The treatment of Diabetic Foot Ulcers (DFUs) is challenging due to their aggressive nature and slow healing, which can result in serious complications such as infection, gangrene, and amputation. Multiclass segmentation of tissues related to DFUs plays a crucial role in the early identification and effective treatment of these complications. Although Convolutional Neural Networks (CNNs) have been widely and successfully used in this context, they face challenges such as class imbalance and high computational demand. To resolve these issues, we propose Multiple Segmentation by Binary Networks (MsBNet), a modular approach based on combining multiple binary segmenters. We evaluate four different architectures: ShuffleNet, UNet, Attentio-nUNet, and PsPNet on our DFUs dataset. The results indicate that MsBNet outperforms DFUs multiclass segmentation models in terms of scalability and ability to deal with class imbalances. It is a flexible and promising solution for expanding medical applications, especially in environments with limited computational resources.

This study introduces MCOF, a multi-camera, computer vision-based system designed to assist visually impaired individuals with mobility on university campuses. The system operates locally and achieves the following performance metrics: (i) detecting body points within 1.2. 10–2seconds, (ii) identifying people, objects, and animals in 2.4. 10–2seconds, and (iii) detecting movements in 5.1. 10−2seconds. These results were obtained using a GTX 1,660 GPU with up to 6 cameras (or a 6,112MB stream) running concurrently. According to the MCOF architecture, events trigger tickets that are sent to an external information system, which can then implement its own safety and personnel protocols. Additionally, MCOF includes modules to handle electrical and network failures and features an obstruction detection routine for the cameras.

We present an image-based approach to estimate the velocity of moving vessels from their traces on the water surface. Vessels moving at constant heading and speed display a familiar V-shaped pattern which only differs from one to another by the wavelength of their transverse and divergent components. Such wavelength is related to vessel velocity. We use planar homography and natural constraints on the geometry of ships' wake crests to compute vessel velocity from single optical images acquired by conventional cameras. Experiments show that our approach produces compelling results, which are in accordance with true data available for the observed vessels.

We present the Spatial Contextualization for Closed Itemset Mining (SCIM) algorithm, an approach that builds a space for the target database in such a way that relevant itemsets can be retrieved regarding the relative spatial location of their items. Our algorithm uses Dual Scaling to map the items of the database to a multidimensional space called Solution Space. The representation of the database in the Solution Space assists in the interpretation and definition of overlapping clusters of related items. Therefore, instead of using the minimum support threshold, a distance threshold is defined concerning the reference and the maximum distances computed per cluster during the mapping procedure. Closed itemsets are efficiently retrieved by a new procedure that uses an FP-Tree, a CFI-Tree and the proposed spatial contextualization. Experiments show that the mean all-confidence measure of itemsets retrieved by our technique outperforms results from state-of-the-art algorithms. Additionally, we use the Minimum Description Length (MDL) metric to verify how descriptive are the collections of mined patterns.

We present an image-based approach to generate truly random numbers from the surface of water bodies such as oceanic bays. As a natural phenomenon, wind-generated gravity waves have non-deterministic behavior. We use the randomness of the angular relation between pairs of estimated surface normals to generate uniformly distributed random binary digits and build random numbers from those digits. Our approach produces compelling geometric models of water surfaces and generates random numbers with high entropy.

In the above titled paper (ibid., vol. 15, no. 6, pp. 1291-1298, Nov./Dec. 09), there were typos in equations (17) and (18). The correct versions are presented here.

Color vision deficiency (CVD) affects approximately 200 million people worldwide, compromising the ability of these individuals to effectively perform color and visualization-related tasks. This has a significant impact on their private and professional lives. We present a physiologically-based model for simulating color vision. Our model is based on the stage theory of human color vision and is derived from data reported in electrophysiological studies. It is the first model to consistently handle normal color vision, anomalous trichromacy, and dichromacy in a unified way. We have validated the proposed model through an experimental evaluation involving groups of color vision deficient individuals and normal color vision ones. Our model can provide insights and feedback on how to improve visualization experiences for individuals with CVD. It also provides a framework for testing hypotheses about some aspects of the retinal photoreceptors in color vision deficient individuals.

We present an efficient and automatic image-recoloring technique for dichromats that highlights important visual details that would otherwise be unnoticed by these individuals. While previous techniques approach this problem by potentially changing all colors of the original image, causing their results to look unnatural to color vision deficients, our approach preserves, as much as possible, the image's original colors. Our approach is about three orders of magnitude faster than previous ones. The results of a paired-comparison evaluation carried out with fourteen color-vision deficients (CVDs) indicated the preference of our technique over the state-of-the-art automatic recoloring technique for dichromats. When considering information visualization examples, the subjects tend to prefer our results over the original images. An extension of our technique that exaggerates color contrast tends to be preferred when CVDs compared pairs of scientific visualization images. These results provide valuable information for guiding the design of visualizations for color-vision deficients.