE) to assess the efficiency of our method. As two times the number of depth images were extracted, our method achieved an MAPE of 12.45%, while the existing method achieved an MAPE of 13.87%. From this result, we have confirmed that our method makes body weight estimation more accurate.

Detection of Pneumonia Using Chest X-Ray Images and Image Processing Algorithms - A Comparative Study

Utilizing exclusively picture handling procedures, this examination proposes an original strategy for distinguishing the presence of pneumonia mists in chest X-rays (CXR). Collected the several analogue chest CXRs from patients with normal and Pneumonia-infected lungs. Indigenous algorithms have been developed for cropping and for extraction of the lung region from the images. To detect pneumonia clouds first conducted the preprocessing of the image then used the image segmentation techniques like Otsu thresholding K-means clustering and global thresholding and then contour detection algorithm was applied which helped to detect lung boundary, the area’s ratio is used to classify the normal lung from pneumonia affected lung.

Automatic Image Characterization of Psoriasis Lesions

Psoriasis is a chronic skin disease that affects 125 million people worldwide and, particularly, 2% of the Spanish population, characterized by the appearance of skin lesions due to a growth of the epidermis that is seven times larger than usual. Its diagnosis and monitoring are based on the use of methodologies for measuring the severity and extent of these spots, and this includes a large subjective component. For this reason, this paper presents an automatic method for characterizing psoriasis images that is divided into four parts: image preparation or pre-processing, feature extraction, classification of the lesions, and the obtaining of parameters. The methodology proposed in this work covers different digital-image processing techniques, namely, marker-based image delimitation, hair removal, nipple detection, lesion contour detection, areal-measurement-based lesion classification, as well as lesion characterization by means of red and white intensity. The results obtained were also endorsed by a professional dermatologist. This methodology provides professionals with a common software tool for monitoring the different existing typologies, which proved satisfactory in the cases analyzed for a set of 20 images corresponding to different types of lesions.