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Colorectal cancer (CRC) stands out as one of the most prevalent global cancers. The accurate localization of colorectal polyps in endoscopy images is pivotal for timely detection and removal, contributing significantly to CRC prevention. The manual analysis of images generated by gastrointestinal screening technologies poses a tedious task for doctors. Therefore, computer vision-assisted cancer detection could serve as an efficient tool for polyp segmentation. Numerous efforts have been dedicated to automating polyp localization, with the majority of studies relying on convolutional neural networks (CNNs) to learn features from polyp images. Despite their success in polyp segmentation tasks, CNNs exhibit significant limitations in precisely determining polyp location and shape due to their sole reliance on learning local features from images. While gastrointestinal images manifest significant variation in their features, encompassing both high- and low-level ones, a framework that combines the ability to learn both features of polyps is desired. This paper introduces UViT-Seg, a framework designed for polyp segmentation in gastrointestinal images. Operating on an encoder-decoder architecture, UViT-Seg employs two distinct feature extraction methods. A vision transformer in the encoder section captures long-range semantic information, while a CNN module, integrating squeeze-excitation and dual attention mechanisms, captures low-level features, focusing on critical image regions. Experimental evaluations conducted on five public datasets, including CVC clinic, ColonDB, Kvasir-SEG, ETIS LaribDB, and Kvasir Capsule-SEG, demonstrate UViT-Seg's effectiveness in polyp localization. To confirm its generalization performance, the model is tested on datasets not used in training. Benchmarking against common segmentation methods and state-of-the-art polyp segmentation approaches, the proposed model yields promising results. For instance, it achieves a mean Dice coefficient of 0.915 and a mean intersection over union of 0.902 on the CVC Colon dataset. Furthermore, UViT-Seg has the advantage of being efficient, requiring fewer computational resources for both training and testing. This feature positions it as an optimal choice for real-world deployment scenarios. [ABSTRACT FROM AUTHOR]

The AFB BJ+ DAC∗ is the latest variant of asynchronous forward bounding algorithms used to solve Distributed Constraint Optimization Problems (DCOPs). It uses Directional Arc Consistency (DAC∗) to remove, from domains of a given DCOP, values that do not belong to its optimal solution. However, in some cases, DAC∗ does not remove all suboptimal values, which causes more unnecessary research to reach the optimal solution. In this paper, to clear more and more suboptimal values from a DCOP, we use a higher level of DAC∗ called Full Directional Arc Consistency (FDAC∗). This level is based on reapplying AC∗ several times, which gives the possibility of making more deletions and thus quickly reaching the optimal solution. Experiments on some benchmarks show that the new algorithm, AFB BJ+ FDAC∗, is better in terms of communication load and computation effort. [ABSTRACT FROM AUTHOR]

With the emergence of consumer RGB-D sensors, discriminative modeling has been shown to perform well in estimating human body pose. However, articulated hand pose estimation remains a challenging problem, mostly due to its high flexibility, occlusions, noisy data, and small area of the fingertips. In this paper, we present an efficient discriminative-based scheme to improve the performance of hand pose estimation from a single depth image. The proposed scheme is inspired by decision forest-based framework, but with several well-motivated modifications. Specifically, we propose a method to estimate 2D in-plane orientation of the hand, which is then utilized to enforce the depth comparison features and make them invariant to in-plane rotation. Subsequently, we investigate the use of random decision forests (RDF) and mean shift algorithm to predict a primary version of hand parts and joint locations. Based on this primary prediction, an adaptive spatial clustering method is applied to correct the misclassified regions, and to deliver the final estimation of hand pose. Along with the proposed scheme, we further develop a new set of highly-distinctive features for static finger sign recognition by utilizing the estimated hand pose configurations and RGB information. The proposed features are straightforward and can effectively capture different aspects of hand pose, such as links from each joint to the closest joints and orientation of each hand part. Extensive experiments on several challenging datasets demonstrate that our approach, compared to decision forest-based methods, is able to provide more precise estimation of hand poses (with up to 21% improvement in joint localization accuracy), and can efficiently recognize more complex static finger signs (93.85% mean recognition accuracy on a challenging 34-finger sign dataset). Our approach is also robust to illumination, inter-hand occlusion, scale, and rotation variance. [ABSTRACT FROM AUTHOR]

Defining the correct number of clusters is one of the most fundamental tasks in graph clustering. When it comes to large graphs, this task becomes more challenging because of the lack of prior information. This paper presents an approach to solve this problem based on the Bat Algorithm, one of the most promising swarm intelligence based algorithms. We chose to call our solution, "Bat-Cluster (BC)." This approach allows an automation of graph clustering based on a balance between global and local search processes. The simulation of four benchmark graphs of different sizes shows that our proposed algorithm is efficient and can provide higher precision and exceed some best-known values. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach to measure the correspondence between objects, and exploit it for object and image classification tasks, using the proposed Partial Dominant Orientation Descriptor (PDOD). In particular, the object is represented by a set of stable and informative key locations sampled using Difference of Gaussian. The proposed PDOD at each extracted key location takes into account the position and partially computes the dominant orientation of other key locations relative to it, thus, offering a global distinctive and discriminative characterization. This allows us to learn features that are largely invariant to common image transformations, including changes in object colors and textures. The correspondence in-between two objects is performed by finding for each key location in one object the key location in the other object that has the most similar descriptor. Object classification proceeds by assigning the most relevant category that has maximally similar stored prototype objects to the query object using k -Nearest Neighbors algorithm with Adaptive Object Distance. For efficiency, we further investigate PDOD for image classification by developing powerful image representations based on the popular Bag-of-Words model. The extensive experiments demonstrate that the proposed approach greatly improves the matching and classification results, while achieving the state-of-the-art performances on several challenging benchmark datasets. The obtained results suggest also broader applicability to other classification modalities. [ABSTRACT FROM AUTHOR]